1642 / 2019-08-24 12:51:22

基于多星融合的遥感土壤湿度产品质量提升研究

7、地理与地理信息科学 > 专题10：水循环遥感：数据、模型与应用

Consistent temporal coverage and accuracy of remotely sensed soil moisture (SM), are very important in many hydrological and environmental applications. The Essential Climate Variable (ECV) SM data set is the first product attempting to address the continuity and reliability issues of individual SM sensors by using multi-satellite observations, but limitations in temporal coverage and accuracy issues still persist. This study proposes a two-step fusion framework to enhance the temporal coverage and accuracy of remotely sensed SM. The first step aims to improve the temporal coverage by filling the gaps of remotely sensed SM with a machine learning method. The second step aims to improve the accuracy by rescaling the reconstructed product against another based on matching the cumulative distribution function (CDF), and then fusing the reconstructed and rescaled SM products by calculating the arithmetic average between the two. We chose the ECV and Fengyun SM products as the test algorithm to improve the quality of ECV over the Tibetan Plateau. Validation at eight sites showed that the R2 between the fused SM and in situ measurements ranged between 0.494 and 0.706, which was much higher than the R2 between the reconstructed ECV SM and in situ measurements, which ranged between 0.368 and 0.647. Moreover, the temporal coverage was improved from 30.84% to 78.12% for the entire Tibetan Plateau (TP). The fused SM provides a better trade-off between temporal coverage and accuracy than the reconstructed ECV SM. This fused SM product has high temporal coverage, high accuracy and high consistency, and is expected to help us better understand the role of SM in the water and energy cycles under global change. This method can also be used to improve the temporal coverage and accuracy for other remotely sensed SM products.