Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data

Abstract

Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-consuming, labour-intensive and limited in spatial coverage, restricting their applicability for regional-scale monitoring. Remote sensing integrated with machine learning provides a promising alternative for generating spatially continuous and timely soil moisture estimates. This study aimed to predict surface soil moisture in the Ranipool-Rumtek administrative region of Sikkim, India, using multimodal remote sensing data and machine-learning techniques. Soil moisture was measured at 85 locations using the gravimetric method and these same locations were used as ground truthing sites. Multi-temporal imagery from Landsat-8 and Sentinel-2 was processed to derive vegetation and moisture-related indices, i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Shortwave-infrared Difference Soil Moisture Index (NSDSI3), Land Surface Temperature (LST), Moisture Stress Index (MSI), and Vegetation Supply Water Index (VSWI). These indices were used as predictor variables to develop artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) models. The results revealed that the ANN model developed using Sentinel-2-derived indices exhibited the highest predictive accuracy, achieving values of coefficient of determination (R2) as 0.82, Root Mean Square Error (RMSE) as 6% and Mean Absolute Error (MAE) of 4.6%. The performance of the Sentinel-2-derived ANN model was found to be better than that of the Landsat-8-based ANN model as well as the SVM and MLR models developed using Sentinel-2 data. The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments. The proposed approach provides a robust framework to support precision agriculture, irrigation scheduling, hydrological modelling, and drought and flood monitoring.