气候变化领域集成服务门户(CCPortal): Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data

CCPortal

DOI	10.1016/j.rse.2020.111692
	Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data
	Shen H.; Jiang Y.; Li T.; Cheng Q.; Zeng C.; Zhang L.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep learning for Ta mapping mainly based on space remote sensing and ground station observations. Considering that Ta varies greatly in space and time and is sensitive to many factors, assimilation data and socioeconomic data are also included for a multi-source data fusion based estimation. Specifically, a 5-layers structured deep belief network (DBN) is employed to better capture the complicated and non-linear relationships between Ta and different predictor variables. Layer-wise pre-training process for essential features extraction and fine-tuning process for weight parameters optimization ensure the robust prediction of Ta spatio-temporal distribution. The DBN model was implemented for 0.01° daily maximum Ta mapping across China. The ten-fold cross-validation results indicate that the DBN model achieves promising results with the RMSE of 1.996 °C, MAE of 1.539 °C, and R of 0.986 at the national scale. Compared with multiple linear regression (MLR), back-propagation neural network (BPNN) and random forest (RF) method, the DBN model reduces the MAE values by 1.340 °C, 0.387 °C and 0.222 °C, respectively. Further analysis on spatial distribution and temporal tendency of prediction errors both validate the great potentials of DBN in Ta estimation. © 2020 Elsevier Inc.
英文关键词	Air temperature; Assimilation data; Deep learning; Land surface temperature; Remotely sensed data; Socioeconomic data
语种	英语
scopus关键词	Atmospheric temperature; Backpropagation; Data fusion; Decision trees; Land surface temperature; Linear regression; Mapping; Neural networks; Random forests; Remote sensing; Space optics; Tantalum; Air temperature; Assimilation data; Back-propagation neural networks; Deep belief network (DBN); Multiple linear regressions; Remotely sensed data; Socio-economic data; Spatiotemporal distributions; Deep learning; air temperature; artificial neural network; back propagation; learning; mapping method; model validation; optimization; regression analysis; remote sensing; spatiotemporal analysis; China
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179402
作者单位	School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; School of Urban Design, Wuhan University, Wuhan, 430079, China; The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China; The Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China
推荐引用方式 GB/T 7714	Shen H.,Jiang Y.,Li T.,et al. Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data[J],2020,240.
APA	Shen H.,Jiang Y.,Li T.,Cheng Q.,Zeng C.,&Zhang L..(2020).Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data.Remote Sensing of Environment,240.
MLA	Shen H.,et al."Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data".Remote Sensing of Environment 240(2020).