Spatial Downscaling of Satellite-Based Precipitation Data over the Qaidam Basin, China.
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| Title: | Spatial Downscaling of Satellite-Based Precipitation Data over the Qaidam Basin, China. |
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| Authors: | Wang, Yuanzheng1,2 (AUTHOR), Yan, Changzhen1,2 (AUTHOR), Ma, Qimin3 (AUTHOR), Jia, Xiaopeng1 (AUTHOR) jiaxp@lzb.ac.cn |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p995. 20p. |
| Subjects: | Downscaling (Climatology), Machine learning, Rainfall measurement, Remote sensing, Climate change, Watersheds, Hydrological research |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? The Cubist model produced the best downscaling results. For the annual and monthly downscaled products, the downscaled data exhibited finer spatial patterns and eliminated the outliers present in the original data. What are the implications of the main findings? The analysis reduces systematic bias and enhances the spatial descriptive capability of precipitation data. The downscaled dataset provides effective data support for hydrological and climate change research in the Qaidam Basin. High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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