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

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

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Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging. / Jin, Y.; Ge, Y.; Wang, J. et al.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 4, 04.2018, p. 2362-2376.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Jin, Y, Ge, Y, Wang, J, Chen, Y, Heuvelink, GBM & Atkinson, PM 2018, 'Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging', IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 4, pp. 2362-2376. https://doi.org/10.1109/TGRS.2017.2778420

APA

Jin, Y., Ge, Y., Wang, J., Chen, Y., Heuvelink, G. B. M., & 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), 2362-2376. https://doi.org/10.1109/TGRS.2017.2778420

Vancouver

Jin Y, Ge Y, Wang J, Chen Y, Heuvelink GBM, Atkinson PM. Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging. IEEE Transactions on Geoscience and Remote Sensing. 2018 Apr;56(4):2362-2376. Epub 2017 Dec 22. doi: 10.1109/TGRS.2017.2778420

Author

Jin, Y. ; Ge, Y. ; Wang, J. et al. / Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging. In: IEEE Transactions on Geoscience and Remote Sensing. 2018 ; Vol. 56, No. 4. pp. 2362-2376.

Bibtex

@article{d98587c5b6e34166925c24d05752c0d2,
title = "Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging",
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.",
keywords = "hydrological techniques, land surface temperature, soil, topography (Earth), vegetation mapping, AMSR-2 soil moisture data, GWATARK method, benchmark methods, coarse spatial resolution, coarse-spatial-resolution passive microwave remote sensing data, daily AMSR-2 SM product, downscaled SM, downscaled predictions, downscaling methods, downscaling process, fine-resolution SM, fine-spatial-resolution optical remote sensing data, geographically weighted area-to-area regression kriging method, hybrid geostatistical approach, land surface energy balance, local spatial heterogeneity, mapping fine-spatial-resolution, microwave SM products, near-surface SM, original data, topography-corrected land surface temperature, vegetation-temperature condition index covariates, water cycle, Land surface, Market research, Microwave radiometry, Microwave theory and techniques, Remote sensing, Sensors, Spatial resolution, Covariance matrices, geospatial analysis, high-resolution imaging, remote sensing, spatial resolution",
author = "Y. Jin and Y. Ge and J. Wang and Y. Chen and Heuvelink, {G. B. M.} and Atkinson, {P. M.}",
note = "{\textcopyright}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.",
year = "2018",
month = apr,
doi = "10.1109/TGRS.2017.2778420",
language = "English",
volume = "56",
pages = "2362--2376",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "4",

}

RIS

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 -