A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products
نویسندگان
چکیده
منابع مشابه
Upscaling Sparse Ground-based Soil Moistureobservations Forthe Validation of Coarse-resolution Satellite Soil Moisture Products
[1] The contrast between the point-scale nature of current ground-based soil moisture instrumentation and the ground resolution (typically >10 km) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from current and upcoming soil moisture satellite missions. Given typical levels of observed spatial variability in soil moisture fields, t...
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Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machi...
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Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate regional soil moisture. However, the application of the retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, many methods were proposed to downscale microwave soil moisture data. The traditional method is the microwave-o...
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FengYun-3B (FY-3B) soil moisture product, retrieved from passive microwave brightness temperature data based on the Qp model, has rarely been applied at the catchment and region scale. One of the reasons for this is its coarse spatial resolution (25-km). The study in this paper presented a new method to obtain a high spatial resolution soil moisture product by downscaling FY-3B soil moisture pr...
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2021
ISSN: 1939-1404,2151-1535
DOI: 10.1109/jstars.2020.3035386