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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging
AU - Jin, Y.
AU - Ge, Y.
AU - Wang, J.
AU - Chen, Y.
AU - Heuvelink, G. B. M.
AU - Atkinson, P. M.
N1 - ©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.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - hydrological techniques
KW - land surface temperature
KW - soil
KW - topography (Earth)
KW - vegetation mapping
KW - AMSR-2 soil moisture data
KW - GWATARK method
KW - benchmark methods
KW - coarse spatial resolution
KW - coarse-spatial-resolution passive microwave remote sensing data
KW - daily AMSR-2 SM product
KW - downscaled SM
KW - downscaled predictions
KW - downscaling methods
KW - downscaling process
KW - fine-resolution SM
KW - fine-spatial-resolution optical remote sensing data
KW - geographically weighted area-to-area regression kriging method
KW - hybrid geostatistical approach
KW - land surface energy balance
KW - local spatial heterogeneity
KW - mapping fine-spatial-resolution
KW - microwave SM products
KW - near-surface SM
KW - original data
KW - topography-corrected land surface temperature
KW - vegetation-temperature condition index covariates
KW - water cycle
KW - Land surface
KW - Market research
KW - Microwave radiometry
KW - Microwave theory and techniques
KW - Remote sensing
KW - Sensors
KW - Spatial resolution
KW - Covariance matrices
KW - geospatial analysis
KW - high-resolution imaging
KW - remote sensing
KW - spatial resolution
U2 - 10.1109/TGRS.2017.2778420
DO - 10.1109/TGRS.2017.2778420
M3 - Journal article
VL - 56
SP - 2362
EP - 2376
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 4
ER -