Publications

Xu, YM; Knudby, A; Shen, Y; Liu, YH (2018). Mapping Monthly Air Temperature in the Tibetan Plateau From MODIS Data Based on Machine Learning Methods. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 11(2), 345-354.

Abstract
Detailed knowledge of air temperature (T-a) is desired for various scientific applications. However, in the Tibetan Plateau (TP), the meteorologically observed Ta is limited due to the low density and uneven distribution of stations. This paper aims to develop a 1-km resolution monthly mean T-a dataset over the TP during 2001-2015 from remote sensing and auxiliary data. 11 environmental variables were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data and topographic index data. Ten machine learning algorithms were implemented and compared to determine the optimal model for T-a estimation in the TP. The Cubist algorithm outperformed other methods, having the highest accuracy and the lowest sensitivity to cloud contamination. To minimize the overfitting problem, a simple forward variable selection method was introduced and six variables were selected from the original 11 environmental variables. Among these six variables, nighttime land surface temperature (T-s) was the most important predictor, followed by elevation and solar radiance. The seasonal performance of the Cubist model was also assessed. The model had good accuracies in all four seasons, with the highest accuracy in winter (R-2 = 0.98 and MAE = 0.63 degrees C) and the lowest accuracy in summer (R-2 = 0.91 and MAE = 0.86 degrees C). Due to the gaps in MODIS data caused by cloud cover, there were 0.39% missing values in the estimated Ta. To improve the data integrity, Delaunay triangulation interpolation was applied to fill the missing Ta values. The final monthly (2001-2015) Ta dataset had an overall accuracy of RMSE = 1.00 degrees C and MAE = 0.73 degrees C. It provides valuable information for climate change assessment and other environmental studies in the TP.

DOI:
10.1109/JSTARS.2017.2787191

ISSN:
1939-1404