Tracking Evapotranspiration Patterns on the Yinchuan Plain with Multispectral Remote Sensing

Junzhen Meng, Xiaoquan Yang, Zhiping Li (), Guizhang Zhao, Peipei He, Yabing Xuan and Yunfei Wang
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Junzhen Meng: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Xiaoquan Yang: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Zhiping Li: Henan Vocational College of Water Conservancy and Environment, The Education Department Henan Province, Zhengzhou 450046, China
Guizhang Zhao: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Peipei He: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yabing Xuan: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Yunfei Wang: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

Sustainability, 2024, vol. 16, issue 18, 1-20

Abstract: Evapotranspiration (ET) is a critical component of the hydrological cycle, and it has a decisive impact on the ecosystem balance in arid and semi-arid regions. The Yinchuan Plain, located in the Gobi of Northwest China, has a strong surface ET, which has a significant impact on the regional water resource cycle. However, there is a current lack of high-resolution evapotranspiration datasets and a substantial amount of time is required for long-time series remote sensing evapotranspiration estimation. In order to assess the ET pattern in this region, we obtained the actual ET (ET a) of the Yinchuan Plain between 1987 and 2020 using the Google Earth Engine (GEE) platform. Specifically, we used Landsat TM+/OLI remote sensing imagery and the GEE Surface Energy Balance Model (geeSEBAL) to analyze the spatial distribution pattern of ET over different seasons. We then reproduced the interannual variation in ET from 1987 to 2020, and statistically analyzed the distribution patterns and contributions of ET with regard to different land use types. The results show that (1) the daily ET a of the Yinchuan Plain is the highest in the central lake wetland area in spring, with a maximum value of 4.32 mm day −1 ; in summer, it is concentrated around the croplands and water bodies, with a maximum value of 6.90 mm day −1 ; in autumn and winter, it is mainly concentrated around the water bodies and impervious areas, with maximum values of 3.93 and 1.56 mm day −1 , respectively. (2) From 1987 to 2020, the ET of the Yinchuan Plain showed an obvious upward and downward trend in some areas with significant land use changes, but the overall ET of the region remained relatively stable without dramatic fluctuations. (3) The ET a values for different land use types in the Yinchuan Plain region are ranked as follows: water body > cultivated land > impervious > grassland > bare land. Our results showed that geeSEBAL is highly applicable in the Yinchuan Plain area. It allows for the accurate and detailed inversion of ET and has great potential for evaluating long-term ET in data-scarce areas due to its low meteorological sensitivity, which facilitates the study of the regional hydrological cycle and water governance.

Keywords: evapotranspiration; hydrological cycle; geeSEBAL; land use types; Landsat remote sensing data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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