Lakes are considered as sentinels of climate change. Water stored in lake is an important component of the land water cycle. Earth Observations offer a unique tool to monitor lakes extent, water levels variations, and hence changes in water volume combing these two variables or one of them with lakes bathymetry. We present here, surface water volume variations combining lake extent from MODIS and water levels from radar altimetry over 01/1995-04/2024 of Lake Titicaca, the second largest lake in South America. Comparisons between water levels from in-situ gauge and from radar altimetry including the recently launched SWOT mission are presented.

Soil Moisture Ocean Salinity (SMOS) mission was the first L-band radiometer launched in 2010 and is operational for retrieving global scale soil moisture and Vegetation Optical Depth (VOD). SMOS-INRA-CESBIO (SMOS-IC) version-2 is the latest retrieval algorithm for SMOS radiometers that outperforms existing SMOS retrieval algorithms. Research is underway to enhance the SMOS-IC product by improving surface roughness information that influences soil moisture and VOD retrievals. In the present study, we developed a new global parameterization of soil roughness using SMOS-IC retrievals. For this purpose, we retrieved the soil moisture and surface roughness (through the Hr parameter) values over bare soils using the SMOS-IC algorithm. A Random Forest (RF) model was trained with soil textural and terrain properties as inputs (explanatory variables) to model Hr over bare soils. Later, we extrapolated the Hr values obtained over bare soils to a global scale using the RF model. The newly calibrated Hr values were further used in the SMOS-IC algorithm for soil moisture and VOD retrievals (SMOS-IC v2.1). The SMOS-IC v2.1, the soil moisture product, is wetter than the original SMOS-IC v2 product and has improved performance compared to in situ ISMN soil moisture and modeled ECMWF soil moisture data sets. Regarding VOD, the SMOS-IC v2.1 VOD product showed improved spatial correlation with the reference aboveground biomass product. In addition, SMOS-IC v2.1 VOD product showed improved temporal correlation with MODIS NDVI over low-to-moderate vegetated regions.

Analyzing the spatio-temporal dynamics of groundwaters is crucial for a better understanding of the global hydrological and biogeochemical cycles, especially with climate change and the intensification of human activities. However, monitoring groundwaters is still complicated due to the lack of in situ data and the weak spatial coverage of these sites. Remote sensing is a good opportunity to address this problem. The method to study water level maps of the groundwater table is based on the use of a dense network of virtual stations produced using radar altimetry and surface water extent obtained by multispectral imagery. The interpolation of these two datasets allows us to produce minimum water level maps associated with the groundwater lebel base at a spatial resolution of 500 m from 2000 to 2022. First results show a link between groundwater anomalies and cumulative rainfall.

Since 2016, land surfaces can be monitored from optical and synthetic aperture radar sensors onboard Sentinel-1 and Sentinel-2 satellites at high spatial and temporal resolutions. Monitoring agricultural surfaces through satellite-based estimations of biophysical parameters is a key issue for agriculture sustainability in the context of an increasing climate change. With its all-weather vision capability, it is important to develop methods based on the use of Sentinel-1 images. In this study, we applied an original algorithm of change detection to monitor the two summer crops (sunflower and sorghum) grown in France and other parts according to a near-real-time detection method called CuSum-NRT applied to Sentinel-1 time-series of images.

The future Copernicus Imaging Microwave Radiometer (CIMR) mission is planned to be launched in the 2027+ time frame. At its present phase, the first version of each Algorithm Theoretical Basis Document (ATBD) must be defined. CIMR will provide observations at L (1.4 GHz), C (6.9 GHz), X (10.65 GHz), Ku (18.7 GHz) and Ka (36.5 GHz) microwave frequencies. These observations will be relevant to develop high resolution land surface products. Here we present a preliminary study with the aim of exploring the future capabilities that the synergy of CIMR frequencies can provide. Focused on the 0th-order Tau-Omega (τ-ω) model, we analysed the influence of soil roughness (H) and scattering albedo (ω) to retrieve soil moisture (SM) and vegetation optical depth (VOD) at L-band and how these parameters can be potentially estimated from higher frequency bands. We evaluated our results over CONUS, concluding that the soil roughness (H) parameter is affecting VOD and ω mainly in non-forested areas: in those areas, the increase of H produces a decrease in VOD. Our maps of ω revealed dependence with land cover type: generally, the lowest ω values were found in forested areas. Instead, our H map yielded patterns that could be mostly associated with topographic effects. Furthermore, by utilizing a depolarization index, TBdep, we discovered that its values were constrained to nearly zero (indicating minimal soil impact) in areas with vegetation, whereas in bare soils, topography had a significant influence on TBdep. We hypothesize that the use of this index could help in finding relationships among the multi-frequency information from CIMR, allowing us to understand the degree of sensitivity of each band to vegetation and topography.

Between 2018 and 2022, INRAE Bordeaux (IB) has developed a series of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) retrieval products from SMOS and SMAP, which are currently the only two operational L-band passive microwave satellite missions. These IB products rely on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information. The products are found to be accurate, and very well-suited for application in hydrology, agriculture, climate and vegetation monitoring. In this communication, we present an overview of the development, evaluation and new applications of these IB SM or L-VOD products.

Climatology (mean seasonal cycle) often dominates the errors in satellite soil moisture (SM) products, which is highly essential for the water-carbon cycle in tropical rainforests. Although microwave observations at L-band are expected to provide more accurate SM information benefiting from their stronger penetration capacity, the SM mapping in rainforests by L-band measurements is still challenging and is less investigated by the community. To bridge the research gap, five L-band satellite SM products from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites, including SMOS-IC, SMAP SCA-V, DCA, MTDCA and IB were assessed by using the FLUXNET SM data in tropical rainforests. To cope with the time inconsistence between satellite and ground data, the SM climatology variations in rainforests for diverse time periods were compared using long-term ERA5 SM. The results indicate the SM climatology is relatively stable over different time periods during 2001-2020 for rainforest sites. Based on the stability of SM climatology, L-band SM products were demonstrated to satisfactorily capture the SM climatology variations in rainforests, especially for SMOS-IC and SMAP-IB. The results are expected to provide guidelines for the hydro-ecological applications using satellite SM products in tropical rainforests.

Blending both active and passive microwave measurements are expected to provide more robust surface soil moisture (SSM) estimations over various environmental conditions compared to that from the single sensor. The integration of the newest L-band passive (i.e., Soil Moisture Active Passive, SMAP) with the similar observation scale active (i.e., the Advanced Scatterometer, ASCAT) sensors provides a considerable opportunity to improve the accuracy of SSM mapping, which however is rarely investigated to date. In this study, we implemented the integration of SMAP brightness temperature (TB) and ASCAT backscattering coefficient (σ) for estimating SSM using the machine learning approach, by fully considering the error sources in physically-based retrieval approaches (e.g., τ–ω model). The independent validation results using ground data from 14 dense networks show the integration of SMAP and ASCAT measurements can satisfactorily achieve better SSM retrievals compared to ASCAT SSM, SMAP-SCA-V SSM and ESA CCI SSM products, with the lowest ubRMSE of 0.042 m3/m3and the highest R of 0.76. This study is expected to enrich the understanding of SSM retrieval from active and passive microwave satellites, and provide SSM product with higher accuracy for eco-hydrological applications.

Analyzing the dynamics of surface water extent and volume is crucial for a better understanding of the global hydrological and biochemical cycles, especially in a context of climate change. However, due to the scarcity of in situ data and their inhomogenous spatial coverage, their use is limited. Remote sensing provides a good opportunity to solve this problem. In this study, we quantify surface water extent (from multispectral imagery) and volume (combining multispectral imagery and radar altimetry) over the Inner Niger Delta (IND) during the 2000-2022 period. The IND, located in Mali, has a wet season (from August to December) where important floods occured. Time series of surface water extent and volume show seasonal variations with maximum values in November and minimal values in April-May. These long time series show important droughts in 2002, 2004, 2011 and 2017, and maximum flood in 2003. In comparison with the digital flooding model, surface water extent from this method show difference of -20%.

Tropical forests are vulnerable to deforestation. This phenomenon led to the development of a wide number of forest monitoring systems based on remotely sensed data. These systems face multiple issues in tropical areas, one of them being the need of a good forest / non-forest map to use as reference for the monitoring. Several of these maps are available online, most of them being based on optical remote sensing data, which is known to be subject to limitations in tropical areas due to high cloud cover. Sentinel-1 C-band Synthetic Aperture Radar (SAR) dense time series of images has already been used for forest / non-forest mapping through combination with X-band SAR images. In this study, a change detection algorithm was applied on timeseries of Sentinel-1 images in the Parà State, Brazil. The Cumulative Sum (CuSum) algorithm was used to assess all non-forest areas as the hypothesis found by preliminary results was that these areas were more varying in terms of backscatter coefficient than undisturbed forests. The validation was made by comparison with a forest / non-forest map derived from Global Forest Watch data. The algorithm detected 78.0 % of the non-forested areas in the study zone of 6,855 km² with a 0.785 F1-score value.

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Lakes and reservoirs are considered sentinels of climate and anthropogenic changes. Lakes and reservoirs surface water storage is an essential hydrological variable but poorly known as this information is scarce. Earth Observation data are a reliable source of information to overcome this scarcity. Among these, the combined use of satellite images, to derive water extent, and radar altimetry, which enables to estimate water levels, provides valuable information on water storage changes. Here, we used Synthetic Aperture Radar images from Sentinel-1 and radar altimetry data from Sentinel-3 to monitor the water volume changes of Lake Bracciano from 2016 to 2021. This lake was affected by a water crisis in 2017 and the water supply to the city of Rome (Italy) was interrupted September 2017 to preserve its ecosystem. Hence, we demonstrate how Sentinel-1 and Sentinel-3 data can be useful to monitor water extent and level, which can be profoundly changed by the climate crisis.

Earth Observation data is often used for land cover classification or change monitoring. It is rarely used for both goals in a single algorithm. The multi-change Cumulative Sum (CuSum) algorithm proposed in this study allows both classification and change monitoring in a single algorithm using Sentinel-1 C-SAR time series. The multi-change CuSum approach allowed to classify pixels belonging to the fused non-forest vegetation and bare soil classes apart from the pixels belonging to new cuts. The distinction of each class is better made using the two polarizations: VV is more accurate for detecting non-forest vegetation (Kappa coefficient of 0.62) and VH for detecting new cuts (Kappa coefficient of 0.65). The algorithm showed an accuracy up to 0.82.

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

The effects of dry and fresh matter on microwave remote sensing data represent a great challenge, for which vegetation permittivity models are essential. To develop this model, in situ permittivity sensors are needed. There are few commercial equipment dedicated to these in situ measurements and none of them are developed in order to leave the instrument on site for automatic measurements with specific constraints such as communication, wide frequency band, non-invasive measurements and low price. We developed an instrument addressing these challenges. In this article, we present the first collected measurements and the computed permittivity data.

Monitoring properties of soil from microwave remote sensing is a very effective tool. In this context, the knowledge of soil permittivity values is important in order to guarantee the accuracy of the monitoring. Previous studies have showed the impact of temperature and moisture gradients in the top soil layer permittivity profile on remote sensing monitoring of soil. To increase our knowledge of these phenomena, we developed a new low cost equipment for continuous in-situ measurements of microwave [100 MHz-3GHz] permittivity soil profiles.

The present study focuses on retrieving soil and canopy temperatures, which are key parameters to estimate soil moisture and vegetation optical depth from multi-frequency microwaves information. Several retrieval algorithms assume that canopy and vegetation temperatures are similar in thermal equilibrium conditions, while others separate their contributions, as SMOS-IC, one of the consolidated retrieval algorithms for the Soil Moisture and Ocean Salinity (SMOS) satellite mission. Soil and canopy temperatures in SMOS-IC are modelled from the ECMWF (European Centre for Medium-Range Weather Forecasts) centre. Both SMOS and the Soil Moisture Active Passive (SMAP) missions are currently the only passive L-band (1.4 GHz) missions in operation, but their lifetime is limited. In this context, the upcoming Copernicus Imaging Microwave Radiometer (CIMR) mission will provide continuity on L-band measurements with complementary information in a range of microwave frequencies, from 1.4 to 36.5 GHz. This study uses in situ soil moisture information from the International Soil Moisture Network (ISMN) as input in the SMOS-IC algorithm to retrieve vegetation optical depth (VOD) and soil/canopy effective temperature (TGC). The retrieved effective temperature is then compared with modelled temperatures from ECMWF and with data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), which acquires the higher frequency bands (C, X, Ka, and Ku) present in the future CIMR mission. Results confirm the potential of all high-frequency bands to estimate TGC, with C and X-bands being the most correlated. This study is a first approach to evaluate how microwave multi-frequency information can help modelling soil and canopy temperatures in the SMOS-IC retrieval algorithm, from which the upcoming CIMR mission may benefit.

This study presents an analysis of radar signature at Ku-band for incidences ranging from 0° to 10° over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, using data from Jason-3 and SWIM. Time-series of radar responses were built over the following environments: stone and sand deserts, Sahelian savannah and floodplain, flooded and non-flooded equatorial forests. Deserts and non-flooded equatorial forest exhibit almost constant responses, decreasing as the incidence angle increases. Similar seasonal variations of the backscattering coefficient between the dry and the wet season are observed at nadir for Jason-3 and SWIM with a decrease in dry season level and amplitude with the increase of the incidence angle. Backscattering at Ku-band can be related to soil roughness, vegetation cover and soil wetness.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

Assessment of the model-based surface soil temperature (ST) products is very important for hydrometeorological and ecological applications, as well as model refinements. Distinguished from previous regional validations using only in situ observations from sparse networks, this study focused on the evaluation of model-based ST products by considering ground observations from 15 dense networks worldwide from April 2015 to December 2017 covering a wide range of ground conditions. Four model-based ST products were selected for the assessment, including the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the ERA-Interim and its successor, the newly developed ERA5. The results indicate the GEOS-5 ST product slightly outperforms other ST products by showing an averaged ubRMSD of 1.84 K. All model-based ST products underestimate in situ ST with a negative bias. All four model-based ST products are demonstrated to well capture the temporal trends of ground observations with very promising$R$values larger than 0.97. The ERA5 shows visible improvements compared to its predecessor ERA-Interim by exhibiting smaller ubRMSD, absolute bias and larger$R$values. These findings are expected to provide useful suggestions for the enhancement and specific usage of the model-based ST products.

Passive microwave L-band remote sensing is well known for its sensitivity to surface soil moisture over land. The signal is also affected by other dynamic variables such as vegetation and soil salinity. In this research, L-band microwave observations of the Soil Moisture Ocean Salinity (SMOS) mission are used to explore soil surface salinity, moisture and vegetation, in the Argentinean Dry Chaco, an area with possible emerging dryland salinity. A Radiative Transfer Model (RTM) with inclusion of a correction to the soil's dielectric constant for salinity was used in forward and inverse mode, using either in situ data or Catchment Land Surface Model (CLSM) simulations as RTM input. The forward analysis pointed out shortcomings in the modeled soil moisture and soil surface temperature estimates. The impact of salinity on forward simulations over the Dry Chaco was limited. The RTM inversion using 10 years of SMOS brightness temperature observations resulted in realistic estimates of vegetation and roughness, both with and without optimizing a correction term for the dielectric constant in terms of salinity equivalents. However, the retrieval of the correction term to the dielectric constant was very uncertain and not representative of soil surface salinity, but rather of open water, texture uncertainty or soil moisture bias.

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing specifically the ill-posed problem of retrieving both SM and VOD from observations which may be strongly cross-correlated. Promising results were obtained particularly in terms of spatial correlation of these alternate VOD indices with biomass.

Tropical forests are vulnerable to deforestation and various monitoring techniques have been developed based on remotely sensed data to map deforestation, but are facing multiple problems in the tropical areas. For instance, the techniques based optical data, which are widely used to monitor deforestation, face severe limitations in the humid tropical forest due to high cloud cover. Sentinel-l C-SAR dense time series can be used for a temporally more accurate monitoring. In this study, a change detection algorithm commonly used in the financial domain, the Cumulative Sum (CuSum) algorithm, was modified to be applied on time-series of Sentinel-l images in a forest concession of Democratic Republic of Congo (DRC) near Kisangani. The validation was made through the visual interpretation of PlanetScope OrthoScene images as in-situ data were missing. The results show a precision up to 0.75, an accuracy up to 0.95 and a kappa coefficient up to 0.40 for clear cut detection. The algorithm is able to detect forest degradation activities before the clear cuts.

Since the first version of the SMOS-IC retrieval product was released in 2017, its soil moisture (SM) and L-band Vegetation Optical depth (VOD) retrievals have proven to be a very interesting alternative product for the SMOS mission. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which is independent of auxiliary data, a key feature making it well-suited for application in hydrology, agriculture, climate, and carbon cycle. This paper describes the development and validation of the most recent SMOS-IC version (V2) soil moisture product. Compared with the previous version (V105), a new constraint was applied on VOD in the cost function which is minimized in the retrieval process. Soil moisture retrievals from SMOS-IC V2 & V105 were inter-compared against the “European Centre for Medium-Range Weather Forecasts” (ECMWF) modelled SM and the “International Soil Moisture Network” (ISMN) in-situ measurements during 2011-2017 over France. It was found that the average retrieval uncertainty of the new version product was lower than that of the old version, particularly when vegetation density increased. The new version of the SMOS-IC soil moisture product will be made available to the public through the CATDS (Centre Aval de Traitements des Données SMOS) website.

Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 9 years. The whole data currently being reprocessed (Version 721 for levels 1 and 2 and version 4 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 9 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

An Emission Models (EM) adequate for a retrieval algorithm requires being simple while still capturing the responses of brightness temperatures TBp,θto the retrieval parameters. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the "Tau-Omega" (TO) EM to retrieve soil Water Content WC and vegetation optical depth τ from L-band TBp,θ. For sparse and low-scattering vegetation TB,EMp,θsimulated with EM = TO and EM = 2S converge, which is not the case for dense and strongly scattering vegetation. WCRCand τRCare retrieved with Retrieval Configurations RC = {TO, 2S} from TBp,θ: i) from a tower within a deciduous forest, and ii) by the "Soil Moisture and Ocean Salinity" (SMOS) mission. Using 2S EM instead of TO EM resulted in marginally lower WCRCretrievals while τRCretrievals are reduced more considerably. With respect to in-situ WCin-situ, retrievals WC2Sderived from tower-based TBp,θperformed better than forest soil water-content WCTOretrieved via the inversion of the "reference" TO EM. Likewise, SMOS based WC2Sretrievals revealed better agreement with ECMWF WC simulations than WCTOachieved with the "reference" RC = TO. In short, our study provides clear evidence that it is meaningful to replace TO EM used for current SMOS and SMAP land retrieval with 2S EM.Further advantages of the 2S EM over the TO EM are outlined in this study.

Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

In this study, we compared coupled and uncoupled land surface model simulations of surface soil moisture (SM) with passive microwave remote sensing observations of SM over the contiguous US (CONUS). For this purpose, we used the following: (i) the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved L3 surface SM; (ii) the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SM derived using LPRM; and (iii) the coupled and uncoupled ORCHIDEE land surface model. The comparison was achieved by computing the temporal mean difference between the normalized remotely-sensed and the model-SM datasets. It was found that both coupled and uncoupled models are drier than the remotely sensed data (particularly the SMOS data) over the principal aquifers of the CONUS. These aquifers are known as one of the places where irrigation is intensive. Therefore, the reason behind this model' dryness could be the fact that the models do not take into account the irrigation activities, which can be monitored by the remotely-sensed observations. Time series of SM over irrigated pixels also showed that SMOS was wetter than the models mainly during the irrigation period.

Monitoring live fuel moisture content (LFMC) in Mediterranean area is of great importance for fire risk assessment. LFMC has extensively been estimated based on optical remote sensing data. But the latter can be affected by atmospheric effects. As a complementary data source, microwave data can be used as they are relatively insensitive to atmospheric effects. Yet further evaluations are needed to investigate the potential of microwave observations to monitor LFMC. In this study, we assess the capability of long-term microwave vegetation optical depth (VOD) to capture the temporal variability of in situ measured LFMC in 14 Mediterranean shrub species in southern France during 1996-2014. Microwave-derived VOD at X band (VODX-15) displayed a high sensitivity to LFMC with correlation coefficients of 0.56. Similar evaluations were made using four optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI). The comparisons showed that VARI performs better than VODX-15 and other optical indices with highest median of correlation coefficients of 0.65. Overall, this study shows that passive microwave-derived VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.

Tropical aboveground carbon changes during 2010–2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010–2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010–2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.

The ongoing ESA funded `SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of dedicated observation operators that link the state of the terrestrial biosphere model to simulated VOD and surface layer soil moisture. We present our carbon assimilation system around a terrestrial biosphere model and demonstrate its operation through simultaneous assimilation of the SMOS VOD product over seven sites covering a range of plant functional types.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

After almost 9 years in orbit L-band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. However, so as to cover specific applications use of other sensors can prove very valuable. In particular use of altimetry, optical and thermal infrared measurements can offer new avenues. Many of them were tested with existing satellites at the time of SMOS launch, but with the Copernicus' Sentinel-3 mission's data now available, new applications and operational products can be envisioned.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

The Radar Vegetation Index (RVI) is widely applied to indicate vegetation cover. The index includes the backscattering intensities of co- and cross-polarization that do not only contain information coming from vegetation scattering at longer wavelength (L-band), but also from the soil underneath. A forward modelling approach using active and passive microwave-derived parameters to obtain the scattering contribution of the soil is pursued. The idea of this research study is a subtraction of the attenuated soil scattering contribution from the measured backscattering intensities, to provide a clean vegetation-based solution, called improved RVI (RVII). For latter analysis, the vegetation volume is forward modeled to calculate vegetation-only RVI-values without any soil scattering contribution. It reveals that, the pre-factor of the standard RVI leads to values up to 1.2, unfavorable for a normalized index running between zero and one. Hence, improvements for the standard RVI equation are proposed here to obtain a better suited value range and for incorporating soil scattering influences and filtering of regions with dominant soil scattering. Moreover, the improved RVI (RVII) is compared with datasets of vegetation and soil parameters (e.g. vegetation water content) for correlation analysis to find the physical parameters contributing to the index.

INRA (National Institute of Agricultural Research) has set up the AQUI network in the Bordeaux-Aquitaine region (southwestern France) in the framework of the SMOS cal/val activities. This network includes sites of in situ measurements which have been equipped with sensors measuring soil moisture (SM) and temperature, at various depths, and the height of the groundwater table. Four sites were installed in the Les Landes forest, which is one of the largest coniferous forests in Europpe, and one site was installed close to vineyard fields of the Bordeaux Graves region. First results of the evaluation of the SM data retrieved from the L-band SMOS and SMAP passive microwave radiometers over the AQUI network are presented. The AQUI network was included in ISMN (International Soil moisture Network) in 2018.

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.

The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization (ΓLR) or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.

In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphère), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

This study presents a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product based on a different scattering albedo and soil roughness parameterization: the SMOS-IC (SMOS INRA-CESBIO) data set. In this study, several parameterizations of the vegetation and soil roughness parameters (ω, HRand NRP, P = H, V) were tested and the retrieved SM was compared against in situ observations obtained from the International Soil Moisture Network (ISMN). Firstly, values of ω = 0.10, HR= 0.4 and NRP= −1 (P = H, V) were found globally. Secondly, a calibration of these parameters was obtained for the different land cover categories of the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HRvaried, respectively, in the ranges 0.08–0.12 and 0.1–0.5. The IGBP-based calibration is currently used in the SMOS-IC product algorithm. Using as reference the ISMN sites, a better performance of the SMOS-IC product over the operational SMOSL3 (SMOS level 3) SM product was found: R = 0.62, bias = −0.019 m3/m3, ubRMSE = 0.061 m3/m3 for SMOS-IC; against R = 0.54, bias = −0.037 m3/m3 and ubRMSE = 0.069 m3/m3 for SMOSL3.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

The GLORIE Campaign was performed in June-July 2015 in order to investigate the sensitivity of airborne GNSS-R measurements to land parameters. In this paper we present the first results focusing on agricultural areas. For this purpose ground truth measurements of soil moisture, roughness, plant water content, leaf area index and plant height were measured over 20 agricultural plots of various crops (cereals, vegetables, bare soil). The correlation with GNSS reflectivity in LHCP polarization confirms noticeable sensitivity to soil moisture, and plant-related parameters especially vegetation cover height.

This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.

This letter aims to estimate the effective scattering albedo ($\omega _{p}$) over the tropical forests using L-band (1.4 GHz) microwave remote sensing. It is carried out using Soil Moisture and Ocean Salinity (SMOS) mission data over five years (2011–2015). We find similar values of$\omega _{p}$computed over the Congo and Amazon forests. The$\omega _{p }$values depend slightly on the polarization. The values of$\omega _{p }$at H-polarization and at 52° ± 5° (40° ± 5°) of incidence angle are within the range 0.064 – 0.069 ± 0.01 (0.061 – 0.067 ± 0.012). At V-polarization, the values of$\omega _{p }$are slightly lower (0.060 – 0.061 ± 0.013 at 52° ± 5° of incidence angle and 0.052 – 0.055 ± 0.013 at 40° ± 5° of incidence angle). These findings should contribute to a better calibration of the value of$\omega _{p }$over the tropical forests in both the SMOS and SM active and passive retrieval algorithms, leading to increase the SM retrieval accuracy over heterogeneous pixels.

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

This study focuses on the calibration of the effective scattering albedo (ω) of vegetation in the soil moisture (SM) retrieval at L-Band. Currently, in the SMOS Level 2 and 3 algorithms, the value of ω is set to 0 for low vegetation and ∼ 0.06 – 0.08 for forests. Different parameterizations of vegetation (in terms of ω values) were tested in this study. The possibility of combining soil roughness and vegetation contributions as a single parameter (“combined” method) leads to an important simplification in the algorithm and was also evaluated here. Following these assumptions, retrieved values of SMOS SM were compared with SM data measured over many in situ sites worldwide from the International Soil Moisture Network. These validation sites were classified using the International Geosphere-Biosphere Programme (IGBP) classification scheme. In situ SM measurements and SM retrievals were compared, and statistical scores were computed. The optimum albedo configuration was then found for each class of the IGBP landcover classification. Preliminary results yield values of albedo between 0.07 to 0.12 under the assumption of homogeneous pixels.

Field experiments have played a critical role in the development and implementation of satellite soil moisture missions. A review of key experiments is presented that includes tower-, aircraft, and satellite-focused efforts conducted over four decades that have supported two dedicated satellite missions; Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active passive (SMAP).

The Soil Moisture and Ocean Salinity mission has been collecting data for 6 years. The whole data set has just been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS). This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 6 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events.

The GLORIE GNSS-R airborne campaign was conducted in the late spring 2015 with the GLORI polarimetric receiver. More than 15 hours or raw data was gathered during 5 flights that spanned over a 3-week period. The aircraft flew over several areas if interest including: 1) agricultural plots with coincident in-situ measurements of soil moisture, vegetation biomass and roughness, 2) in situ monitored forest plots with a wide range of above ground biomass values and 3) inland water bodies in order to test phase altimetry retrievals. Apparent reflectivity was computed from the data, showing a good dynamics above various types of terrains. Phase altimetry was performed over calm water, showing a precision in the range of the centimeter level.

Soil Moisture and Ocean Salinity (SMOS) satellite has been providing surface soil moisture (SSM) and ocean salinity (OS) retrievals at L-band for five years (2010-2014). During these five years, the SSM retrieval algorithm i.e. the L-MEB (L-Band Microwave Emission of the Biosphere [1] model has been progressively improved and hence results in different versions of the SMOS SSM products. This study aims at evaluating the last improvement in the SSM products of the most recent SMOS level 3 (SMOSL3) reprocessing (SMOSL3_2.72) vs. an earlier version (SMOSL3_246). Correlation, bias, Root Mean Square Difference (RMSD) and unbiased RMSD (unbRMSD) were used as performance criteria in this study using the ECMWF SM-DAS-2 product as a reference. Results show that the SMOS SSM estimates have been improved: (i) SMOSL3_272 was closer to SM-DAS-2 over most of the globe-with the exception of arid regions-in terms of unbRMSD (ii) SMOSL3_272 was closer to SM-DAS-2 over Spain, Brazil, parts of Sahel, high latitude and equator regions but comparable with SMOSL3_246 over most of the rest of the globe in terms of correlations.

This paper focuses on a new approach to account for soil roughness effects in the retrieval of soil moisture (SM) at L-band in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission: the Simplified Roughness Parameterization (SRP). While the classical retrieval approach considers SM and τNAD(vegetation optical depth) as retrieved parameters, this approach is based on the retrieval of SM and the TR parameter combining τNADand soil roughness (TR = τNAD+ HR/2). Different roughness parameterizations were tested to find the best correlation (R), bias and unbiased RMSE (ubRMSE) when comparing homogeneous retrievals of SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) vineyard field. The highest R (0.68) and lowest ubRMSE (0.056 m3m−3) were found using the SRP method. Using the SMOS observations comparisons against several SM networks were also made: AACES, SCAN, watersheds and SMOSMANIA. SM was retrieved over all these stations. The SRP and another similar approach (SRP2) improved the averaged ubRMSE, while the SRP2 method leaded to higher correlation values (R). A global underestimation of SM was noticed, which may be linked to the differences in the sampling depths of the L-band observations (∼ 0–3cm for both Elbara-II and SMOS) and of the in situ measurements (∼ 0–5 cm).

The availability of Landsat data (Landsat 4, 5, 7 and 8) from ~30 years makes it possible to analyze the forest long term dynamics at high resolution (30m). The performances of the Landsat time-series have been already demonstrated for mapping and monitoring the annual clear-cuts and the storm damage in the Landes Forest, that covers ~1 million ha in southwestern France and that is heavily managed with even-aged stands with rather short rotations after clear-cut harvesting. Our objectives aimed at improving, automating, and enriching these previous methods. This was to operationally produce over the whole Landes Forest not only (1) the annual maps of clear-cutting from 1984 up the current year but also (2) the map of the current age that was derived from the forest change detected every year since 1984. The developed methodology used the time-series of surface reflectance and cloud mask provided for Landsat by USGS and sought to cope the possible absence of cloud-free image during the interest season or the numerous missing data in Landsat 7 images after 2002. The retrospective processing of the Landsat time-series from 1984 to 2014 made it possible the prediction of actual current age with a satisfactory accuracy.

Soil moisture is one of the biosphere's most essential climatic variables. However, its periodical monitoring at regional scales using in-situ measurements is complex. In this context, L-band microwaves observations from passive remote sensors appear as an important tool for the periodical estimation of soil moisture at regional scales. In this work, a semi-empirical model to estimate soil moisture values from L-band observation was calibrated and evaluated over several land cover classes in a heterogeneous study area in Chile. For this, 3 years (2010, 2011 and 2012) of brightness temperature data from SMOS, soil temperature and volumetric water content from ERA-Interim, and NDVI from MODIS were used. Results showed an increase in the average r2when a vegetation index was used in the calibration of the approach. These increases ranged from a 3% for Crops, to a 49% for Closed Shrublands. The ubRMSD showed a decrease in its value of up to 1% m3/m3for Woodlands, Open Shrublands and Woody Shrublands and of up to 2% m3/m3for Closed Shrublands.

The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The retrieval algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS retrieval algorithm converges to realistic soil moisture retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.

This study investigated the use of physically based statistical regressions to retrieve a global and long term (e.g. 2003–2014) surface soil moisture (SSM) record based on a combination of passive microwave remote sensing observations from the Advanced Microwave Scanning Radiometer (AMSR-E; 2003-Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS; 2010–2014) sensors. Statistical regression methods based on bi-polarization (horizontal and vertical) brightness temperatures (Tb) observations obtained from AMSR-E. The coefficients of these regression equations were calibrated using SMOS level 3 SSM maps (SMOSL3) as a reference. 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 global SSM maps could be computed from the AMSR-E Tb observations over the whole 2003–2011 period. In this study, the SSM maps were successfully evaluated against the SMOSL3 SSM products over the period of calibration (Jun. 2010-Sept. 2011). Correlations (R) and Root Mean Square Error (RMSE) were computed between the AMSR-E retrievals and the reference (SMOSL3) SSM products. The R (mostly > 0.75) and RMSE (mostly3/m3) maps showed a good agreement between the retrieved and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In conclusion, the statistical regression method is capable of retrieving a coherent "SMOS-AMSR-E" SSM time series for the period 2003–2014.

Performances of two global satellite-based surface soil moisture (SSM) retrievals with respect to model-based SSM derived from the MERRA (Modern-Era Retrospective analysis for Research and Applications) rea-nalysis were explored in this paper: (i) Soil Moisture and Ocean Salinity (SMOS; passive) Level-3 SSM (SMOSL3) and (ii) the Advanced Scatterometer (ASCAT; active) SSM. Temporal correlation was used to investigate the performance of SMOSL3 and ASCAT SSM products during the period 05/2010–2012 on a global basis. Both SMOSL3 and ASCAT (slightly better) captured well (R>0.70) the long-term variability of the modelled SSM, particularly, over the Indian subcontinent, the Great Plains of North America, and the Sahel. However, ASCAT had negative correlations in arid regions, in particular across the Sahara and the Arabian Peninsula. This may be due to complex scattering mechanisms over very dry surfaces. To explore the land cover dependence of the analyzed statistical indicators, the global correlation results were averaged per biome extracted from a global map of biomes. In general, SMOSL3 and ASCAT performances behaved differently from one biome to another. For SMOSL3, the highest average correlation was observed over “tropical semi-arid” (R = ∼ 0.5) and “temperate semi-arid” biomes, whereas for ASCAT, the highest correlations were observed over “tropical semi-arid” (R = ∼ 0.7) and “tropical humid” biomes. The poorest agreement for both SMOSL3 and ASCAT was generally found over “tundra” and “desert temperate” biomes, particularly for ASCAT. This study showed that the performance of both SMOSL3 and ASCAT is highly dependent on vegetation. We also showed that both of them provide complementary information on SSM, which implies a potential for data fusion which would be pertinent for the ESA climate change initiative (CCI).

Microwave remote sensing instruments are an adequate way to provide soil moisture information at large scale. Microwave remote sensing data are linked to the electromagnetic properties of soil. In that context, the objective of this study is to develop an integrated approach to estimate effective electromagnetic properties of soils layers at different scale using ground-penetrating radar (GPR), L-band radiometer, dielectric laboratory measurements, modelling approaches and in situ measurements of essential state variables.

In this paper, roughness parameterizations providing best retrievals of soil moisture (SM) at L-band were evaluated. Different parameterizations were tested to find the best correlation R, bias and ubRMSE when comparing retrieved SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) over a vineyard field. Roughness measurements were always performed after the agricultural practices in the vineyard. These in situ data was used as input of the L-MEB (L-band Microwave Emission of the Biosphere) model, which permits the retrieval of SM and TAU (vegetation optical depth). In addition, a simplified method consisting on the retrieval of a parameter which combines the effects of roughness and TAU was tested. Significantly higher correlation (R=0.86) for SM was found using this method, while the absolute bias (-0.062) and RMSE (0.069) were slightly higher than for other roughness parameterizations.

Since several years, more and more studies aim at developing phenology products from satellite time-series at high temporal frequency such as those provided by the VEGETATION or MODIS sensors. Reflectance times-series at high spatial resolution, such as those that will be obtained from SENTINEL2, will soon available to monitor the phenology response of forests under climate change at the level of the forest stand or the tree species. There is a great need for continuous in situ monitoring of phenology to calibrate and validate the current and future remotely-sensed phenology products. In this context, we proposed a method based on near surface remote sensing techniques to monitor the seasonal change in LAI (Leaf area Index) and date key stages of the leaf phenology. As it is important to use a network of sensors with autonomous recording systems at a minimal cost in order to maximize the spatial sampling, we selected a method based on the transmittance continuous measurement of photosynthetic active radiation (PAR). We evaluated and validated the method for a deciduous forest species (Quercus petraea) over two sites encompassing a large variation in the timing of spring leafing, where direct visual phenology observations were performed. A specific preprocessing and modeling of the measured temporal signal was developed. The performances for dating the leaf unfolding in spring are satisfying: the bias is lower than ~1 day and the RMSE is (generally) lower than ~ 4 days.

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture (SM). SMOS operates at L-band and at this frequency, the signal depends on soil moisture but is also significantly affected by surface soil roughness. Using the Combined soil Roughness & Vegetation Effects (CRVE) method detailed in this paper, the effect of vegetation and soil roughness can be combined using a single parameter, referred to as TR here. SM and TR were retrieved by inverting the SMOS observations using the forward emission model (L-MEB). Assuming a linear relationship between TR and LAI obtained by the MODIS data, an Australian map of soil roughness was computed. This map could lead to improved soil moisture retrievals for present and future microwave remote sensing missions such as SMOS and the Soil Moisture Active Passive (SMAP) scheduled for launch in November 2014.

In this study, vegetation optical depth from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite mission is combined with other existing remote sensing, meteorological and literature data in order to obtain values of gravimetric vegetation water content (Mg). The methodology combines an effective medium model valid at passive microwave frequencies with a vegetation dielectric constant model. The algorithm is calibrated for 11 global vegetation classes. The resulting product consists of temporally dynamic ~25 km global grids of Mg. The first maps clearly show seasonal differences in vegetation water, which vary for the different continental regions due to variations in e.g. latitude, climate and landcover type. This new vegetation water product is unique and offers important complementary information to existing vegetation indices.

The usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

In this letter, a monofrequent dielectric model for moist soils taking into account dependences on the temperature and texture is proposed, in the case of an electromagnetic frequency equal to 1.4 GHz. The proposed model is deduced from a more general model proposed by Mironov and Fomin (2009) that provides estimations of the complex relative permittivity (CRP) of moist soils as a function of frequency, temperature, moisture, and texture of soils. The latter employs the physical laws of Debye and Clausius-Mossotti and the law of ion conductance to calculate the CRP of water solutions in the soil. The parameters of the respective physical laws were determined by using the CRPs of moist soils measured by Curtis (1995) for a wide ensemble of soil textures (clay content from 0% to 76%), moistures (from drying at 105 °C to nearly saturation), temperatures (10 °C -40 °C), and frequencies (0.3-26.5 GHz). This model has standard deviations of calculated CRPs from the measured values equal to 1.9 and 1.3 for the real and imaginary parts of CRP, respectively. In the model proposed in this letter, the respective standard deviations were decreased to the values of 0.87 and 0.26. In addition, the equations to calculate the complex dielectric permittivity as a function of moisture, temperature, and texture were represented in a simple form of the refractive mixing dielectric model, which is commonly used in the algorithms of radiometric and radar remote sensing to retrieve moisture in the soil.

In this paper, a numerical modeling approach was used to evaluate the semiempirical H-Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H-Q model uses four parameters, HR, QR, NRH, and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights σ and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H -Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H -Q model. It was found that the retrieved HRcould be related directly to Zs = σ2/Lcand that QR, NRV, and NRHwere dependent on HR. HRwas found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H-Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of ~ 0.03 m3/m3, which was within the desired 0.04-m3/m3error margin for the SMOS mission.

In the above paper (ibid., vol. 49, no. 4, pp. 1177-1189, Apr. 2011), there is an error in equation (7). The explanation and corrected equation are presented here.

Insect related damage has severe ecological and socio-economic impacts by causing large-scale mortality of vegetation and thereby affecting biogeochemical cycles and forest industry. The objective of this study was to evaluate the potential use of 250 m MODIS data time-series to monitor damage caused by the pine processionary moth (PPM), which is the most important pine defoliator of the maritime-pine forest in southwest France. We compared the in situ observed defoliation rates (DR) and the multi-seasonal variations of the MODIS-sensor derived MOD13Q1 product. We exploited the winter anomalies in the EVI signals to empirically detect and quantify the PPM defoliation. Different EVI-based anomaly metrics were tested by linear regression over a test site of 18 km2. The best correlated relationship (R2=0.908, p<;0.0001) to observed DR was used to define an empirical model. The predictions of DR after model inversion were validated at another site with a larger extent (16 km ×16 km). A robust empirical model based on the winter decrease in EVI-based anomaly metrics was found to be suitable to retrieve the spatial pattern of defoliation at the landscape scale.

In the context of the Soil Moisture and Ocean Salinity mission, we present a study of the emission of rough surfaces at 1.4 GHz and the effects of moisture and temperature gradients.

This study investigates the temporal behavior of the gravimetric vegetation water content (Mg) derived from SMOS (L-band) optical depth (τ) values. The analysis is done for the year 2010 over a coniferous forest site in the U.S. Resulting values of Mgare compared to values of leaf water potential obtained with the Soil-Plant-Atmosphere model and in situ data. A significant nonlinear correlation is found between the two (R=0.72, pg, based on plant water status, is different from that of existing optical-based vegetation indices, which are mainly based on chlorophyll/biomass changes. Therefore, τ-derived Mgcould provide complementary information for future global-scale dynamic monitoring of vegetation water status.

In this paper a single-frequent temperature and texture dependent dielectric model for moist soils is proposed. This model is designed for the frequency of 1.4 GHz at which the European sensor SMOS operates to monitor moisture of the Earth surface from space. Earlier, the dielectric model was created, that provides estimations of the complex dielectric constant of moist soils as a function of frequency, temperature, moisture and mineralogy of soils. This model was developed based on extensive measurements of the dielectric constant for a wide ensemble of soils (clay content from 0 to 76%), moistures (from zero to molecular moisture capacity), temperatures (10° C to 40° C), and frequencies (0.3 to 26.5 GHz). This model provides for fairly good accuracy. But, being cumbersome to account for frequency dependencies, the model is not always convenient for practical use at a single frequency. Exclusion of the frequency dependence allowed us to obtain a substantially more simple dielectric model to estimate the complex dielectric constant of soil at a single frequency of 1.4 GHz as a function of moisture, temperature, and clay content.

Spatial datasets of biophysical parameters at multiple scales can be important for the modeling of landsurface processes. In this study, we compared how decametric resolution and kilometric resolution LAI retrievals vary over a maritime pine dominated ecosystem in Southern France. Firstly, we used atmospherically corrected Landsat ETM+ and SPOT4 HRVIR reflectances along with ground-based LAI measurements to derive empirical relationships between vegetation indices and measured LAI. These algorithms were later inverted to map LAI over the landscape. RSR-based algorithms showed the best performance for both ETM+ (r2= 0.788) and HRVIR (r2= 0.780) sensors and were more stable than SR and NDVI. Further, after upscaling to 1km, comparison with global LAI products revealed that the CYCLOPES product was more robust in capturing the fine scale signatures. These results show that modeling of landsurface processes could be improved by adopting a multiscale and multisensor approach.

Numerous studies showed the impact of droughts events on the seasonal dynamic of satellite-based vegetation index but validation and relation to ground observations is still lacking. Focusing on the impacts of 2003 and 2011 droughts events, we defined and validated forest health indicators using extensive in-situ damage inventories carried out on all French forests. Analysis of vegetation indices dynamics (EVI, LAI) at the national scale was performed using MODIS and SPOT VEGETATION products (MOD13A2 V5 and CYCLOPES V3.1). Satellite-based indicators of (i) crown condition - which is itself a commonly used forest health indicator - and (ii) leaf phenology anomalies were calibrated and validated. In our study, average of CYCLOPES LAI during the growing season is a good proxi of crown condition (r2=0.79, p<;0.001). Inter-annual MODIS EVI anomalies can effectively capture drought effects on the vegetation dynamics and its recovery both in time and space.

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The bestwSestimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

Land surface temperature data are rarely available at high temporal and spatial resolutions at the same locations. To fill this gap, the low spatial resolution data can be disaggregated at high temporal frequency using empirical relationships between remotely sensed temperature and fractional green (photosynthetically active) and senescent vegetation covers. In this paper, a new disaggregation methodology is developed by physically linking remotely sensed surface temperature to fractional green and senescent vegetation covers using a radiative transfer equation. Moreover, the methodology is implemented with two additional factors related to the energy budget of irrigated areas, being the fraction of open water and soil evaporative efficiency (ratio of actual to potential soil evaporation). The approach is tested over a 5 km by 32 km irrigated agricultural area in Australia using airborne Polarimetric L-band Multibeam Radiometer brightness temperature and spaceborne Advanced Scanning Thermal Emission and Reflection radiometer (ASTER) multispectral data. Fractional green vegetation cover, fractional senescent vegetation cover, fractional open water, and soil evaporative efficiency are derived from red, near-infrared, shortwave-infrared, and microwave-L band data. Low-resolution land surface temperature is simulated by aggregating ASTER land surface temperature to 1-km resolution, and the disaggregated temperature is verified against the high-resolution ASTER temperature data initially used in the aggregation process. The error in disaggregated temperature is successively reduced from 1.65$^{\circ}\hbox{C}$to 1.16$^{\circ}\hbox{C}$by including each of the four parameters. The correlation coefficient and slope between the disaggregated and ASTER temperatures are improved from 0.79 to 0.89 and from 0.63 to 0.88, respectively. Moreover, the radiative transfer equation allows quantification of the impact on disaggregation of the temperature at high resolution for each parameter: fractional green vegetation cover is responsible for 42% of the variability in disaggregated temperature, fractional senescent vegetation cover for 11%, fractional open water for 20%, and soil evaporative efficiency for 27%.

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parametersNand σ was established, while NHand NVappeared to be lower for a rough (NH~ 0.59 and NV~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter.

Radiative properties at 1.4 GHz of vine vegetation are investigated by measuring brightness temperatures with the ETH L-band Radiometer II (ELBARA II) operated on a tower at the Mediterranean Ecosystem L-band Characterisation Experiment III (MELBEX III) field site in Spain. To this aim, experiments with and without a reflecting foil placed under the vines were performed for the vegetation winter and summer states, respectively, to provide prevailingly information on vegetation transmissivities. The resulting parameters, which can be considered as “ground truth” for the MELBEX III vineyard, were retrieved from brightness temperature at horizontal and vertical polarization measured at observation angles between 30° and 60°. These MELBEX III “ground-truth” values are representative for the Mediterranean Soil Moisture and Ocean Salinity (SMOS) Valencia Anchor Station (VAS) and therefore valuable for the corresponding calibration and validation activities over the VAS site. Likewise, quantifying the uncertainties of the measured brightness temperatures was also important, particularly as several equivalent ELBARA II instruments are currently operative in ongoing SMOS-related field campaigns.

We evaluate a new 3-D numerical modeling approach for calculating the rough-surface scattering and emission of a soil layer. The approach relies on the use of Ansoft's numerical computation software High-Frequency Structure Simulator, which solves Maxwell's equations directly using the finite-element method. The interest of this approach is that it can be easily extended to studies of heterogeneous media. However, before being applied in this way, it must first be validated for the rough-surface case. In this letter, we perform this validation by comparing the results of rough-surface scattering and emission with the results of the method of moments (MoM) for a range of different roughness and permittivity conditions and with both Gaussian and exponential rough-surface autocorrelation functions. For the scattering case, we obtain results that are in agreement with the MoM to within approximately 1-3 dB for angles up to and including 40° and 2-4 dB for angles from 50° to 70°. Agreement for emissivity is to within 3.2 and 3.6 K for low and high roughness conditions, respectively. We then illustrate the application of the new approach by calculating the emission of a two-layer system with rough surfaces, representing the soil-litter system in forests.

Ground-based multifrequency (L-band to W-band, 1.41-90 GHz) and multiangular (20°-50°) bipolarized (V and H) microwave radiometer observations, acquired over a dense wheat field, are analyzed in order to assess the sensitivity of brightness temperatures (Tb) to land surface properties: surface soil moisture (mv) and vegetation water content (VWC). For each frequency, a combination of microwaveTbobserved at either two contrasting incidence angles or two polarizations is used to retrievemvand VWC, through regressed empirical logarithmic equations. The retrieval performance of the regression is used as an indicator of the sensitivity of the microwave signal to eithermvor VWC. In general, L-band measurements are shown to be sensitive to bothmvand VWC, with lowest root mean square errors (0.04 m3·m-3and 0.52 kg ·m-2, respectively) obtained at H polarization, 20° and 50° incidence angles. In spite of the dense vegetation, it is shown thatmvinfluences the microwave observations from L-band to K-band (23.8 GHz). The highest sensitivity to soil moisture is observed at L-band in all configurations, while observations at higher frequencies, from C-band (5.05 GHz) to K-band, are only moderately influenced bymvat low incidence angles (e.g., 20°). These frequencies are also shown to be very sensitive to VWC in all the configurations tested. The highest frequencies (Q- and W-bands) are shown to be moderately sensitive to VWC only. These results are used to analyze the response of W-band emissivities derived from the Advanced Microwave Sounding Unit instruments over northern France.

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters:$H_{R}$,$Q_{R}$, and$N_{R}$. In many studies, the two parameters$Q_{R}$and$N_{R}$are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that$Q_{R}$could be set to zero. Second, a refinement of the L-MEB soil model, considering values of$N_{R}$for both polarizations (namely,$N_{\rm RV}$and$N_{\rm RH}$), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters$H_{R}$and$(N_{\rm RH} - N_{\rm RV})$to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions.

The error induced by some soil dielectric models used to retrieve the soil moisture from the SMOS radiobrightnesses is statistically evaluated, based on the measured dielectric data. Three dielectrical models most frequently used in the radio thermal remote sensing algorithms are tested in conjunction with the dielectric data base covering all soil types and moisture variations.

The effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. With recent physical model developments, the advanced integral equation model (AIEM) has been proven to provide accurate representation over a wide range of surface-roughness conditions. We evaluated the capability of the AIEM model in simulating multiangular surface emission signals in comparison with a field experiment data set. A simplified multiangular surface emission model was developed based on simulated database using the AIEM model. Based on the parameterized model, an inversion procedure was developed using dual-polarization microwave brightness temperatures to retrieve soil moisture. Two data sets were used to test the inversion algorithm, and the accuracies in root-mean-square error were about 4% for incidence angles from 20° to 50°. This new simple model is suitable for soil moisture retrieval from future L-band satellite data.

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

The emission of bare soils at microwave L-band (1-2 GHz) frequencies is known to be correlated with surface soil moisture. Roughness plays an important role in determining soil emissivity although it is not clear which roughness length scales are most relevant. Small-scale (i.e., smaller than the resolution limit) inhomogeneities across the soil surface and with soil depth caused by both spatially varying soil properties and topographic features may affect soil emissivity. In this paper, roughness effects were investigated by comparing measured brightness temperatures of well-characterized bare soil surfaces with the results from two reflectivity models. The selected models are the air-to-soil transition model and Shi's parameterization of the integral equation model (IEM). The experimental data taken from the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) consist of surface profiles, soil permittivities and temperatures, and brightness temperatures at 1.4 GHz with horizontal and vertical polarizations. The types of correlation functions of the rough surfaces were investigated as required to evaluate Shi's parameterization of the IEM. The correlation functions were found to be clearly more exponential than Gaussian. Over the experimental period, the diurnal mean root mean square (rms) height decreased, while the correlation length and the type of correlation function did not change. Comparing the reflectivity models with respect to their sensitivities to the surface rms height and correlation length revealed distinct differences. Modeled reflectivities were tested against reflectivities derived from measured brightness, which showed that the two models perform differently depending on the polarization and the observation angle.

Knowledge of the variability of microwave signatures of vegetation and roughness are necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. Conventional, vegetation indices are often limited by the effects of atmosphere, background soil conditions. The Soil Moisture and Ocean Salinity (SMOS) satellite will provide global multi-angular microwave brightness temperature observations at L-band, in dual polarization and multi-angles. Through the analysis of the simulations by the advanced integral equation model (AIEM), we found that the polarization difference for the bare surface emission signals at different view angles can be well characterized by a line function with parameters that are dependent on the pair of view angles to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-angular radiometer measurements. The developed microwave vegetation index (MVI) is independent of soil surface emission signals and can provide new vegetation information since the L-band microwave measurements are sensitive to the properties of the overall vegetation. We compared the developed MVI with the Leaf Area Index (LAI) with SMOSREX data in 2003. The results showed that the general distribution and the change patterns of the MVI are consistent with those of LAI and VWC derived by the field experiments. This method provides a new opportunity to monitor vegetation cover for future SMOS applications.

In the context of the SMOS mission, a new numerical method based on the finite element method is presented which can be used to model the radiometric L-band emission of soil and litter layers in forests. Many different characteristics of these layers that affect the soil-litter emission can be incorporated into the model, including surface roughness, inclusions and volume effects. Soil moisture is incorporated into the model as a function of the dielectric permittivity constant. The model is validated for a single dielectric layer with a surface roughness of Gaussian autocorrelation function by comparing results of the backscattering coefficient with those calculated by the 2D method of moments. Good general agreement is obtained between these results. An emissivity calculation for a single layer rough surface is also presented and compared with the emissivity of a flat layer.

The future Soil Moisture and Ocean Salinity satellite time of overpass is 6 A.M. and 6 P.M. at the equator. In many regions, morning dew is expected at the time of the satellite overpass and might play a role in soil moisture retrievals. The aim of this study was to assess the effects of dew on L-band measurements. Radiometric, biomass, and dew measurements were performed over a natural grass field. Our results show that at the diurnal scale, vegetation internal water content changes play a major role in emission. A direct impact of dew on measurements was not identified. However, a brightness temperature increase of 0.5 and 1 K was observed at vertical and horizontal polarizations, respectively. This increase was detected later after dew, suggesting that it was not directly caused by the presence of dew on the grass blades. Our hypothesis is that the observed increase in brightness temperatures is due to the absorption of dew water by the litter layer.

Recent studies of passive L-band observations over forests have shown that the average canopy transmissivity in temperate coniferous and deciduous forests is on the order of 0.4-0.5. Although the canopy would therefore be expected to transmit a reasonable amount of ground emission, the total emission observed above the canopy shows very little variation with varying soil moisture content. Moist litter present on the forest floor is known to obscure the soil emission. Therefore, more knowledge on the L-band radiative properties of litter and understory layers is needed to better understand the emission of the whole forest system. In order to contribute toward this issue, a field experiment was conducted in a pine forest in southwest France. Radiometric observations were done on the canopy and on different configurations of the forest floor, following sequential stripping of each forest floor layer. In combination with a long-term data set of above-canopy observations, this resulted in emissivity values of bare soil, soil-litter, soil-litter-grass, and soil-litter-grass-canopy configurations for a range of soil and litter moisture values. Calculations involved the use of the Wilheit and L-MEB models. The sensitivity to soil moisture was found to be substantially suppressed by the presence of a grass understory and litter. This corroborates the low correlation between soil moisture and L-band brightness temperature measured above the canopy. Several results of recent modeling and laboratory studies are also confirmed by this paper, which is, to our knowledge, the first to useinsituexperimental data in this context.

The community microwave emission model (CMEM) is the low frequency forward observation operator developed at ECMWF. It is used in this paper to simulate brightness temperatures at local and regional scales over SMOSREX (France) and AMMA (West Africa), respectively. Background errors in simulated brightness temperatures are quantified at different frequencies and incidence angles for these two sites.

The objective of this study is to estimate the potential of remote sensing observations over forests in order to distinguish the seasonal dynamic of LAI and fAPAR of understory and tree canopy over the large maritime pine forest "Les Landes" in south-western France. First, we measured the seasonal dynamic of the vegetation index PVI using a time series of seasonal reflectance data acquired from 2002 to 2007 with the VEGETATION radiometer. Also, we monitored the seasonal changes of LAI and fAPAR of the understory and tree canopy layers from ground measurements with hemispherical photographs and PAR sensors on four sites, which were representative of the different ecological conditions found in "Les Landes". Finally, we compared the seasonal dynamic of PVI with the seasonal dynamic of LAI and fAPAR measured for both layers. The first results showed that the seasonal variation of PVI could mainly be explained by the seasonal variation of LAI and fAPAR of understory vegetation.

The CAROLS, L band radiometer, is built and designed as a copy of EMIRAD II radiometer of DTU team. It is a Correlation radiometer with direct sampling and fully polarimetric (i.e 4 Stockes). It will be used in conjunction with other airborne instruments (in particular the C-Band scatterometer (STORM) and IEEC GPS system, Infrared CIMEL radiometer, one visible camera), in coordination with in situ field campaigns for SMOS CAL/VAL. The instruments are implemented on board the French research airplane ATR42. A validation campaign with four flights was made over south west of France, Hourtin Lake and Bay of Biscay (Atlantic Ocean) in September 2007. In order to qualify the radiometer data, different types of aircraft movements were realized: circle flights, wing and nose wags. Simultaneously to flights, different ground measurements were made over continental surfaces and ocean. First results show a good quality of data over ocean surfaces. For continental surfaces, important Radio-Frequency Interferences (RFI) were observed over a large part of the studied region.

Methods to retrieve surface soil moisture were assessed at the global scale for one entire year by using simulated Soil Moisture and Ocean Salinity brightness temperatures (TB) and vegetation coverage information which can be derived from optical sensors. The globalTBdatabase consists of half-degree continental pixels and accounts for within-pixel heterogeneity, based on 1-km resolution land cover maps. The retrievals were performed by using a three-parameter inversion method applied to the L-band Microwave Emission of the Biosphere model. By using a Bayesian approach, vegetation data were injected as a priori information. Two options were investigated to profit from normalized difference vegetation index products: providing an a priori knowledge either on vegetation optical depth or on the vegetation cover fraction (fcover). The latter option allows for a better description of the surface heterogeneity by considering a bare soil fraction. When an error of 1 K is applied to theTB, both synergistic schemes significantly improved the soil moisture accuracy compared with methods using microwave data only. Using the vegetation a priori information, about 80% of the pixels present soil moisture retrieval accuracy less than 0.04 m3middotm-3in terms of root-mean-square error, whereas methods based only on the microwave data provide 63% of pixels of the studied area with this accuracy. If the error inTBis larger (2 or 3 K), the soil moisture retrieval accuracy decreases significantly for both methods. The use of optical data to give a priori value of vegetation optical option is then the best for these cases.

In this paper, the L-band Microwave Emission of the Biosphere (L-MEB) model used in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm is calibrated using L-band (1.4 GHz) microwave measurements over a coniferous (pine) and a deciduous (mixed/beech) forest. This resulted in working values of the main canopy parameters optical depth (tau), single scattering albedo (omega), and structural parameterstt(H) andtt(V), besides the soil roughness parametersHRandNR. Using these calibrated values in the forward model resulted in a root mean-square error in brightness temperatures from 2.8 to 3.8 K, depending on data set and polarization. Furthermore, the relationship between canopy optical depth and leaf area index is investigated for the deciduous site. Finally, a sensitivity study is conducted for the focus parameters, temperature, soil moisture, and precipitation. The results found in this paper will be integrated in the operational SMOS Level 2 Soil Moisture algorithm and used in future inversions of the L-MEB model, for soil moisture retrievals over heterogeneous, partly forested areas.

The European Centre for Medium-range Weather Forecasts (ECMWF) brightness will use temperatures from the soil moisture and ocean salinity mission to analyze root zone soil moisture through a variational data assimilation system. The first guess is obtained from numerical weather prediction (NWP) model fields, an auxiliary database, and a land surface microwave emission model. In this paper, we present the community microwave emission model and research the first-guess errors in L-band brightness temperatures. An error propagation study is performed on errors introduced through: (1) uncertainties in the parameterizations of the radiative transfer model; (2) auxiliary geophysical quantities for the radiative transfer computations; and (3) an imperfect NWP model. It is found that the vegetation and dielectric models introduce uncertainties with a difference of up to 25 K between models. However, the biggest error in brightness temperature is likely related to the use of an auxiliary vegetation database, which results in differences of -20 to +20 K in our simulations. These potential errors are in many regions higher than the variance in brightness temperatures related to an imperfect NWP model.

Soil moisture retrieval models from the Soil Moisture and Ocean Salinity (SMOS) mission, which is an L-band microwave interferometer, are based on multiangular measurements and make use of the emissivity angular signature. Mountainous areas modify local incidence angles, implying significant impacts on brightness temperatures and, consequently, on soil moisture retrievals. The purpose of this paper is to establish a criterion in quantifying the relevance of topographic impacts at the SMOS scale ( ~ 40 km). The goal is thus to define a method of flagging the pixels according to the relative impact of topography on the brightness temperature. The proposed method uses the variogram of digital elevation model images. As a result, a map of the pixels to be flagged is produced to ensure that no soil moisture retrievals are carried out on pixels that are affected by strong topographic effects. As validation, a model was also used to simulate differences between brightness temperature variations between mountainous areas and flat surfaces.

To retrieve soil moisture from L-band microwave radiometry, it is necessary to account for the effects of temperature within both vegetation and soil media. To compute the effective soil temperatureTG, several simple formulations accounting for soil temperatures at the surface and at depth and surface soil moisture have been developed. However, the effects of the soil physical properties in terms of texture, density, or structure, which all may be important variables in the modeling ofTG, have never been investigated. In this paper, several simple formulations ofTGat L-band, accounting for or ignoring the effects of soil texture and density, were developed and compared based on a very large simulated data set. The best configurations and parameterizations of these simple formulations were computed and could be directly used for operational applications in future soil moisture retrieval studies. For instance, we showed that the use of the surface temperature in the estimation ofTGcan be significantly improved by using additional information on the soil temperature at depth (the average error in the estimation ofTGdecreased from ~ 4 to ~ 1.8 K). On the contrary, almost no improvement was obtained if air temperature was used instead of surface temperature. Also, it is shown that the use of additional information on the soil properties, mainly the soil clay content and density, led to improved results by about 0.2 K in the estimation ofTG. The improvement was found to be larger for sandy and dry soils: simplified formulations accounting for soil properties are able to represent the fact thatTGis closer to the soil temperature at depth for these soil conditions.

SMOS (Soil Moisture and Ocean Salinity), is the second mission of “Earth Explorer” to be developed within the program “Living Planet” of the European Space Agency (ESA). This satellite, containing the very first 1.4GHz interferometric radiometer 2D, will carry out the first cartography on a planetary scale of the moisture of the grounds and the salinity of the oceans. The forests are relatively opaque, and the knowledge of moisture remains problematic. The effect of the vegetation can be corrected thanks a simple radiative model. Nevertheless simulations show that the effect of the litter on the emissivity of a system litter + ground is not negligible. Our objective is to highlight the effects of this layer on the total multi layer system. This will make it possible to lead to a simple analytical formulation of a model of litter which can be integrated into the calculation algorithm of SMOS. Radiometer measurements, coupled to dielectric characterizations of samples in laboratory can enable us to characterize the geological structure. The goal of this article is to present the step which we chose to validate this analytical model.

Soil moisture retrieval from passive microwave remote sensing over densely vegetated areas, such as forests, has become a pertinent subject in the light of ESA’s upcoming Soil Moisture and Ocean Salinity (SMOS) mission. Even though it is probably not possible to retrieve soil moisture over forested areas with the accuracy required by SMOS, understanding forest radiative transfer properties at L-band (1.1-1.7 GHz) is still important for soil moisture retrieval in partly forested pixels. The forward model to be used by the SMOS Level 2 Soil Moisture algorithm is “L-MEB”, from “L-band Microwave Emission of the Biosphere”. This paper shows the results of L-MEB model calibration over two forest types in terms of various soil and vegetation parameters, together with an assessment of model performance in both forward and inverse mode. While, as expected, the retrieval of soil moisture over forest areas does not give results up to the standard required by SMOS, the forward model does result in an acceptable root mean square error in terms of “reconstructed” brightness temperatures.

For the preparation of the Soil Moisture and Ocean Salinity (SMOS) mission, due for launch in 2008, a synthetic study for the aggregation and disaggregation of L-band brightness temperature fields is currently being undertaken for a 5-year period (2000–2005) over south-western France. The observed soil moisture is derived from offline simulations obtained from the Météo-France land surface model ISBA-A-gs. The radiative transfer model L-MEB is used to estimate the corresponding brightness temperature at 8km resolution, with a large number of possible incidence angles for each time step. Aggregation of the distributed information is then undertaken by simulating overpasses of SMOS over the region. Finally, the disaggregation method is an extension of the approach presented by Merlin et al. (2005).

The CoSMOS (Campaign for validating the Operation of the Soil Moisture and Ocean Salinity mission) campaign was conducted during November of 2005 in the Goulburn River Catchment, in SE Australia. The main objective of CoSMOS was to obtain a series of L-band measurements from the air in order to validate the L-band emission model that will be used by the SMOS (Soil Moisture and Ocean Salinity) ground segment processor. In addition, the campaign was designed to investigate open questions including the sun-glint effect over land, the application of polarimetric measurements over land, and to clarify the importance of dew and interception for soil moisture retrievals. This paper summarises the campaign activities, and presents progress on the analysis of the CoSMOS data set.

This paper compares L-band measurements from three different experiments in areas covered by grass. The main objective is to assess soil moisture retrievals based on the L-band Microwave Emission of the Biosphere model (L-MEB) used by the Soil Moisture and Ocean Salinity mission (SMOS). Results indicate that over grass the vegetation is isotropic to the microwave propagation at horizontal polarisation, while at vertical polarisation non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil roughness and litter emission are calibrated beforehand. The study also highlights the importance of detecting strong attenuation by wet vegetation and litter due to rainfall interception. We show that strong rainfall interception can be flagged using a microwave polarisation index.

This contribution summarizes prominent features of the Level 2 algorithm aimed at processing land surface geophysical quantities from the ESA-led SMOS mission. It emphasizes the soil moisture retrieval and describes the decision tree built in order to select appropriate retrieval configurations. The expected performance is illustrated by preliminary results of the algorithm validation.

This letter describes recent advances in modeling forest emissivity at L-band. The formulation is based on a previously developed discrete model and includes a new representation of forest litter. Comparisons with multitemporal radiometric data collected in the framework of the ldquoBray 2004rdquo experiment, which was carried out within Les Landes forest, are shown and discussed. Input variables are given by using detailed ground measurements. In general, the model reproduces both absolute values and temporal variations of measured brightness temperature. The contribution of the litter to overall emission was found to be important.

A simple reflectivity model of a bare soil at L-band is developed to account for the effects of soil roughness at different angles and polarizations. This model was developed using a long-term dataset acquired over the bare soil in the framework of the Surface Monitoring Of the Soil Reservoir EXperiment (SMOSREX). It is shown that the roughness effects are different depending on the measurement configuration, in terms of incidence angle and polarization. However, in this paper, a simple parameterization that is based on a single roughness parameter was calibrated in order to account for this angular and polarization dependencies. This parameter was found to be dependent on soil moisture: drier conditions were associated to higher ldquoroughnessrdquo conditions. The root-mean-square error between the measured and modeled reflectivities on days when no precipitation events were detected at vertical polarization (V-pol) is 0.0275, and at horizontal polarization (H-pol), the rmse is 0.0237; all incidence angles were considered. When all data are considered, the rmsd for V-pol is 0.0350, and for H-pol, the rmse is 0.0373. This new simple model is suitable for soil moisture retrieval from Soil Moisture and Ocean Salinity data. By means of this simple parameterization, almost two years of soil moisture data were retrieved with a good accuracy. The SMOSREX dataset allowed to ensure a long-term suitability of the proposed parameterization.

SMOS mission will provide micro-wave soil emission data at L-band (1.4 GHz) at global scale with a half-degree spatial resolution. Inversion of model emission permits to retrieve surface soil moisture from SMOS data. These retrievals suffer from a lack of accuracy mainly because of the heterogeneity of land coverage encountered inside a given pixel. The addition of land surface parameters such as vegetation fractions coverage and surface temperature permits to overcome this problem. A feasibility study is presently undertaken with synthetic SMOS data and simulated optical satellite information. The results present significant improvements of soil moisture retrieval accuracy. A future operational scheme including these features will be of relevance for a global surface soil moisture mapping.

A discrete model, based on the radiative transfer theory, is used to simulate coniferous forest emissivity at L-band. Inputs to the model are given by using a detailed geometrical representation of Les Landes forest. Simulated emissivities are compared against EuroSTARRS campaign measurements, which were made over the same forest at nominally vertical polarization and several angles. The model has also been used to investigate the sensitivity of L-band radiometers to soil moisture under forests. Results of this investigation indicate that the soil contribution to emission is potentially appreciable, even under developed forests. This may be a useful result, in view of future satellite missions, such as SMOS and HYDROS

In the framework of the Soil Moisture and Ocean Salinity mission, a two-year (1987-1988) global simulation of brightness temperatures (TB) at L-band was performed using a simple model [L-band microwave emission of the biosphere, (L-MEB)] based on radiative transfer equations. However, the lack of alternative L-band spaceborne measurements corresponding to real-world data prevented from assessing the realism of the simulated global-scale TB fields. In this study, using a similar modeling approach, TB simulations were performed at C-band and X-band. These simulations required the development of C-MEB and X-MEB models, corresponding to the equivalent of L-MEB at C-band and X-band, respectively. These simulations were compared with Scanning Multichannel Microwave Radiometer (SMMR) measurements during the period January to August 1987 (corresponding to the end of life of the SMMR mission). A sensitivity study was also carried out to assess, at a global scale, the relative contributions of the main MEB parameters (particularly the roughness and vegetation model parameters). Regional differences between simulated and measured TBs were analyzed, discriminating possible issues either linked to the radiative transfer model (C-MEB and X-MEB) or due to land surface simulations. A global agreement between observations and simulations was discussed and allowed to evaluate regions where soil moisture retrievals would give best results. This comparison step made at C-band and X-band allowed to better assess how realistic and/or accurate the L-band simulations could be

A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Qpmodel, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Qpat different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface emissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronomiques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50deg and 60deg. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50deg and 55deg

In the microwave region, discrete physical models are often used to simulate surface emission and backscattering of forests. These models require detailed information on the geometry of the forest constituents, typically represented by canonical shapes. This paper presents a forest geometric description (FGD) of a Maritime pine tree forest (Pinus pinaster A/spl inodot//spl uml/t) suitable for discrete models at low microwave frequencies. The FGD contains a number of allometric equations developed from the analysis of a very large set of ground-based measurements of the forest structure. These allometric equations reproduce the density, shape, and dimension of branches, trunks, and needles, represented by cylinders in the FGD. An original approach to describe the tree architecture is presented: primary branches (branches inserted in the trunk) are split into segments or growth units (GUs) so that the full branch curvature can be accounted for, and probability distribution functions are given for the orientation and diameter of the GUs. The FGD uses only two variables, tree density (number of trees per ha) and tree age, as input information to simulate the forest geometric characteristics at three layers: the upper crown, the lower crown, and a crown-free layer. Input parameters to compute the dielectric constant of the forest constituents are also given.

This study develops a parameterized bare surface emission model for the applications in analyses of the passive microwave satellite measurements from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). We first evaluated the capability of the advanced integral equation model (AIEM) in simulating wide-band and high-incidence surface emission signals in comparison with INRA's field experimental data obtained in 1993. The evaluation results showed a very good agreement. With the confirmed confidence, we generated a bare surface emission database for a wide range of surface dielectric and roughness properties under AMSR-E sensor configurations using the AIEM model. Through the evaluations of the commonly used semiempirical models with both the AIEM simulated and the field experimental data, we developed a parameterized multifrequency-polarization surface emission model-the Qp model. This model relates the effects of the surface roughness on the emission signals through the roughness variable Qp at the polarization p. The Qp can be simply described as a single-surface roughness property-the ratio of the surface rms height and the correlation length. The comparison of the emissivity simulations by the Qp and AIEM models indicated that the absolute error is extremely small at the magnitude of 10/sup -3/. The newly developed surface emission model should be very useful in modeling, improving our understanding, analyses, and predictions of the AMSR-E measurements.

A recently published feasibility study on the consequences of surface heterogeneity for parameter retrieval from multi-angle L-band SMOS observations indicated that even small portions of forest (>10%) may have a dramatic effect on the retrieval of pixel-average soil moisture, resulting in retrieval errors exceeding the required precision of 4% by volume formulated for the SMOS mission. These retrievals were done without a priori knowledge of surface temperature and without a priori knowledge of the within-pixel forest cover fraction. This study demonstrates that inclusion of a priori knowledge of the within-pixel forest fraction allows the retrieval of the soil dielectric constant (or the soil emissivity) of the non-forested area with a precision better than the requirement defined for SMOS. It was also found that a bias of plusmn2 K in the assumed thermodynamic surface temperature has a relatively small effect on the retrieval precision

Currently, there is a strong interest in studying the L-band emission of forests to exploit the sensitivity of the microwave signature to surface soil moisture and forest biophysical parameters such as biomass or LAI. In this paper we contribute to the understanding of the forest emission problem through simulations of the L-band brightness temperature based on a discrete radiative transfer model. Prior to L-band simulations, a geometric model of a maritime pine tree forest has been developed to get a close description of a real forest. L-band simulations are then analysed and compared to airborne radiometric data over the Les Landes (SW France) Maritime pine tree forest.

This paper is part of an ongoing study aimed at exploring the potential of biangular measurements at L band to estimate near surface soil moisture. A statistical approach based on a physical radiative transfer model is tested over a natural grassland. Soil moisture shows an encouraging correlation with a combination of brightness temperatures measurements performed at two different angles. Nevertheless, the assumptions involved into the definition of a valid statistical index need to be studied. For that purpose, an insight into the modeling of grassland L band emission is also presented

A simple approach for correcting the effect of vegetation in the estimation of soil moisture (w/sub S/) from L-band passive microwave observations is presented in this study. The approach is based on statistical relationships, calibrated from simulated datasets, which requires only two observations made at distinct incidence angles (/spl theta//sub 1/,/spl theta//sub 2/). A sensitivity study was carried out, and best retrieval remote sensing configurations, in terms of polarization and couple of incidence angles (/spl theta//sub 1/,/spl theta//sub 2/), were investigated. Best estimations of w/sub S/ could be made at H polarization, for /spl theta//sub 1/ varying between 15/spl deg/ and 30/spl deg/, and with a difference (/spl theta//sub 2/-/spl theta//sub 1/) larger than 30/spl deg/. The method was tested against two experimental datasets acquired over crop fields (soybean and wheat). The average accuracy in the soil moisture retrievals during the whole crop cycle was found to be about 0.05 m/sup 3//m/sup 3/ for both crops.

For anticipated synergistic approaches of the L-band radiometer on the Soil Moisture and Ocean Salinity (SMOS) mission with higher frequency microwave radiometers such as the Advanced Microwave Scanning Radiometer (AMSR) (C-band), a reanalysis has been performed on the frequency dependence of the linear relationship between vegetation optical depth (/spl tau//sub o/) and vegetation water content (W), given by /spl tau//sub o/=b/spl middot/W. Insight into the frequency dependence of the b-factor is important for the retrieval of surface moisture from dual- or multifrequency microwave brightness temperature observations from space over vegetation-covered regions using model inversion techniques. The b-values presented in the literature are based on different methods and approaches. Therefore, a direct comparison is not straightforward and requires a critical analysis. This paper confirms that when a large frequency domain is considered, the b-factor is inversely proportional to the power of the wavelength b=c/(/spl lambda/)/sup x/, which is in line with theoretical considerations. It was found that different canopy types could be separated into different groups, each with a different combination of values for log(c) and x, which characterize the linearized relationship log(b)=log(c)-x/spl middot/log(/spl lambda/). A comparison of ratios b/sub C//b/sub L/ (with C and L denoting C- and L-band, respectively) also resulted in basically the same groups.

In support of algorithm development for the multiangle interferometric synthetic L-band radiometer on the Soil Moisture and Ocean Salinity sensor (SMOS) and for possible synergistic approaches with higher frequency microwave radiometers such as the Advanced Microwave Scanning Radiometer (C-band), an inventory has been made of polarization, angular, and frequency dependencies of vegetation optical depth and single-scattering albedo. Both parameters form the basis of a zero-order radiative transfer model, which is often used for inverse modeling of microwave observations from space. The inventory is based on experimental data published in the literature. Underlying models have been reviewed because data comparison is impossible without due consideration of the theoretical background. In general, it can be concluded that both single-scattering albedo and optical depth are angular, polarization, and frequency dependent. This dependence, however, depends on the canopy type and structure. Angular dependence implies that the cosine correction for the slant path through the canopy is no longer valid. Knowledge of these dependencies, therefore, is important for processing multiangle observations such as those anticipated for the planned SMOS and for possible synergistic approaches with C-band observations. Because of the existing variety of methods and procedures found in the literature, some guidelines for a protocol for field experiments are proposed in order to facilitate intercomparison of experimental results and proper incorporation of the parameters in zero-order transfer models.

A number of studies have shown the feasibility of estimating surface soil moisture from L-band passive microwave measurements. Such measurements should be acquired in the near future by the Soil Moisture and Ocean Salinity (SMOS) mission. The SMOS measurements will be done at many incidence angles and two polarizations. This multiconfiguration capability could be very useful in soil moisture retrieval studies for decoupling between the effects of soil moisture and of the various surface parameters that also influence the surface emission (surface temperature, vegetation attenuation, soil roughness, etc.). The possibility to implement N-parameter (N-P) retrieval methods (where N = 2, 3, 4, ..., corresponds to the number of parameters that are retrieved) was investigated in this study based on experimental datasets acquired over a variety of crop fields. A large number of configurations of the N-P retrievals were studied, using several initializations of the model input parameters that were considered to be fixed or free. The best general configuration using no ancillary information (same configuration for all datasets) provided an rms error of about 0.059 m/sup 3//m/sup 3/ in the soil moisture retrievals. If a priori information was available on soil roughness and at least one vegetation model parameter, the rms error decreased to 0.049 m/sup 3//m/sup 3/. Using specific retrieval configurations for each dataset, the rms error was generally lower than 0.04 m/sup 3//m/sup 3/.

To retrieve soil moisture over vegetation-covered areas from microwave radiometry, it is necessary to account for vegetation effects. At L-band, many retrieval approaches are based on a simple model that relies on two vegetation parameters: the optical depth (/spl tau/) and the single-scattering albedo (/spl omega/). When the retrievals are based on multiconfiguration measurements, it is necessary to take into account the dependence of /spl tau/ and /spl omega/ on the system configuration, in terms of incidence angle and polarization. In this paper, this dependence was investigated for several crop types (corn, soybean, wheat, grass, and alfalfa) based on L-band experimental datasets. The results should be useful for developing more accurate forward modeling and retrieval methods over mixed pixels including a variety of vegetation types.

Surface soil moisture is a key variable of water and energy exchanges at the land surface/atmosphere interface. But currently there are no means to assess it on a global and timely fashion. The ESA Earth Explorer Opportunity mission Soil Moisture and Ocean Salinity (SMOS) is the first attempt to fill such a gap. SMOS is based upon an L-band 2-D interferometer, an innovative concept of bi-dimensional aperture synthesis method to obtain surface measurement with an appropriate resolution from a tractable (in terms of dimensions) space-borne instrument. Moreover, the sensor has new and very significant capabilities especially in terms of multi-angular view configuration. However as for most of space borne instrument, retrieval of surface parameters/variables will be hampered by several factors. This fact is enhanced for sensors having a coarse spatial resolution. In the specific case of SMOS the ground resolution of 40 km means that the influence of different contributors to the signal has to be accurately assessed and eventually corrected. Finally many pixels will be affected by topography effects. It is thus important to assess exactly which level of topography distorts significantly the brightness temperatures and to which extend so as to be able to either correct soil moisture retrieval for topography effects or flag the data. The goal of this paper is to present the topography effects. The latter has been analyzed in depth by modeling the signal issued from mountainous terrain including moisture and vegetation gradient. The adjacency and shadowing effects were in particular addressed. The second step was to develop a simplified characterization of the topography through a statistical description which is then used to assess exactly the level of topography which has an influence of the signal and from which one has to take it into account in the retrieval process. The potential of SMOS, depending on the view angle configuration and the use of the sole 1.4 GHz is thus investigated over complex targets. These questions are key issues to define the exact range of configurations where SMOS meets the scientific requirements of the mission.

Surface soil moisture is a key variable of water and energy exchanges at the land surface/atmosphere interface. But currently there are no means to assess it on a global and timely fashion. Similarly, our current knowledge of sea surface salinity is very reduced. One way to overcome this issue would be to use an adequate space-borne instrument. The most promising instrument would then be an L-band microwave remote sensing sensors as they are able to provide estimates of surface soil moisture and sea surface salinity, on spatial and temporal scales compatible with applications in the fields of climatology, meteorology and large scale hydrology. The ESA Earth Explorer Opportunity mission SMOS is the first to attempt to fulfill such a gap. SMOS is based upon an L-band 2-D interferometer. It is thus an innovative concept of bi-dimensional aperture synthesis method to obtain surface measurement with an appropriate resolution from a tractable (in terms of dimensions) space-borne instrument. Moreover, the sensor has new and very significant capabilities especially in terms of multi-angular view configuration. This paper will describe the SMOS concept in terms of instrument (characteristics) and will investigate the main aspects of the retrieval capabilities of the 2-D microwave interferometer for monitoring soil moisture, vegetation biomass and sea surface salinity. The analysis is based on model inversion taking into account the instrument characteristics. The standard error of estimate of the surface variables is computed as a function of the sensor configuration system and of the uncertainties associated with the spatial measurements. The inversion process is based on a standard minimisation routine that computes both retrieved variables and standard error associated with the retrievals. Nevertheless, retrieving surface variables from such as instrument is not necessarily straightforward. Over the oceans, a very high sensitivity and accuracy are acquired. Over the land the main issues are linked to mixed pixels and topography. Using other sensors/mission (such as Aquarius over the oceans) and assimilation techniques will be used to address these issues. The potential of SMOS, depending on the view angle configuration and the use of the sole 1.4 GHz is thus investigated. These questions are key issues to define the observation configuration of SMOS that meets the scientific requirements and the technical constraints of the spatial missions.

The objective of this work is to evaluate soil moisture retrieval methods over different canopy, with the view of developing an operational algorithm (for the SMOS project for instance). We tested different inversion process of the /spl tau/-/spl omega/ model, using or not using ancillary information, to estimate soil moisture and /spl tau/-/spl omega/ parameters (vegetation parameters and soil roughness) simultaneously under different crop types. The best general method (same for all data sets) provides a RMSE=0.059 m/sup 3// m/sup 3/ using no ancillary information and a RMSE=0.048 m/sup 3// m/sup 3/ if we use soil and vegetation a priori information. Moreover, using specific methods to each data set provide RMSE values between 0.04 and 0.05m/sup 3// m/sup 3/.

National audience

The main objective of the SMOS (Soil Moisture and Ocean Salinity) mission over the land surfaces, is to monitor soil moisture (SM) with a frequent (3-day revisit) and global coverage (ground resolution of /spl sim/50 km). In this paper, we present some examples of the current activities aiming at developing and improving SM retrieval methods.

The L-band (1.4 GHz) two-dimensional microwave interferometric radiometer, the payload of the Soil Moisture and Ocean Salinity (SMOS) mission, will observe elements of the Earth's surface simultaneously at multiple angles. Compared to single-angle observations, this multiangle observation technology is expected to significantly improve the capability of passive microwave remote sensing to retrieve soil moisture and vegetation properties from space. Although multiangle retrieval algorithms have been developed and successfully evaluated for homogeneous surfaces on the basis of simulation studies, the inherently large footprint of microwave observations from space has serious consequences for parameter retrieval from "real-world" inhomogeneous surfaces. At the spatial scale of SMOS (/spl sim/30 km for nadir observations), the Earth's surface is inhomogeneous almost by default. This aspect has not been fully accounted for yet. This study gives some insight into the consequences of vegetation spatial heterogeneity for the retrieval of "effective" surface parameters (soil moisture, canopy microwave transmissivity, and effective surface temperature) from inhomogeneous surfaces without prior knowledge of the within-pixel canopy heterogeneity.

Using a global simulation of L-band (1.4 GHz) brightness temperature (T/sub B/) for two years (1987 and 1988), the relationship between L-band brightness temperatures and surface soil moisture was analyzed using simple regression models. The global T/sub B/ dataset describes continental pixels at a half-degree spatial resolution and accounts for within-pixel heterogeneity, based on 1-km resolution land cover maps. Two different statistical methods were investigated. First, a single regression model was obtained using a linear combination of T/sub B/ indexes. This method consisted in retrieving surface soil moisture using the same global regression model for all the pixels. Second, a regression model was calibrated over each pixel using similar linear combinations of the T/sub B/ indexes. In both cases, the influence of the radiometric noise on T/sub B/ was investigated. Applying these two methods, the capability of L-band T/sub B/ observations to monitor surface soil moisture was evaluated at the global scale and during a two-year time period. Global maps of the estimated accuracy of the soil moisture retrievals were produced. These results contribute to better define the potential of the observations from future spaceborne missions such as the Soil Moisture and Ocean Salinity (SMOS) mission.

This letter presents a synthetic L-band (1.4 GHz) multiangular brightness temperature dataset over land surfaces that was simulated at a half-degree resolution and at the global scale. The microwave emission of various land-covers (herbaceous and woody vegetation, frozen and unfrozen bare soil, snow, etc.) was computed using a simple model [L-band Microwave Emission of the Biosphere (L-MEB)] based on radiative transfer equations. The soil and vegetation characteristics needed to initialize the L-MEB model were derived from existing land-cover maps. Continuous simulations from a land-surface scheme for 1987 and 1988 provided time series of the main variables driving the L-MEB model: soil temperature at the surface and at depth, surface soil moisture, proportion of frozen surface soil moisture, and snow cover characteristics. The obtained global maps constitute a useful dataset for a first evaluation of the sensitivity of future satellite-based L-band radiometry data to soil moisture.

The US Salinity Temperature and Roughness Remote Scanner (STARRS) was exploited during an European campaign in 2001 (EuroSTARRS) supporting the scientific preparation of the Soil Moisture and Ocean Salinity (SMOS) mission. This paper is intended to introduce the EuroSTARRS experiment set-up and to provide an overview of all activities performed during this campaign.

This paper describes the ESA-funded airborne EUROSTARRS experiment over land. A multiangle L-band radiometer was flown over a large variety of landscapes in France and Spain during the autumn of 2001. The data analysis will be performed in the framework of an ESA project to assess the feasibility of retrieving soil moisture by a future spaceborne Earth observation mission.

In preparation of the SMOS mission, synthetic L-band brightness temperatures are produced at the global scale and soil moisture retrieval algorithms are assessed.

The Soil Moisture and Ocean Salinity (SMOS) space mission is currently undergoing phase-B studies at the European Space Agency. The SMOS payload is an L-band interferometric radiometer based on a two-dimensional aperture synthesis concept. This paper presents the first images obtained by a demonstrator of the SMOS instrument over land surfaces at the Avignon test site in 1999.

The microwave model developed at the Tor Vergata University is used to simulate the emissivity of forests, in order to study the performance of an L-band spaceborne radiometer, similar to that carried by the Soil Moisture Ocean Salinity mission. The model is first validated, and the importance of a correct vegetation growth parametrization in the modeling procedure is pointed out. This model is also used to calibrate a simple zero-order radiative transfer model, since simple models have been recognized to be useful in retrieval applications at a global scale. We show that although a zero-order approximation cannot be directly used for forests, a simple formulation may be applied, provided the albedo and the optical depth are defined as equivalent parameters.

Physically based land surface process/radiobrightness (LSP/R) models may characterize well the relationship between radiometric signatures and surface parameters. They can be used to develop and improve the means of sensing surface parameters by microwave radiometry. However, due to a lack in the skill to properly understand the behavior of the data, a statistical approach is often adopted. In this paper, we present the retrieval of wheat plant water content (PWC) and soil moisture content (SMC) profiles from the measured H-polarized and V-polarized brightness temperatures at 1.4 (L-band), and 10.65 (X-band) GHz by an error propagation learning back propagation (EPLBP) neural network. The PWC is defined as the total water content in the vegetation. The brightness temperatures were taken by the PORTOS radiometer over wheat fields through three month growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). Note that, through the neural network, there is no requirement of ancillary information on the complex surface parameters such as vegetation biomass, surface temperature, and surface roughness, etc. During both field campaigns, the L-band radiometer was used to measure brightness temperatures at incident angles from 0 to 50/spl deg/ at L-band and at an incident angle of 50/spl deg/ at X-band. The SMC profiles were measured to the depths of 10 cm in 1993 and 5 cm in 1996. The wheat was sampled approximately once a week in 1993 and 1996 to obtain its dry and wet biomass (i.e., PWC). The EPLBP neural network was trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the remaining data from the same set. The trained neural network is further evaluated with the PORTOS-96 data.

Microwave radiometry at low frequencies (L-band: 1.4 GHz, 21 cm) is an established technique for estimating surface soil moisture and sea surface salinity with a suitable sensitivity. However, from space, large antennas (several meters) are required to achieve an adequate spatial resolution at L-band. So as to reduce the problem of putting into orbit a large filled antenna, the possibility of using antenna synthesis methods has been investigated. Such a system, relying on a deployable structure, has now proved to be feasible and has led to the Soil Moisture and Ocean Salinity (SMOS) mission, which is described. The main objective of the SMOS mission is to deliver key variables of the land surfaces (soil moisture fields), and of ocean surfaces (sea surface salinity fields). The SMOS mission is based on a dual polarized L-band radiometer using aperture synthesis (two-dimensional [2D] interferometer) so as to achieve a ground resolution of 50 km at the swath edges coupled with multiangular acquisitions. The radiometer will enable frequent and global coverage of the globe and deliver surface soil moisture fields over land and sea surface salinity over the oceans. The SMOS mission was proposed to the European Space Agency (ESA) in the framework of the Earth Explorer Opportunity Missions. It was selected for a tentative launch in 2005. The goal of this paper is to present the main aspects of the baseline mission and describe how soil moisture will be retrieved from SMOS data.

A simple model for simulating the L-band microwave emission from bare soils is developed. The model is calibrated on a large set of measurements obtained during a three-month period over seven plots covering a wide range of surface roughness (representing the total range which can be expected on agricultural fields), soil moisture, and temperature conditions. The approach is based on the parameterization of an effective roughness parameter as a function of surface characteristics: surface roughness (standard deviation of height and correlation length) and the surface soil moisture. The parameterizations that are developed are independent of incidence angle and polarization and are valid over a large range in surface roughness conditions, representative of most of typical agricultural bare fields, from very smooth (rolled field after sowing) to very rough surfaces (deeply plowed soil). This approach will enable the use of microwave radiometric observations for soil moisture retrieval over agricultural areas.

The microwave brightness temperatures of a wheat field were measured in 1993, during the whole growth cycle by the six-frequency PORTOS radiometer, at two polarizations and several angles. In this paper, the emissivities measured at L-, C-, X-, and K-band are compared with those simulated by a discrete multiple scattering model based on the radiative transfer theory. The agreement between experimental and simulated data is generally good at all frequencies, although a unique set of input parameters has been used. It is demonstrated that the model simulations lead to a reliable identification of the radiometric configurations most sensitive to ground variables. Moreover, they aid the development of a soil moisture inversion algorithm that performs well even with soils covered by developed vegetation.

To retrieve temperature and humidity profiles from special sensor microwave/temperature (SSM/T) and advanced microwave sounding units (AMSU), it is important to quantify the contribution of the Earth surface emission. So far, no global estimates of the land surface emissivities are available at SSM/T and AMSU frequencies and scanning conditions. The land surface emissivities have been previously calculated for the globe from the SSM/I conical scanner between 19 and 85 GHz. To analyze the feasibility of deriving SSM/T and AMSU land surface emissivities from SSM/I emissivities, the spectral and angular variations of the emissivities are studied, with the help of ground-based measurements, models, and satellite estimates. Up to 100 GHz, for snow and ice free areas, the SSM/T and AMSU emissivities can be derived with useful accuracy from the SSM/I emissivities. The emissivities can be linearly interpolated in frequency. Based on ground-based emissivity measurements of various surface types, a simple model is proposed to estimate SSM/T and AMSU emissivities for all zenith angles knowing only the emissivities for the vertical and horizontal polarizations at 53/spl deg/ zenith angle. The method is tested on the SSM/T-2 91.655 GHz channels. The mean difference between the SSM/T-2 and SSM/I-derived emissivities is /spl les/0.01 for all zenith angles with a root mean squared (RMS) difference of /spl ap/0.02. Above 100 GHz, preliminary results are presented at 150 GHz based on SSM/T-2 observations and are compared with the very few estimations, available in the literature.

This work investigates the potential use of passive and active microwave observations to monitor soil moisture and vegetation biomass over a soybean cover. The work is based on a sensitivity analysis from a large set of data generated by radiative-transfer models. Some appropriate configurations are identified. In particular, the combined use of passive data at 1.4 GHz, with multiangle active measurements at 5 GHz, is found to be promising.

The soil microwave effective temperature (T/sub e/) is an important parameter which improves the accuracy of the soil surface moisture derived from low frequency microwave radiometric observations. A new semi-empirical model of T/sub e/ at L- and C-bands is proposed. The model is based on the following inputs: the air temperature (T/sub a/), a deep soil temperature (T/sub d/), and the microwave brightness temperature measured at X-band (/spl lambda//spl sime/3 cm) and V polarization (T/sub BXV/). Unlike other approaches based on the surface temperature (T/sub s/), the proposed model can be implemented without being dependent on the clear sky conditions required to measure T/sub s/ with a spaceborne infrared radiometer. However, the proposed model may also use T/sub s/ when available. The model was designed from a large data set simulated by a physical model for smooth bare soil. The model of T/sub e/ was then successfully validated with experimental data acquired during a ground based experiment with the multifrequency PORTOS radiometer. This model designed from smooth soil data was successfully tested on rough bare soil using experimental data.

Bipolarized microwave brightness temperatures of Sahel semiarid landscapes are analyzed at two frequencies: 5.05 and 36.5 GHz. These measurements were performed in Niger, West Africa, by the radiometer PORTOS in the framework of the Hydrologic Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel), during the end of the rainy season (August-September 1992). The airborne microwave data were collected simultaneously with radiosoundings of the atmosphere, and ground measurements of surface temperature, soil moisture, and biomass of several vegetation types. After estimating the soil roughness parameters, it is shown that two kinds of vegetation canopies must be considered: sparse canopies and patchy canopies including bare soil strips. The mixed soil vegetation microwave emission is analyzed using a random continuous approach. The sparse canopy emission is efficiently described by considering the vegetation layer as homogeneous. Conversely, a simple soil-vegetation mixing equation must be used for the patchy canopies. The problem with retrieving the canopy temperature and the near-surface soil moisture is addressed. For every canopy, soil moisture retrieval is possible. Soil moisture maps are proposed. The canopy temperature can also be retrieved with good accuracy provided both vertical (v) and horizontal (h) polarizations are available. It is shown that the retrieved variables can be used to separate landscape units through a classification procedure.

Microwave brightness temperatures at three frequencies (23.8, 36.5, and 90 GHz) of a carefully smoothed silt-loam were measured over a large range of near-surface soil moisture conditions. The microwave data were collected simultaneously with ground observations of soil moisture, soil temperature, and bulk density. The atmospheric downwelling radiation is computed from the French weather forecast model outputs and cloud coverage surface observations. Assuming that roughness effects are negligible for the studied surface, Fresnel coefficients are used to model soil reflectivities. The relationship between soil moisture and soil reflectivity at high frequencies is then analyzed through existing semi-empirical models of the soil complex dielectric permittivity.>

A new approach to model the microwave emission of vegetation is described in the paper. The modeling is based on the combination of both the discrete approach to simulate single scattering albedo /spl omega/ and the continuous approach to simulate vegetation scattering effects. This composite model COMPOS is designed first to account for absorption effects in a more accurate way than the continuous approach and secondly to remain relevant for inversion procedures. Sensitivity studies showed that the use of a priori information about the vegetation structure is relevant to simulate /spl omega/. So, a reduced number of input parameters can be used in the composite model. Simulations of single scattering albedo /spl omega/, of canopy opacity and of wheat emissivity have been compared with several sets of radiometric data. The comparisons show that the composite approach simulations are consistent with the microwave observations.>

An attempt is made to derive the evolution of the temperature and the water status of the Amazon forest canopy from satellite microwave radiometry. The Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) temperature-corrected tapes data are analyzed for the 6.6, 10.7, 18, and 37 GHz frequencies, at daytime and nighttime, over a zone near Manaus (3/spl deg/S, 60/spl deg/W), Brazil. Two periods are investigated: the wet (April-May) and dry (July-August) seasons of 1985. After separating forest- from river-contaminated pixels, atmospheric corrections are performed for water vapor, clouds, and rain, using surface and satellite data. Algorithms are developed to model the microwave thermal emission of vegetation following a continuous approach and a discrete approach. A sensitivity study is performed in order to determine which frequencies are relevant to retrieve land surface parameters. The models are then used along with an optimization procedure so as to carry out the inversion of the canopy structure parameters. The vegetation temperature and water content are retrieved through the continuous model.>

The effects of leaf characteristics on the microwave emission of land surfaces are analyzed. In order to simulate these effects, a radiative transfer model is presented. The medium consists of a vegetated layer containing randomly oriented leaves, modeled as elliptic-shaped scatterers, over the ground surface. Radiative transfer equations are solved with a discrete-ordinate-eigenanalysis method. The calculation of the phase matrix of the elliptic scatterers is based on the generalized Rayleigh-Gans approximation, which increases the frequency range of the modeling. The sensitivity of brightness temperature and polarization ratio to leaf characteristics, volume fraction, gravimetric moisture, size, shape, and inclination distribution is investigated at C-, and X-band. The behavior of the simulated emission of a soybean canopy versus frequency and incidence angle is studied for different soil moisture levels. Up to 10 GHz the microwave emission appears to contain significant information on underlying soil moisture.>