A New Fully Constrained Least Squares-Based Fusion Approach of Optical, Thermal, and SAR Remote Sensing Data for Soil Moisture Content Estimation

This paper introduces a new multilevel fusion approach for Soil Moisture Content (SMC) estimation. This approach includes the following indices and models; the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI), the Perpendicular Drought Index (PDI), and the Temperature Vegetation Dryness Index (TVDI), extracted from Landsat-8 data, and the inversion of the Integral Equation Model (IEM) from Sentinel-1 data with the support of surface roughness measurements. The proposed fusion occurs on the feature and decision levels. At the feature level, features extracted from each of the above indices/models are combined to obtain three feature vectors, those vectors are later used in the decision level via the Fully Constrained Least Squares (FCLS) technique. The areas of interest of this study are; Blackwell Farms, Guildford, United Kingdom, and Sidi Rached, Tipasa, Algeria. The proposed system yielded lower Root Mean Square Errors (RMSE) (1.09% on average) than that of IEM inversion.