Estimation of the Instantaneous Soil Surface Moisture Content in Cold Seasons by using Optical and Thermal Remote Sensing Data under Clear Sky

A limited number of agricultural weather stations measure moisture in the soil surface. Furthermore, soil moisture information may be required in areas where there is no weather station. The aim of the present study was to use Landsat 8 satellite images to estimate soil surface moisture in an area without agricultural meteorological stations. Gravimetric soil moisture for a total of 14 samples was calculated in the cold season in depths of 0-10 cm when Landsat 8 satellite was overpassing poor rangeland of North of Sabzevar. Furthermore, the first four principal components were extracted from seven Landsat-derived vegetation indices and bio-physical factors affecting soil moisture. Afterwards, the first four components were used to estimate soil surface moisture at the moment of the satellite passing the region using a multivariate linear regression and neural networks. The obtained results of instantaneous soil surface moisture showed that the neural networks had mean absolute percentage error of while classical regression analysis had mean absolute percentage error of 40%. The results also showed the benefits of using both in-situ soil moisture data and Landsat 8 satellite images to model instantaneous soil surface moisture content for areas lacking meteorological networks.