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Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. ispartof: REMOTE SENSING OF ENVIRONMENT vol:224 pages:289-303 status: published

L-band passive microwave remote sensing is currently considered a robust technique for global monitoring of soil moisture. However, soil roughness complicates the relationship between brightness temperature and soil moisture, with current soil moisture retrieval algorithms typically assuming a constant roughness parameter globally, leading to a potential degradation in retrieval accuracy. This current investigation established a tower-based experiment site in Victoria, Australia. P-band (~40-cm wavelength/0.75 GHz) was compared with L-band (~21-cm wavelength/1.41 GHz) over random and periodic soil surfaces to determine if there is an improvement in brightness temperature simulation and soil moisture retrieval accuracy for bare soil conditions, due to reduced roughness impact when using a longer wavelength. The results showed that P-band was less impacted by random and periodic roughness than L-band, evidenced by more comparable statistics across different roughness conditions. The roughness effect from smooth surfaces (e.g., 0.8-cm root-mean-square height and 11.1-cm correlation length) could be potentially ignored at both P- and L-band with satisfactory simulation and retrieval performance. However, for rougher soil (e.g., 1.6-cm root-mean-square height and 6.8-cm correlation length), the roughness impact needed to be accounted for at both P- and L-band, with P-band observations showing less impact than L-band. Moreover, a sinusoidal soil surface with 10-cm amplitude and 80-cm period substantially impacted the brightness temperature simulation and soil moisture retrieval at both P- and L-band, which could not be fully accounted for using the SMOS and SMAP default roughness parameters. However, when retrieving roughness parameters along with soil moisture, the ubRMSE at P-band over periodic soil was improved to a similar level (0.01‐0.02 m3/m3) as that of smooth flat soil (0.01 m3/m3), while L-band showed higher ubRMSE over the periodic soil (0.03‐0.04 m3/m3) than over smooth flat soil (0.01 m3/m3). Accordingly, periodic roughness effects were reduced by using observations at P-band.

ElectroMagnetic (EM) reasons resulting in temperature dependence of L-band Vegetation Optical Depth (L-VOD) are currently overlooked in remote sensing products. Discrepancies in retrievals of geophysical surface properties over vegetated areas can result from this incompleteness. This perception motivated to explore EM considerations in how temperature drives L-VOD of a boreal forest. Thereto, a novel physics-based model is developed and evaluated to assess L-VOD sensitivities to canopy temperature and some other model parameters. The L-VOD model is compared to L-VOD derived from close-range L-band brightness temperatures measured through the tree canopy at the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankyla (Finland) during a 4-week and a 1-day period in 2019. Furthermore, the model's ability to explain L-VOD retrieved from brightness temperatures of the “Soil Moisture and Ocean Salinity” (SMOS) satellite over the “Sodankyla grid cell” is investigated. Experimental L-VOD are maximal at around 0 °C and decrease when canopy temperature is moving away from zero degree Celsius. This temperature response, observed at different temporal- and spatial scales, is captured by the proposed L-VOD model and explained by freezing tree sap-water and the dependence of water permittivity on temperature. The demonstrated EM-induced temperature dependence suggest caution with interpreting satellite-based L-VOD, because increased L-VOD around the freezing point is not solely due to increased biomass or rehydration of the vegetation. Further, our study can find future application to compensate L-VOD for EM-induced temperature sensitivity. This potentially leads to improved explanatory power of temperature normalized L-VOD for characterization of forest phenology. Furthermore, we suggest examining the presence and strength of the demonstrated L-VOD temperature response as a practical L-VOD retrieval quality assessment method under steady forest phenology.

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX ( Surface Externalisee )). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale.

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1 cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “Q/H” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between ΔN and root-mean-square height (σ) or σ2/LC can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters (Q, Np and h) generally increase when soil get rougher. The calibrated Q, Nh and Nv show exponential dependence on the effective parameter h. The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero-Q fitting models have comparable performance in Tb modeling with zero-Q models but may be more physically realistic.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

This paper evaluates the performance of different soil moisture products from AMSR2 and SMOS against the most representative stations within the Yanco study area in the Murrumbidgee catchment, in southeast Australia. AMSR2 Level 3 (L3) soil moisture products retrieved from two versions of brightness temperatures using the Japanese Aerospace eXploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithm were included. For the LPRM algorithm, two different parameterization methods were applied. Furthermore, two versions of SMOS L3 soil moisture product were assessed. The results are contrasted against the use of “random” stations. Accounting for all versions, frequencies and overpasses, the latest versions of the JAXA (JX2) and LPRM (LP3) products were found to surpass the earlier versions (JX1, LP1 and LP2). Soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used but not X-band. However, X-band retrievals (r: 0.71, MAE: 0.07, RMSD: 0.08 m 3 /m 3 ) were found to perform better than C-band (r: 0.68–0.70, MAE: 0.07–0.09 m 3 /m 3 , RMSD: 0.09–0.10 m 3 /m 3 ). Moreover, an intercomparison between different acquisition times (morning and evening) of AMSR2 X-band products found a better performance from evening overpasses (1:30 pm; r: 0.69–0.77) as opposed to morning overpasses (1:30 am; r: 0.47–0.66). In the case of SMOS, morning (6:00 am; r: 0.77) retrievals were found to be superior over evening (6:00 pm; r: 0.69) retrievals. Overall, both versions of JAXA products, the second and third versions of LPRM X-band products, and two versions of SMOS products were found to meet the mean average error (MAE) goal accuracy of the AMSR2 mission (MAE 3 /m 3 ) but none of the products achieved the SMOS goal of RMSD 3 /m 3 . Furthermore, performance of the products differed depending on the statistic used to evaluate them. Consequently, considering the results in this study, JX2 products are recommended if both absolute and temporal accuracy of the soil moisture product is of importance, whereas LP3 X products from evening observations and SMOS version 3.00 (SMOS2) products from morning overpasses are recommended if temporal accuracy is of greater importance.

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

International audience; The remote signatures measured at microwave frequency above land surfaces are strongly dependent on the permittivity of the soil, which is linked to its moisture content. Thus, soil permittivity is a key parameter when algorithms are developed for the retrieval of hydrologic parameters from remote sensing data. Soil permittivity measurements are generally carried out in the laboratory because in-situ measurements are more difficult to obtain. The study presents the development of two probes (N and SMA probes) for in situ soil permittivity measurements (i.e. measurements of dielectric properties). They are based on the end effect phenomenon of a coaxial waveguide and so are called end effect probes in this paper. Results obtained on well-known materials (water and polytetrafluoroethene) are compared with corresponding data obtained by laboratory approaches (Von Hippel’s method and resonant cavity) and show good agreement from 0.5 GHz up to ∼3.5 GHz and 6 GHz for N and SMA probes respectively. Then measurements made on concrete and mineral soil are reported to underline the efficiency of end effect probes for in-situ dielectric measurements. Finally, through work undertaken in the framework of the European Space Agency’s SMOSHiLat project, we demonstrate the applicability of the two probes for measurements performed within these frequency ranges in complex material such as organic soil horizons.

European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by Radio Frequency Interference (RFI) that jeopardizes part of its scientific retrieval in certain areas of the world. Areas affected by RFI might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put several strategies into place that help improve the RFI situation, filter the SMOS data from RFI perturbed measurements and bring awareness to the RFI problem.

International audience; Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.

L-band passive measurements (1.4 GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31 K and 12 K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~ 40 km).

The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. Although the L-band is a protected band, the data collected by SMOS are contaminated by radio frequency interferences (RFI) which degrade the performance of the mission. A precise location of the RFI emitters is required for switching off illegal transmissions or for fixing malfunctioning equipments. This work is concerned with the geolocation of such sources with a sub-kilometric accuracy from SMOS interferometric data themselves. Such a precise location has never been reached by any other published methods using only SMOS measurements.

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

In this study, we used the Disaggregation based on Physical And Theoretical scale Change (DISPATCH) algorithm under very wet soil conditions in Western Canada for the disaggregation of coarse resolution 40-km soil moisture derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The algorithm relies on the Soil Evaporative Efficiency (SEE), which was estimated using the 1-km resolution data from the MODerate resolution Imaging Spectoradiometer (MODIS). The study aimed to: (i) evaluate DISPATCH under wet soil conditions, (ii) test the linearity/non-linearity of the relationship between soil moisture and SEE, and (iii) propose a more robust procedure to calibrate the SEE model under very wet soil conditions. The disaggregated soil moisture values were compared to 0-5 cm in situ measurements and the soil moisture derived from the L-MEB (L-band Microwave Emission of the Biosphere) model from airborne brightness temperature at 1.4 GHz collected during the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) field campaign. The results show a correlation between 0.7 and 0.8 and bias values similar to 0 m(3)/m(3). The DISPATCH algorithm shows better disaggregation results under very wet soil conditions when a non-linear relationship is considered between SEE and soil moisture instead of a linear model. This is mainly due to the small variability of surface temperature inside the area covered by the SMOS pixel under very wet soil conditions, and the difficulty in accurately estimating the maximum soil temperature (Ts-max), which is a driving factor for SEE. A sensitivity analysis was conducted and it shows that the linear model performs well only if Ts-max can be determined more accurately. The possibility to determine Ts-max using high resolution MODIS data over a larger area than the SMOS pixel is examined and discussed in the paper.

Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe.

Summary Since soil moisture is an important influencing factor of the hydrological cycle, knowledge of its spatio-temporal dynamics is crucial for climate and hydrological modeling. In recent years several soil moisture data products from satellite information have become available with global coverage and sub-monthly resolution. Since the remote sensing of soil moisture is an indirect measurement method and influenced by a large number of factors (e.g. atmospheric correction, vegetation, soil roughness, etc.), a comprehensive validation of the resulting soil moisture products is required. However, the coarse spatial resolution of these products hampers the comparison with point-scale in situ measurements. Therefore, upscaling of in situ to the scale of the satellite data is needed. We present the validation results of the soil moisture products of the years 2010–2012 retrieved from the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Scatterometer (ASCAT) for the Rur and Erft catchments in western Germany. For the upscaling of in situ data obtained from three test sites of the Terrestrial Environmental Observatories (TERENO) initiative we used the hydrological model WaSiM ETH. Correlation of the SMOS product to modeled and upscaled soil moisture resulted in a mean correlation coefficient of 0.28 whereas for ASCAT a correlation coefficient of 0.50 was obtained. However, for specific regions the SMOS product showed similar correlation coefficients as the ASCAT product. While for ASCAT correlation was mainly dependent on topography and vegetation, SMOS was also influenced by radiofrequency interferences in our study area. Both products show dry biases as compared to the soil moisture reference. However, while SMOS showed relatively constant bias values, ASCAT bias is variable throughout the year. As an additional validation method we performed a temporal stability analysis of the retrieved spatio-temporal soil moisture data. Through investigation of mean relative differences of soil moisture for every pixel, their standard deviations and their rankings, we analyzed the temporal persistence of spatial patterns. Our results show high standard deviations for both SMOS and ASCAT soil moisture products as compared to modeled soil moisture, indicating a lower temporal persistence. The consistence of ranks of mean relative differences was low for SMOS and relative ASCAT soil moisture compared to modeled soil moisture, while ASCAT soil moisture, converted to absolute values, showed higher rank consistence.

This paper focuses on the sensitivity of L-band multi-parameter retrievals across the range of angular measurements available from the SMOS (Soil Moisture and Ocean Salinity) mission. The SMOS core algorithm was used to evaluate two-parameter retrieval scenarios including soil moisture and one of either i) vegetation water content, ii) surface roughness, iii) vegetation temperature, or iv) surface soil temperature. For all pairs a range of parameter value combinations were compiled to run the model in forward mode. Subsequently, the resulting angular brightness temperature simulations with two unknown parameters were compared against the brightness temperature response derived from reference simulations using data from the National Airborne Field Experiment 2005 (NAFE'05) in Australia. This paper showed that the two-parameter retrieval accuracy of soil moisture is strongly affected by the surface moisture conditions, the polarization of the brightness temperature data, and the choice of the secondary ancillary parameter to be retrieved. The synthetic analysis demonstrated a tendency for better retrievals from dual-polarized data at large incidence angles (40–50°). Validation with airborne brightness temperature observations at L-band did not demonstrate such a strong angular dependency, although it confirmed that the simultaneous retrieval of soil moisture and vegetation properties is not preferable as opposed to i) soil moisture and surface roughness or ii) soil moisture and surface soil temperature, especially under dry moisture conditions.

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the growing season and decrease during senescence in these areas, as did MODIS vegetation indices. SMOS' tau values generally peaked later than MODES LAI values, with an estimated time difference of about 19 days. A linear regression between tau and the MODIS products was carried out for each node and values of the determination coefficient, R-2, slope, b' and intercept, b '' were found. The average R-2 value varied from 0.32 to 035 for the different vegetation indices. The linear regression between LAI and tau produced an average slope of b' = 0.06, and an average intercept of b '' = 0.14. The effects of crop fraction and dominant crop type were investigated and crop fraction was found to have a low effect on R-2 values. R-2 values appeared to be lower for wheat and hay and higher for corn. b' and b '' values had higher standard deviations for wheat but were generally close to the mean values for corn, soybean and hay. (C) 2013 Published by Elsevier Inc. (Less)

A deterministic approach for downscaling ∼ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.

International audience; The L-band brightness temperature of natural grass fields is strongly influenced by rainfall interception. In wet conditions, the contribution of the soil, mulch, and vegetation to the overall microwave emission is difficult to decouple, thus rendering the retrieval of surface soil moisture from a direct emission model difficult. This paper investigates the development and assesses the performances of statistical regressions linking passive microwave measurements to surface soil moisture in order to assess the potential of soil moisture retrievals over natural grass. First, statistical regressions were analytically derived from the L-Band Emission of the Biosphere model (L-MEB). Single configuration (1 angle, 1 polarisation), and multi-configuration regressions (2 angles, or 2 polarisations) were developed. Second, the performance of statistical regressions was evaluated under different rainfall interception conditions. For that purpose, a modified polarisation ratio at L-band was used to build three data sets with different interception levels. In the presence of interception, a regression based on one observation angle (50°) and two polarisations was able to reduce the effects of vegetation and soil roughness on the soil moisture retrievals. The methodology presented in this study is also able to provide estimates of the vegetation and soil roughness contribution to the brightness temperature.

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface–atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150km 2 study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.

This paper explores the effect of rain intercepted by vegetation and mulch on the L-band emission of natural grass. The study is based on radiometric, meteorological, and biophysical measurements obtained during the SMOSREX Experiment (Toulouse, France). Several approaches were followed to evaluate interception effects. Firstly, the analysis of microwave brightness temperature (TB) measurements at L-band indicated that interception increases vegetation emission at both polarisations. Secondly, the use of microwave indices to detect the presence/absence of interception was examined. In particular, a modified polarisation ratio at 50° was found to be well related to the interception status of the standing vegetation. Finally, the vegetation optical depth (?), which parameterises the extinction across the vegetation layer, was retrieved from the TB observations. It was found that ? increases with the increase in the water content stored within the vegetation and mulch after rainfall. The study highlights the strong impact of intercepted water in otherwise weakly attenuating covers such as grasses. Interception might therefore be an issue to consider in order to improve soil moisture retrieval algorithms from L-band observations.

This paper reports on a new soil moisture disaggregation scheme based on topography and soil depth information. It is designed for low resolution remote sensing data assimilation into hydrological modelling. The scheme makes use of a simple Soil Vegetation Atmosphere Transfer model coupled to the TOPMODEL formalism. Water and energy balance are computed at the catchment scale, taking lateral flows due to topography into account. Lumped values of near-surface and deep soil water content are then disaggregated at local scale using simple relationship between mean quantities, local topography and soil depth information. Results for a small water catchment in South-eastern Australia show satisfactory reproduction of the local soil moisture patterns using a combination of topography and soil depth information. Due to subgrid variability and differences between the simulation and observation scale (the Digital Elevation Model pixel versus the point measurement), the point-to-point comparison between observations and simulations shows a poor correlation. Rescaling shows that a good correlation is obtained when averaging the simulated and observed soil moisture over a length of 100 m.

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (s o ) to soil and plant conditions, we found that SAR s o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR s o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r 2 =.67) between normalized SAR s o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness.

In this study, an experimental design was conceived, as part of the Semi-Arid-Land-Surface-Atmosphere (SALSA) program, to document the effect of view angle variation on surface radiative temperature measurements. The results indicated differences between nadir and off-nadir radiative temperature of up to 5 K. The data also illustrated that, under clear sky and constant vegetation conditions, this difference is well correlated with surface soil moisture. However, the correlation decreased when the same comparison was made under changing vegetation conditions. To investigate the possibility of deriving component surface temperatures (soil and vegetation) using dual-angle observations of directional radiative temperature, two radiative transfer models (RTM) with different degrees of complexity were used. The results showed that despite their differences, the two models performed similarly in predicting the directional radiative temperature at a third angle. In contrast to other investigations, our study indicated that the impact of ignoring the cavity effect term is not very significant. However, omitting the contribution of the incoming long-wave radiation on measured directional radiance seemed to have a much larger impact. Finally, sensitivity analysis showed that an accuracy of better than 10% on the plant area index (PAI) was required for achieving a precision of 1 K for inverted vegetation temperature. An error of 1 K in measured directional radiative temperature can lead to an error of about 1 K in the soil and vegetation temperatures derived by inverting the RTM.

This paper presents a new method to retrieve soil moisture and roughness from ERS-1. Wind scatterometer (WSC) data measured over the HAPEX-Sahel area (semiarid environment). The retrieval algorithm makes full use of the multiangular acquisitions and the high temporal repetition of the measured backscattering coefficients. The vegetation contribution to the signal is taken into account through a first-order radiative transfer model. The soil moisture and roughness are subsequently retrieved, throughout the rainy season, using the copolarization ratio as expressed by Oh et al. [IEEE Transactions on Geoscience and Remote Sensing GE-30 (1992) 370–381]. The paper describes the data and the approach used, together with the results gained. A good sensitivity of the backscattering coefficient to soil moisture is obtained. The results are compared with data collected during the HAPEX-Sahel campaign.

The objective of this paper is to provide an overview of the primary results of the Semi-Arid Land-Surface-Atmosphere (SALSA) Program in the context of improvements to our overall understanding of hydrologic, ecologic, and atmospheric processes and their interactions in a semi-arid basin. The major findings and future research needs associated with the different core components of the program are emphasized. First, remote-sensing investigations are discussed, especially those directed toward taking full advantage of the capabilities of the new generation of satellites (ERS2/ATSR2, VEGETATION, LANDSAT7, NASA-EOS). Second, we discuss parameterization of the water and energy fluxes in arid and semi-arid regions, with special emphasis on methods to aggregate these fluxes from patch scale to grid scale. Third, we address the issues related to grassland ecology and competition for water between native grass and invasive mesquite species. Fourth, findings related to the interactions between surface water, ground water, and vegetation in a semi-arid riparian system are discussed. Next, an assessment of land use and land cover change over the entire basin over a quarter century is reviewed. Finally, unsolved issues and the needs for further research are outlined.

In many semi-arid basins during extended periods when surface snowmelt or storm runoff is absent, groundwater constitutes the primary water source for human habitation, agriculture and riparian ecosystems. Utilizing regional groundwater models in the management of these water resources requires accurate estimates of basin boundary conditions. A critical groundwater boundary condition that is closely coupled to atmospheric processes and is typically known with little certainty is seasonal riparian evapotranspiration (ET). This quantity can often be a significant factor in the basin water balance in semi-arid regions yet is very difficult to estimate over a large area. Better understanding and quantification of seasonal, large-area riparian ET is a primary objective of the Semi-Arid Land-Surface-Atmosphere (SALSA) Program. To address this objective, a series of interdisciplinary experimental campaigns were conducted in 1997 in the San Pedro Basin in southeastern Arizona. The riparian system in this basin is primarily made up of three vegetation communities: mesquite (Prosopis velutina), sacaton grasses (Sporobolus wrightii), and a cottonwood (Populus fremontii)/willow (Salix goodingii) forest gallery. Micrometeorological measurement techniques were used to estimate ET from the mesquite and grasses. These techniques could not be utilized to estimate fluxes from the cottonwood/willow (C/W) forest gallery due to the height (20‐30 m) and non-uniform linear nature of the forest gallery. Short-term (2‐4 days) sap flux measurements were made to estimate canopy transpiration over several periods of the riparian growing season. Simultaneous remote sensing measurements were used to spatially extrapolate tree and stand measurements. Scaled C/W stand level sap flux estimates were utilized to calibrate a Penman‐Monteith model to enable temporal extrapolation between synoptic measurement periods. With this model and set of measurements, seasonal riparian vegetation water use estimates for the riparian corridor were obtained. To validate these models, a 90-day pre-monsoon water balance over a 10 km section of the river was carried out. All components of the water balance, including riparian ET, were

Changes in climate and land management practices in the San Pedro River basin have altered the vegetation patterns and dynamics. Therefore, there is a need to map the spatial and temporal distribution of the vegetation community in order to understand how climate and human activities affect the ecosystem in the arid and semi-arid region. Remote sensing provides a means to derive vegetation properties such as fractional green vegetation cover (fc) and green leaf area index (GLAI). However, to map such vegetation properties using multitemporal remote sensing imagery requires ancillary data for atmospheric corrections that are often not available. In this study, we developed a new approach to circumvent atmospheric effects in deriving spatial and temporal distributions of fc and GLAI. The proposed approach employed a concept, analogous to the pseudoinvariant object method that uses objects void of vegetation as a baseline to adjust multitemporal images. Imagery acquired with Landsat TM, SPOT 4 VEGETATION, and aircraft based sensors was used in this study to map the spatial and temporal distribution of fractional green vegetation cover and GLAI of the San Pedro River riparian corridor and southwest United States. The results suggest that remote sensing imagery can provide a reasonable estimate of vegetation dynamics using multitemporal remote sensing imagery without atmospheric corrections.

The issue of using remotely sensed surface temperature to estimate the area-average sensible heat flux over surfaces made up of different vegetated patches has been investigated. The performance of three aggregation procedures, ranging from physically based through semi-empirical, to entirely empirical has been assessed by comparing measured and simulated area-average sensible heat flux. The results show that the physically based scheme perform very well. The performance of the entirely empirical scheme was reasonable but that of the semi-empirical scheme, which actually takes full advantage of remotely sensed data, was very poor. This result suggests that unlike the case of surface fluxes, it is not appropriate to use relationships between model and observational variables (here radiative and aerodynamic surface temperature) that were developed and calibrated at a local/patch scale, for an application at a larger/grid scale just by scaling the parameters. Therefore, future research should be directed towards building robust relationships between model and observational variables directly at the large-scale.

A process-based model for semi-arid grassland ecosystems was developed. It is driven by standard daily meteorological data and simulates with a daily time step the seasonal course of root, aboveground green, and dead biomass. Water infiltration and redistribution in the soil, transpiration and evaporation are simulated in a coupled water budget submodel. The main plant processes are photosynthesis, allocation of assimilates between aboveground and belowground compartments, shoots and roots respiration and senescence, and litter fall. Structural parameters of the canopy such as fractional cover and LAI are also simulated. This model was validated in southwest Arizona on a semi-arid grassland site. In spite of simplifications inherent to the process-based modelling approach, this model is useful for elucidating interactions between the shortgrass ecosystem and environmental variables, for interpreting H2O exchange measurements, and for predicting the temporal variation of above- and belowground biomass and the ecosystem carbon budget.

The aim of this study is to use the information provided by the ERS-1 wind scatterometer (WSC) over land surfaces in arid and semi-arid environments to infer soil moisture in the presence of vegetation. Driven by dielectric properties and surface roughness, the soil contribution is attenuated by a factor which depends on canopy characteristics (water content, shape, height, density) and scatterometer viewing conditions. To describe the influence of vegetation on the signal, a semi-empirical ‘water-cloud’ model (a first-order radiative transfer solution) was used. The optical thickness (τ) and the single scattering albedo (ω) are the parameters used to quantify vegetation contribution to the measured signal. Through a simulation analysis for different soil moisture and viewing (incidence angle) conditions, we show the importance of τ and ω on the signal partition between vegetation and soil contributions. To quantify the effect of vegetation on the signal, we used information on the gree vegetation acquired from NOAA-AVHRR, visible and near-IR data combined with WSC satellite data in a water-cloud model to extract τ and ω. The temporal evolution of the various contributions to the signal was then compared for different angular ranges. This semi-empirical model was then applied within suitable angular ranges to retrieve soil moisture.

The study presents a model of top of the atmosphere (TOA) and surface reflectances in the visible and near-IR. The reflectance is parameterized as the product of an isotropic component (constant) of the reflection and normalized temporal and bidirectional functions. The bidirectional function uses a simple physical representation of viewing geometry. The temporal function is represented as a development in modified Fourier series. An iterative scheme is used to adjust the constants of the model. The analysis is applied to three locations consisting of 15 × 15 NOAA-AVHRR pixels acquired in 1992 during the HAPEX-Sahel experiment (Niger). Clouds were screened using a threshold standard deviation of reflectance (visible) and mean brightness temperature (thermal Channel 4). The study allowed the highest frequency fluctuations in the dataset to be reduced substantially (about 85% of the variance is explained) and allowed the temporal variation of the land surface cover to be detected. Comparisons between results obtained with TOA and atmospherically corrected surface reflectances show that there is a need to improve the monitoring of aerosols; however, the angular effects were the largest contributors to high-frequency fluctuations in the NOAA-AVHRR data.

This paper describes the results of Lettau's evapoclimatonomy model at daily time scales as applied to the Central East and Southern supersites of the HAPEX-Sahel region in Niger, West Africa. A revised version of the evapoclimatonomy model has been applied to the millet and bush fallow (Guiera senegalensis) fields at both supersites during the intensive observation period (IOP; 20 August–12 October, 1992), using daily means of precipitation, potential evapotranspiration, solar radiation, normalized difference vegetation index (NDVI) from the HAPEX-Sahel observations, as well as vegetation and soil parameters for the region. Soil moisture and immediate and delayed evapotranspiration and runoff are predicted. It has been found that the model predicts the soil moisture at the Central Eastern supersite quite well. However, it overestimates soil moisture at the Southern supersite even though its variability is captured by the model. Model results also indicate that soil moisture estimates are very sensitive to the NDVI-evaporivity relationship, which is robust at monthly scales but needs more revision for application at the daily scale. Overall the model performance when applied to the IOP observations is sufficiently good to indicate the suitability of the climatonomy for water balance studies on daily time scales.

Airborne microwave radiometric measurements in the framework of the HAPEX-Sahel Experiment were performed by the Push Broom Microwave Radiometer (PBMR) and the PORTOS radiometer. The flights of both radiometers produced an original set of data covering the 1.4–90 GHz range of frequency. The East and West Central Super Sites were the areas most intensively observed by the microwave radiometers. Over those sites, several brightness temperature (TB) maps are available at seven dates distributed over a 1 month period in the middle of the rainy season. A comparison of the two radiometers demonstrates their radiometric quality and the precision of the localization of the microwave observations. At 1.4 GHz, the vegetation had very little effect on the soil microwave emission. Maps of soil moisture were developed using a single linear relationship between TB and the surface soil moisture. There is an important spatial heterogeneity in the soil moisture distribution, which is explained by both the soil moisture hydrodynamic properties and the localization of the precipitation fields. At 5.05 GHz, the vegetation must be accounted for to infer soil moisture from the microwave observations. A method based on a simple radiative transfer model and on microwave data has shown encouraging results.

Workshop on Thermal Remote Sensing of the Energy and Water Balance Over Vegetation in Conjunction with Other Sensors, La-Londe-des-Maures, 20-24 septembre 1993; International audience; Surface temperature is a key parameter for assessing fluxes at the surface-atmosphere interface. Proper estimation of radiative surface temperature requires corrections for perturbating factors such as atmospheric contributions and angular effects. Several models have been derived to address angular effects, but relevant data for validating such models is still scarce. This paper describes a field experiment dedicated to collecting angular measurements of brightness surface temperature over several types of surfaces (bare soils with different roughnesses, corn, grass, alfalfa), using a unique measurement protocol with simultaneous temperature readings at two angles. For each surface zenithal and azimuthal angular effects are quantified. In some cases (unstressed, fully-covering alfalfa) the difference between oblique and vertical brightness temperatures is within ±0.5 K. Over stressed corn the temperature measured at angles of ±60° is about 4 K less than the nadir looking temperature, but it is 3.5 K higher over a ploughed bare soil, when the inclined radiometer faces the sunlit side of the furrows. Over a bare smooth soil the observed angular variations are shown to be compatible with those due to possible angular variations in emissivity. All the results are discussed in terms of surface geometry and microclimatic conditions, and compared to previous studies. Implications are deduced for the interpretation of satellite measurements of surface temperature.

There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a "vegetation signal" to "soil noise" ratio. © 1994.

In order to detect thermal anomalies prior to an eruption with remotely sensed thermal infrared data, fifteen images collected by the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA7 satellite were acquired over a period of three months before the eruption of Mt. Etna which took place on 28th March 1983. This data set was processed in order to get a surface temperature as close as possible to reality and to suppress the spatial variations not directly linked with internal activity (i.e. altitude effects, climatic environment, sea proximity, etc...). Subsequently two anomalies were made conspicuous; the first one is related to the permanent activity of the summit craters and the second one, visible during the month prior to the eruption seems to be highly correlated to the intrusion. A possible interpretative model assuming natural convective heat transfer is then proposed and discussed.

Earth observation satellites provide us with the opportunity to survey the land surface on a frequent and global basis for agronomy applications or climate studies. Meteosat and NOAA satellites have been used in this work to estimate land surface parameters useful for climate modelling, albedo, ground temperature, vegetation index, and to study their time changes on a seasonal and year-to-year basis. Results obtained monthly from 1983 over Africa will be shown and discussed. They include: - the seasonal and interannual variabilities of the surface albedo, and the impact of the vegetation on the surface albedo in the subtropical areas ; - monthly mean values of the downward solar radiation at the surface which can be combined with surface albedo to compute the net solar radiation at the surface; - minimum, maximum and daily range of the surface temperatures in connection with the water budget in the Sahel region; - the relationship between precipitations and the vegetation biomass through the vegetation index in the Sahel region.

The monitoring of water balance from satellite thermal infra-red data is limited by the frequency of clear days necessary to obtain images: interpolation between precise data is therefore possible only by the modelling of crop energy and water balance. The agrometeorological model of Choisnel is improved in order to get a better estimation of hourly surface temperature of a short crop (grass ...). It is then compared to thermal infra-red data of NOAA satellite on South-East of France. This remote sensing and modelling approach permits the study of the spatial variability of soil water holding capacity, the estimation of irrigation water supply in a homogeneous area and the drawing of a first map of the daily evapotranspiration in the lower Rhone Valley.