Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Yongkang Li, Qing He (), Yongqiang Liu, Amina Maituerdi, Yang Yan and Jiao Tan
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Yongkang Li: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China
Qing He: Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Yongqiang Liu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China
Amina Maituerdi: Xinjiang Uygur Autonomous Region Meteorological Service, Urumqi 830002, China
Yang Yan: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China
Jiao Tan: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830052, China

Land, 2024, vol. 13, issue 11, 1-26

Abstract: Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R 2 ) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Keywords: air-to-land temperature conversion; machine learning; cloud cover impact; Kunlun Mountain gradient observation system; mountain land (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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