文献类型： 外文期刊

作者： Sun, Di ¹ ; Zhang, Hang ² ; Qi, Yanbing ² ; Ren, Yanmin ⁴ ; Zhang, Zhengxian ⁵ ; Li, Xuemin ² ; Lv, Yuping ⁶ ; Cheng, Minghan ⁷ ;

作者机构： 1.Beijing Water Author, Beijing 101117, Peoples R China

2.Beijing Water Sci & Technol Inst, Beijing 100048, Peoples R China

3.Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

5.Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Jiangsu Prov Key Lab Soil Eros & Ecol Restorat, Nanjing 210037, Peoples R China

6.Yangzhou Univ, Coll Hydraul Sci & Engn, Yangzhou 225009, Peoples R China

7.Yangzhou Univ, Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr, Yangzhou 225009, Peoples R China

8.Yangzhou Univ, Agr Coll, Jiangsu Key Lab Crop Genet & Physiol, Jiangsu Key Lab Crop Cultivat & Physiol, Yangzhou 225009, Peoples R China

关键词： evapotranspiration; LST-VI space; long-time-series; machine learning; spatial distribution

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 4 期

页码：

收录情况： SCI

摘要： Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature-vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = -0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = -0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET.