Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

• Published in: Forests Vol. 12; no. 12; p. 1632
• Main Authors: Li, Yufu, Sui, Xinxin, Yao, Yunjun, Cheng, Haixia, Zhang, Lilin, Wang, Lu, Ning, Jing, Shang, Ke, Yang, Junming, Yu, Ruiyang, Liu, Lu, Guo, Xiaozheng, Xie, Zijing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Datasets; eddy covariance; Evaporation; Evapotranspiration; Fluctuations; Haihe River basin; Heat flux; Heat transfer; Latent heat; Machine learning; Methods; Remote sensing; River basins; Rivers; Root-mean-square errors; satellite; Satellites; Spectroradiometers; Terrestrial environments; terrestrial latent heat flux; Vegetation; Water balance; water balance method; Water budget; Water resources; Heihe River; Haihe River Basin; China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f12121632

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p < 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.