Improving Land Surface Temperature Estimation in Cloud Cover Scenarios Using Graph-Based Propagation

被引：1

作者：

Rolland, Iain ^{[1
]}

Selvakumaran, Sivasakthy ^{[1
]}

Shaikh, Shaikh Fairul Edros Ahmad ^{[2
]}

Hamel, Perrine ^{[2
]}

Marinoni, Andrea ^{[1
,3
]}

机构：

[1] Univ Cambridge, Dept Engn, Cambridge, England

[2] Nanyang Technol Univ, Asian Sch Environm, Singapore, Singapore

[3] UiT Arctic Univ Norway, Dept Phys & Technol, Tromso, Norway

来源：

GEOPHYSICAL RESEARCH LETTERS | 2024年 / 51卷 / 23期

基金：

英国工程与自然科学研究理事会;

关键词：

missing data; land surface temperature; urban heat islands; cloud cover; graph propagation; DROUGHT; ALGORITHM; IMAGES; IMPACT;

D O I：

10.1029/2024GL108263

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Land surface temperature (LST) serves as an important climate variable which is relevant to a number of studies related to energy and water exchanges, vegetation growth and urban heat island effects. Although LST can be derived from satellite observations, these approaches rely on cloud-free acquisitions. This represents a significant obstacle in regions which are prone to cloud cover. In this paper, a graph-based propagation method, referred to as GraphProp, is introduced. This method can accurately obtain LST values which would otherwise have been missing due to cloud cover. To validate this approach, a series of experiments are presented using synthetically obscured Landsat acquisitions. The validation takes place over scenarios ranging from between 10% and 90% cloud cover across six urban locations. In presented experiments, GraphProp recovers missing LST values with a mean absolute error of less than 1.1 degrees C, 1.0 degrees C and 1.8 degrees C in 90% cloud cover scenarios across the studied locations respectively.

引用

页数：11

共 46 条

[1] The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts [J].