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.

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.

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.

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.

This paper shows results obtained by using the SMOS retrieval algorithm over forests at the prototype level. In each SMOS node, the algorithm estimates the soil moisture and the vegetation optical depth. For the optical depth, values retrieved in July 2011 in all forests of the American continent are shown and compared against forest height estimated by GLAS LIDAR of ICESAT satellite. A significant correlation between the two variables is observed. For each forest height estimated by LIDAR, the standard deviation of optical depth is slightly higher than 0.1. For soil moisture, 30 nodes of the SCAN/SNOTEL network have been considered. Over one year of data, retrieved values are compared against ground measurements. Overall, the rms error is of the order of 0.1 m3/m3. In general better results are obtained in the Eastern deciduous forest. The algorithm was run using different versions, corresponding to different initial guesses of soil permittivity and Leaf Area Index, but variations in the retrieved values are moderate.