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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
Downscaling AMSR-2 soil moisture data with geographically weighted area-to-area regression kriging

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.

Covariance matrices, Geospatial analysis, High-resolution imaging, Remote sensing, Spatial resolution
0196-2892
2362-2376
Jin, Yan
f4f0d1e5-8f59-4f8b-8cb5-fa944ec96e55
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Wang, Jianghao
824eda0f-b65e-41c4-bb75-b0b604f96454
Chen, Yuehong
827c7a53-1f3c-4baa-bf6b-ee4a57a3ee7d
Heuvelink, Gerard B.M.
958daf54-59ef-4257-b988-4b2f2d6a6caa
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Jin, Yan
f4f0d1e5-8f59-4f8b-8cb5-fa944ec96e55
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Wang, Jianghao
824eda0f-b65e-41c4-bb75-b0b604f96454
Chen, Yuehong
827c7a53-1f3c-4baa-bf6b-ee4a57a3ee7d
Heuvelink, Gerard B.M.
958daf54-59ef-4257-b988-4b2f2d6a6caa
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Jin, Yan, Ge, Yong, Wang, Jianghao, Chen, Yuehong, Heuvelink, Gerard B.M. and Atkinson, Peter 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), 2362-2376. (doi:10.1109/TGRS.2017.2778420).

Record type: Article

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.

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More information

e-pub ahead of print date: 22 December 2017
Published date: 1 April 2018
Keywords: Covariance matrices, Geospatial analysis, High-resolution imaging, Remote sensing, Spatial resolution

Identifiers

Local EPrints ID: 422508
URI: http://eprints.soton.ac.uk/id/eprint/422508
ISSN: 0196-2892
PURE UUID: a757ad9a-5416-42de-b08b-683bae515542
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 24 Jul 2018 16:31
Last modified: 07 Oct 2020 01:37

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Contributors

Author: Yan Jin
Author: Yong Ge
Author: Jianghao Wang
Author: Yuehong Chen
Author: Gerard B.M. Heuvelink
Author: Peter M. Atkinson ORCID iD

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