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遥感技术与应用  2022, Vol. 37 Issue (4): 839-853    DOI: 10.11873/j.issn.1004-0323.2022.4.0839
蒸散发遥感专栏     
基于遥感的地表蒸散发研究进展
孟莹(),姜鹏,董巍
中国气象局气象干部培训学院辽宁分院,辽宁 沈阳 110166
Progress in the Evapotranspiration Estimation Using Remotely Sensed Data
Ying Meng(),Peng Jiang,Wei Dong
Liaoning Branch of China Meteorological Administration Training Centre,Shenyang 110166,China
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摘要:

地表蒸散发是整个生物圈、大气圈和水圈中水分循环和能量传输的重要控制因素。遥感技术的应用使得区域尺度的蒸散发估算成为可能,并在过去的几十年中快速发展。研究对遥感蒸散发估算进行了总结与归纳,在此基础上展望了今后的发展方向,明确指出了遥感蒸散发未来研究的突破点及发展方向。提出未来应加强蒸散发尺度效应、夜间蒸散发、不同蒸散发产品的统一真实性检验、国产卫星数据的使用、更高时空分辨率产品的研发以及机器学习在遥感蒸散发产品中的应用。

关键词: 地表蒸散发遥感模型    
Abstract:

The surface Evapotranspiration (ET) is an important controlling factor to water cycle and energy transmission in the biosphere, atmosphere and hydrosphere. Satellite provides an unprecedented spatial distribution of ET in the past decades. In this paper,the estimation methods of evapotranspiration using remotely sensed data were summarized,and the existing issues that should be further studied were discussed. In the future research,we should strengthen the improvement of the evapotranspiration regarding scale effect, nighttime ET, the general validation method of different ET products, remotely sensed data in China, the ET products with higher spatial-temporal resolution, and the new ET model using the machine learning methods.

Key words: Evapotranspiration    Remote sensing    Model
收稿日期: 2021-04-19 出版日期: 2022-09-28
:  P426.2  
作者简介: 孟 莹(1973-),女,辽宁营口人,高级工程师,主要从事气候变化与气象教育培训。E?mail: mylnqx@163.com
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引用本文:

孟莹,姜鹏,董巍. 基于遥感的地表蒸散发研究进展[J]. 遥感技术与应用, 2022, 37(4): 839-853.

Ying Meng,Peng Jiang,Wei Dong. Progress in the Evapotranspiration Estimation Using Remotely Sensed Data. Remote Sensing Technology and Application, 2022, 37(4): 839-853.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.4.0839        http://www.rsta.ac.cn/CN/Y2022/V37/I4/839

产品名称理论基础时空分辨率覆盖范围时间范围下载地址参考文献
BESSPM公式

0.01?/0.5?

8 d

全球2000-2015https://www.environment.snu.ac.kr[93]
GLASS集成模型

5 km/1 km

8 d

全球1981-2019http://www.geodata.cn/[20]
GLEAMPT公式

0.25?

日/月/年

全球1980-2018https://www.gleam.eu/[56]
MOD16PM公式

500 m/1 km

8 d

全球2000-2020https://modis.gsfc.nasa.gov/data/[53]
MTE机器学习

0.08?/0.5?

全球1982-2016——[94]
FLUXCOM机器学习

0.08?/0.5?

8 d/d

全球

2002-2015

1980-2019

http://www.fluxcom.org/[7]
PLSHPM公式

0.08?/1?

全球1982-2013——[55]
PML_V2PM公式

500 m

8 d

全球2002-2020

https://data.tpdc.ac.cn/

zh-hans/data/

[95]
SSEBopSSEB

1 km

8 d/月

全球2003-2018

https://earlywarning.usgs.

gov/ssebop/modis

[96]
ETWatch

SEBAL/SEBS

PM公式

30 m/1 km

月/年

中国黑河流域2000-2012https://data.tpdc.ac.cn/[97]
ETMonitor

Shuttleworth-

Wallace公式

250 m/1 km

日/月/年

中国黑河流域2009-2012https://data.tpdc.ac.cn/[44]
PT-JPLPT公式

0.5?/1?

全球1984-2006http://josh.yosh.org/[3]
L3_ET_PT-JPLPT公式

70 m

1-5 d

全球2018-2021https://search.earthdata.nasa.gov[49]
EB-ETSEBS0.1?/0.05?日/月中国/全球2000-2017https://data.tpdc.ac.cn/[98]
CR-ET互补原理

0.1?

中国1982-2015https://data.tpdc.ac.cn/[13]
LSA-SAFSVAT

3 km

h/d

欧洲/非洲/南美洲2010-2021https://landsaf.ipma.pt/[99]
HiTLLSEBS

100 m

黑河流域2010-2016https://data.tpdc.ac.cn/[100]
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