Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions
文献类型：期刊论文
| 作者 | Du, Tao3,4; Wang, Li1; Yuan, Guofu3,4; Sun, Xiaomin3,4; Wang, Shusen2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-12-01 |
| 卷号 | 11期号:23页码:18 |
| 关键词 | evapotranspiration remote sensing arid ecosystems Landsat NDVI Tamarix ramosissima Populus euphratica |
| DOI | 10.3390/rs11232856 |
| 英文摘要 | 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. |
| WOS关键词 | LOWER TARIM RIVER ; NDVI TIME-SERIES ; DESERT RIPARIAN FORESTS ; ENERGY-BALANCE CLOSURE ; LOWER COLORADO RIVER ; WATER-USE STRATEGIES ; GROUNDWATER EVAPOTRANSPIRATION ; EDDY-COVARIANCE ; LOWER REACHES ; POPULUS-EUPHRATICA |
| 资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences[XDA20060301] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000508382100137 |
| 出版者 | MDPI |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/131271]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.China Univ Geosci Beijing, Beijing Key Lab Water Resources & Environm Engn, Beijing 100083, Peoples R China 2.Nat Resources Canada, Canada Ctr Remote Sensing, Ottawa, ON K1A 0E4, Canada 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Du, Tao,Wang, Li,Yuan, Guofu,et al. Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions[J]. REMOTE SENSING,2019,11(23):18. |
| APA | Du, Tao,Wang, Li,Yuan, Guofu,Sun, Xiaomin,&Wang, Shusen.(2019).Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions.REMOTE SENSING,11(23),18. |
| MLA | Du, Tao,et al."Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions".REMOTE SENSING 11.23(2019):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。