Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface. Issue 5 (4th May 2018)

• Record Type: Journal Article
• Authors: Wu, Xiaodan; Wen, Jianguang; Xiao, Qing; You, Dongqin; Liu, Qiang; Lin, Xingwen
• Is Part Of: International journal of digital earth. Volume 11:Issue 5(2018)
• Page Start: 470
• Page End: 484
• Publication Date: 2018-05-04
• Subjects: Trend surface -- upscaling -- spatiotemporal -- heterogeneous -- albedo; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2017.1334097
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: 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.