Estimation of High-Resolution Surface Soil Moisture Through GIS-Based Frequency Ratio Modeling

Abstract

This article presents a methodology for estimating higher-resolution soil moisture using GIS and frequency ratio (FR) modeling techniques. A global soil moisture database with a 9 km spatial resolution was used as reference data. A total of 283 reference points were selected through spatial fishnet analysis with optimum soil moisture. Eighty percent (80%) of these reference points served as inputs to the FR model, with the remaining twenty percent (20%) reserved for validation. Key independent variables incorporated in the FR modeling process included land use and land cover, soil characteristics, vegetation index, wetness index, surface temperature, rainfall, elevation, slope, and distance from rivers. This research was conducted in the final drainage basin of the Markham River basin. The resulting high-resolution surface soil moisture was further classified into five basic zones, namely very low (< 6), low (6 - 7), moderate (7 - 8), high (8 - 9), and very high (> 9). The result indicates almost 26.10% and 56.89% of the Basin area come under high and very high soil moisture zones respectively. The FR model evinced a prediction accuracy of 93.98% along with a succession rate of 91.59%. These results provide useful data for scientific applications in various domains, specifically in the agricultural sector, local government administrator, researcher, and planner.