Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Jin, Y. and Ge, Y. and Wang, J. and Chen, Y. and Heuvelink, G. B. M. and Atkinson, P. M. (2018) Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging. IEEE Transactions on Geoscience and Remote Sensing, 56 (4). pp. 2362-2376. ISSN 0196-2892

[img]
Preview
PDF (Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging)
Downscaling_AMSR_2_Soil_Moisture_Data_With_Geographically_Weighted_Area_to_Area_Regression_Kriging.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (2MB)

Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Geoscience and Remote Sensing
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1900
Subjects:
ID Code:
125975
Deposited By:
Deposited On:
20 Jun 2018 08:32
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Nov 2020 05:23