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遥感技术与应用  2021, Vol. 36 Issue (1): 103-120    DOI: 10.11873/j.issn.1004-0323.2021.1.0103
蒸散发遥感专栏     
地表蒸散发遥感产品比较与分析
李佳1,2(),辛晓洲1(),彭志晴1,2,李小军1,2
1.中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
2.中国科学院大学,北京 100049
Remote Sensing Products of Terrestrial Evapotranspiration: Comparison and Outlook
Jia Li1,2(),Xiaozhou Xin1(),Zhiqing Peng1,2,Xiaojun Li1,2
1.State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

蒸散发遥感估算与其他定量遥感反演参数相比存在算法复杂,环节多,输入参数多,不确定性来源多等不利因素,因而造成蒸散发遥感产品成熟度较低,产品较少,不能满足高时空分辨率、高精度、实时获取等应用需求的问题。从蒸散发遥感产品角度出发,选择目前国内外较为常用和知名的蒸散发遥感产品,系统分析了各产品的实际蒸散发估算模型方法、阻抗的参数化方案、地表可用能量的估算方法、时间尺度扩展方法,以及计算流程和输入数据等,重点比较各产品在模型算法和计算流程上的不同设计思路和处理方法,以便更加深入地理解地表蒸散遥感估算的复杂性和不确定性,对未来研发出精度更高,普适性更广的蒸散发遥感产品提供重要的参考和基础框架。最后对这些产品的验证和应用情况进行了分析对比,总结了目前蒸散发遥感产品在产品生产、验证等方面存在的问题,探讨了未来蒸散发遥感产品的发展趋势。

关键词: 遥感产品地表蒸散发对比分析模型算法未来趋势    
Abstract:

Evapotranspiration(ET) is one of the most important term of land surface, which is an indicator of agriculture growth conditions and yield. Remote sensing technology has advantages in monitoring evapotranspiration, however, remote sensing evapotranspiration products develop slower than other remote sensing products. This paper compared seven widely used ET products and two ET systems, including MODIS-MOD16, SSEBop, ET_PT-JPL, ET_ALEXI, LSA-SAF, GLEAM, BESS, ETWatch and ETMonitor. We introduced the details of these ET products. In order to propose a framework of estimating high accuracy and widely used remote sensing ET products, and to better understand the complexity and uncertainty in ET estimation, we focused on comparing the design ideas and processing procedure of these ET products through summarizing algorithms of estimating actual ET, resistances parameterizations, available energy calculating methods, temporal upscaling methods, estimating procedures and input data sources, respectively. In addition, the current problems of ET calculating from remote sensing are proposed, including complex algorithms with multi parameters, inconsistent spatial and temporal resolutions, and difficult to be validated. Finally, we discussed possible directions of the future remote sensing ET products.

Key words: Evapotranspiration    Remote sensing    Comparison and analysis    Models and algorithms    Future development and tendency
收稿日期: 2019-02-18 出版日期: 2021-04-13
ZTFLH:  TP701  
基金资助: 国家自然科学基金项目(41871252);中国科学院战略性先导科技专项(A类)课题(XDA15012400);国家重点研发计划项目(2018YFA060550202)
通讯作者: 辛晓洲     E-mail: lijia@radi.ac.cn;xin_xzh@163.com
作者简介: 李 佳(1992—),女,河北石家庄人,硕士研究生,主要从事地表能量平衡遥感估算方面的研究。E?mail: lijia@radi.ac.cn
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引用本文:

李佳,辛晓洲,彭志晴,李小军. 地表蒸散发遥感产品比较与分析[J]. 遥感技术与应用, 2021, 36(1): 103-120.

Jia Li,Xiaozhou Xin,Zhiqing Peng,Xiaojun Li. Remote Sensing Products of Terrestrial Evapotranspiration: Comparison and Outlook. Remote Sensing Technology and Application, 2021, 36(1): 103-120.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.1.0103        http://www.rsta.ac.cn/CN/Y2021/V36/I1/103

研发机构产品名称理论基础时间分辨率空间分辨率覆盖范围产品精度参考文献
美国国家航天局NASAMODIS-MOD16P-M8 d、1 m、1 a500 m、1 km全球植被覆盖地区日均蒸散平均误差为0.31 mm/d(由GMAO驱动数据得到的结果)和0.33 mm/d(站点气象数据的驱动结果)[36-37]
美国国家航天局NASASSEBopSSEB8 d、1 m、3 m、 1 a1 km全球陆地、非洲、亚洲、美洲与FLUXNET的16站点的EC数据对比,R2为0.85[38]
美国喷气实验室JPLET_PT-JPLP-T取决于ISS的过境时间70 m全球陆地-[33]
美国喷气实验室JPLET_ALEXIALEXI1 d30 m农业监测站-[39]
欧洲气象卫星组织EUMETSATLSA-SAFSVAT1 h、1 d3 km欧洲、非洲、南美洲在欧洲与地面站点观测结果一致性良好,在欧洲、南美及非洲与ECMWF及GLDAS的结果一致性良好,相关系数为0.612~0.850,偏差为-22.94~9.11,Nash指数为0.44~0.67[40]
荷兰阿姆斯特丹大学、根特大学GLEAMP-T1 d0.25°全球陆地与43个FLUXNET站点数据对比,年尺度R2=0.83,月尺度0.90[34,41]
韩国首尔大学BESSP-M

8 d平均、

1 m

1 km、

0.5°

全球陆地与FLUXNET的观测数据对比,在不同植被类型的站点R2在0.32~0.83之间[35,42]
中国科学院遥感与数字地球研究所ETWatchSEBAL/SEBS、P-M

1 m、3 m、

1 a

30 m、

1 km

中国黑河流域与观测站实测蒸发比相关性较高;与GRACE估算结果相关系数为0.7,均方根误差为12.7 mm,ETWatch估算结果与降雨量相关性强,相关系数为0.64~0.79[43-44]
中国科学院遥感与数字地球研究所ETMonitorShuttleworth-Wallace

1 d,8 d平均,

1 m、1 a累积

250 m、1 km中国黑河流域与MOD16相比ETMonitor估算结果与地面EC观测的实际蒸散一致性较高,相关系数为0.87~0.96,优于MOD16的0.64~0.71;春季在盈科站,由于土壤湿度的低估而存在低估[45-46]
表1  蒸散发遥感产品概览
蒸散发产品算法参数化方案参考文献
MODIS-MOD16λE=λEwet_c+λEtrans+λEsoilλE为潜热通量,λEwet_cλEtransλEsoil分别表示湿冠层、干冠层及土壤的蒸散通量[36-37]
SSEBopETa=ETf×kET0

ETa为实际蒸散,ETf为蒸发比例,k为系数,ET0为草地的参考蒸散值

ETf=Th-TsTh-Tc=Th-TsdT=ρaCpRnrah(Th-Ts)

k=1.25

[35,38]
ET_PT-JPLAET=?ETs+ETc+ETi

ETc=(1-fwet)fgfTfMαΔΔ+γRnc 植被的潜热通量

ETs=fwet+fSM1-fwetαΔΔ+γ(Rn-G) 土壤的潜热通量

ETi=fwetαΔΔ+γRnc冠层截留的潜热通量

[33]
ET_ALEXIλE=λEs+λEcλEC=αPTfgss+γRn,c,?λEC为植被部分的潜热通量[39]
LSA-SAF

LEi=ζiLEi

H=?ζiHi

LEiHi分别为斑块的潜热通量和显热通量

LEi=Lvρarai+rciqsatTsk,i-qaTa

LEi=Lvρa(rai+rci)[qsatTsk,i-qa(Ta)]

[40]
GLEAME=?Ep×S+I-βI

Ep为潜在蒸散,S为土壤胁迫系数,I为降雨截留蒸散,β为截留系数取0.07

λEp=αΔΔ+ψ(Rn-G)

[34,41]
BESS

aλEj2+bλEj+c=0

λEsoil=ss+γRnsoil-Gsoil×RHD/1000

a、b、c为相应的系数,j分别表示叶片的光照和阴影部分

λEsoil表示土壤部分的潜热通量,s为饱和水汽压随气温变化的速率,γ为湿度常数,RnsoilGsoil分别表示土壤表面的净辐射和土壤热通量,RH为相对湿度,D为饱和水汽压差。

[35,42]
ETWatchLE=ΔRn-G+ρCpraes(Ta-ea)Δ+r(1+rsra)LE为潜热通量,ETWatch先由地表能量平衡模型获取晴好日蒸散发结果,将空气动力学阻抗输入至P-M模型获取连续日蒸散发[43-44]
ETMonitorET=?CpPMc+CsPMs

CcCs分别为冠层和土壤的表面阻抗系数,PMcPMs分别表示植被蒸腾和土壤水分蒸发的变量

PMc=ΔRn-G0+(ρCpVPD-ΔracRns-G0)/(raa+rac))(Δ+γ(1+rss/(raa+rac)))λ

PMs=ΔRn-G0+(ρCpVPD-ΔracRnc/(raa+ras))(Δ+γ(1+rss/(raa+ras)))λ

[45-46]
表2  各产品潜热通量及显热通量计算公式对比
产品名称数据类型数据源主要数据空间分辨率时间分辨率参考文献
MODIS-MOD16气象数据GMAOTEM、PRS、RHU等1°×1°、1°×1.25°6 h[36-37]
遥感数据MODISAlbedo、LAI、Landcover等1 km8 d合成、16 d合成
SSEBop遥感数据MODIS、SRTMAlbedo、LST、NDVI等1 km8 d合成[35,38]
模型模拟PRISMTEM、dT、ra等1 km1 d
同化数据GDASET01 km1 d
ET_PT-JPL遥感数据ECOSTRESS、MODISLST、Albedo、LAI、NDVI等70 m、1 km1 d[33]
再分析数据MERRA代替缺失值0.5~0.67°1 d
同化数据GMAO代替缺失值0.5~0.67°1 d
ET_ALEXI遥感数据ECOSTRESS、MODIS、LandsatLST、LAI、地表反射率70 m、30 m8 d[39]
再分析数据CFSR日曝辐量、TEM、PRS等0.25°1 d
地表覆盖类型 数据集NLCDLandcover30 m1 a
LSA-SAF气象数据ECMWFTEM、PRS、tem_soil等0.25°×0.25°3 h[40]
遥感数据MSG-SEVIRIDSSF、DSLF、Albedo3 km

30 min或

15 min

地表参数ECOCLIMAPLandcover、tile、LAI_tile等1 km1 m
GLEAM遥感数据GEWEX-SRB辐射产品、TEM、SM等1°、0.25°3 h、1 d[41]
再分析数据ERA-Interim辐射数据、气温0.75°3 h、1 d
融合数据TMPA 3B42v7降雨0.25°1 d
BESS遥感数据MODISAlbedo、LAI、LST1 km或5 km8 d、16 d[35,42]
再分析数据NCEPTEM、WDS、tem_dewpoint1 km6 h
ETWatch气象数据气象数据集TEM、PRS、WDS等1 h[43-44]
遥感数据MODIS或LandsatAlbedo、LAI、NDVI等1 km或30 m8 d合成、16 d合成
高程数据数字地形数据高程、坡度、坡向30 m
ETMonitor气象数据WRF模式TEM、PRS、WDS等5 km1 h[45-46]
遥感数据GLASS、MODIS、AMSR-EAlbedo、Preciptation、SM1 km8 d合成
地表覆盖类型数据多源合成数据集Landcover1 km1 a
表3  驱动数据对比
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