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

Yan Jin, Yong Ge, Jianghao Wang, Yuehong Chen, Gerard B.M. Heuvelink, Peter M. Atkinson

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2362-2376
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume56
Issue number4
Early online date22 Dec 2017
DOIs
Publication statusPublished - Apr 2018

Fingerprint

Soil moisture
downscaling
kriging
soil moisture
spatial resolution
Remote sensing
Microwaves
remote sensing
land surface
surface energy
prediction
Energy balance
Interfacial energy
Mean square error
Topography
energy balance
method
surface temperature
topography
Temperature

Keywords

  • Covariance matrices
  • geospatial analysis
  • high-resolution imaging
  • Land surface
  • Market research
  • Microwave radiometry
  • Microwave theory and techniques
  • remote sensing
  • Sensors
  • Spatial resolution
  • spatial resolution.

Cite this

Jin, Yan ; Ge, Yong ; Wang, Jianghao ; Chen, Yuehong ; Heuvelink, Gerard B.M. ; Atkinson, Peter M. / 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.
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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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Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging. / Jin, Yan; Ge, Yong; Wang, Jianghao; Chen, Yuehong; Heuvelink, Gerard B.M.; Atkinson, Peter M.

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

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

AU - Jin, Yan

AU - Ge, Yong

AU - Wang, Jianghao

AU - Chen, Yuehong

AU - Heuvelink, Gerard B.M.

AU - Atkinson, Peter M.

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 - Covariance matrices

KW - geospatial analysis

KW - high-resolution imaging

KW - Land surface

KW - Market research

KW - Microwave radiometry

KW - Microwave theory and techniques

KW - remote sensing

KW - Sensors

KW - Spatial resolution

KW - spatial resolution.

U2 - 10.1109/TGRS.2017.2778420

DO - 10.1109/TGRS.2017.2778420

M3 - 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 -