C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

Abstract

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).