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.

This paper aims to investigate the potential of SAR and optical satellite data for high temporal monitoring of rapeseed crops and the retrieval of biophysical parameters. To do so, dense temporal series of Landsat-8, Sentinel-2 and Sentinel-1 have been used to derive NDVI, GAI and backscattering coefficients, respectively. These signals have been physically interpreted and compared each other and to in situ measured crop biophysical parameters (i.e., height, dry biomass, fresh biomass and plant water content) for two rapeseed fields. This study pointed out the complementarity of multi-orbit SAR and multi-sensor optical data for rapeseed crop monitoring throughout its phenological cycle. We also demonstrated that the SAR derived VH-VV ratio could be successfully used for biomasses retrieval ($\mathrm{R}^{2}\geq 0.7$) thus showing its potential for assimilation in crop models.

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.

The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from SAR (synthetic aperture radar) images and in situ soil surface parameter measurements (moisture content and roughness) is used. This dataset contains a wide range of incidence angles (18°-57°) and radar wavelengths (L, C, X), well distributed geographically for regions with different climate conditions (humid, semi-arid and arid sites) and involving many SAR sensors. The proposed model, developed in HH, HV and VV polarizations, uses a formulation of radar signals based on physical principles that validated in numerous studies. The results show that the new model shows a very good performance for different radar wavelength (L, C, X), incidence angles, and polarizations (Root Mean Square Error “RMSE” about 2 dB).

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.

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.

The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for SAR data at C- and X-bands to SAR data at L band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. A semi-empirical calibration of the IEM was performed at L band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.

The aim of this article is to evaluate the potential of optical and multi-polarization SAR images for soybean yield estimation by their assimilations into a simple agro-meteorological model. Satellite and ground data were acquired over two sites during the MCM'10 experiment. Optical and radar images were provided by Formosat-2, Spot-4, Spot-5 and Radarsat-2 satellites during the whole vegetation cycle of soybean. Results show that the assimilation of optical or SAR offer similar performances for the estimation of crop parameters (i.e. LAI and dry biomass) and crop yield (rRMSE = 18% in the worst case). Concerning SAR data, results highlighted the interest of using backscattering coefficients acquired at VV polarization (rRMSE = 2%).

This paper aims to compare the crop yield retrieval performances, obtained by assimilating the leaf area index derived from multi-temporal satellite signatures (i.e. reflectances and backscattering coefficients) into an agro-meteorological model. The study is based on the Multispectral Crop Monitoring experimental campaign, conducted in 2010 by the CESBIO laboratory. During the agricultural season of sunflower, regular satellite images were quasi-synchronously acquired by 6 sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2 and Alos), over a region located in the south west of France. Calibration and validation steps take advantage of the dense network of monitored fields. Among the wide range of the tested image configurations (multi-frequency and multi-polarization), promising results are offered by optical and co-polarized C-band (i.e. HH and VV) data for yield estimate, with correlation superior to 0.74.

The floods are an annual phenomenon on the Pacific coast of Ecuador and can become devastating during El Niño years, especially in the Guayas watershed (32,300 km2), the largest drainage basin on the South American western side of the Andes. In this study, we used ENVISAT ASAR GM SAR images with a spatial resolution of 1 km to map the flooded areas between 2004 and 2008 and study the spatio-temporal dynamics of floods in the Guayas Basin. Maximum of likelihood supervised classification performed on ASAR images acquired during four consecutive dry seasons allowed us to identify five classes of land cover consistent with land use map. From the four wet seasons, we computed standardized anomalies of backscattering coefficient to detect changes between dry and wet season and tested different thresholds to identify flooded areas.

As multipaths still represent a major problem for reaching precise GNSS positioning, the mitigation of their influence has been widely investigated. However, previous studies have lately proposed to use these interferences of GNSS electromagnetic waves to estimate parameters related to the reflecting surface (e.g., antenna heights, rugosity,...). Variations of the nature of the surface is likely to modify the properties of the reflected waves, and consequently lead to variations of amplitude / phase of the signal-to-noise ratio (SNR), e.g. recorded at 1 Hz by a GNSS receiver. By analyzing the time variations of SNR measurements linked to the dielectric constant of the surrounding soil, we use a method to recover the local fluctuations of the soil moisture content. It is simply based on the obvious linear correlation between SNR amplitude / phase and retrieved antenna height time series and independent measurements of humidity probe at 2 and 5 cm depths. This method of combination is applied to determine soil moisture in a corn and soya field at Lamasquère, France, for 21 successive days. Results show a good correlation (e.g. 0.96 with GPS PRN-01 satellite) between SNR inversion and humidity probes for most satellites.

This study presents three complementary approaches to determine the volume of water in small lakes (2altimetry=0.98, RMSEaltimetry=5.0%, R2imagery=0.90, and RMSEimagery=7.4%). The third method combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2=0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

This paper aims to establish and to analyze the temporal reflectance signatures of sunflower according to optical and radar satellite images. The study is performed in the south west of France, and takes advantage of the MCM'10 experiment (Multispectral Crop Monitoring), conducted in 2010 by the CESBIO laboratory. Images are provided by 6 satellites sensors (Formosat-2, Spot-4 and -5, TerraSAR-X, Radarsat-2 and Alos). The proposed method consists in correcting the angular effect of radar signal, and in analyzing the different temporal signatures depending on the phenological cycle of the sunflower (at parcel and landscape scales). Results highlight the importance of the radar angular normalization and show the importance of multi-frequency approaches in the context of Sentinel-1, TerraSAR-X and Alos-2 missions. Among the wide range of tested radar signal combinations, the C- and L-bands appear more adapted to monitor sunflower, and further estimate its biophysical parameters.

The MCM'10 experiment (Multi-sensors Crop Monitoring, 2010) aims to evaluate the potentialities of optical, microwave and thermal satellite images for monitoring agricultural surfaces. The Experiment is conducted during ten month in 2010, from February to November over a super-site located in the South West of France. Remote sensing data (>;150 images) are provided by nine low orbit satellites, from high to low spatial resolutions (several meters to 50km). Ground data are collected over winter and summer crops, quasi synchronously with satellite images. More than 30 000 measurements are collected over 387 agricultural fields. They concern soil and vegetation parameters (moisture, roughness, height, biomass...). Results show great complementarities of multi-sensors and multiwavelength data for monitoring agricultural landscape. The ground data collection highlights the importance of field-scale approaches, linked to the strong heterogeneity in space and time of surface parameters (soil properties, vegetation type, farmers' practices...).

The aim of this work is to investigate the sensitivity of multi-incidence, multi-frequency and multi-polarized radar signatures over different crops (wheat, rapeseed, soybean, corn and sunflower). Time series of SAR images are acquired by TerraSAR-X (HH), RADARSAT-2 (full polarization) and ALOS (HH) over an agricultural site located in South West of France during the MCM'10 experiment synchronously with ground measurements. The angular normalization of radar signals is performed per crop type at X- and C-bands. The angular sensitivity of the backscatters decreases with the increase of the vegetation index (from 0.4 dB.°-1over bare soils to 0.05 dB.°-1for fully vegetated fields). Analyses of the radar time series show that X-, C- and L-bands behaviors are strongly contrasted, depending on the phenological stages of crops, highlighting the interest of using multi-frequency data. Crop height retrieval based on microwave data indicates promising results, with R2ranging from 0.7 to 0.93.

The objective of this letter is to evaluate the surface radar backscattering models, namely, integral equation model (IEM), Oh, and Dubois, for synthetic aperture radar data in X-band over bare soils. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture “mv” and surface roughness “h_rms”). Oh's model correctly simulates the radar signal forHHandVVpolarizations, whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR-X data and model. The backscattering IEM simulates correctly the backscattering coefficient only forh_rms; 1.5 cm in using Gaussian function. However, the results are not satisfactory for the use of IEM in the inversion of TerraSAR-X data. A semiempirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR-X data and the simulations using the calibrated version of the IEM.

The validity of a coherent Sahelian-grassland scattering model is determined by comparing the model predictions with satellite measurements of a representative site. This model considers the realistic botanical structure of grassland. The site Agoufou, located in the Northern Mali, was selected as the test target. This site is governed by a semi-arid tropical climate. Its vegetation is mainly composed of shrubs and annual grass. HH polarization backscattering data was collected over an entire growing season at different incidence angles by means of the ENVISAT ASAR. Simulations provided by the coherent model show a good agreement with measured data having a correlation coefficient equal to 0.92. Model predictions show that the HH polarization component is higher than the W polarization component during all growing season. Significant parameters are shown to be the grass density, the soil moisture content and the grass moisture content. The most sensitive parameter is the ground soil moisture content. Moreover, it is observed that the variation of the backscattering coefficient for all parameters can be represented by a linear regression function.

This paper presents an analysis of ENSIVAT advanced synthetic aperture radar data acquired over a Sahelian region located in Mali, West Africa. The considered period is 2004-2005 and includes two rainy seasons. Emphasis is put on two ScanSAR modes, namely, the global monitoring (GM) and the wide swath (WS) modes characterized by spatial resolutions of about 1 km and 150 m, respectively. Results show that the WS mode offers better performance in terms of radiometric resolution, radiometric stability, and speckle reduction than the GM mode. The latter is more appropriate for studies at large scale (> 10 times 10 km). In both modes, pronounced angular and temporal signatures are observed for most soil surfaces, and azimuthal effects are observed on markedly orientated rocky surfaces. In contrast, polarization differences (VV/HH) are small during the dry season except on flat loamy soil surfaces. Finally, a relationship is observed between the normalized WS backscattering signal at HH polarization and the surface soil moisture of sandy soils.