Retrieving Soil Moisture Over Continental U.S. via Multi-View Multi-Task Learning
IEEE Geoscience and Remote Sensing Letters(2019)
摘要
Soil moisture (SM) is an essential variable in the hydrological cycle. Quantifying the magnitude of SM is crucial for the climate system. In this letter, we present a new model with multi-view multi-task learning (MVMTL) to estimate SM over continental U.S. Specifically, the multi-view component is used to make full use of spatial and temporal features of each grid cell (
$0.25^{\circ }\times 0.25^{\circ }$
). Meanwhile, the multi-task component aims to capture the spatial correlations and to perform coestimations between different grid cells in the study area. To evaluate the effectiveness of MVMTL, we compare it with several retrieval methods in terms of the SM product from the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim) and
in situ
SM measurements. The experimental results show that the MVMTL model can achieve higher performance than the other methods.
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关键词
Feature extraction,Meteorology,Soil,Satellites,Temperature measurement,Correlation,Soil measurements
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