Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface

Upscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse...

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Main Authors: Xiaodan Wu, Jianguang Wen, Qing Xiao, Dongqin You, Qiang Liu, Xingwen Lin
Format: Article
Language:English
Published: Taylor & Francis Group 2018-05-01
Series:International Journal of Digital Earth
Subjects:
Online Access:http://dx.doi.org/10.1080/17538947.2017.1334097
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author Xiaodan Wu
Jianguang Wen
Qing Xiao
Dongqin You
Qiang Liu
Xingwen Lin
author_facet Xiaodan Wu
Jianguang Wen
Qing Xiao
Dongqin You
Qiang Liu
Xingwen Lin
author_sort Xiaodan Wu
collection DOAJ
description Upscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces. One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling. However, due to the low frequency of updating of high-resolution albedo maps, upscaling time series of ground-based albedo measurements is difficult. This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo. The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations. This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER. The results indicate that this method can characterize the spatiotemporal variations in surface albedo well, and the overall correlation coefficient was 0.702 during the study period.
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spelling doaj.art-a79c7ef715d649818b38e8d94baf75dd2023-09-21T14:38:06ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552018-05-0111547048410.1080/17538947.2017.13340971334097Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surfaceXiaodan Wu0Jianguang Wen1Qing Xiao2Dongqin You3Qiang Liu4Xingwen Lin5State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesCollege of Global Change and Earth System Science, Beijing Normal UniversityState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of SciencesUpscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces. One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling. However, due to the low frequency of updating of high-resolution albedo maps, upscaling time series of ground-based albedo measurements is difficult. This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo. The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations. This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER. The results indicate that this method can characterize the spatiotemporal variations in surface albedo well, and the overall correlation coefficient was 0.702 during the study period.http://dx.doi.org/10.1080/17538947.2017.1334097trend surfaceupscalingspatiotemporalheterogeneousalbedo
spellingShingle Xiaodan Wu
Jianguang Wen
Qing Xiao
Dongqin You
Qiang Liu
Xingwen Lin
Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
International Journal of Digital Earth
trend surface
upscaling
spatiotemporal
heterogeneous
albedo
title Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
title_full Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
title_fullStr Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
title_full_unstemmed Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
title_short Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface
title_sort forward a spatio temporal trend surface for long term ground measured albedo upscaling over heterogeneous land surface
topic trend surface
upscaling
spatiotemporal
heterogeneous
albedo
url http://dx.doi.org/10.1080/17538947.2017.1334097
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