This study analyses a long-time series (from January 2016 to December 2021) of optical and SAR signatures. The data were processed into two contexts: 1) over a station-monitored field called Auradé in southwestern France (part of the ICOS network) to examine the impact of surface states on satellite signals and, 2) over 939 fields of sunflower to investigate the impact of climatic conditions on crop development. Results show that backscatters can be used with moderate confidence to replace or gap fill NDVI ($r_{6-years}^{Aurad\acute{e}} = 0.73$ over the Auradé field and $r_{6 - years}^{sunflower} = 0.77$ over the 939 fields of sunflower). Nevertheless, additional analyses highlighted the effectiveness of combining γ0VH, γ0VH/VV with NDVI, GAI (Green Area Index), and Fcover indices for complementary monitoring of crop phenology. Superpositions of satellite signals observed on sunflowers suggest that it is possible to detect development anomalies using optical or radar signals based on yearly climatic conditions. Results also show the importance of considering both sunflower architecture (orientation, inter-row spacing, Fcover) and radar acquisition geometry (orbit pass, viewing angles), in contrast to denser or more covering crops. The joint use of the 2 SAR orbits enabled the detection of phenological stages (from flowering to harvest) not as well detectable with a single orbit. This long-time series analysis provided insights under multiple climatic conditions, including the hottest year in France since the beginning of meteorological records.

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.

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.

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.

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) 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.