A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm - Nguyen, Thu Thuy; Ngo, Huu Hao; Guo, Wenshan; Chang, Soon Woong; Nguyen, Dinh Duc; Nguyen, Chi Trung; Zhang, Jian; Liang, Shuang; Bui, Xuan Thanh; Hoang, Ngoc Bich (The Science of the total environment, 2022)
A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production. [Display omitted] •Data fusion and machine learning are of importance for SM retrieval.•The predictor variables derived from S-1, S-2, and ALOS DSM data were generated.•21 optimal features were identified using genetic algorithm.•The extreme gradient boosting regression performed best in SM estimation.•Inverted Red-Edge Chlorophyll Index was the most influential feature.