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Open AccessArticle

Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions

by Tao Du 1,2, Li Wang 3, Guofu Yuan 1,2,*, Xiaomin Sun 1,2 and Shusen Wang 4
1
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
3
Beijing Key Laboratory of Water Resources & Environmental Engineering, China University of Geoscicences (Beijing), Beijing 100083, China
4
Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2856; https://doi.org/10.3390/rs11232856
Received: 21 October 2019 / Revised: 24 November 2019 / Accepted: 28 November 2019 / Published: 1 December 2019
Accurate estimates of evapotranspiration (ET) in arid ecosystems are important for sustainable water resource management due to competing water demands between human and ecological environments. Several empirical remotely sensed ET models have been constructed and their potential for regional scale ET estimation in arid ecosystems has been demonstrated. Generally, these models were built using combined measured ET and corresponding remotely sensed and meteorological data from diverse sites. However, there are usually different vegetation types or mixed vegetation types in these sites, and little information is available on the estimation uncertainty of these models induced by combining different vegetation types from diverse sites. In this study, we employed the most popular one of these models and recalibrated it using datasets from two typical vegetation types (shrub Tamarix ramosissima and arbor Populus euphratica) in arid ecosystems of northwestern China. The recalibration was performed in the following two ways: using combined datasets from the two vegetation types, and using a single dataset from specific vegetation type. By comparing the performance of the two methods in ET estimation for Tamarix ramosissima and Populus euphratica, we investigated and compared the accuracy of ET estimation at the site scale and the difference in annual ET estimation at the regional scale. The results showed that the estimation accuracy of daily, monthly, and yearly ET was improved by distinguishing the vegetation types. The method based on the combined vegetation types had a great influence on the estimation accuracy of annual ET, which overestimated annual ET about 9.19% for Tamarix ramosissima and underestimated annual ET about 11.50% for Populus euphratica. Furthermore, substantial difference in annual ET estimation at regional scale was found between the two methods. The higher the vegetation coverage, the greater the difference in annual ET. Our results provide valuable information on evaluating the estimation accuracy of regional scale ET using empirical remotely sensed ET models for arid ecosystems. View Full-Text
Keywords: evapotranspiration; remote sensing; arid ecosystems; Landsat; NDVI; Tamarix ramosissima; Populus euphratica evapotranspiration; remote sensing; arid ecosystems; Landsat; NDVI; Tamarix ramosissima; Populus euphratica
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MDPI and ACS Style

Du, T.; Wang, L.; Yuan, G.; Sun, X.; Wang, S. Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions. Remote Sens. 2019, 11, 2856.

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