Information from the abstract
Accurate soil moisture (SM) information is essential for irrigation management, hydrological modelling, and drought monitoring, but sparse in-situ observations limit large-scale applications. This study proposes a novel three-segment quantile mapping cumulative distribution function (QM 3-segment) framework to downscale and bias-correct 9-km Soil Moisture Active Passive (SMAP) data to 1-km resolution across Nebraska, USA. Performance was compared with monthly mean-adjusted SMAP (SMAP-Adj) and conventional single-segment quantile mapping (QM single) using observations from 26 Nebraska Mesonet stations during the 2015–2018 growing seasons. The QM 3-segment method consistently outperformed both benchmark approaches, reducing daily unbiased root mean square error (ubRMSE) by more than 44% at most stations while accurately capturing temporal soil moisture dynamics. It also corrected distribution-dependent biases, improved spatial soil moisture patterns with ubRMSE of 0.02–0.03 m3 m⁻3, and enhanced soil moisture anomaly detection (ubRMSE: 0.40–0.60). The proposed framework provides an efficient, reliable approach for producing high-resolution, bias-corrected SM products.
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This record has an Impact Signal of 71/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.
Related topics: Soil Moisture and Remote Sensing · Soil Geostatistics and Mapping · Hydrology and Watershed Management Studies
Thai researcher and institutional participation
Jayanga Nishadi Samararathna Kodikara · Mohanasundaram Shanmugam · Sangam Shrestha · Natthachet Tangdamrongsub · Sarawut Ninsawat · Asian Institute of Technology
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