Merging Microwave, Optical, and Reanalysis Data for 1 Km Daily Soil Moisture by Triple Collocation

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless products is challenging due to the inability of optical bands penetrate clouds coarse spatiotemporal microwave reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on triple collocation (TC) method with explicit physical mechanism, which was dedicated generating 1 km daily Current techniques TC often lack input. remedy deficiency, our performed reconstruction MODIS LST NDVI, retrieved Then, optical-derived sm1, microwave-retrieved sm2 (ESA CCI combined), sm3 (CLDAS) were matched by cumulative distribution function (CDF) eliminate bias, their weights determined method. Finally, least squares algorithm significance judgment adopted complete merging. Although CLDAS presented anomalies over several stations, can detect reduce impact minimizing its weight, shows robustness This implemented Naqu region, results showed that merged captured temporal variability SM depicted spatial information detail; validation situ measurement obtained average ubRMSE 0.046 m³/m³. Additionally, transferrable any area measured sites better hydrological applications.