Successful retrieval of surface properties from space is hampered by the presence of atmospheric aerosol particles that contribute significantly to the measured signal. Our ability to obtain reliable information about surface properties depends to a large extent on how well we can account for the influence of aerosols. The problem is complicated by the fact that these aerosols often consist of a multi-component mixture of particles with different chemical compositions and different affinities to water. For example, in order to predict how the optical properties of such particles change with increasing humidity, we need to make assumptions about how the particles grow, change their refractive indices, and mix as a function of humidity. The purpose of this paper is to discuss possible strategies for reliable atmospheric correction over dark as well as bright surfaces. The role of realistic simulations of the radiative transfer process in the coupled atmosphere-surface system in order to solve the inverse problem required to retrieve surface properties will also be discussed.

Aircraft routinely used for agricultural spray application are finding utility for remote sensing. Data obtained from remote sensing can be used for prescription application of pesticides, fertilizers, cotton growth regulators, and water (the latter with the assistance of hyperspectral indices and thermal imaging). Digital video was used to detect weeds in early cotton, and preliminary data were obtained to see if nitrogen status could be detected in early soybeans. Weeds were differentiable from early cotton at very low altitudes (65-m), with the aid of supervised classification algorithms in the ENVI image analysis software. The camera was flown at very low altitude for acceptable pixel resolution. Nitrogen status was not detectable by statistical analysis of digital numbers (DNs) obtained from images, but soybean cultivar differences were statistically discernable (F=26, p=0.01). Spectroradiometer data are being analyzed to identify narrow spectral bands that might aid in selecting camera filters for determination of plant nitrogen status. Multiple camera configurations are proposed to allow vegetative indices to be developed more readily. Both remotely sensed field images and ground data are to be used for decision-making in a proposed variable-rate application system for agricultural aircraft. For this system, prescriptions generated from digital imagery and data will be coupled with GPS-based swath guidance and programmable flow control.

Comparison of extracting areas of paddy fields of Zhejiang Province in 2002 using Moderate-Resolution Imaging Spectroradiometer (MODIS), Advance Very High Resolution Radiometer (AVHRR), geographic information system (GIS) and global position system (GPS) was reported in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy. A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. MODIS data of September 1st, 2002 and NOAA/AVHRR data of September 2st, 2002 covering the whole of Zhejiang Province were acquired. By integration of Remote Sensing (RS), GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.1% percent for MODIS and 92.7 percent for NOAA compared with the statistical data of the agricultural bureau of Zhejiang Province.

We measure directional reflectance and daytime temperature of a wintertime coniferous forest from space using data acquired by the Department of Energy's Multispectral Thermal Imager (MTI). The study site is the Howland experimental forest in central Maine. The data include measurements from all seasons over a one-year period from 2001-2002 but with a concentration in late winter and early spring. The results show variation in both reflectance and temperature with direction and season. The reflectance results compare favorably with previous bidirectional measurements performed at the Howland site. Near-nadir reflectance in the visible bands varies periodically over the year with a high in summer and a low in winter. Near-infrared (NIR) reflectance shows dual variation. The canopy reflectance varies as a function of solar and satellite zenith angle, presumably due to a changing proportion of shadows. Furthermore, a NIR pseudo-BRDF (bidirectional reflectance distribution function) shows that the canopy brightens in the NIR during fall and winter. Retrieved canopy temperatures are consistently warmer in the off-nadir view by about 2°C, with a small seasonal variation. The seasonal canopy temperature trend is well exhibited, and days with snow on the ground are easily distinguished from days with no snow on the ground. The results also show that the retrieved temperatures are consistently warmer than above-canopy air temperature by about 4°C. This difference is greater for off-nadir views and also appears to be larger in the spring and summer than in the fall and winter.

Remotely sensed imagery, coupled with wildlife habitat models provide a powerful tool for the implementation, assessment, and monitoring of wildlife conservation/restoration initiatives. Observed, empirical relationships between a species abundance metric and landscape structure/composition are used to structure models. Habitat suitability models always represent a trade off between breadth of applicability and specificity. Large-spatial extent, coarse spatial resolution data sets may be useful for characterizing potential animal distributions at regional or continental scales; however, habitat models developed at this spatial scale may have little applicability for predicting suitability at finer spatial resolutions. Whereas numerous issues related to multi-scale analysis have been acknowledged with respect to wildlife habitat models, only recently have sources of high-resolution imagery been readily available for site-specific analyses. We outline a multi-scale approach to habitat modeling and demonstrate this approach with northern bobwhite. We developed a coarse resolution model appropriate for identifying focal regions likely to support bobwhite using classified LandSat imagery and relative abundance measures from breeding season call counts. Then we developed a fine resolution model based on 4-m multispectral IKONOS imagery and animal space-use for planning and implementing conservation practices at the local scale. We discuss the application of this hierarchical approach to conservation planning.

With the large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible. Currently, the moderate resolution imaging spectroradiometers on board NASA’s Earth Observing Satellites (EOS) Terra and Aqua make it possible to derive a global coverage of land surface vegetation indices, leaf area index, and surface temperature data products at 1 km spatial resolution every day. The advanced microwave scanning radiometers (AMSR) on board Aqua and Japan's ADEOS satellites start sending back a global coverage of rainfall and land surface soil moisture data products at up to 25km spatial resolution every two to three days. It is also well known that these land surface remote sensing products contain uncertainties due to imperfect instrumentation calibration and inversion algorithms, geophysical noise, representativeness error, communication breakdowns, and other sources while land surface model can continuously simulate these land surface state or storage variables for all time steps and all covered areas. Therefore a combination of satellite remote sensing products and land surface model simulations may provide more continuous, precise and comprehensive depiction of the dynamics of the land surface states. This paper introduces the state-of-the-arts technologies in the development of NASA's Land Data Assimilation System, and then proposes a procedure to combine the simulations of a simple land surface model and the remote sensing products from MODIS and AMSR. After the results of testing the procedure for an arid area in Southwest USA are presented, the application of the procedure for the oases in Fukang Count of Xinjiang Autonomous Region is proposed.

The use of soil and topography information to explain crop yield variation across fields is often applied for crop management purposes. Remote sensed data is a potential source of information for site-specific crop management, providing both spatial and temporal information about soil and crop condition. Studies were conducted in a 104-acre (42-hectare) dryland cotton field in 2001 and 2002 in order to (1) qualitatively assess the spatial variability of soil physical properties from kriged estimates, (2) compare actual yields with normalized difference vegetation reflectance indices (NDVI) obtained from multispectral imagery and from in situ radiometer data, and (3) predict site-specific cotton yields using a crop simulation model, GOSSYM. An NDVI map of soybean in 2000 obtained from a multispectral image was used to establish four sites in each low, medium and high NDVI class. These 12 sites were studied in 2001 and 12 more sites selected at random were studied in 2002 (n = 24). Site-specific measurements included leaf area index (LAI), canopy hyperspectral reflectance, and three-band multispectral image data for green, red, and near-infrared reflectance wavebands at spatial resolutions of 2 m in 2001 and 0.5 m in 2002. Imagery was imported into the image analysis software Imagine (ERDAS, v. 8.5) for georegistration and image analysis. A 6x6 pixels (144 m²) area of interest was established on top of each field plot site and digital numbers (DN) from reflectance imagery were extracted from each band for derivation of NDVI maps for each of four sampling dates. Lint yield from each plot site was collected by hand and also by a cotton picker equipped with AgLeader yield monitor and OmniStar differential global positioning system. We found plant height, leaf area index, and lint yield were closely associated with NDVI maps and with NIR band values acquired from either an aircraft or handheld (GER-1500) sensor during peak bloom in mid July. Results indicate NDVI and NIR bands could be used to produce estimated field maps of plant height, leaf area index and yield, which offer a potentially attractive mid-season management tool for site specific farming in dryland cotton.

In the last several decades, the responses of vegetation to global changes at regional and global scales have been studied with many mathematical models primarily driven by point meteorological observations. In this study, the net primary productivity (NPP) of Xinjiang, China is simulated using the GLObal Production Efficiency Model (GLO-PEM) which is a semi-mechanistic model of plant photosynthesis and respiration and driven entirely by satellite observations. With the available satellite observation data acquired from NOAA’s Advanced Very High Resolution Radiometer (AVHRR), the seasonal and inter-annual changes of NPP in the Xinjiang area are analyzed for the time period of 20 years from 1981 to 2000. Large spatial variability of NPP is found in this area. The temporal trends of NPP in different regions of the area differed significantly. However, for the whole area the mean annual NPP decreased in the 1980s and increased in the 1990s. Seasonal variations of NPP are large and inter-annual changes are moderate. The correlations between the simulated NPP and the precipitation and temperature suggested that precipitation and temperature played major roles in the variations of NPP.

Precision agriculture, a holistic approach to micro-manage agricultural landscapes based on information, knowledge, and new technologies, will accelerate the application of remote sensing techniques to agricultural management. In recent years there has been a wealth of new research developments, particularly based on ground platforms, but also on airborne and spatial platforms. The paper provides a summary of applications by platform. Presently, utilizations by producers are still rare but, in the past few years, several new programs have been offered for nutrient management, particularly nitrogen, crop status monitoring, and irrigation management. There are specific and unique technical and managerial barriers and requirements for the application of remote sensing to soil and crop management. The four principal requirements relate to: spatial resolution, timeliness, coverage frequency, and imagery management infrastructure. Through precision agriculture, specialists trained in imagery analysis, efficient infrastructure for the transfer and management of imagery, better sensor systems, all needed to successfully use remote sensing to precision agriculture include various aspects of soil monitoring, crop condition monitoring and management, and machinery performance evaluation. This will bring a more profitable and sustainable agriculture where optimal agricultural production is made while protecting environmental quality.

The hyperspectral reflectance of the canopy in field, the first and the third unfold leaves from the top of corn are measured indoor in different stages by a ASD FieldSpec Pro FR. The concentrations of chlorophyll and carotenoid of leaves corresponding to the spectra are determined by biochemical method. The correlation between the pigment concentrations, leaf area indices, above ground biomass and fresh leaf mass and the red edge parameters of corn are analyzed. The hyperspectral reflectance are gradually getting smaller in the visible region and bigger in the near infrared region along with growth. The difference of reflectance between in the near infrared region and in the visible region is the biggest in flowering stage. There are “two peak” phenomena for the red edge of canopy spectra of corn. These phenomena are first the clearer with growth, then the clearest in flowering stage and after that are gradually weaken. The position of red edge (λ_red) of canopy spectra are between 710nm and 740nm. There are "red shift’ phenomena for λ_red before flowering stage, the slope of red edge (Dλ_red) and the area of red edge (S_red) before the elongation stage, but are gradually smaller and "blue shift’ after flowering stage for the slope of red edge (Dλ_red) and the area of red edge (S_red) of the canopy spectra. The leaf area indices (LAI), above ground fresh biomass, above ground dry biomass and fresh leaf mass are very significantly correlative to the red edge parameters _λred, Dλ_red and S_red of the canopy spectra, and the concentrations of chlorophyll-a, chlorophyll-b, total chlorophyll and carotenoid of leaves also significantly correlative to their red edge parameters λ_red and Dλ_red. These prove that the red edge parameters (λ_red, Dλ_red and S_red) can be used to estimate LAI, above ground biomass and fresh leaf mass. The parameters λ_red and Dλ_red can be used to estimate the concentrations chlorophyll and carotenoid of leaves for corn.

Fresh market fruit crops such as apples have not employed precision agriculture tools, partially due to the labor intensive nature of the cropping systems. In this paper we describe new research in the development of precision agriculture tools for tree fruit, including the ability to track spatially variable orchard data before harvest through to the packing plant. Remote sensing is a key component of this system, and remote sensing products are being evaluated for their usefulness in guiding orchard management.

Irrigated potato production in sandy soils can be impacted by low nitrogen (N) and water retention in the soil. A field study was conducted to use canopy spectral reflectance as a primary means to characterize N fertilizer rates and soil texture variations as growth and yield limiting factors in potato. A hand-held 16-band spectral radiometer was used to obtain reflectance readings of the potato canopies. Reflectance measurements were made in field plots that received four rates of N or in four areas where the soil textures were different. At later stages of plant growth, canopy reflectance in the 760 to 1000 nm spectral range was consistently higher in plots that received higher rates of N or in areas where the soil contained higher clay and silt fractions. Russet Burbank potatoes, with increasing rate of N fertilizer, showed a decreasing trend in total tuber yield and an increasing trend in percent of tubers with weight exceeding 170 g. Canopy reflectance was inversely related to tuber yield or size for Russet Burbank potatoes when soil texture was the only variable.

Based on Landsat TM data combined with practical investigation information obtained using Global Positioning Systems (GPS), we created a training field of land use classification. Using the methods of spectral distance analysis, we analyzed spectral signature value of different training fields in TM3, TM4, TM5 and TM7 band, and compared these with the standard deviation analysis. Based on these results, we selected the best spectral bands for classification and created remote sensing interpretation marks of land use classification. Supervising classification was used with the image classification of TM and the maximum likelihood was used for parametric rule of supervised classification. We applied the method of spectral signature analysis to the individual study of land use classification of Poyang Lake region. The land use was classified into 9 classes: paddy field, non-irrigated farmland, forestland, grassland, water area, lake beach, grass beach, sandy land and residential area. Based on the data of GPS investigation, we assessed the classification accuracy. Result indicated that classification accuracy reached 91.43% and the classification effect was better than the common supervised classifying and unsupervised classifying.

If farmers could predict yield on a spatially variable basis, they could better understand risks and returns in applying costly inputs such as fertilizers, etc. To this end, several remotely sensed images of a cotton field were collected during the 2002 growing season, along with daily high and low temperatures. Image data were converted to normalized-difference vegetation index (NDVI), and temperature data were used to normalize NDVI changes over periods between image collections. Remote-sensing and weather data were overlaid in a geographic information system (GIS) with data from the field: topography, soil texture, and historical cotton yield. All these data were used to develop relationships with yield data collected at the end of the 2002 season. Stepwise regression was conducted at grid-cell sizes from 10 m square (100 m²) to 100 m square (10,000 m²) in 10-m increments. Relationships at each cell size were calculated with data available at the beginning of the season, at the first image date, at the second image date, and so on. Stepwise linear regression was used to select variables at each date that would constitute an appropriate model to predict yield. Results indicated that, at most dates, model accuracy was highest at the 100-m cell size. Remotely sensed data combined with weather data contributed much information to the models, particularly with data collected within 2.5 months of planting. The most appropriate model had an R² value of 0.63, and its average prediction error was about 0.5 bale/ha (0.2 bale/ac, or roughly 100 lb/ac).

Infrared cameras are well established as a useful tool for fire detection, but their use for quantitative forest fire measurements faces difficulties, due to the complex spatial and spectral structure of fires. In this work it is shown that some of these difficulties can be overcome by applying classification techniques, a standard tool for the analysis of satellite multispectral images, to bi-spectral images of fires. Images were acquired by two cameras that operate in the medium infrared (MIR) and thermal infrared (TIR) bands. They provide simultaneous and co-registered images, calibrated in brightness temperatures. The MIR-TIR scatterplot of these images can be used to classify the scene into different fire regions (background, ashes, and several ember and flame regions). It is shown that classification makes possible to obtain quantitative measurements of physical fire parameters like rate of spread, embers temperature, and radiated power in the MIR and TIR bands. An estimation of total radiated power and heat release per unit area is also made and compared with values derived from heat of combustion and fuel consumption.

Remote sensing techniques for monitoring soil moisture, e.g., that of thermal inertia, are confined to the top level of soil, generally with useful measurements only at the 0~20 cm interval due to the fact that the thermal inertia method is built mainly on the difference in daily temperature, part of whose patterns are limited largely to soil surface level without attacking its depth. The paper makes an approach to the problem, proposing a scheme and a model for estimating soil moisture at depth from NOAA/AVHRR sensings, based upon the apparent thermal inertia (ATI) and the aid of Geographic Information System (GIS), and with the effect of soil quality allowed for. Evidence suggests a rather high nonlinear relationship between the surface and deep levels of soil and its model is in the form S=Ax(d-d₀)+S₀x[1+Bx(d-d₀)²]+S_c, with which to estimate the water at depth by means of remotely sensed top-level moisture. As demonstrated in the practical applications to moisture sensing on a long-term and a multi-temporal phase basis in Henan Province, the developed model raises the mean accuracy by 5.5%~8.1% compared to the direct monitoring from satellite sensings of soil moisture at depth. On the other hand, owing to the limitation to the data of deep level moisture the water conditions at depth retrieved from the presented method and the developed model do not exceed 100 cm. And on land just irrigated or after rain the precision would be affected to noticeable degree because of the nonlinear relation available no longer.

Ability to estimate crop information from remotely sensed imagery is fundamental in precision agriculture. Traditional approach using optical remote sensing is often limited by cloud-free quality imagery while microwave radar has not been fully explored to infer crop conditions. There is a need to develop an alternative to infer crop information that overcomes these limitations. In this study, an optical/radar synergy was developed and used to examine its potential for extracting soil and plant information. The synergy uses a microwave scattering model developed by Karam and his colleagues but modified to (1) take into account underneath soil backscattering properties and (2) use optical remote sensing as direct input variables to the model. The synergistic method was applied to two data sets from Maricopa Agricultural Center, Maricopa, Arizona, and the experimental fields of the National Institute for Agro-Environmental Sciences, Tsukuba, Japan. The data sets included images from Landsat and ERS satellites as well as some ground based soil and plant measurements. The preliminary results indicate that radar imagery can be effectively integrated with optical imagery for extracting both soil and plant information. There exist potentials to use such synergy for site-specific agricultural management practices.

Using the observations from 8 weather stations in northern Xinjiang, eight weather stations in southern Xinjiang and 8 weather stations in Tianshan Mountains area, we analyzed changing features of sandstorm, floating dust, blowing-sand. The observations were collected from 1961-2000 in Xinjiang, China. The results show that southern Xinjiang was the area where sandstorm and blowing-sand occurred more often, and the occurrence was 3-5 times higher than those in northern Xinjiang. Days of floating-dust appearing in southern Xinjiang were 50 times more than those in northern Xinjiang; in Tianshan Mountains area sand-dust weather appeared less. In the last 40 years, the long-term change trend of these sand-dust weather in southern Xinjiang was similar to those in northern Xinjiang, that had been obviously decreasing since the 1990’s; the total days of sand-dust weather in southern Xinjiang in spring had a linear relationship with air temperature and precipitation of the same period, respectively.

Correlation analyses between the spectral reflectance and the pigment contents of five crop leaves including rice, cotton, color cotton, corn and sugarcane were reported in this paper. Spectral reflectance over the 350-2500 nm range with a spectral resolution of 3 nm and the content of chlorophyll a, b, a+b, and total carotenoids were determined for leaves from five crops covering a wide range of chlorophyll a content (0.2440 -3.8755mg/g). Maximum sensitivity of reflectance to variation in pigment content was found in the green wavelength region at 550 nm and at 707 nm. The reflectance in the main the pigment absorption regions in the blue (400-500 nm) and red (660-690 nm) wavelengths proved to be insensitive to variation in pigment content. The ratio R670/(R550*R707) correlated best with chlorophyll a, a+b, and carotenoids contents. The ratio R670/R550 correlated best with chlorophyll b content.

The hyperspectral reflectances of the canopy, the sword leaf, the third unfolding leaf from the top and ear of the main stem of two varieties of rice are measured by a ASD FieldSpec Pro FR in field and indoor under 3 nitrogen support levels in mature process. The concentrations of chlorophyll and carotenoid of leaves and ears corresponding to the spectra were determined by biochemical method. The spectral differences are significant for the canopy and leaves of rice under differet nitrogen support level, and the concentrations of chlorophyll and carotenoid of leaves increase with the increasing of nitrogen applying. There exist significant differences for the pigment concentrations of the leaves of rice under different nitrogen levels. The spectral reflectances of the canopy are gradually getting bigger in the visible region and smaller in the near infrared region as the growth stage goes on. 'Blue shift' phenomena for the spectra red edge position of the canopy, leaves and ears were proved. The concentrations of chlorophyll and carotenoid of leaves and ears are very significantly correlative to the spectral vegetation indices VI1(= R₉₉₀/R₅₅₃), VI2(=R₁₂₀₀/R₅₅₃), VI3(=R₇₅₀/R₅₅₃), VI4(=R₆₇₀/R₄₄₀), VI5(= R₅₅₃/R₆₇₀), PRVI(=R₈₀₀/R₅₅₃), PSSRa, PSNDa and λ_red (the red edge position). The results show that these VIs can be used to estimate the concentrations of chlorophyll and carotenoid of leaves and ears of rice.

The Relationships between Narrow Band Normalized Vegetation Index and Rice Agronomics Variables are reported in this paper. The data for this study comes from ground-level hyperspectral reflectance measurements of rice at different stage of 2002 growing period. Reflectance was measured in discrete narrow bands between 350 and 2500 nm. Observed rice agronomics variables included wet biomass, leaf area index. Narrow band normalized difference vegetation index (NBNDVI) involving all possible two-band combinations of discrete channels was tested. Special narrow band lambda (λ₁) versus lambda (λ₂) plots of R² values illustrates the most effective wavelength combinations (λ₁ and λ₂) and band-width (Δλ₁ and Δλ₂) for predicting rice agronomics variables at different development stage. The best of the NBNDVI models explained 53% to 83% variability rice agronomics variables at different development stage. A strong relationship with rice agronomics variables is located in red-edge, 700 nm to 750 nm, the longer portion of red, 650 nm to 700 nm, moisture-sensitive NIR, 950 nm to 1000 nm, longer portion of the blue band, 450 nm to 500 nm, longer portion of the green, 550 nm to 600 nm, the intermediate portion of SWIR, 1600 nm to 1700 nm, and the longer portion of SWIR, 2150 nm to 2250 nm.

Based on the relationship between water balance and crop-water, water-saving irrigation model was integrated with monitoring and prediction of soil moisture, forming a system of decision-making of irrigation. It is demonstrated that straw mulching for winter wheat is an effective way to reduce soil evaporation at early stages and increase yield and improve water utilization efficiency. Combination of water-saving irrigation and straw mulching plays an important role in China water-saving agriculture.

Field studies were conducted in 1998 and 1999 in Livingston Field at Perthshire Farm, Bolivar County which is located in west-central Mississippi along the Mississippi River. It is a 162 ha field and has a 2-m elevation range. The dominant soil series of the field are Commerce silt loam, Robinsonville fine sandy loam and Souva silty clay loam. The objectives of the study were to (1) compare GOSSYM simulated yield with actual yield, (2) study spatial and temporal pattern of cotton crop across two growing seasons using multispectral imagery, 3) predict field based lint yield from remote sensed data, and determine age of the crop most suitable for aerial image acquisition in predicting yield and/or discriminating differences in cotton growth. Two transects were selected for GOSSYM study, each containing twelve sites. A 1-m length of single row plot was established at each profile. Plant mapping was done five times in 1998 and seven times in 1999 growing seasons. GOSSYM simulation runs were made for each profile and compared with actual crop parameters using root mean square error (RMSE). Results based on averaging common soil mapping units indicate that GOSSYM accuracy in predicting yield varied from 0.45 bales acre^-1 to 0.61 bales acre^-1. To monitor and estimate the biophysical condition of the cotton crop, airborne multispectral images were acquired on 10 dates in 1998 and 17 dates in 1999 from April to September. In both years site-specific yield and normalized difference vegetation index (NDVI) were significantly (p < 0.0001) correlated in July. Changes in NDVI in 1999 across sampling dates for the different sites showed the least distinctiveness due to somewhat wetter weather conditions, as compared to drier weather in 1998. Crop growing in or near the drainage areas were low in NDVI and lint yield. Multispectral images acquired between ~ 300 - 600 growing degree days above 60°C (GDD60) may be useful decision tools for replanting certain areas of the field, especially in dry weather conditions when variability in crop growth pattern is enhanced due to spatial variability in soil texture, which influences the capacity of a soil to hold moisture and to release it to plants for growth. Results suggest that 2-3 multispectral images acquired between 800 and 1500 GDD60 can be used to monitor crop health and predict yield in a normal weather condition.

In this paper, the characteristics of climate change in the west Tianshan Mountains for last 40 years are discussed. The trend of climate change and its effect combined with the oasis climate effect in the irrigated areas are analyzed. The regional characteristics of climate change and the temporal and spatial distribution characteristics of sand-dust storms, floating dusts and strong winds are compared and analyzed based on the observed data collected from 14 meteorological stations in the source stream area of Aksu River, Aksu Irrigated Area and Yarkant River Irrigated Area during the period of 1961-2000. We concluded that both temperature and precipitation have increased in these regions since 1990’s. It is observed with higher precipitation in the area of Aksu River there are fewer days when sand-dust storms, floating dusts and strong winds happened. The interaction between the climate change and the oasis development is also discussed.

Based on forty-one years observational data (1960-2000) from seventies weather stations, we analyze the characteristics of climate changes in winter growing season that is defined from November to April the next year. Supported by Geographical Information System (GIS) techniques, relative models are built between climate factors in winter growing season and heights above the sea level. In the context of recent decades climate conditions and assumed climate warming in the future, Climate Potential Productivity (CPP) for five winters crops are calculated, with making CPP distribution maps also. The features of climate changes in winter growing season in recent 41 years can be expressed as increases of mean temperature and precipitation and decrease of sunshine hours, the case appearing especially in 1990's. Temperature is a crucial factor in CPP model. Climate warming can improve the CPP. When the mean temperature increase 0.5°C, 1.0°C, 1.5°C and 2.0°C with unchangeable of other factors, the CPP will increase by 2.1%, 4.1%, 6.3%, and 8.3%. For the proportions of actual field per unit area to CPP for five winter crops are only 14% to 21%, it is beneficial to utilize winter climate resources rationally and increase crop's field.

Decision support system (DSS) is a flexible information technology system that is useful in making semi-structure and non-structure decisions. This paper takes Toutun river basin in Xinjiang as a typical study region, combines "3S" (RS, GIS, GPS), digital 3D virtual emulation and seamless integration of multi-source spatial data with hydrological basin model, forecasting model, reservoir regulating model and damage estimation model to crete a DSS for flood prevention. With this DSS, some difficult issues concerning flood prevention are explored. The characteristics of the DSS for flood prevention for this river basin include: decisions are made spatially-dstributed, real-time, mutual and by group. The DSS is a software platform with diversity and expandability. It contains intelligent and visualization functions.

Soil samples collected from vegetation zone of Fukang oasis in South Junggar Basin were analyzed. The soil samples were air-dried and their PH values, electrical conductivity and total salts were analyzed in the laboratory. Soil samples were weighted and mixed with distilled water by a ratio of 1:5 after stirred and settled over night. Moisture content of soil was measured by the dry-method, PH value was measured by SM-10 digital acidity-meter, and electrical conductivity was measured by weight-method. The PH value, electrical conductivity and total salts of soil were measured from 0-10cm, 10-30cm and 30-50cm vertically. The study uses SAS software to analyze the statistical characteristic of the moisture content, electrical conductivity and total salts of the soil samples. The results show that soil properties are inhomogeneous. From the surface to below 50cm, moisture content and electrical conductivity increase successively, the change of PH value is not significant. The soil is alkalescence. In most circumstances, the data of moisture content, PH value and electrical conductivity are normally distributed. But, in the procession, soil properties influenced by systematic variance of soil properties deviated for normal distribution with various degrees. Soil moisture content, PH value and electrical conductivity do not satisfy simple linear relation at the vegetation zone of Fukang oasis. Using the method of trend surface analysis, the polynomial relation of variances (P<0.01) was obtained and found that moisture content is higher related with electrical conductivity at different depths, but the correlation between moisture content and PH value, and the correlation between PH value and electrical conductivity change with different environment factors.

Since we started the National Key Project of Fundamental Research (China), “The research on eco-environmental evolution, control and adjustment for arid land in Western China,” four years ago we have made a number of important progresses. These progresses include many research fields: the palaeoclimate rebuilding and environmental change; the development process and driving forces of oases; the water-salt balance and main hydrology processes of oases; the oasis ecosystem’s structure, ecological process and stability; the coupling mechanism of three major ecosystems of mountain, oasis, and desert; the ecological landscape patterns’ evolution and its responses climate change; the ecological environmental effects of large industrial (construction) projects; the cause and spatial-temporal distribution of desertification; the establishment of experimental demonstration bases of ecological recovery and rebuilding; and adjustment and control of ecosystem and management models.

Xinjiang plays an important part in China Western Development Project and becomes an important region supported by the Chinese government. The advantages in resource and geography provide the favorable conditions to Xinjiang’s development sustainedly. Xinjiang economic development has been limited by unreasonable economic structure, undeveloped science and technology and education, poor investment environment and ecological environment. This paper provides an analysis of the advantages and restrictive factors of Xinjiang in China Western Development.

Under the conditions of global warming, the degenerated ecological environment has threatened human survival. Therefore, people will gradually pay more attention to the environmental problem of climate change. This paper analyzes the distribution features of air temperature, relative humidity (precipitation), and the horizontal stream field in Xinjiang for July 1997. In order to do the integration of one month (July, 1997) we ran the model NCAP/PENN MM5V3. Data from WLCCD (the latest World Land Cover Characteristics Database) was used to relate the two research domains of the model, and also to replace the vegetation in the MM5. The data in the WWLCD depends on the actual land surface characteristics. It was found that the general law of air temperature, relative humidity (precipitation) and the horizontal stream field of 1000hPa in Xinjiang in July of 1997 by means of the model. The mean regional data for July helped prefect the theory that humans are controlling the ecological environment in order to prevent and control desertification. Among these results, the simulated air temperature was the best.

Three field experiments of nitrogen (N) rates, plant growth regulator (PIX) applications, and irrigation regimes were conducted in 2001 and 2002 to investigate relationships between hyperspectral reflectance (400-2500 nm) and cotton (Gossypium hirsutum L.) growth, physiology, and yield. Leaf and canopy spectral reflectance and leaf N concentration were measured weekly or biweekly during the growing season. Plant height, mainstem nodes, leaf area, and aboveground biomass were also determined by harvesting 1-m row plants in each plot at different growth stages. Cotton seed and lint yields were obtained by mechanical harvest. From canopy hyperspectral reflectance data, several reflectance indices, including simple ratio (SR) and normalized difference vegetation index (NDVI), were calculated. Linear relationships were found between leaf N concentration and a ratio of leaf reflectance at wavelengths 517 and 413 nm (R₅₁₇/R₄₁₃) (r² = 0.70, n = 150). Nitrogen deficiency significantly increased leaf and canopy reflectance in the visible range. Plant height and mainstem nodes were related closely to a SR (R₇₅₀/R₅₅₀) according to either a logarithmic or linear function (r² = 0.63~0.68). The relationships between LAI or biomass and canopy reflectance could be expressed in an exponential fashion with the SR or NDVI [(R₉₃₅-R₆₆₁)/(R₉₃₅+R₆₆₁)] (r² = 0.67~0.78). Lint yields were highly correlated with the NDVI around the first flower stage (r² = 0.64). Therefore, leaf reflectance ratio of R₅₁₇/R₄₁₃ may be used to estimate leaf N concentration. The NDVI around first flower stage may provide a useful tool to predict lint yield in cotton.

Heat, precipitation and humidity affect vegetation types and their distribution. However, their degree of effects is highly spatial and temporal dependent. When we study the major factors which affect vegetation cover, we need define a specific region and a time period. In order to study land cover and vegetation change in Xinjiang and to probe its driving force from 1992 to 2000, we analyzed sensitivity of land-cover to climate change using Remote Sensing (RS) and Geographic Information System (GIS) with multi-temporal NOAA/AVHRR NDVI images. Major factors we considered in this study were temperature, precipitation, humidity and their long-term and seasonal impacts on land cover and vegetation change. Results provided different sensitive levels as following: bare lands, partially vegetated lands, agriculture uses and water bodies. Concerning meteorological parameters impact we found in eastern Xinjiang humidity was more important than temperature and precipitation, in southern Xinjiang precipitation had more impact than temperature and humidity, and in both northern Xinjiang and Ili area temperature was more important than precipitation and humidity.

This study was conducted to develop an appropriate assessment technique to define impact of mountain-desert-oasis ecosystem on net primary productivity (NPP) in northern foothills of Tianshan Mountains. Geographic Information System (GIS) was used to estimate land use/land cover of the mountain, desert and oasis zones. An ecological process model was used to estimate NPP by using data entirely derived from satellite. The results show that landscape heterogeneity was important factor to affect NPP values in mountain-desert-oasis ecosystem. Simulated results indicated a total annual NPP of 1.5081×10¹⁴ g C for selected transect in 2002. There was 32.67% of total NPP which came from oasis areas, 28.16% from alpine meadows areas, 9.15% from forests area. Mean NPP values over the selected transect was 150.29 g C m^-2 year^-1 in 2002. However, NPP values varied greatly with different geography and season.