Generation of Spatial-Seamless AMSR2 Land Surface Temperature in China During 20122020 Using a Deep Neural Network
文献类型: 外文期刊
第一作者: Lian, Yihua
作者: Lian, Yihua;Duan, Si-Bo;Huang, Cheng;Han, Wenjing;Liu, Meng
作者机构: Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China
关键词: AMSR2; deep neural network (DNN); gap filling; land surface temperature (LST); passive microwave (PMW)
期刊名称:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING ( 2022影响因子:8.2; 五年影响因子:8.8 )
ISSN: 0196-2892
年卷期: 2023 年 61 卷
收录情况: SCI
摘要: Land surface temperature (LST) reflects the cold and hot conditions of the land surface and is one of the most important geophysical parameters in the study and research of the land-atmosphere system. Passive microwave (PMW) is one of the primary techniques for obtaining spatially continuous LST at regional, continental, and global scales. However, there is an orbital gap in the LST retrieved from PMW (PMW LST) due to the scanning scheme of the PMW sensor, which limits the application of PMW LST, so it is necessary for the proposed some methods to fill the orbital gap of PMW LST. In this study, a new orbital gap-filling method based on a deep neural network (DNN) was developed to address the issue of PMW LST orbital gaps. This method first established the DNN model based on the nonlinear relationship between AMSR2 LST and 11 environmental variables and then used the DNN model to generate a new spatially continuous LST product, namely, DNN-LST, and, finally, used DNN-LST to fill the orbital gaps of AMSR2 LST to generate the daytime/nighttime spatially seamless gap-filled LST (GF-LST) product for China from 2012 to 2020. GF-LST can more correctly represent the spatiotemporal variation of surface temperature in China than AMSR2 LST because it has continuous spatial texture information and no obvious boundary reconstruction effect. After verifying the accuracy of GF-LST products through simulated gap region validation and in situ validation, it can be found that: 1) DNN-LST in simulated gap regions showed high accuracy during the daytime and nighttime on July 15, 2012-2020, and the mean values of bias and root mean square error (RMSE) compared with AMSR2 LST at day (night) were, respectively, -0.08 K (-0.22 K) and 1.89 K (2.23 K); 2) the accuracy of DNN-LST was the best in autumn (mean RMSE values of 1.43 K at day and 1.89 K at night) and the worst in winter (mean RMSE values of 2.35 K at day and 2.36 K at night), no matter during daytime or nighttime, in different seasons in 2015-2017; 3) the RMSE value of DNN-LST during nighttime was slightly higher than the RMSE value of DNN-LST during daytime; and 4) the accuracy of DNN-LST was equivalent to AMSR2 LST, that is, the unbiased RMSE (ubRMSE) of DNNLST and AMSR2 LST was all about 4 K compared with in situ LST, but the ubRMSE of DNN-LST was slightly lower than AMSR2 LST. The above accuracy validation analysis shows that DNN-LST has good robustness and good spatial consistency with AMSR2 LST and can be well used to fill the orbital gap of AMSR2 LST to generate spatial seamless GF-LST product.
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