Paper Info
Title
Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China
Abstract
Meteorologically observed air temperature (T-a) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface T-a on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous T-a when satellites overpass. This study aims to produce both daily and instantaneous T-a datasets at 1 km resolution for the Jingjinji area, China during 2018-2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of T-a estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 degrees C, mean absolute error (MAE) of 0.94 degrees C, daytime instantaneous RMSE of 1.88 degrees C, MAE of 1.35 degrees C, nighttime instantaneous RMSE of 2.47 degrees C, and MAE of 1.83 degrees C. The corresponding R-2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating T-a. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous T-a at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.
Year
DOI
Venue
2022
10.3390/rs14081916
REMOTE SENSING
Keywords
DocType
Volume
near-surface air temperature, land surface temperature, random forest model, Jingjinji area, machine learning, remote sensing
Journal
14
Issue
ISSN
Citations 
8
2072-4292
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Chunling Wang100.34
Xu Bi200.34
Qingzu Luan300.34
Zhanqing Li43513.98