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.

Determination of agricultural fields carbon budget is needed to devise the best sustainable agriculture strategies, and quantify future subsidies in the agricultural sector. One major sustainable practice is cover crops that are planted in between main crops cycles, enabling storage of carbon and reduction of reflected heat. Assimilation of Earth Observation data is crucial for such exercise due to the sheer heterogeneity and complexity of crop modeling in the real environment. In this paper, we present the impact of cover crops on Net Ecosystem Exchange (NEE) and Biomass over a large area in the south-west of France using the AgriCarbon-EO. This tool is an end-to-end solution that enables a Bayesian assimilation of multi-temporal and entire tiles (100x100 km) Sentinel-2 reflectance data into a radiative model and a crop model. Our results show an increase of NEE of about due to cover crops over maize fields.

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.

The Intergovernmental Panel on Climate Change (IPCC) Tier 1 approach is applied to estimate emissions of N2O over an agricultural landscape located in southwestern of France. Yearly maps of potential N2O emissions are derived at a territory scale from land cover classifications, theoretical amount of mineral nitrogen (N) applied and an emission factor. The methodology takes advantage of the regular high resolution optical remote sensing images acquired during the 2006–2016 period to provide land cover maps with an overall accuracy superior to 0.87. Over the study area, the first yearly total N2O emissions estimations range from 105 to 113 tons for the 2014–2016 period. Future works will allow analyzing the estimates of potential N2O emissions and the potential associated error sources over the entire last decade.

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 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.

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.

Artificial sources emitting in the protected part of the L-band are polluting the retrievals of ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. Detection and localization of such sources are of interest for the exploitation of science products as well as for the identification of the emitters. A simple and fast method that provides snapshot-wise information is presented. From a statistical analysis of the results, some systematic errors are reported along with their potential causes and an approach to mitigate them. In the case of sources at high geomagnetic latitudes a seasonal variation of the localization error is also noticed; the origin of such phenomenon is still under investigation.

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.