Surface and vegetation monitoring is a key activity in analyzing and understanding how climate change is impacting natural resources. Moreover, identifying vegetation stress using remote-sensed data has proven to be essential in assessing said understanding, as well as in the effort to prevent or act upon extreme phenomena, such as premature land and forest dryness due to summer heatwaves in the Mediterranean area. Typically used satellite indices for this purpose are the well-known NDVI, followed by Leaf Area Index (LAI) and Surface Soil Moisture (ssm), together with physical parameters such as surface and air temperature close to the surface (the latter retrieved by both remote-sensed data and in situ measurements). However, it is a known fact that NDVI is not able to differentiate between barren soil and suffering vegetation, while surface temperature and air temperature correlate poorly with soil moisture. The analysis carried out in this paper is aimed at proving the effectiveness of two newly designed thermodynamical indices, ECI and wdi, in assessing vegetation stress and woodland degradation in southern Italy between 2014 and 2022. ECI is based on infrared surface emissivity, which is closely related to land cover, while wdi directly measures surface water loss. Said indices have been estimated from both ECMWF operational analysis and IASI L2 data, the latter upscaled and remapped on a regular grid using an optimal interpolation scheme. Moreover, a comparison with other traditional indices is presented, further validating the applied methodology.
Exploiting the Infrared Atmospheric Sounder Interferometer (IASI) profiling capability for surface parameters, atmospheric temperature, and water vapour we have designed a new Water Deficit Index (wdi) to monitor drought and heatwaves. Because of climate change at a global level, drought is becoming a strong emergency also in countries which never experienced it, such as the Mediterranean mid-latitude area and, in particular, Italy. The last two years strongly affected the northern part of Italy, i.e. the Po Valley, causing high vegetation and soil water stress. Satellite data can provide a large spatial coverage (locally and globally) as well as a continuous data supply and are an important help to ground monitoring stations, especially in remote regions with dense vegetation. In this paper, we used the wdi to investigate the 2022 intense drought over the Po Valley region. We integrated the study considering both the Surface Soil Moisture (SSM) from Copernicus Sentinel-1 C-SAR and the Normalized Difference Moisture Index (NDMI) from Sentinel-2 images. We also considered the Fractional Vegetation Cover (FVC), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and the Leaf Area Index (LAI) data from the Drought & Vegetation Data Cube (D&V Data Cube) from the European Organization for the Exploitation of Meteorological Satellites - Satellite Application Facilities (EUMETSAT SAFs). Overall, we found that the wdi compares well to other indices related to vegetation stress and can be used as a tool for risk assessment of forest fires and agriculture productivity.
The paper uses Level 2 IASI (Infrared Atmospheric Sounder Interferometer) products to analyse long-standing heatwaves and related droughts. The paper is mostly interested in studying and assessing the effect of drought on vegetation. To this end, we have devised a series of indices sensitive to the water deficit. IASI retrievals are used to derive indices from the surface temperature, emissivity, and temperature/humidity atmospheric profiles. We define the emissivity contrast index, which is sensitive to the land cover and type, and the water deficit index, which combines the surface and air dew point temperatures. These two indices are assessed by considering the heatwave, which hit most of Europe and the Mediterranean basin in 2017. The application of the methodology will be shown by considering a target area in Southern Italy, where woodlands are suffering from climate change. It will be shown that the two indices are sensitive to the water deficit caused by long-lasting droughts.
In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman filter estimation. It has been applied to 15-minutes temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and temperature products and to Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) retrievals, a gas affecting the air quality. Results have been exemplified for target areas over Italy. In particular temperature retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. Our findings show that the proposed strategy is quite effective to fill gaps because of data voids due, e.g., to clouds, gains more efficiency in capturing the daily cycle for surface parameters and provides valuable information on NH3 concentration and variability in regions not yet covered by ground-based instruments.
Remote sensing of atmosphere is changing rapidly thanks to the
development of high spectral resolution infrared space-borne
sensors. The aim is to provide more and more accurate information
on the lower atmosphere, as requested by the World Meteorological
Organization (WMO), to improve reliability and time span of
weather forecasts plus Earth's monitoring. In this paper a new
channel selection strategy for water vapor is presented and
analyzed both on simulated spectra and on real spectra.
Remote sensing of atmosphere is changing rapidly thanks to the development of high spectral resolution infrared space- born sensors. The aim is to provide more and more accurate information on the lower atmosphere, as requested by the World Meteorological Organization (WMO), to improve reliability and time span of weather forecasts plus Earth's monitoring. In this paper the performance of the Infrared Atmospheric Sounding Interferometer (IASI) is analyzed looking directly at the products: temperature and water vapor.
The IASI has 8461 potential channels to be exploited for inversions of geophysical parameters. In this paper we analyze two different strategies for their reduction. The first one looks for suitable spectral ranges where the inverse problem is as linear as possible; the second one is based on the cluster analysis theory. Our aim is to minimize the potential information loss evaluated by directly comparing the retrieved temperature and water vapor profiles on a complete set of test atmospheres.
REFIR is a Fourier Transform Spectrometer designed to measure the upwelling IR Earth's emission from space in the spectral range 100 to 1000 cm-1 with an unapodized spectral resolution of about 0.5 cm-1. One of the main scientific objectives of REFIR is to monitor the far IR planetary emission and the principal drivers of this emission, with particular attention being paid to the poorly understood mid and upper troposphere. The expected retrieval performance for temperature and water vapor profiles, from a subset of REFIR measurements, is evaluated and presented in this paper.
Inversion of the radiative transfer equation to retrieve the vertical profile of temperature from high resolution radiance spectra is an important problem in remote sensing of atmosphere. Because of its non linearity and ill conditioning, regularization techniques have been resorted in order to reduce the error of the retrieval. In this paper Generalized Singular Value Decomposition (GSVD) and Truncated Generalized Singular Value Decomposition (TGSVD) have been used to solve the linear model; the optimal regularization parameter for the proper amount of smoothing have been chosen by the L-curve criterion. A significant test problem has been worked out with reference to the Infrared Atmospheric Sounding Interferometer (IASI). The effectiveness of the methods to reduce variance and bias in the output profile has been addressed. We show that GSVD plus L-curve criterion or TGSVD plus L-curve are really effective in reducing error, variance and bias of the retrieved profile.
Retrieving of temperature profiles from radiance data obtained by interferograms is an important problem in
remote sensing of atmosphere. The great amount of data to process and the ill-conditioning of the problem demand
objective procedures able to reduce the error of the retrieval. In this paper we use Generalized Singular Value
Decomposition (GSVD), which is able to deal with deficient-rank smoothing functionals in order to regularize the
problem and the L-Curve criterion for choosing the optimal regularization parameter and then the proper amount of
smoothing.
Some test problems of temperature inversion are carried out to examine the effectiveness of the methods considered;
to this purpose we use some indicators based on the bias and variance of the output temperature.
We show that the objective L-Curve criterion does not perform fully satisfactory in estimating the optimal
regularization parameter and then in reducing output error at best. In any case GSVD plus L-Curve criterion prove
effective in reducing output error (with respect to the ordinary least squares method). In particular, reduction of
variance over troposphere and stratosphere is high for all tested cases; reduction of bias depends on the first-guess
profile. An important role in the latter is played by the choice of deficient-rank smoothing functional.
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