Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 5; p. 730
• Main Authors: Du, Changmin, Jiang, Shouzheng, Chen, Chuqiang, Guo, Qianyue, He, Qingyan, Zhan, Cun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2024
• Subjects: Accuracy; Agricultural land; Agricultural production; Air temperature; Algorithms; Atmospheric models; Back propagation networks; Climate; Climate prediction; climate zone; Climatic conditions; cropland evapotranspiration; eddy flux; Efficiency; Evapotranspiration; Fluctuations; Flux density; Heat; Heat flux; Hydrologic cycle; Hydrology; input factor combinations; Irrigation water; Leaf area; Leaf area index; Learning algorithms; Machine learning; Net radiation; Neural networks; Physiological aspects; Radiation; Remote sensing; Resource management; Simulation; Simulation methods; Soil temperature; Spectroradiometers; Support vector machines; Vapor pressure; Variables; Water resources management; Wind speed; China; United States > US
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16050730

The accurate prediction of cropland evapotranspiration (ET) is of utmost importance for effective irrigation and optimal water resource management. To evaluate the feasibility and accuracy of ET estimation in various climatic conditions using machine learning models, three-, six-, and nine-factor combinations (V3, V6, and V9) were examined based on the data obtained from global cropland eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Four machine learning models, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and backpropagation neural network (BP), were used for this purpose. The input factors included daily mean air temperature (Ta), net radiation (Rn), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pressure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four machine learning models exhibited significant simulation accuracy across various climate zones, reflected by their global performance indicator (GPI) values ranging from −3.504 to 0.670 for RF, −3.522 to 1.616 for SVM, −3.704 to 0.972 for XGB, and −3.654 to 1.831 for BP. The choice of suitable models and the different input factors varied across different climatic regions. Specifically, in the temperate–continental zone (TCCZ), subtropical–Mediterranean zone (SMCZ), and temperate zone (TCZ), the models of BPC-V9, SVMS-V6, and SVMT-V6 demonstrated the highest simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d−1, average MAE values of 0.177, 0.263, and 0.248 mm d−1, average R2 values of 0.949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respectively. In climate zones with a lower average LAI (TCCZ), there was a strong correlation between LAI and ET, making LAI more crucial for ET predictions. Conversely, in climate zones with a higher average LAI (TCZ, SMCZ), the significance of the LAI for ET prediction was reduced. This study recognizes the impact of climate zones on ET simulations and highlights the necessity for region-specific considerations when selecting machine learning models and input factor combinations.