An effective approach for mapping permafrost in a large area using subregion maps and satellite data. (22nd May 2020)

• Record Type: Journal Article
• Authors: Hu, Jianan; Zhao, Shuping; Nan, Zhuotong; Wu, Xiaobo; Sun, Xuehui; Cheng, Guodong
• Is Part Of: Permafrost and periglacial processes. Volume 31:Number 4(2020)
• Page Start: 548
• Page End: 560
• Publication Date: 2020-05-22
• Subjects: permafrost distribution, permafrost mapping, Qinghai–Tibet plateau, remote sensing, surface frost number model; Frozen ground -- Periodicals; Sols gelés -- Périodiques; Périglaciaire -- Périodiques; 551.3805
• DOI: 10.1002/ppp.2068
• Languages: English
• ISSNs: 1045-6740

Abstract

Abstract: Permafrost distribution maps are of importance for environmental assessment, climate system modeling, and practical engineering applications. The scarcity of forcing data and parameters often limits the uses of permafrost models over large areas. However, detailed data are often available in a few subregions through field investigations. In this study, we propose a novel approach for mapping permafrost distribution in a large and data‐scarce area using an empirical model with subregion permafrost maps and satellite data as inputs. The surface frost number model (FROSTNUM) was re‐inferred to include an extra soil parameter to represent the thermal and moisture conditions in soils. The optimal soil parameters were determined from the subregion maps of permafrost distribution through spatial clustering, parameter optimization, and the decision tree method. FROSTNUM was fed with satellite‐derived ground surface freezing and thawing indices to map the permafrost distribution over the study area. The proposed approach was evaluated in the Gaize area on the Qinghai–Tibet Plateau, where intensive field studies have been done. The simulated permafrost distribution is consistent with a map of permafrost distribution made from borehole observations and field surveys in Gaize. Due to excellent accuracy, the approach is effective and can be used in large areas with limited data.