Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing

Yan Jin, Yong Ge*, Jianghao Wang, Gerard B.M. Heuvelink, Le Wang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.
Original languageEnglish
Article number579
JournalRemote Sensing
Volume10
Issue number4
DOIs
Publication statusPublished - 1 Apr 2018

Fingerprint

downscaling
kriging
remote sensing
spatial resolution
soil moisture
spatial variation
prediction
product
method

Keywords

  • High-resolution imaging
  • Soil moisture
  • Spatial downscaling

Cite this

@article{41f9671d88794cbea20465368179310d,
title = "Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing",
abstract = "Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5{\%}. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.",
keywords = "High-resolution imaging, Soil moisture, Spatial downscaling",
author = "Yan Jin and Yong Ge and Jianghao Wang and Heuvelink, {Gerard B.M.} and Le Wang",
year = "2018",
month = "4",
day = "1",
doi = "10.3390/rs10040579",
language = "English",
volume = "10",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI",
number = "4",

}

Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing. / Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B.M.; Wang, Le.

In: Remote Sensing, Vol. 10, No. 4, 579, 01.04.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing

AU - Jin, Yan

AU - Ge, Yong

AU - Wang, Jianghao

AU - Heuvelink, Gerard B.M.

AU - Wang, Le

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.

AB - Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.

KW - High-resolution imaging

KW - Soil moisture

KW - Spatial downscaling

U2 - 10.3390/rs10040579

DO - 10.3390/rs10040579

M3 - Article

VL - 10

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 4

M1 - 579

ER -