In The Science of the total environment
A good knowledge in eco-hydrological processes requires significant understanding of geospatial distribution of soil moisture (SM). However, SM monitoring remains challenging due to its large spatial variability and its dynamic time response. This study was performed to assess the performance of a particle swarm optimization (PSO)-based optimized Cerebellar Model Articulation Controller (CMAC) in generating high-resolution surface SM estimates using sentinel-2 imagery over a Mediterranean agro-ecosystem. Furthermore, the results were compared with those of PSO-based optimized group method of data handling (GMDH) as a more common data-driven method. Two different modeling approaches i.e. modeling in homogenous clusters (local approach) and modeling in entire area as an entity (global approach) were examined. Candidate predictors namely sentinel-2 spectral bands, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), digital elevation model (DEM), slope and aspect were used as the input variables to estimate SM. An intensive field survey had been done to gather in-situ SM data using a time-domain reflectometer (TDR). K-fold validation based on in-situ SM measurements demonstrated the reasonability of the SM estimation of the proposed methodology. Detecting homogeneous areas was done using genetic and particle swarm optimization algorithms. Synthesized SM product of PSO-GMDH showed a mean Normalized Root-Mean-Square Error (NRMSE) of 13.6 to 8.91 for global and local approaches in the test phase. PSO-CMAC method with an average NRMSE of 12.47 to 8.72 for global and local approaches shows the highest accuracy and outperforms the PSO-GMDH method at both local and global approaches. Overall, results revealed that clustering study area prior to running machine learning (ML) models coupled with optical satellite imagery and geophysical properties, boosts their predictive performance and can lead to more accurate mapping of SM with more heterogeneity. The results also showed that the global approach had a moderate performance in capturing the SM heterogeneity.
Moosavi Vahid, Karami Ayoob, Aliramaee Ramyar
2022-Oct-15
Clustering, Local and global modeling, Remote sensing, Surface soil moisture
