Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region

Estimation of soil moisture content (SMC) is an important aspect of precision irrigation water management. Soil moisture content affects several factors such as vegetation cover, evapotranspiration (ET) and crop growth. This study aims to predict soil moisture content using optical remote sensing data and Synthetic Aperture Radar (SAR) Sentinel-1 data and the correlation with crop pattern. The study was carried out in the east of Nile Delta of Egypt (30 & DEG; 31 to 30 & DEG; 33 N, 31 & DEG; 55 to 31 & DEG; 05 E). A number of 100 surface soil samples (0-10) were collected to represent different soil types in the study area. Soil moisture index (SMI) is assessed based on thermal remote sensing data as Land Surface Temperature (LST) besides, Sentinel-1 data. The results showed a high correlation between SMC and SMI, coefficient of determination (R2) reached 0.81 between actual soil moisture and SMI. Furthermore, a significant correlation was also shown by Sentinel-1 data, with R2 0.83 between actual soil moisture content and backscattering coefficient (dB). The thermal data gives significant results to predict soil moisture content. The Accurate discrimination of crop varieties is considered as effective factor in explaining the distribution of soil moisture where moisture is associated with the crop type. These results can be useful as a good indicator for irrigation control at large scale.& COPY; 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).