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Uncertainty Analysis and Data Fusion of Microwave Remote Sensing Soil Moisture Products based on Triple-Collocation Method |
Shuguo Wang(),Wei Liu,Liang Liang |
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China |
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Abstract Microwave remote sensing can provide large scale soil moisture information, and even further derive soil moisture products at global scale. Due to impacts of observation configuration and retrieval method etc., different soil moisture products feature different accuracies and reliabilities. Based on Triple-Collocation method, this study analyzes the uncertainties among AMSR2, SMAP and SMOS soil moisture products at two spatial scales in Naqu study area, i.e., 0.25°× 0.25° and 1.0°×1.0°, and further performs data fusion based on analyzed random errors to obtain more reliable soil moisture product. The uncertainty analysis indicates that the three products have distinct random error distribution in spatial. In this case, the traditional arithmetic mean method may not be appropriate. Hence, data fusion is performed by proposed optimizing weighting method based on the analyzed uncertainties. In comparison with the three original soil moisture products, the fusion result shows a more effective data size and improved accuracy. When different original products present similar errors, the fusion products of equal weighting and optimized weighting methods show the similar performance. Oppositely, the uncertainties analysis based fusion method is superior to equal weighting method in terms of effective data size and accuracy.
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Received: 15 January 2019
Published: 23 March 2020
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