Publications

Wang, ZT; Zhang, M (2022). Evaluation and Comparison of Different Machine Learning Models for NSAT Retrieval from Various Multispectral Satellite Images. ATMOSPHERE, 13(9), 1429.

Abstract
As a key parameter of land surface energy balance models, near surface air temperature (NSAT) is an important indicator of the surface atmospheric environment and the urban thermal environment. At present, NSAT data are mainly captured by meteorological ground stations. In areas with a sparse distribution of meteorological stations, however, it is not possible to describe the heterogeneity of NSAT in continuous space. With the rapid development of satellite remote sensing technologies, there is now a significant method to retrieve NSAT from multispectral satellite images based on machine learning methods. In the literatures published so far, there is little reported research concerning the comprehensive evaluation and/or the systematic comparison of NSAT retrieval performances based on different machine learning models. Hence, the three most commonly-used machine learning models, Support Vector Regression (SVR), Multilayer Perceptron Neural Network (MLBPN), and Random Forest (RF), have been employed for the NSAT retrieval from various multispectral satellite images of MODIS daytime and nighttime data, Landsat 8 data, and Sentinel-2 data. Comparison of the NSAT retrieval results generated by the different machine learning models from the different types of satellite images reveals that (a) the RF-based model has a better NSAT retrieval performance than the SVR- or MLBPN-based models with respect to both the accuracy and stability, and (b) the NSAT results retrieved from the MODIS data were generally better than those from the Landsat 8 and Sentinel-2 data. To sum up, the conducted research in this article does not only provide a reference for practical applications relevant to NSAT retrievals, but also proposes an efficient RF-based model for NSAT retrieval from multispectral satellite images in continuous space.

DOI:
10.3390/atmos13091429

ISSN:
2073-4433