文献类型： 外文期刊

作者： Guo, Xiaozheng ¹ ; Yao, Yunjun ¹ ; Tang, Qingxin ² ; Liang, Shunlin ³ ; Shao, Changliang ⁴ ; Fisher, Joshua B. ⁵ ; Chen, Jiquan ⁶ ; Jia, Kun ¹ ; Zhang, Xiaotong ¹ ; Shang, Ke ⁷ ; Yang, Junming ¹ ; Yu, Ruiyang ¹ ; Xie, Zijing ¹ ; Liu, Lu ¹ ; Ning, Jing ¹ ; Zhang, Lilin ⁸ ;

作者机构： 1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

2.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Peoples R China

3.Univ Hong Kong, Dept Geog Sci, Hongkong 999077, Peoples R China

4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Natl Hulunber Grassland Ecosyst Observat & Res Stn, Beijing 100081, Peoples R China

5.Chapman Univ, Schmid Coll Sci & Technol, Univ Dr, Orange, CA 92866 USA

6.Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48823 USA

7.Space Engn Univ, Sch Space Informat, Beijing 101416, Peoples R China

8.Univ Twente, Fac Geoinformat & Earth Observat ITC, NL-7500 AE Enschede, Netherlands

关键词： Latent heat flux; Integration algorithm; CNN-LSTM; Landsat; High -spatial -resolution products

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：6.2； 五年影响因子：6.9 ）

ISSN： 0168-1923

年卷期： 2024 年 349 卷

页码：

收录情况： SCI

摘要： Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsatderived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5-8 W/m2 and increasing Kling-Gupta efficiency (KGE) of 0.05-0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R2 (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables.