International audience

C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020.

Surface soil moisture (SM) is an essential component for crop water stress detection and irrigation management. It controls soil evaporation and plant transpiration. SM dynamics is temporally related to root zone soil moisture which is the primary measure of the plant's water status. The aim of this work is to assess the robustness of high-resolution SM product derived from remote sensing on the energy balance based latent and sensible heat fluxes. Radar SM products retrieved from Sentinel-1 data only combined with Landsat Normalized Difference Vegetation index and land surface temperature are used together to feed the energy balance model TSEB-SM to estimate turbulent fluxes. The model estimates have been evaluated against the Eddy-covariance measurements over an irrigated wheat field situated in the Haouz plain in the center of Morocco. The results are very encouraging, with few observed anomalies meanly linked to the retrieved Priestley Taylor coefficient that is affected by SM.

This study aims to establish a quantitative framework for soil nitrogen (N), phosphorus (P), and potassium (K) availability in crop fields, using hyperspectral remote sensing imagery and 30m resolution landmark map charting soil fertility across the whole of Africa that has been recently developed by a group of international scientists (iSDA soil). The iSDA map data will be analysed against PRISMA derived Level-2 surface reflectance imagery. We expect to highlight spectral absorption features of N, P and K that relate with their variability in soil. We will investigate the performance of random forest, principal component analysis, support vector regression, partial least squares regression and artificial neural networks in estimating soil total N and extractable P and K quantities from hyperspectral imagery. These models will be assessed using descriptive statistical indices and analysis of variance. We expect to demonstrate the suitability of remote sensing in precision for African agriculture.

Optimizing irrigation (timing and amount) is a worldwide requirement for water resource management, especially in semi-arid regions suffering already from limited water supply. In this study, an approach for estimating daily to seasonal irrigation amount is developed. The approach assimilates the surface soil moisture (SSM) estimated from Sentinel-1 radar data using a particle filter algorithm into the FAO-56 double coefficient model. The approach is tested over a drip irrigated wheat field located in the center of Morocco during two successive growing seasons. It is evaluated using in situ SSM measurements and using Sentinel-1 SSM products. Assimilation of Sentinel-1 SSM products, available every 6 days, yielded accurate irrigation estimates. The seasonal amounts are estimated with a maximum difference of 28 mm (8%) and 15-days cumulative irrigation amounts are reasonable with R=0.64, RMSE=28.78 mm and bias=1.99 mm.

C-band radar observations are well known to have great potentials for the monitoring of crop hydric conditions. Recent studies suggested that the observed difference of backscattering coefficient ($\sigma_{o}$) between ascending and descending pass over tropical forest could be related to the physiological functioning of the trees. Likewise, the water movement within annual crops could lead to a daily cycle of both$\sigma_{o}$and temporal coherence ($\rho$). The objective of this paper is to present the preliminary results of an experiment carried out on a winter wheat field in Morocco that was instrumented with six C-band antennas during 2020 growing season. The preliminary results showed strong daily cycles of$\rho$and$\sigma_{o}$that are analyzed in relation to wind speed, surface soil moisture and evapotranspiration. This work open insights for the monitoring of the crop hydric status using C-band radar data acquired by Sentinel-1 and by potential future radar geostationary missions.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0-5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Données SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

This paper presents a new approach to aggregate spatially distributed land surface heat fluxes and landscape characteristics, adopting a probabilistic point of view and using remote sensing data. This method is tested in the Marrakech plain (Morocco), with different remote sensing sensors, and different probabilistic approaches, in order to reach the best evaluation of large scale heat fluxes and characteristics probability distributions.