This PDF file contains the front matter associated with SPIE Proceedings Volume 9998, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Knowing the exact growth stage of agricultural crops can be valuable information for crop management and monitoring. In Canada, canola fields are particularly vulnerable for crop disease development during their flowering stage, especially when the fields are under persistent wet conditions. Clubroot and sclerotinia are diseases that can occur in canola when these two factors come together. Remote sensing can provide an interesting tool for the monitoring of crop phenological stages over large agriculture landscapes. Reliable and frequent access to data is needed to determine field-specific growth stages. Given their all-weather capability, radar sensors are optimal for monitoring such a dynamic crop parameter. In 2014, Agriculture and Agri-Food Canada collected crop phenology information over multiple canola fields in the area of Carman, Manitoba. Coincidental to ground data collection, fully polarimetric RADARSAT-2 and dual-polarimetric TerraSAR-X satellite data were acquired over the study site. In collaboration with A. U. G. Signals Ltd., a methodology will be developed and validated for the identification of inflorescence emergence and flowering in canola fields. Analysis of the polarimetric datasets from this study determined that several polarimetric parameters were sensitive to the emergence of flower buds and the flowering stage in canola. The alpha angle and entropy in both the C- and X-band were able to identify these growth stages, in addition to any of the reflectivity ratios and differential reflectivity responses that incorporated an HV response. The RADARSAT-2 scatter diversity, degree of purity and depolarization index also demonstrated great potential at identifying canola flower emergence and flowering. These latter polarimetric parameters along with the reflectivity ratios may be advantageous given their ease in implementation within a larger risk assessment satellite-derived methodology for canola crop disease.

Grasslands are among the largest ecosystems worldwide and according to the FAO they contribute to the livelihoods of more than 800 million people. Harvest dates and frequency can be utilised for an improved estimation of grassland yields.

In the presented project a highly automatised methodology for detecting harvest dates and frequency using SARamplitude data was developed based on an amplitude change detection techniques. This was achieved by evaluating spatial statistics over field boundaries provided by the European Integrated Administration and Control System (IACS) to identify changes between pre- and post-harvest acquisitions. The combination of this method with a grassland yield model will result in more reliable and regional-wide numbers of grassland yields. In our contribution we will focus on SAR-remote sensing for monitoring harvest frequencies, discuss the requirements concerning the acquisition system, present the technical approach and analyse the verified results.

In terms of the acquisition system a high temporal acquisition rate is required, which is generally met by using SARsatellite constellations providing a revisit time of few days. COSMO-SkyMed data were utilised for the pilot study for developing and prototyping a monitoring system. Subsequently the approach was adapted to the use of the C-Band system Sentinel-1A becoming fully operational with the availability of Sentinal-1B.

The study area is situated northeast of Munich, Germany, extending to an area of approx. 40km to 40km and covering major verification sites and in-situ data provided by research farms or continuously surveyed in-situ campaigns. An extended time series of SAR data was collected during the cultivation and vegetation cycles between March 2014 and March 2016. All data were processed and harmonised in a GIS database to be analysed and verified according to corresponding in-situ data.

Stream network delineation based on LiDAR-derived digital terrain model (DTM) may produce stream segments that are inexistent or incomplete because of limitations imposed by extraction procedure, terrain and data. The applicability of a common threshold value in defining streams such as those implemented through the D8 algorithm also remains in question because the threshold varies depending on the geomorphology of the area. Flat areas and improper hydrologic conditioning produce erratic stream network. To counteract these limitations, this study proposes a workflow that improves the stream network produced by the D8 algorithm. It incorporates user-defined channel initiation points as inputs to a tool developed to automatically trace the flow of water into the next actual stream segment. Spurious streams along digital dams and flat areas are also manually reshaped. The proposed workflow is implemented in Iligan River Basin, Philippines using LiDARderived DTM of 1-meter resolution. The Flow Path Tracing (FPT) method counteracts the limits imposed by extraction procedure, terrain and data. It is applicable to different typologies of watersheds by eliminating the need to use site-specific threshold in determining streams. FPT is implemented as a Phyton script to automate the tracing of the streams using the flow direction raster. The FPT method is compared to the blue line digitization and the D8 method using morphometric parameters, such as stream number, stream order and stream length, to assess its performance. Results show that streams derived from the FPT method has higher stream order, number and length. An accuracy of 93.5% produced from field validation of the FPT method’s streams strengthens the findings that integrating manual channel head initiation and flow path tracing can be used for nationwide extraction of streams using LiDAR-derived-DTM in the Philippines.

The aim of this work is to analyse grapevine temporal signatures over a viticultural landscape with remote sensing data in order to evaluate the impact of the perennial practices on optical and SAR signals. For this, the effects of different combinations of vineyard organisations on multi-temporal high and very high spatial resolution SAR and optical data have been analysed. The study area is located in Mendoza (Argentina). All the ground data were acquired during an experimental campaign performed in 2014-2015 over 153 vineyards (around 400ha). The data of the perennial practices were recorded over each of the 153 vineyards. The schedule of the annual practices were also provided over the all site. The grapevine phenological cycle was finally monitored on 14 fields through GLAI (Green Leaf Area index) estimation using hemispherical images and grapevine canopy height measurements. The satellite images dataset is composed of 6 Dual-Pol TerraSAR-X images, 5 Pleiades images, and 6 Landsat-8 images. After calibrating the images, backscattering coefficients and polarimetric parameters were extracted from Terrasar-X images (entropy and alpha angle) and the NDVI from optical images. The analysis of the temporal signatures regarding perennial practices revealed a strong impact of the inter-row spacing management. Ranges of values of backscattering coefficients differ with the type of management but their temporal variation seems to be linked to soil moisture changes. Depending on the roughness of the inter-row spacing the alpha angle evolution is linked to grapevine growth in the case of a chemically weeded inter-row spacing. But it remains at high values when the inter-row spacing is ploughed or grassed. In those cases, the entropy increases along the growth cycle. The vegetation indexes series are increasing with the grapevine growth depending on canopy width and soil management. Thus this work highlighted the variability of the SAR and optical signals due to heterogeneities in vineyard organisation and management.

Glacier change in central Karakorum is known as ‘anomony’ in the late 1990s, where many glaciers expanded and numbers of glacier surged while most of glaciers in the Greater Himalaya rapidly retreated. However, the understanding of glacier change in this region is still poor. Glacier changes for the Hunza river basin (HRB) in central Karakorum during 2003 to 2008 were investigated from different data sources. The mass variation in HRB were estimated from the DEOS Mass Transport Model (DMT-1) GRACE data and the Variable Infiltration Capacity (VIC) model, and compared with the simulated glacier mass balance by one monthly degree-day model. The surface elevation difference of glaciers between ASTER DEM and SRTM were calculated. The mass variations from GRACE data suggest that the glacier mass balance in HRB during 2003-2007 has no clear trend. The cumulative mass balance is positive during 2003-2008. The average glacier surface elevation difference between SRTM DEM and ASTER DEM is 11.8±3.2 m. The average differences of glacier surface elevation of Batura glaciers in accumulation zones is increased with 0.88m.a-1, These results indicate that there is no significant glacier retreat during 1999 to 2008. The seasonal amplitude of simulated mass variation of the monthly degree-day model agreed well with that estimated from DMT-1 GRACE data, but the simulated glacier accumulation is less than that calculated from GRACE data. The main reason probably lies in that the precipitation of glaciers and ungalciated areas were underestimated, especially in alpine areas.

This study aims to assess the results of runoff volume, peak flow and the lag time between peak rainfall and peak river discharge or peak flow when no proper land use management is done and another is when sound land use management is adopted. Hence, two (2) land cover/use scenarios were created. The first scenario is the Projected Land Cover. This scenario was created using a Trend Analysis function from MS Excel derived from the 1973, 1989, 1998, 2008 and 2010 land cover scenarios. The second scenario is the Desired Land Use wherein it makes use of slope as the basis in assigning the different land uses. Specifically, agriculture and built-up were assign to 0-18% slope, 18-30% slope for agroforestry, 30-50% for production forest and >50% for protection forest. Limitedly available LiDAR DEM strips was integrated into the IFSAR DEM to generate a detailed basin model and slope in GIS. The HEC-HMS was used for simulating runoff models. The Projected Land Cover has a higher total runoff volume and peak flow and shorter Lag time as compared to the Desired Land Use scenario in all the four (4) Rainfall Return Period these are 5 years, 25 years, 50 years and 100 years. The latter has twice as much forest vegetation that the projected land cover scenario, it has better forest cover quality and plus the presence of agroforestry. Such condition helps improve soil infiltration and thus reduces runoff volume and peak time. The study shows when land cover conditions are left by itself without any intervention, the impact of flood disaster is more likely to be magnified. The study also shows that flood disaster can be mitigated if the Desired Land Use scenario will be adopted as one of the course of action in flood disaster risk reduction management.

Flood extent maps derived from Synthetic Aperture Radar (SAR) data can communicate spatially-explicit information in a timely and cost-effective manner to support disaster management. Automated processing chains for SAR-based flood mapping have the potential to substantially reduce the critical time delay between the delivery of post-event satellite data and the subsequent provision of satellite derived crisis information to emergency management authorities. However, the accuracy of SAR-based flood mapping can vary drastically due to the prevalent land cover and topography of a given scene. While expert-based image interpretation with the consideration of contextual information can effectively isolate flood surface features, a fully-automated feature differentiation algorithm mainly based on the grey levels of a given pixel is comparatively more limited for features with similar SAR-backscattering characteristics. The inclusion of ancillary data in the automatic classification procedure can effectively reduce instances of misclassification. In this work, a near-global ‘Height Above Nearest Drainage’ (HAND) index [10] was calculated with digital elevation data and drainage directions from the HydroSHEDS mapping project [2]. The index can be used to separate flood-prone regions from areas with a low probability of flood occurrence. Based on the HAND-index, an exclusion mask was computed to reduce water look-alikes with respect to the hydrologictopographic setting. The applicability of this near-global ancillary data set for the thematic improvement of Sentinel-1 and TerraSAR-X based services for flood and surface water monitoring has been validated both qualitatively and quantitatively. Application of a HAND-based exclusion mask resulted in improvements to the classification accuracy of SAR scenes with high amounts of water look-alikes and considerable elevation differences.

Over the last 2-3 decades, the economy of many countries around the world has been developed rapidly but it was unbalanced development because of expecting on economic growth only. Meanwhile it lacked of effective planning in the use of natural resources. This can significantly induce climate change which is major cause of natural disaster. Hereby, Thailand has also suffered from natural disaster for ages. Especially, the flood which is most hazardous disaster in Thailand can annually result in the great loss of life and property, environment and economy. Since the flood management of country is inadequate efficiency. It is unable to support the flood analysis comprehensively. This paper applied Geographic Information System and Multi-Criteria Decision Making to create flood risk model at regional scale. Angthong province in Thailand was used as the study area.

In practical process, Fuzzy logic technique has been used to improve specialist’s assessment by implementing with Fuzzy membership because human decision is flawed under uncertainty then AHP technique was processed orderly. The hierarchy structure in this paper was categorized the spatial flood factors into two levels as following: 6 criteria (Meteorology, Geology, Topography, Hydrology, Human and Flood history) and 8 factors (Average Rainfall, Distance from Stream, Soil drainage capability, Slope, Elevation, Land use, Distance from road and Flooded area in the past). The validity of the pair-wise comparison in AHP was shown as C.R. value which indicated that the specialist judgment was reasonably consistent. FAHP computation result has shown that the first priority of criteria was Meteorology. In addition, the Rainfall was the most influencing factor for flooding. Finally, the output was displayed in thematic map of Angthong province with flood risk level processed by GIS tools. The map was classified into: High Risk, Moderate Risk and Low Risk (13.20%, 75.58%, and 11.22% of total area).

For water productivity (WP) assessments, the SAFER (Surface Algorithm for Evapotranspiration Retrieving) algorithm for evapotranspiration (ET) and the Monteith’s light use efficiency (LUE) model for biomass production (BIO), were applied to Landsat and RapidEye satellite images, in the Brazilian semiarid region, inside the dry season of 2011, in a mixture of irrigated and rainfed agro-ecosystems. Firstly, with the Landsat image, the methodology from which the surface temperature (T0) is derived as a residue in the radiation balance was tested. Low differences were detected, being Landsat ET with the thermal band averaged 0.9 ± 1.5 mm d^-1, while without it the mean value was 0.8 ± 1.5 mm d^-1. The corresponding Landsat BIO values were respectively 28 ± 59 and 28 ± 58 kg ha^-1 d^-1, resulting in mean WP of 1.3 ± 1.3 kg m^-3, in both cases. After having confidence on the residual methodology for retrieving T₀ it was applied to the RapidEye image, resulting in average pixel values for ET, BIO and WP of 0.6 ± 1.5 mm d^-1, 26 ± 58 kg ha^-1 d^-1 and 0.9 ± 1.3 kg m^-3, representing 75%, 93% and 69% of the Landsat ones obtained without the thermal band. In addition, the Surface Resistance Algorithm (SUREAL) was used to classify the agro-ecosystems into irrigated crops and natural vegetation by using the RapidEye image. The incremental values for ET, BIO and WP in 2011 were 2.0 ± 1.3 mm d-1, 88 ± 87 kg ha d^-1 and 2.5 ± 0.6 kg m^-3, respectively, as a result of the replacement of the natural species by crops.

Agriculture contributes greatly to the economy of South Africa (SA), through job creation and produce exports. SA is classified as a semi-arid country and due to its low rainfall, fierce competition exists for the available water resources. Balancing the need for water resources on the one hand, with the importance of agricultural production on the other, is often challenging. A lot of emphasis is placed on prudent water management and enhanced crop water use efficiency. Suitable information and tools are key in empowering both water resources managers and (crop) producers for sustainable agricultural production. Information and tools available at frequent intervals throughout the production season and at a range of levels - from the field to the catchment and for the entire country – has become essential. The frequency and availability of remote sensing data, developments in algorithms to produce information related to the water cycle and crop growth and hence the actual information sets produced over time, makes for fitting solutions. Though much progress has been made over the past years to integrate these spatial data products into water management and agricultural systems, it is likely still in its infancy. In the paper, some flagship projects related to sustainable agriculture and water management – both research and applied - are showcased.

Among indirect estimations of the soil water content in the upper layer, the "triangle method" is based on the relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at surface and within root zone, but also by meteorological forcing including air temperature and humidity, and solar radiation. Night and day-time MODIS composite land-surface temperature (LST) allowed applying the thermal admittance version of the method; by taking into account the temporal admittance of the soil, this version was previously found achieving high accuracy in estimate the soil water content at high spatial resolution within a short time period (a single irrigation season). In this study, the method has been applied on a long time series to analyse the seasonal influence of the meteorological forcing on the triangle method index (or temperature vegetation index, TVX). The Imera Meridionale hydrological basin (≈ 2000 km², Sicily) has been chosen to test the method over a decade time series, since its climate varies during the year from arid to temperate. The climate is arid for ≈3-7 months (from April-May to August- October) depending on altitude. The temporal analysis reveals that NDVI and LST pairs moves circularly within the optical and thermal diachronic feature space. Concordantly, the boundaries of the triangle move during the seasons. Results suggest that the contribution of soil water content fluctuations need to be isolated from other environmental stress factors, or at least, the conceptual meaning of TVX have to be better interpreted.

Drought is one of the most complex natural hazards because of its slow onset and long-term impact; it has the potential to negatively affect many people. There are several advantages to using remote sensing to monitor drought, especially in developing countries with limited historical meteorological records and a low weather station density. In the present study, we assessed agricultural drought in the croplands of the BioBio Region in Chile. The vegetation condition index (VCI) allows identifying the temporal and spatial variations of vegetation conditions associated with stress because of rainfall deficit. The VCI was derived at a 250m spatial resolution for the 2000-2015 period with the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product. We evaluated VCI for cropland areas using the land cover MCD12Q1 version 5.1 product and compared it to the in situ Standardized Precipitation Index (SPI) for six-time scales (1-6 months) from 26 weather stations. Results showed that the 3-month SPI (SPI-3), calculated for the modified growing season (Nov-Apr) instead of the regular growing season (Sept-Apr), has the best Pearson correlation with VCI values with an overall correlation of 0.63 and between 0.40 and 0.78 for the administrative units. These results show a very short-term vegetation response to rainfall deficit in September, which is reflected in the vegetation in November, and also explains to a large degree the variation in vegetation stress. It is shown that for the last 16 years in the BioBio Region we could identify the 2007/2008, 2008/2009, and 2014/2015 seasons as the three most important drought events; this is reflected in both the overall regional and administrative unit analyses. These results concur with drought emergencies declared by the regional government. Future studies are needed to associate the remote sensing values observed at high resolution (250m) with the measured crop yield to identify more detailed individual crop responses.

The development of an efficient ground sampling strategy which can sample the natural dynamics of variations in variables of interest, is critical to ensuring the validation of remotely sensed products. This study attempts to take a fresh look at geostatistical methods for ground sampling and pixel-mean estimating in remote sensing validation campaigns. Spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Non-homogeneity (MSN) were implemented to estimate the fractional vegetation cover mean values at GEVO1 1 km² pixel level using Landsat 8 OLI and SPOT4 HRVIR1 fine-resolution FVC maps respectively derived from a homogeneous area covered by forest and a heterogeneous area covered by crop. The GEOV1 FVC product was validated using the means estimated by SRS, BK, and MSN. Root square error (RMSE), mean absolute percentage error (MAPE) and product accuracy (PA) were used to evaluate the validation. Results showed that the MSN method performs well for estimating the means of the surface with non-homogeneity, with a high accuracy of the GEOV1 FVC product (RMSE=0.12, MAPE=29.37 and PA= 77.39%). The statistical values outputted by BK were respectively 0.13, 31.46% and 76.21%. These values of SRS were respectively 0.13, 31.16% and 76.10%. For homogeneous surface, the statistical parameters outputted by these three methods were similar. These results revealed that MSN is an effective method for estimating the spatial means for heterogeneous surface and validating remote sensing product. We can conclude that choosing an appropriate sampling method has a significant impact on the validation of remote sensing product.

Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.

Precision agriculture (PA) has offered a multitude of benefits to farmers, such as cost reduction, accuracy and speed in decision making. Among the tools that work with PA, the aerial image mosaics have key role in accurate mapping of diseases and pests in crops. A mosaic is the combination of multiple images, creating a new image that covers the property or plots accurately. One of the important analysis for farmers is based on the properties of the reflectance in each range of the electromagnetic spectrum of vegetation. Performing mathematical combinations of the different spectral bands has a better understanding of the spectral response of the vegetation. These combinations are called vegetation index (VI) and are useful for the control of the biomass, water content in leaf, chlorophyll content and others. It is usually calculated VI after the construction of the mosaic, as well the farmer has an accurate analysis of its vegetation. However, building a mosaic of images, it has a high computational cost, taking hours to complete and then apply the VI and to have the first test results. In order to reduce the computational cost of this process, this work aims to present a mosaic of images constructed from images with the VI already pre-calculated providing faster analysis to the farmer, given the fact that applying VI on the image came a this reduction in density image and thus have the gain in computational cost to build the mosaic.

Prosopis spp. are a fast growing invasive tree originating from the American dry zones, introduced to Kenya in the 1970s for the restoration of degraded pastoral lands after prolonged droughts and overgrazing. Its deep rooting system is capable of tapping into the ground water table reducing its dependency on rain water and increasing its drought tolerance. It is believed that the Prosopis invasion was eased by a hybridization process, described as the Prosopis Juliflora – Prosopis Pallida complex, suggesting that introduced Prosopis spp. evolved into a hybrid, specifically adapted to the environmental conditions, rendering it a superior and aggressive competitor to endemic species. In many dry lands in Kenya Prosopis has expanded rapidly and has become challenging to control. On the other hand, in some cases, an economic use seems possible. In both cases, detailed and accurate maps are necessary to support stakeholders and design management strategies. The aim of this study is to map the distribution of Prosopis spp. in a selected area in north-west Turkana (Kenya), covering a section of the Tarach water basin. The study is funded by the European Union through the National Drought Management Authority (NDMA) in Kenya, and the main purpose is to assess the potential production of Prosopis pods, which can be used to manufacture emergency livestock feeds to support animals during drought events. The classification was performed using novel Sentinel-2 data through a non-parametric Random Forest classifier. A selection of reference sites was visited in the field and used to train the classifier. Very high classification accuracies were obtained.

Under land and climate change scenarios, agriculture has experienced water competitions among other sectors in the São Paulo state, Brazil. On the one hand, in several occasions, in the northeastern side of this state, nowadays sugar-cane is expanding, while coffee plantations are losing space. On the other hand, both crops have replaced the natural vegetation composed by Savannah and Atlantic Coastal Forest species. Under this dynamic situation, geosciences are valuable tools for evaluating the large-scale energy and mass exchanges between these different agro-ecosystems and the lower atmosphere. For quantification of the energy balance components in these mixed agro-ecosystems, the bands 1 and 2 from the MODIS product MOD13Q1 were used throughout SAFER (Surface Algorithm for Evapotranspiration Retrieving) algorithm, which was applied together with a net of 12 automatic weather stations, during the year 2015 in the main sugar cane and coffee growing regions, located at the northeastern side of the state. The fraction of the global solar radiation (R_G) transformed into net radiation (Rn) was 52% for sugar cane and 53% for both, coffee and natural vegetation. The respective annual fractions of Rn used as λE were 0.68, 0.87 and 0.77, while for the sensible heat (H) fluxes they were 0.27, 0.07 and 0.16. From April to July, heat advection raised λE values above Rn promoting negative H, however these effects were much and less strong in coffee and sugar cane crops, respectively. The smallest daily Rn fraction for all agro-ecosystems was for the soil heat flux (G), with averages of 5%, 6% and 7% in sugar cane, coffee and natural vegetation. From the energy balance analyses, we could conclude that, sugar-cane crop presented lower annual water consumption than that for coffee crop, what can be seen as an advantage in situations of water scarcity. However, the replacement of natural vegetation by sugar cane can contribute for warming the environment, while when this occur with coffee crop there was noticed cooling conditions. The large scale modeling satisfactory results confirm the suitability of using MODIS products together with weather stations to study the energy balance components in mixed agro-ecosystems under land-use and climate change conditions.

The regular monitoring of the evapotranspiration rates and their links with vegetation conditions and soil moisture may support management and hydrological planning leading to reduce the economic and environmental vulnerability of complex water-controlled Mediterranean ecosystems. In this work, the monitoring of water use over a basin with a predominant oak savanna (known in Spain as dehesa) was conducted for two years, 2013 and 2014, monitoring ET at both fine spatial and temporal resolution in different seasons.

A global 5 km daily ET product, developed with the ALEXI model and MODIS day-night temperature difference, was used as starting point. Flux estimations with higher spatial resolutions were obtained with the associated flux disaggregation scheme, DisALEXI, using surface temperature data from the polar orbiting satellites MODIS (1 Km, daily) and Landsat 7/8 (60-120m and sharpened to 30m, 16 days) and the previously estimated coarse resolution fluxes. The results achieved supported the ability of this scheme to accurately estimate daytime-integrated energy fluxes over this system, using input data with different spatio-temporal resolution and without the need for ground observations. Daily ET series at 30 m spatial resolution, generated using STARFM fusion technique, has provided a significant improvement in spatial heterogeneity assessment of the ET series, with RMSE values of 0.56 and 0.68 mm/day for each year, representing an enhancement with respect to interpolated Landsat series. In summary, this approach was demostrated to be robust and operative to map ET at watershed scale with a suitable spatial and temporal resolution for applications over the dehesa ecosystem.

The region of Ilha Solteira, in the Northwestern of São Paulo State, has been undergoing significant changes in agricultural land use and cover since 2006, as pasture fields have been replaced by sugarcane crop. This drastic change can lead to a disturbance in the energy balance, affecting the local climate. The aim of this paper was to assess some parameters related to the energy balance of Ipê's watershed, that changed since no sugarcane cultivation in 2006 to 2,164 hectares in 2011, occupying 31% of the catchment area with this important energy crop for the economy and the environment of Brazil. This study was carried out using remote sensing combined with weather data and using the SAFER (Simple Algorithm for Retrieving Evapotranspiration) model applied in 9 Landsat images collected between 2003 and 2011. The results showed a wide variation between the components of energy balance and when considering only the sugarcane crop were verified the increase values of ETa (Actual Evapotranspiration), H/Rn (Sensible Heat Flux/Net Radiation), TS (Surface Temperature), Rl↑ (Emitted longwave), Rl↓ (Incidente longwave) and surface albedo after the sugarcane production over these years. On the other hand, the NDVI, λE/Rn (Latent Heat Flux/Net Radiation) and Rn values (data) decreased in the same period. Also there was satisfactory correlation between NDVI and ETa. The SAFER model showed satisfactory results for studies of energy balance applied in the Northwestern of São Paulo State.

Evapotranspiration (ET) is one of the least understood components of the water cycle, particularly in data scarce areas. In a context of climate change, evaluating water vapour fluxes of a particular area is crucial to help understand dynamics in water balance. In data scarce areas, ET modelling becomes vital. The study modelled ET using the Penman-Monteith- Leuning (PML) equation forced by Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and MODIS albedo with ancillary meteorological data from an automatic weather station. The study area is located on the Albany Thicket (AT) biome of South Africa and the dominant plant species is Portulacaria afra. The biggest challenge to the implementation of the PML is the parameterisation of surface and stomatal conductance. We tested the use of volumetric soil water content (f_swc), precipitation and equilibrium evaporation ratio (f_zhang) and soil drying after precipitation (f) approaches to account for the fraction (f) of evaporation from the soil. ET from the model was validated using an eddy covariance system (EC). Post processing of eddy covariance data was implement using EddyPro software. The f_drying method performed better with a root mean square observations standard deviation ratio (RSR) of 0.97. The results suggest that modelling ET over the AT vegetation is delicate owing to strong vegetation phenological control of the ET process. The convergent evolution of the vegetation has resulted in high plant available water than the model can detect. It is vital to quantify plant available water in order to improve ET modelling in thicket vegetation.

Vineyards are agricultural surfaces that present a unique structural perturbation to the mean wind flow. As part of the Grape Remote Sensing Atmospheric Profiling and Evapotranspiration Experiment (GRAPEX) a 10-m profile tower of three sonic anemometers at 2, 3.75 and 8 m above ground level in in a mature vineyard having was deployed to measure high frequency diurnal variations of 3-dimensional velocity components (u, v, w) and temperature (T) throughout the growing season. Previous work has been published involving eddy covariance measurements in vineyards but these mostly represented convective daytime unstable conditions. Significantly less has been published about turbulence in vineyards during stable nocturnal periods. Hence, in this study we focused on the nocturnal stable periods under clear skies and relatively light winds typical of the northern portion of California's Central Valley. Our objective was to characterize and evaluate turbulent exchange processes in the layer near the top of a vine canopy during nocturnal periods which are often characterized by weak and intermittent turbulence. Spectra, cospectra and coherence plots were evaluated for nocturnal periods. The spectra suggest there are periods of intermittent turbulence with features indicative of local and regional scale processes. Additionally the impact of the vine structure and spacing on slow meandering flows enhance the decomposition of organized turbulent eddies resulting in intricate mechanical turbulence generated by intermittent eddies that are rapidly decomposed as eddies interact with the vine structure and spacing. Preliminary results will be discussed that provide insight into turbulence characteristics at several heights above a canopy vineyard as affected by vine structure and spacing, wind speed, direction and stable conditions.

Remote sensing-based models are a potential technique when evapotranspiration (ET) estimates are needed on a regional scale. These remote sensing methods are typically validated and calibrated using in situ measurements. Eddy covariance (EC) and lysimetry are two of the most prevalent techniques for measuring ET. Some discrepancies arise between these two techniques consequence of the measurement footprint or the spatial variability in atmospheric and surface conditions. An experiment was carried out in the growing season of 2015 in a ~4 ha row-crop vineyard in a semi-arid advective location in Central Spain, encouraged by the necessity to assess the feasibility of EC measurements in this area and under these conditions. A 9-m2 monolithic weighting lysimeter was available. An EC system was deployed together with a net radiometer and a set of soil heat flux plates. Data of the different terms of the energy balance equation were stored every 15 min, and then averaged at an hourly and daily scales. In this work we focus on the comparison between ET measurements from the two methods, EC and lysimetry. The imbalance in the surface energy budget was first analyzed. A lack of closure around 20% was observed. After forcing the closure, discrepancies between EC and lysimeter measurements still remained. Average estimation errors of ±0.09 mm h^-1 and ±0.5 mm d^-1 were obtained at hourly and daily scales, respectively, whereas a deviation of only 2% was observed in the accumulated ET for the entire experiment. These results support the use of adjusted EC technique to monitor accurate ET in vineyards.

Determining water and carbon fluxes over a vegetated surface is important in a context of global environmental changes and the fluxes help in understanding ecosystem functioning. Pursuant to this, the study measured evapotranspiration (ET) using an eddy covariance (EC) system installed over an intact example of the Albany Thicket (AT) vegetation in the Eastern Cape, South Africa. Environmental constraints to ET were also assessed by examining the response of ET to biotic and abiotic factors. The EC system comprised of an open path Infrared Gas Analyser and Sonic anemometer and an attendant weather station to measure bi-meteorological variables. Post processing of eddy covariance data was conducted using EddyPro software. Quality assessment of fluxes was also performed and rejected and missing data were filled using the method of mean diurnal variations (MDV). Much of the variation in ET was accounted for by the leaf area index (LAI, p < 0.001, 41%) and soil moisture content (SWC, p < 0.001, 32%). Total measured ET during the experiment was greater than total rainfall received owing to the high water storage capacity of the vegetation and the possibility of vegetation accessing ground water. Most of the net radiation was consumed by sensible heat flux and this means that ET in the area is essentially water limited since abundant energy was available to drive turbulent transfers of energy. Understanding the environmental constraints to ET is crucial in predicting the ecosystem response to environmental forces such as climate change.

This paper discusses a novel way of generating sampling points of hydrologic features, specifically streams, irrigation network and inland wetlands, that could provide a promising measure of accuracy using combinations of traditional statistical sampling methods. Traditional statistical sampling techniques such as simple random sampling, systematic sampling, stratified sampling and disproportionate random sampling were all designed to generate points in an area where all the cells are classified and subjected to actual field validation. However, these sampling techniques are not applicable when generating points along linear features. This paper presents the Weighted Disproportionate Stratified Systematic Random Sampling (WDSSRS), a tool that combines the systematic and disproportionate stratified random sampling methods in generating points for accuracy computation. This tool makes use of a map series boundary shapefile covering around 27 by 27 kilometers at a scale of 1:50000, and the LiDAR-extracted hydrologic features shapefiles (e.g. wetland polygons and linear features of stream and irrigation network). Using the map sheet shapefile, a 10 x 10 grid is generated, and grid cells with water and non-water features are tagged accordingly. Cells with water features are checked for the presence of intersecting linear features, and the intersections are given higher weights in the selection of validation points. The grid cells with non-intersecting linear features are then evaluated and the remaining points are generated randomly along these features. For grid cells with nonwater features, the sample points are generated randomly.

In semi-arid regions, small reservoirs are widely employed for facing seasonal in water availability due to the alternation of a short rainy season and of a very long dry season. Therefore, their monitoring is fundamental for local rural communities wellness. In this paper, we present a novel framework for water resources management exploiting the synergy of synthetic aperture radar (SAR) data and hydrological models. The pilot project was implemented in Burkina Faso, showing good potentialities for cheap and continuous monitoring of the environment through the exploitation of a multi-disciplinary framework.

Iligan river basin located in Northern Mindanao, Philippines covers 165.7 km² of basin area. In December 2011, tropical storm Sendong (Washi) hit Iligan City, leaving a trail of wrecked infrastructures and about 490 persons reported dead. What transpired was a wake up call to mitigate future flood disasters. Fundamental to mitigation is understanding runoff behavior inside a basin considering that this is the main source of flooding. For this reason, the present study evaluated total runoff volume, peak discharge and lag time given land cover scenarios in four different years- 1973, 1989, 1998 and 2008. IFSAR and LIDAR DEM were integrated to generate the basin model in ArcGIS. HEC-HMS was used in simulating models for each scenario with Soil Conservation Service Curve Number (SCS CN) as the loss parameter method. Four simulation models of the runoff with varying CN values were established using RIDF as rainfall input with 5 year, 10 year, 25 year, 50 year and 100 year Rainfall Return Period (RRP). Total Runoff volume, peak discharge and lag time were progressively higher from 1973 to 2008 with 1989 land cover as exception where runoff parameters was its lowest. The total runoff volume, peak discharge and lag time is governed by vegetation type. When vegetation is characterized predominantly with woody perennials, runoff volume and peak time is lower. Conversely, when the presence of woody perennials is minimal, these parameters are higher. This study shows that an important way to mitigate flooding is to reduce surface runoff by maintaining vegetation predominantly composed of woody perennials.

A study conducted in the scope of the Alcantara initiative in Angola shown that optical and SAR images allows the estimation of crop's irrigation requirements (CIR) based on a soil water balance model (IrrigRotation). The methodology was applied to east central Portugal, to evaluate its transferability in cases of different climatic conditions and crop types. SPOT-5 Take-5 and Sentinel-1A data from April to September 2015 are used to generate NDVI and backscattering maize crop time series. Both time series are then correlated and a linear regression equation is computed for some maize parcels identified in the test area. Next, basal crop coefficients (K_cb) are determined empirically from the K_cb-NDVI relationships applied within the PLEIADeS project and also from the K_cb-SAR relationships retrieved from the linear fit of both EO data for other maize parcels. These K_cb allow to overcome a major drawback related to the use of the FAO tabulated K_cb, only available for the initial, mid and late season of a certain crop type. More frequent K_cb values also allow a better identification of the crop's phenological stages lengths. CIR estimated from EO data are comparable to the ones obtained with tabulated FAO 56 K_cb values for crops produced under standard conditions, while for crops produced in suboptimal conditions, EO data allow to improve the estimation of the CIR. Although CIR results are promising, further research is required in order to improve the K_cb initial and K_cb end values to avoid the overestimation of the CIR.

Irrigation networks are important in distributing water resources to areas where rainfall is not enough to sustain agriculture. They are also crucial when it comes to being able to redirect vast amounts of water to decrease the risks of flooding in flat areas, especially near sources of water. With the lack of studies about irrigation feature extraction, which range from wide canals to small ditches, this study aims to present a method of extracting these features from LiDAR-derived digital terrain models (DTMs) using Geographic Information Systems (GIS) tools such as ArcGIS and Whitebox Geospatial Analysis Tools (Whitebox GAT). High-resolution LiDAR DTMs with 1-meter horizontal and 0.25-meter vertical accuracies were processed to generate the gully depth map. This map was then reclassified, converted to vector, and filtered according to segment length, and sinuosity to be able to isolate these irrigation features. Initial results in the test area show that the extraction completeness is greater than 80% when compared with data obtained from the National Irrigation Administration (NIA).

A parameterized numerical model is constructed to compare platform options for collecting aerial imagery to support agriculture electronic information services in developing countries like India. A sensitivity analysis shows that when Unmanned Aerial Vehicles, UAVs, are limited in flight altitude by regulations, the velocity and altitude available to manned aircraft lead to a lower cost of operation at altitudes greater than 2000ft above ground level, AGL. If, however, the UAVs are allowed to fly higher, they become cost-competitive once again at approximately 1000ft AGL or higher. Examination of assumptions in the model highlights two areas for additional technology development: baseline-dependent feature-based image registration to enable wider area coverage, and reflectance reconstruction for ratio-based agriculture indices.

The metabolic status of carbon (C) and nitrogen (N) as two essential elements of crop plants has significant influence on the ultimate formation of yield and quality in crop production. The ratio of carbon to nitrogen (C/N) from crop leaves, defined as ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is an important index that can be used to diagnose the balance between carbon and nitrogen, nutrient status, growth vigor and disease resistance in crop plants. Thus, it is very significant for effectively evaluating crop growth in field to monitor changes of leaf C/N quickly and accurately. In this study, some typical indices aimed at N estimation and chlorophyll evaluation were tested to assess leaf C/N in winter wheat and spring barley. The multi-temporal hyperspectral measurements from the flag-leaf, anthesis, filling, and milk-ripe stages were used to extract these selected spectral indices to estimate leaf C/N in wheat and barley. The analyses showed that some tested indices such as MTCI, MCARI/OSAVI2, and R-M had the better performance of assessing C/N for both of crops. Besides, a mathematic algorithm, Branch-and-Bound (BB) method was coupled with the spectral indices to assess leaf C/N in wheat and barley, and yielded the R² values of 0.795 for winter wheat, R² of 0.727 for spring barley, 0.788 for both crops combined. It demonstrates that using hyperspectral data has a good potential for remote assessment of leaf C/N in crops.

With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Airborne and satellite hyperspectral remote sensing is a key technology to observe finite change in ecosystems and environments. The role of such sensors will improve our ability to monitor and mitigate natural and agricultural environments on a much larger spatial scale than can be achieved using field measurements such as soil coring or proximal sensors to estimate the chemistry of vegetation. Hyperspectral sensors for commentarial and scientific activities are increasingly available and cost effective, providing a great opportunity to measure and detect changes in the environment and ecosystem. This can be used to extract critical information to develop more advanced management practices.

In this research, we provide an overview of the data acquisition, processing and analysis of airborne, full-spectrum hyperspectral imagery from a small-scale aerial mapping project in hill-country farms in New Zealand, using an AISA Fenix sensor (Specim, Finland). The imagery has been radiometrically and atmospherically corrected, georectified and mosaicked. The hyperspectral data cube was then spectrally and spatially smoothed using Savitzky-Golay and median filter, respectively. The mosaicked imagery used to calculate bio-chemical properties of surface vegetation, such as pasture. Ground samples (n = 200) were collected a few days after the over-flight are used to develop a calibration model using partial least squares regression method. In-leaf nitrogen, potassium and phosphorous concentration were calculated using the reflectance values from the airborne hyperspectral imagery. In total, three surveys of an example property have been acquired that show changes in the pattern of availability of a major element in vegetation canopy, in this case nitrogen.

The European Water Framework Directive (Directive 2000/60/EC) is a mandatory agreement that guides the member states of the European Union in the field of water policy to fulfill the requirements for reaching the aim of the good ecological status of water bodies. In the last years several workflows and methods were developed to determine and evaluate the characteristics and the status of the water bodies. Due to their area measurements remote sensing methods are a promising approach to constitute a substantial additional value. With increasing availability of optical and radar remote sensing data the development of new methods to extract information from both types of remote sensing data is still in progress. Since most limitations of these data sets do not agree the fusion of both data sets to gain data with higher spectral resolution features the potential to obtain additional information in contrast to the separate processing of the data. Based thereupon this study shall research the potential of multispectral and radar remote sensing data and the potential of their fusion for the assessment of the parameters of water body structure. Due to the medium spatial resolution of the freely available multispectral Sentinel-2 data sets especially the surroundings of the water bodies and their land use are part of this study. SAR data is provided by the Sentinel-1 satellite. Different image fusion methods are tested and the combined products of both data sets are evaluated afterwards. The evaluation of the single data sets and the fused data sets is performed by means of a maximum-likelihood classification and several statistical measurements. The results indicate that the combined use of different remote sensing data sets can have an added value.

We computed Net Basin Supplies to the Great Lakes by estimated over-lake evaporation using the bulk aerodynamic method and derived over-lake precipitation from MERRA associated with the runoff data provided by GLERL. The long-term trend of NBS components that are over-lake evaporation rate, over-lake precipitation and runoff were also addressed. We also evaluated the relationship between NBS and change in water level for each of the Great Lakes. The simple correlation analysis was performed in order to determine the relationship between the climate teleconnection indices with NBS, over-lake precipitation, and over-lake evaporation. A brief review on the effect of climate teleconnection on the Great Lakes NBS, over-lake evaporation, and over-lake precipitation were given in the last section. The distribution of over-lake precipitation is spread throughout the year with two peaks in April and October. The monthly average of runoff entering to the Great Lakes was highest in April while the lowest monthly mean runoff was in September. The evaporation rate was highest in January for all Lakes but Lake Ontario, which was highest in early December. The evaporation rate then sharply drops in March the evaporation processes continue again in August. The calculations of NBS were limited by the availability of runoff data. The highest NBS was in April while the lowest monthly average NBS was varied in December, January, and February. The correlation analysis was based on various teleconnection indices and NBS, over-lake evaporation, and over-lake precipitation. NAO was positively correlated with NBS in all Lakes but Lake Superior while over-lake evaporation showed a negative correlation with PDO and Niño 3.4.

The agricultural expansion in the Southern Brazilian Amazon has long been pointed out due to its severe impacts on tropical forests. But the last decade has been marked by a rapid agricultural transition which enabled to reduce pressure on forests through (i) the adoption of intensive agricultural practices and (ii) the diversification of activities. However, we suggest that this new agricultural model implies new pressures on environment and especially on water resources since many artificial water reservoirs have been built to ensure crop irrigation, generate energy, farm fishes, enable access to water for cattle or just for leisure. In this paper, we implemented a method to automatically map artificial water reservoirs based on time series of Landsat images. The method was tested in the county of Sorriso (State of Mato Grosso, Brazil) where we identified 521 water reservoirs by visual inspection on very high resolution images. 68 Landsat-8 images covering 4 scenes in 2015 were pre-classified and a final class (Terrestrial or Aquatic) was determined for each pixel based on a Dempster-Shafer fusion approach. Results confirmed the potential of the methodology to automatically and efficiently detect water reservoirs in the study area (overall accuracy = 0.952 and Kappa index = 0.904) although the methodology underestimates the total area in water bodies because of the spatial resolution of Landsat images. In the case of Sorriso, we mapped 19.4 km2 of the 20.8 km2 of water reservoirs initially delimited by visual interpretation, i.e. we underestimated the area by 5.9%.

The applications of remote sensing in hydrology are diverse and offer significant benefits for water monitoring. Up to now, operational river monitoring and sediment management in Germany mainly rely on in-situ measurements and on results obtained from numerical modelling. Remote sensing by satellites has a great potential to supplement existing data with two-dimensional information on near-surface turbidity distributions at greater spatial scales than in-situ measurements can offer. Within the project WasMon-CT (WaterMonitoring-Chlorophyll/Turbidity), the Federal Institute of Hydrology (BfG) aims at the implementation of an operational monitoring of turbidity distributions based on satellite images (esp. Sentinel-2, Landsat7 and 8). Initially, selected federal inland and estuarine waterways will be addressed: Rhine, Elbe, Ems, Weser. WasMon-CT is funded within the German Copernicus activities.

Within the project, a database of atmospherically corrected, geo-referenced turbidity data will be assembled. The collected corresponding meta-data will include aspects of satellite data as well as hydrological data, e.g. cloud cover and river run-off. Based on this catalogue of spatially linked meta-data, the satellite data will be selected by e.g. cloud cover or run-off. The permanently updated database will include past as well as recent satellite images. It is designed with a long-term perspective to optimize the existing in-situ measurement network, which will serve partly for calibration and partly as validation data set. The aim is to extend, but not to substitute, the existing frequent point measurements with spatially extensive, satellite-derived data from the near surface part of the water column. Here, turbidity is used as proxy for corresponding suspended sediment concentrations. For this, the relationship between turbidity and suspended sediment concentrations will be investigated. Products as e.g. longitudinal profiles or virtual measurement stations will be developed from an application toolbox to specifically match requirements of operational monitoring tasks and to allow for a better integration into the existing monitoring system. The toolbox demonstrates the benefits of remote sensing by applying the established processing chain to diverse hydrological questions, such as for the investigation of tidal-affected sediment loads or mixing processes at river confluences.

This new application will be of great value to assess, evaluate and monitor the status or the change of large-scale sediment processes at the system level. Accordingly, the satellite-derived turbidity data will strongly enhance federal consulting activities and thus ensure a modern river monitoring of Germany’s federal water ways.

The European Water Framework Directive commits the member states to achieve the good ecological status for all water bodies. For this purpose on the level of the national states monitoring programs are established with the aim to verify the actual status by means of regular surveys. Already in the past remote sensing data in conjunction with methods of geospatial data analysis revealed the added value in terms of monitoring strategies regarding the European Water Framework Directive. Depending on the type of data they can be used for example for the determination of several parameters of rivers and streams. The present analyses show how it is possible to determine the parameter of width variation of small and medium rivers based on digital orthophotos. Because this parameter strongly depends on the geometric quality of the riverbank line, its determination is given particular attention. It turns out that mainly riparian vegetation has a large impact on the visibility of the riverbank line. In a multi-stage process different methods for the identification of affected areas are developed with the aim to reconstruct the true riverbank line in a second step. Finally these data form the basis for the determination of river width variations.

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

The Simple Algorithm for Evapotranspiration Retrieving (SAFER) was used to estimate biophysical parameters and the energy balance components in two different pasture experimental areas, in the São Paulo state, Brazil. The experimental pastures consist in six rotational (RGS) and three continuous grazing systems (CGS) paddocks. Landsat-8 images from 2013 and 2015 dry and rainy seasons were used, as these presented similar hydrological cycle, with 1,600 mm and 1,613 mm of annual precipitation, resulting in 19 cloud-free images. Bands 1 to 7 and thermal bands 10 and 11 were used with weather data from a station located near the experimental area. NDVI, biomass, evapotranspiration and latent heat flux (λE) temporal values statistically differ CGS from RGS areas. Grazing systems influences the energy partition and these results indicate that RGS benefits biomass production, evapotranspiration and the microclimate, due higher LE values. SAFER is a feasible tool to estimate biophysical parameters and energy balance components in pasture and has potential to discriminate continuous and rotation grazing systems in a temporal analysis.

In the northwestern side of the São Paulo state, Brazil, irrigated areas are expanding, because rainfall is not enough to supply the crop water requirements. Under the actual climate and land-use change scenarios, large-scale evapotranspiration (ET) and biomass production (BIO) acquirements are relevant. Eleven Landsat 8 images, from May 2013 to October 2014, were used together with a net of eight agrometeorological stations for modelling these water productivity (WP) parameters in the main agricultural growing irrigated areas inside three hydrological basins in this region. Some of these areas inside of each basin were highlighted for more in-depth WP analyses. The SAFER algorithm estimated the ratio of actual (ET) to reference (ET0) evapotranspiration and this ratio was used for both, to calculate ET and to include the soil moisture effects in the Monteith’s Radiation Use Efficiency (RUE) model. The highlighted agricultural growing regions were Paranapuã, Populina and Santa Rita d’Oeste in the Turvo/Grande basin; Rubinéia, Santa Fé do Sul, Suzanópolis and Ilha Solteira, in the São José dos Dourados basin; and Pereira Barreto and Sud Mennucci, in the Baixo Tietê basin. The highest averages of both ET (1.7 ± 0.9 mm d^-1) and BIO (47 ± 31 kg ha^-1 d^-1) were for Ilha Solteira, while the lowest ones happened in Sud Mennucci (1.3 ± 0.7 mm d^-1 and 40 ± 27). These ET and BIO ranges returned WP values varying from 2.2 ± 0.6 to 2.6 ± 0.8 kg m^-3, with the higher end of this range happening in the Turvo/Grande hydrological basin. Considering the annual time-scale, crops will consume around 770, 828 and 786 mm yr^-1 with the corresponding BIO values of 27, 26 and 25 t ha^-1 yr^-1, respectively in Turvo/Grande, São José do Dourados and Baixo Tietê. It was concluded that increments in agricultural irrigated areas should be stimulated in the northwestern side of the state, mainly in the first basin, to retrieve good yield with less water use.

The Cabeceira Comprida stream's watershed, located in Santa Fé do Sul, Brazil, is an agroecosystem with great demand of water for the population and agriculture. During the dry season the water demand exceeds the amount generated by the watershed. It is important to know the dynamics of the water above the ground to improve the water resources management. Ten Landsat 8 images were used combined with Northwestern São Paulo State Weather Network data under different thermohydrological conditions of the year 2014 to quantify actual evapotranspiration (ETa), biomass production (BIO) and water productivity (WP) based on ETa. Using the Simple Algorithm for Retrieving evapotranspiration (SAFER) for calculating ETa, the Monteith's radiation model was applied for estimating the BIO and for calculation of WP the ratio of BIO and ETa. The average pixels for ETa, BIO and WP ranged respectively from 0.38 ± 0.35 to 2.05 ± 0.76 mm day^-1; 10.15 ± 12.19 to 71.61 ± 35.54 kg ha^-1 day^-1; 1.89 ± 0.76 to 3.88 ± 0.86 kg m^-3. The lower values of ETa (0.38 mm day^-1; DOY 220), BIO (10.15 kg ha^-1 day^-1; DOY 220) and WP (1.89 kg m^-3; DOY 204) were obtained in winter, and highest values of ETa (2.05 mm day^-1; DOY 364) and BIO (71.64 kg ha^-1 day^-1; DOY 364) in the summer and WP (3.88 kg m^-3; DOY 92) in the autumn. The water productivity components can subsidize the monitoring of the agro-ecosystems, being a useful tool to quantify the annual variability of ETa and BIO.

Monitoring dry biomass of crop timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin in China was chose as study area to map the spatial distribution of paddy biomass. The study derived 12 vegetation indexes from HJ-CCD image, which were closely related to crop growth. After screening sensitive vegetation index with in-situ samples by correlation analysis, the study developed the inversion model by single variable and multiple variables. The determination coefficient (R²) and root mean square error (RMSE) was used to evaluate the accuracy of models. Results showed that the accuracies of multivariable models were better than these of single-variable models, of which the average R² reached 0.647 and the average RMSE was 0.059. It indicated that the multi-variable models were input in more information than those of single-variable models, which improved the accuracies of estimating paddy biomass in to a certain degree. The average overall accuracies of multi-variable models were 92.7%, while that of singe-variable models were 87.8%. The model with multiple linear regressions could be used to map the paddy biomass in the study area by using HJ-CCD image.

The use of remote sensing satellite in conjunction with models and meteorological data enable the mapping of biophysical properties of agroecosystems with satisfactory accuracy. The main goal of this research was to determine the spatial-temporal agro-ecological indicators of water productivity in watersheds with different types of land use and occupation, using Landsat 8 images, agro-meteorological stations and application of Monteith and SAFER (Simple Algorithm for Retrieving Evapotranspiration) models to estimate the production biomass (BIO) and the actual evapotranspiration (ET), respectively. Incident global solar radiation (RS ↓) is observed seasonality of radiation during the year. Higher RS ↓levels happen during the first and the last four months, when the Sun is around its zenith positions in the study region. During the natural dry period in the region, the RS↓ is lower because winter solstice time for the Southern Hemisphere, this condition it is verified the reducing in the values of ET and BIO. Average values of biophysical properties for the study period were 0.54, 0.16 and 301 K for Normalized Difference Vegetation Index, albedo and surface temperature, respectively. The highest value of BIO was 105 kg ha^-1d^-1 and occurred in July 2013. The lowest value was 15.9 kg ha^-1d^-1 and occurred in October 2014. ET showed a value of 1.65 mm d^-1 in the rainy period and 0.64 during the dry period in the study area. The highest average ET occurred in the irrigated area (June 2014), with a value of 1.89 mm d^-1 and a maximum of 2.46 mm d^-1. WP average for the evaluated period was 3.06 Kg m^-3, with the largest value of 4.91 Kg m^-3 in June 2013 and a minimum value of 2.45 Kg m^-3 in September 2013.

The objective of the study was to determine whether rice identification and acreage estimation accuracy at the early stage of the rice growth season using single fine quad Radarsat-2 images could meet the demand of rice monitoring. The Leizhou site (20°52’N, 110°05’E) is located in Guangdong Province in southern China and is dominated by rice paddies. There was a lack of optical data acquisitions during certain years. There are two rice growth seasons per year. Multitemporal Radarsat-2 products were acquired in the early rice growth season of 2014. First, multi-temporal backscattering coefficients and polarimetric parameters based on H/A/Alpha and Yamaguchi polarimetric decomposition theories were extracted and analyzed to distinguish between rice paddies and other typical land cover types, which could help select the optimal rice growth stage and image characteristics for early season classification. Second, an object-oriented technique was applied to time series backscattering coefficient images (HH, HV, VH, VV), H/A/Alpha images (Entropy, Anistropy and Alpha) and Yamaguchi images (volume, double-bounce, surface and helix). Based on the segmentation images, the supervised Bayes, KNN, SVM and Decision tree classifier were used to identify rice. The results indicated that in flat area, 1) Backscatter images and Yamaguchi images were better than H/A/Alpha images in the rice identification application, and 2) At the early rice growth season, the 0430-Yamaguchi image combined with the objectoriented decision tree algorithm were capable of delivering highly accurate maps of rice (0.9236), while the 0524- backscatter image combined with the object-oriented decision tree algorithm could acquire the highest classification accuracy (0.9278) at the end of the rice growth season. Jointing stage is considered to be appropriate for rice identification at the early season. The study demonstrated that it was possible to identify rice at the early season with single quad C-SAR imagery.

Leaf nitrogen accumulation (LNA) has important influence on the formation of crop yield and grain protein. Monitoring leaf nitrogen accumulation of crop canopy quantitively and real-timely is helpful for mastering crop nutrition status, diagnosing group growth and managing fertilization precisely. The study aimed to develop a universal method to monitor LNA of maize by hyperspectrum data, which could provide mechanism support for mapping LNA of maize at county scale. The correlations between LNA and hyperspectrum reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimal model of estimating LNA based on multiple linear regression method. The in-situ samples were used to evaluate the accuracy of the estimating model. Results showed that the estimating model with one differential logarithmic transformation (lgP') of reflectivity could reach highest correlation coefficient (0.889) with lowest RMSE (0.646 g·m^-2), which was considered as the optimal model for estimating LNA in maize. The determination coefficient (R²) of testing samples was 0.831, while the RMSE was 1.901 g·m^-2. It indicated that the one differential logarithmic transformation of hyperspectrum had good response with LNA of maize. Based on this transformation, the optimal estimating model of LNA could reach good accuracy with high stability.

Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop needs or health problems and provide solutions for a better crop management. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation.

In the present study, the experimental area is located near the village Eleftherion of Larissa Prefecture in the Thessaly Plain, and consisted of two adjacent agricultural fields of cotton and corn.

Imagery from WorldView-2 (WV2) satellite platform was obtained from European Space Imaging and Landsat-8 (L8) free of charge data were downloaded from the United States Geological Survey (USGS) archive. The images were selected for a four month span to evaluate continuity with respect to vegetation growth variation.

VIs for each satellite platform data such as the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Fraction Photosynthetically Radiation (FPAR) were calculated. The comparison of these VIs produced from the two satellite systems with different spatial and spectral resolution was made for each growth stage of the crops and their results were analyzed in order to examine their correlation.

Utilizing the WV2 new spectral data, several innovative chlorophyll and vegetation indices were created and evaluated so as to reveal their effectiveness in the detection of problematic plant growth areas. The Green Chlorophyll index appeared to be the most efficient index for the delineation of these areas.

Reliable and updated maps of vegetation in protected natural areas are essential for a proper management and conservation. Remote sensing is a valid tool for this purpose. In this study, a methodology based on a WorldView-2 (WV-2) satellite image and in situ spectral signatures measurements was applied to map the Canarian Monteverde ecosystem located in the north of the Tenerife Island (Canary Islands, Spain). Due to the high spectral similarity of vegetation species in the study zone, a Multiple Endmember Spectral Mixture Analysis (MESMA) was performed. MESMA determines the fractional cover of different components within one pixel and it allows for a pixel-by-pixel variation of endmembers. Two libraries of endmembers were collected for the most abundant species in the test area. The first library was collected from in situ spectral signatures measured with an ASD spectroradiometer during a field campaign in June 2015. The second library was obtained from pure pixels identified in the satellite image for the same species. The accuracy of the mapping process was assessed from a set of independent validation plots. The overall accuracy for the ASD-based method was 60.51 % compared to the 86.67 % reached for the WV-2 based mapping. The results suggest the possibility of using WV-2 images for monitoring and regularly updating the maps of the Monteverde forest on the island of Tenerife.

The Jaíba Irrigated Perimeter is a large irrigated agriculture area, located in the region Forest Jaíba between the São Francisco and Verde Grande rivers, in the Brazilian semi-arid region. In 2014, irrigators this the region face losses in the interruption of new plantings in irrigated areas due to water scarcity. The objective of this study is combine the model to estimate the Monteith BIO with the SAFER algorithm in the case of obtaining ET, to analyze the dynamics of natural vegetation and irrigated crops in water scarcity period. For application of the model are necessary data from meteorological stations and satellite images. Were used 23 satellite images of MODIS with spatial resolution of 250m and temporal 16 days, of 2014 year. For analyze the results, we used central pivots irrigation mask of Minas Gerais state, Brazil. In areas with irrigated agriculture with central pivot, the mean values of BIO over the year 2014 were 88.96 kg.ha^-1.d^-1. The highest values occurred between April 23 and May 8, with BIO 139 kg.ha^-1.d^-1. For areas with natural vegetation, the average BIO was 88.34 kg.ha^-1.d^-1 with lower values in September. Estimates of ET varied with the lowest values of ET observed in natural vegetation 1.91±1.22 mm.d^-1 and the highest values in irrigated area is observed 3.51±0.97 mm.d^-1. Results of this study can assist in monitoring of river basins, contributing to the management irrigated agriculture, with the trend of scarcity of water resources and increasing conflicts for the water use.

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

The most important parameters, should be considered during application of remote sensing techniques in agricultural sector, is to acquire image data in appropriate moment in accordance with the growth of the crop. Unmanned Aerial Vehicles (UAVs) have several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly with a comparatively lower flight altitude i.e. 80~400m nullifying the effect of extreme weather and cloud. This study discussed the use of low cost-effective UAV based remote sensing application in inspection of agricultural subsidy. The study area includes 129.1km² of Miwon town. UAV images acquired 41 times from July 17 to August 10, 2015 for 7 days. The UAV images identify a significant amount of incorrect applications for agricultural subsidy, almost 29.6% (559 of 1,889). Surveying with UAV for agricultural payment instead of field stuff can reduce the time as much as 76.7 % and increase the effectiveness of inspection methods.

The advancements in remote sensing in combination with sensor technology (both passive and active) enable growers to analyze an entire crop field as well as its local features.

In particular, changes of actual evapo-transpiration (ET) as a function of water availability can be measured remotely with infrared radiometers. Detection of crop water stress and ET and combining it with the soil water flow model enable rational irrigation timing and application amounts.

Nutrient deficiency, and in particular nitrogen deficiency, causes substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield.

In the present work, to retrieve ET a novel deterministic approach was used which is based on the remote sensing data. The algorithm can automatically provide timely valuable information on plant and soil water status, which can improve the management of irrigated crops. The solution is capable of bridging between Penman-Monteith ET model and Richards soil water flow model. This bridging can serve as a preliminary tool for expert irrigation system.

To support decisions regarding fertilizers the greenness of plant canopies is assessed and quantified by using the spectral reflectance sensors and digital color imaging.

Fertilization management can be provided on the basis of sampling and monitoring of crop nitrogen conditions using RS technique and translating measured N concentration in crop to kg/ha N application in the field.

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods.

This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

We studied the Paraíba do Sul river watershed, São Paulo state (PSWSP), Southeastern Brazil, in order to assess the land use and cover (LULC) and their implications to the amount of carbon (C) stored in the forest cover between the years 1985 and 2015. The region covers an area of 1,395,975 ha. We used images made by the Operational Land Imager (OLI) sensor (OLI/Landsat-8) to produce mappings, and image segmentation techniques to produce vectors with homogeneous characteristics. The training samples and the samples used for classification and validation were collected from the segmented image. To quantify the C stocked in aboveground live biomass (AGLB), we used an indirect method and applied literature-based reference values. The recovery of 205,690 ha of a secondary Native Forest (NF) after 1985 sequestered 9.7 Tg (Teragram) of C. Considering the whole NF area (455,232 ha), the amount of C accumulated along the whole watershed was 35.5 Tg, and the whole Eucalyptus crop (EU) area (113,600 ha) sequestered 4.4 Tg of C. Thus, the total amount of C sequestered in the whole watershed (NF + EU) was 39.9 Tg of C or 145.6 Tg of CO₂, and the NF areas were responsible for the largest C stock at the watershed (89%). Therefore, the increase of the NF cover contributes positively to the reduction of CO₂ concentration in the atmosphere, and Reducing Emissions from Deforestation and Forest Degradation (REDD+) may become one of the most promising compensation mechanisms for the farmers who increased forest cover at their farms.

The vertical distribution of leaf water content (LWC) within plant canopy plays an important role in light penetration and scattering, thus affecting reflectance simulation with radiative transfer models. Although passive remote sensing techniques have been widely applied to estimate LWC, they are unable to retrieve the LWC vertical distribution within canopy. By providing vertical information, terrestrial LiDAR can potentially overcome this limitation. In this paper we investigated the applicability of the terrestrial full-waveform LiDAR to estimate the LWC vertical profile within the canopy of individual plants.

A standard radiometric calibration was applied to convert the amplitude and the echo width to a physically well-defined radiometric quantity, namely the backscatter coefficient. However, the backscatter coefficient is strongly affected by the incidence angle between the laser beam and the leaf normal. In order to compensate for incidence angle effects, reference reflectors (Spectralon from Labsphere, Inc.) were used to build a look-up table to calibrated the backscatter coefficient. Our results showed that the backscatter coefficient had a strong correlation (R² = 0.66) with LWC after correcting for the incidence angle effect. Good agreements were achieved between the predicted vertical profile of LWC and the measured vertical profile of LWC with a mean RMSE (root mean square error) value of 0.001 g/cm² and a mean MAPE (mean absolute percent error) value of 4.46 %. Our study successfully demonstrated the feasibility of retrieving LWC vertical distribution within plant canopy from a terrestrial full-waveform LiDAR.

Forest areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Satellite remote sensing provides a useful tool to capture the temporal dynamics of forest vegetation change in response to climate shifts, at spatial resolutions fine enough to capture the spatial heterogeneity. Frequent satellite data products, for example, can provide the basis for studying time-series of biophysical parameters related to vegetation dynamics. Vegetation index time series provide a useful way to monitor forest vegetation phenological variations. In this study, we used MODIS Terra/Aqua time-series data, along with yearly and monthly net radiation, air temperature, and precipitation data to examine the feedback mechanisms between climate and forest vegetation. Have been quantitatively described Normalized Difference Vegetation Index(NDVI) /Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Evapotranspiration (ET) and Gross Primary Production (GPP) temporal changes for Cernica- Branesti forest area, a periurban zone of Bucharest city in Romania, from the perspective of vegetation phenology and its relation with climate changes and extreme climate events (summer heat waves). A time series from 2000 to 2016 of the MODIS Terra was analyzed to extract forest biophysical parameters anomalies. Forest vegetation phenology analyses were developed for diverse forest land-covers providing a useful way to analyze and understand the phenology associated to those landcovers. Correlations between NDVI/EVI , LAI, ET and GPP time series and climatic variables have been computed.

Land degradation, a phenomenon referring to (drought) in arid, semi-arid and dry sub-humid regions as a result of climatic variations and anthropogenic activities most especially in the semi-arid lands of Sudan, where vast majority of the rural population depend solely on agriculture and pasture for their daily livelihood, the ecological pattern had been greatly influenced thereby leading to loss of vegetation cover coupled with climatic variability and replacement of the natural tree composition with invasive mesquite species. The principal aim of this study is to quantitatively examine the vigour of vegetation in Sudan through different vegetation indices. The assessment was done based on indicators such as soil adjusted vegetation index (SAVI). Cloud free multi-spectral remotely sensed data from LANDSAT imagery for the dry season periods of 1984 and 2009 were used in this study. Results of this study shows conversion of vegetation to other land use type. In general, an increase in area covered by vegetation was observed from the NDVI results of 2009 which is a contrast of that of 1984. The results of the vegetation indices for NDVI in 1984 (vegetated area) showed that about 21% was covered by vegetation while 49% of the area were covered with vegetation in 2009. Similar increase in vegetated area were observed from the result of SAVI. The decrease in vegetation observed in 1984 is as a result of extensive drought period which affects vegetation productivity thereby accelerating expansion of bare surfaces and sand accumulation. Although, increase in vegetated area were observed from the result of this study, this increase has a negative impact as the natural vegetation are degraded due to human induced activities which gradually led to the replacement of the natural vegetation with invasive tree species. The results of the study shows that NDVI perform better than by SAVI.

Vegetation forms the basis for the existence of animal and human. Due to changes in climate and human perturbation, most of the natural vegetation of the world has undergone some form of transformation both in composition and structure. Increased anthropogenic activities over the last decades had pose serious threat on the natural vegetation in Nigeria, many vegetated areas are either transformed to other land use such as deforestation for agricultural purpose or completely lost due to indiscriminate removal of trees for charcoal, fuelwood and timber production. This study therefore aims at examining the rate of change in vegetation cover, the degree of change and the application of Principal Component Analysis (PCA) in the dry sub-humid region of Nigeria using Normalized Difference Vegetation Index (NDVI) data spanning from 1983-2011. The method used for the analysis is the T-mode orientation approach also known as standardized PCA, while trends are examined using ordinary least square, median trend (Theil-Sen) and monotonic trend. The result of the trend analysis shows both positive and negative trend in vegetation change dynamics over the 29 years period examined. Five components were used for the Principal Component Analysis. The results of the first component explains about 98 % of the total variance of the vegetation (NDVI) while components 2-5 have lower variance percentage (< 1%). Two ancillary land use land cover data of 2000 and 2009 from European Space Agency (ESA) were used to further explain changes observed in the Normalized Difference Vegetation Index. The result of the land use data shows changes in land use pattern which can be attributed to anthropogenic activities such as cutting of trees for charcoal production, fuelwood and agricultural practices. The result of this study shows the ability of remote sensing data for monitoring vegetation change in the dry-sub humid region of Nigeria.

Although the spatial context is expected to be a major influence in the interactions among organisms and their environment, it is commonly ignored in ecological studies. This study is part of an investigation on home ranges and their influence in the escape behaviour of Iberian lizards. Fieldwork was conducted inside a 400 m² mesocosm, using three acclimatized adult male individuals. In order to perform analyses at this local scale, tools with high spatial accuracy are needed. A total of 3016 GPS points were recorded and processed into a Digital Elevation Model (DEM), with a pixel resolution of 2 cm. Then, 1156 aerial photos were taken and processed to create an orthophoto. A refuge map, containing possible locations for retreats was generated with supervised image classification algorithms, obtaining four classes (refuges, vegetation, bare soil and organic soil). Furthermore, 50 data-loggers were randomly placed, recording evenly through the area temperature and humidity every 15'. After a month of recording, all environmental variables were interpolated using Kriging. The study area presented an irregular elevation. The humidity varied according to the topography and the temperature presented a West-East pattern. Both variables are of paramount importance for lizard activity and performance. In a predation risk scenario, a lizard located in a temperature close to its thermal optimum will be able to escape more efficiently. Integration of such ecologically relevant elements in a spatial context exemplifies how remote sensing tools can contribute to improve inference in behavioural ecology.

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth’s energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 ~ 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer’s Accuracy), UA (User’s Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Regular updating of fuels maps is important for forest fire management. Nevertheless complex and time consuming field work is usually necessary for this purpose, which prevents a more frequent update. That is why the assessment of the usefulness of satellite data and the development of remote sensing techniques that enable the automatic updating of these maps, is of vital interest. In this work, we have tested the use of the spectral bands of OLI (Operational Land Imager) sensor on board Landsat 8 satellite, for updating the fuels map of El Hierro Island (Spain). From previously digitized map, a set of 200 reference plots for different fuel types was created. A 50% of the plots were randomly used as a training set and the rest were considered for validation. Six supervised and 2 unsupervised classification methods were applied, considering two levels of detail. A first level with only 5 classes (Meadow, Brushwood, Undergrowth canopy cover >50%, Undergrowth canopy cover <15%, and Xeric formations), and the second one containing 19 fuel types. The level 1 classification methods yielded an overall accuracy ranging from 44% for Parellelepided to an 84% for Maximun Likelihood. Meanwhile, level 2 results showed at best, an unacceptable overall accuracy of 34%, which prevents the use of this data for such a detailed characterization. Anyway it has been demonstrated that in some conditions, images of medium spatial resolution, like Landsat 8-OLI, could be a valid tool for an automatic upgrade of fuels maps, minimizing costs and complementing traditional methodologies.

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30 years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and evaluate their applicability for agricultural drought evaluation when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in-situ rainfall measurements across Chile were initially compared to the satellite-based precipitation estimates. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated precipitation in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, further CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in-situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in-situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR data sets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of precipitation geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.