Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network

doi:10.3390/rs12172691

	Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network
	Wang, Shaofei 1; Zhou, Ji 1; Lei, Tianjie 2; Wu, Hua 3; Zhang, Xiaodong 1; Ma, Jin 1; Zhong, Hailing 1
刊名	REMOTE SENSING
	2020-09-01
卷号	12 期号:17 页码:27
关键词	land surface temperature neural network deep belief network convolutional neural network passive microwave
DOI	10.3390/rs12172691
通讯作者	Zhou, Ji(jzhou233@uestc.edu.cn)
英文摘要	Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25 degrees. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1 degrees, respectively. MODIS LST played the role of "ground truth" in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1-0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19-3.58 K for the daytime and 1.43-2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10-4.72 K for forest and cropland sites. Further intercomparison indicated that similar to 50% of the CNN LSTs were closer to the MODIS LSTs than ESA's GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA's global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.
资助项目	National Key Research and Development Program of China[2018YFC1505205] ; National Natural Science Foundation of China[41871241] ; Fundamental Research Funds for the Central Universities of China[ZYGX2019J069] ; ESA-MOST Dragon 5 Cooperation Programme[59318]
WOS关键词	SIMPLE RETRIEVAL METHOD ; AMSR-E ; WINDOW ALGORITHM ; EDDY-COVARIANCE ; WEATHER ; EVAPOTRANSPIRATION ; EMISSIVITIES ; EMISSION ; RECORD
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000569668500001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities of China ; ESA-MOST Dragon 5 Cooperation Programme
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156865]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhou, Ji
作者单位	1.Univ Elect Sci & Technol China, Ctr Informat Geosci, Sch Resources & Environm, Chengdu 611731, Peoples R China 2.China Inst Water Resources & Hydropower Res IWHR, Beijing 100038, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shaofei,Zhou, Ji,Lei, Tianjie,et al. Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network[J]. REMOTE SENSING,2020,12(17):27.
APA	Wang, Shaofei.,Zhou, Ji.,Lei, Tianjie.,Wu, Hua.,Zhang, Xiaodong.,...&Zhong, Hailing.(2020).Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network.REMOTE SENSING,12(17),27.
MLA	Wang, Shaofei,et al."Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network".REMOTE SENSING 12.17(2020):27.