|Title||Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging|
|Author(s)||Jin, Yan; Ge, Yong; Wang, Jianghao; Chen, Yuehong; Heuvelink, Gerard B.M.; Atkinson, Peter M.|
|Source||IEEE Transactions on Geoscience and Remote Sensing 56 (2018)4. - ISSN 0196-2892 - p. 2362 - 2376.|
ISRIC - World Soil Information
Soil Geography and Landscape
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Covariance matrices - geospatial analysis - high-resolution imaging - Land surface - Market research - Microwave radiometry - Microwave theory and techniques - remote sensing - Sensors - Spatial resolution - spatial resolution.|
Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates.