Spatial Heterogeneity of Albedo at Subpixel Satellite Scales and its Effect in Validation: Airborne Remote Sensing Results From HiWATER

Characterizing the subpixel heterogeneity within satellite pixels is a key issue in validation. Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators i...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Hauptverfasser: Wu, Xiaodan, Wen, Jianguang, Xiao, Qing, You, Dongqin, Gong, Baochang, Wang, Jingping, Ma, Dujuan, Lin, Xingwen
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container_title IEEE transactions on geoscience and remote sensing
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Wen, Jianguang
Xiao, Qing
You, Dongqin
Gong, Baochang
Wang, Jingping
Ma, Dujuan
Lin, Xingwen
description Characterizing the subpixel heterogeneity within satellite pixels is a key issue in validation. Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation including spatial heterogeneity (SH), representativeness errors, and representative area with subpixel size were analyzed and discussed. Furthermore, this article discussed the optimal subpixel size to assess SH within a coarse pixel and the optimal footprint of in situ measurements for building dense and sparse validation networks. SH decreases with the increase of subpixel size. And a reduction of about 10% can be obtained from 5 m \times 5 m to 150 m \times150 m subpixel size, depending on the degree of SH within the typical satellite pixels. And the sensitiveness of SH to subpixel size decreases gradually with the increasing of subpixel size. Ideally, SH should be assessed using maps with pixel sizes corresponding to the footprint of in situ measurements. Regarding the deployment of future validation networks, the footprint of in situ sites should be designed at least larger than 25 m for dense networks. And much larger footprints (e.g., 100 m) are preferred in designing sparse networks. The representativeness error is not fully related to subpixel sizes because it is affected by many factors. The findings are also transferable to model evaluation when comparing model grid values to local observations.
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Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation including spatial heterogeneity (SH), representativeness errors, and representative area with subpixel size were analyzed and discussed. Furthermore, this article discussed the optimal subpixel size to assess SH within a coarse pixel and the optimal footprint of in situ measurements for building dense and sparse validation networks. SH decreases with the increase of subpixel size. And a reduction of about 10% can be obtained from 5 m <inline-formula> <tex-math notation="LaTeX">\times 5 </tex-math></inline-formula> m to 150 m <inline-formula> <tex-math notation="LaTeX">\times150 </tex-math></inline-formula> m subpixel size, depending on the degree of SH within the typical satellite pixels. And the sensitiveness of SH to subpixel size decreases gradually with the increasing of subpixel size. Ideally, SH should be assessed using maps with pixel sizes corresponding to the footprint of in situ measurements. Regarding the deployment of future validation networks, the footprint of in situ sites should be designed at least larger than 25 m for dense networks. And much larger footprints (e.g., 100 m) are preferred in designing sparse networks. The representativeness error is not fully related to subpixel sizes because it is affected by many factors. 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Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation including spatial heterogeneity (SH), representativeness errors, and representative area with subpixel size were analyzed and discussed. Furthermore, this article discussed the optimal subpixel size to assess SH within a coarse pixel and the optimal footprint of in situ measurements for building dense and sparse validation networks. SH decreases with the increase of subpixel size. And a reduction of about 10% can be obtained from 5 m <inline-formula> <tex-math notation="LaTeX">\times 5 </tex-math></inline-formula> m to 150 m <inline-formula> <tex-math notation="LaTeX">\times150 </tex-math></inline-formula> m subpixel size, depending on the degree of SH within the typical satellite pixels. 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subjects Airborne remote sensing
Airborne sensing
Albedo
Atmospheric measurements
Atmospheric modeling
Geologic measurements
Heterogeneity
In situ measurement
Measurement uncertainty
Networks
Patchiness
Pixels
Remote sensing
representativeness errors
Satellites
Size measurement
Spatial heterogeneity
spatial heterogeneity (SH)
Spatial resolution
subpixel size
validation
title Spatial Heterogeneity of Albedo at Subpixel Satellite Scales and its Effect in Validation: Airborne Remote Sensing Results From HiWATER
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