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遥感技术与应用  2022, Vol. 37 Issue (1): 137-147    DOI: 10.11873/j.issn.1004-0323.2022.1.0137
1.辽宁工程技术大学 测绘与地理科学学院,辽宁 阜新 123000
2.中国科学院地理科学与资源研究所,陆地水循环及地表过程院重点实验室,北京 100101
Remote Sensing Estimation of Terrestrial Evapotranspiration and Analysis of Its Temporal-spatial Distribution Characteristics over the Three-River Headwater Region
Tianwei Zhao1,2(),Wenbin Zhu2(),Liang Pei1,Kangni Bao1,2
1.School of Geomatics,Liaoning Technical University,Fuxin 123000,China
2.Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
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蒸散发是地表水热平衡的基本变量,也是衡量植被生长水分适应性的重要指标。针对三江源地面实测资料匮乏的现状,以MODIS系列产品为主要数据源,通过对地表温度—植被指数特征空间法的改进,在日尺度实现了该地区2011~2019年蒸散发的连续遥感估算,并进一步解析其时空变化特征与影响因子,揭示不同土地覆被类型的蒸散发差异,以期为三江源畜牧业可持续发展与生态环境保护提供支撑。对比分析表明:蒸散发的估算结果达到了现有遥感蒸散发产品的精度要求,可用于分析三江源地区蒸散发的时空变化特征。近9年,三江源蒸散发总体呈现先减少后增加趋势,多年平均值为420.04 mm;受海拔与降水控制,蒸散发空间分布异质性明显,从东南向西北逐渐减少;3 194~4 620 m海拔范围内,蒸散发随海拔高度增加呈单峰型变化,站点尺度年蒸散发与降水量之间的相关系数为0.71。虽然不同土地覆被分类系统下蒸散发的统计结果存在差异,但单位面积蒸散发具有林地>灌丛/灌木林>草地/草甸>裸土地/无植被区的明显特征,像元尺度多年平均蒸散发与植被覆盖度的相关系数高达0.77。

关键词: 蒸散发时空分布遥感估算土地覆被三江源    

Terrestrial Evapotranspiration (ET), defined as the sum of water lost to atmosphere from soil through evaporation and plant transpiration, is a primary process driving the energy and water exchange among the atmosphere, hydrosphere and biosphere. Facing a significant warm-wet change in the Three-River Headwater Region (TRHR), accurate ET information is of great importance for a wide range of applications including water resources management, hydrometeorological predictions and ecological protection. However, due to the complex topography and sparse distribution of ground-based meteorological observations, the accurate estimation of ET over the TRHR is always not easy. The traditional surface temperature-vegetation index triangular/trapezoidal characteristic space was transformed from regional to pixel scale based on land surface energy balance principle, so daily ET over the TRHR from 2011 to 2019 could be retrieved continuously from a series of MODIS (Moderate-resolution Imaging Spectroradiometer) products. Then we analyzed the temporal-spatial distribution characteristics of ET and its influencing factors over the study region with special focus on ET difference over a variety of land cover types. Comparison between our estimation with other remote sensing-based ET products shows that the accuracy of our algorithms has reached a comparable level, which lays a good basis for further analysis. Results show that ET in recent nine years over the whole TRHR decreased first and then increased with annual average value of 420.04 mm. Controlled by altitude and precipitation, the distribution of ET varied significantly in space with the high values in the southeast and low values in the northwest. ET with the elevation between 3 194 m and 4 620 m increased first and then deceased with altitude. The Pearson correlation coefficient (r) between annual precipitation and ET at site scale was 0.71. The ET statistics of natural ecosystems varied with different land use/cover maps, but all statistics show clearly that ET per unit area followed the order: forest land > shrubland > grassland > bare land. The r between vegetation coverage and annual ET was as high as 0.77 at pixel scale.

Key words: Evapotranspiration    Temporal-spatial distribution    Remote sensing estimation    Land cover    Three-River Headwater Region
收稿日期: 2021-10-27 出版日期: 2022-04-08
ZTFLH:  P426.2  
基金资助: 国家自然科学基金面上项目“大尺度土壤含水量与陆面蒸散发耦合优化模拟研究”(42077032);青海三江源生态保护和建设二期工程科研和推广项目(2018?S?3);中国科学院青年创新促进会资助项目(2020056)
通讯作者: 朱文彬     E-mail:;
作者简介: 赵天玮(1997-),女,辽宁营口人,硕士研究生,主要从事遥感影像信息识别与提取研究。E?
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赵天玮,朱文彬,裴亮,宝康妮. 三江源蒸散发遥感估算及其时空分布特征研究[J]. 遥感技术与应用, 2022, 37(1): 137-147.

Tianwei Zhao,Wenbin Zhu,Liang Pei,Kangni Bao. Remote Sensing Estimation of Terrestrial Evapotranspiration and Analysis of Its Temporal-spatial Distribution Characteristics over the Three-River Headwater Region. Remote Sensing Technology and Application, 2022, 37(1): 137-147.


图1  三江源地理位置与海拔分布图审图号:GS(2016)2556号
MOD031 km太阳天顶角
MOD06_L21 km



MOD07_L25 km空气温度与露点温度
MOD11A11 km地表温度与地表发射率
MOD13A21 km归一化植被指数
MCD12Q1500 m土地覆被类型
MCD43B31 km白空与黑空反照率
表1  研究中涉及的MODIS产品
图2  本研究蒸散发估算结果与GLEAM、甘海洪(ET_甘)估算结果的对比分析图[48]
图3  三江源地区蒸散发时间变化趋势图
图4  三江源地区多年平均蒸散发空间分布图 审图号:GS(2016)2556
图5  海拔高度—蒸散发—降水量关系图
图6  植被覆盖度与陆面蒸散发之间的关系图
图7  三江源不同土地覆被类型单位面积年均蒸散发箱形图
1 Liu Xiaoqiong, Wu Zezhou, Liu Yansui, et al. Spatial-temporal characteristics of precipitation from 1960 to 2015 in the Three Rivers' Headstream Region, Qinghai, China[J]. Acta Geographica Sinica,2019,74(9):1803-1820.
1 刘晓琼, 吴泽洲, 刘彦随, 等. 1960~2015年青海三江源地区降水时空特征[J]. 地理学报,2019,74(9):1803-1820.
2 Meng Xianhong, Chen Hao, Li Zhaoguo, et al. Review of climate change and its environmental influence on the Three-River Regions[J]. Plateau Meteorology,2020,39(6):1133-1143.
2 孟宪红, 陈昊, 李照国, 等. 三江源区气候变化及其环境影响研究综述[J]. 高原气象,2020,39(6):1133-1143.
3 Jin Yanli, Xu Maolin, Gao Shuai, et al. Analysis on the dynamic changes and driving forces of surface water in the Three-River Headwater Region from 2001 to 2018[J]. Remote Sensing Tech-nology and Application,2021,36(5):1147-1154.
3 金岩丽, 徐茂林, 高帅, 等. 2001~2018年三江源地表水动态变化及驱动力分析[J]. 遥感技术与应用, 2021,36(5):1147-1154.
4 Zheng Zimei, Meixia Lü, Ma Zhuguo. Climate, hydrology, and vegetation coverage changes in source region of YellowRiver and countermeasures for challenges[J]. Bulletin of Chinese Academy of Sciences, 2020,35(1):61-72.
4 郑子彦, 吕美霞, 马柱国. 黄河源区气候水文和植被覆盖变化及面临问题的对策建议[J]. 中国科学院院刊, 2020, 35(1):61-72.
5 Bai Xiaolan, Wei Jiahua, Xie Hongwei. Characteristics of wetness/ dryness variation and their influences in the Three-River Headwaters Region[J]. Acta Ecologica Sinica,2017,37(24):8397-8410.
5 白晓兰, 魏加华, 解宏伟. 三江源区干湿变化特征及其影响[J]. 生态学报, 2017, 37(24):8397-8410.
6 Jung M, Reichstein M, Ciais P, et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply[J]. Nature, 2010, 467(7318):951-954.
7 Xiong Y J, Zhao S H, Tian F. An evapotranspiration product for arid regions based on the Three-Temperature model and thermal remote sensing[J]. Journal of Hydrology,2015,530:392-404.
8 Adam J P, Joshua B F, Michael L G, et al. SMAP soil moisture improves global evapotranspiration[J]. Remote Sensing of Environment,2018,219:1-14. DOI:10.1016/j.rse. 2018. 09.023 .
doi: 10.1016/j.rse. 2018. 09.023
9 Deng Xingyao, Liu Yang, Liu Zhihui, et al. Temporal-spatial dynamic change characteristics of evapotranspiration in arid region of Northwest China[J]. Acta Ecologica Sinica, 2017, 37(9):2994-3008.
9 邓兴耀, 刘洋, 刘志辉, 等. 中国西北干旱区蒸散发时空动态特征[J]. 生态学报,2017,37(9):2994-3008.
10 Zhao Wei, Huang Pan, Li Ainong. A review of evapotranspiration estimation using remotely sensed data in mountainous region[J]. Journal of Mountain Science,2017,35(6):908-918.
10 赵伟, 黄盼, 李爱农. 山地地表蒸散发遥感估算研究现状[J]. 山地学报,2017,35(6):908-918.
11 Li Xiaoyuan, Yu Deyong. Progress on evapotranspiration estimation methods and driving forces in arid and semiarid regions[J]. Arid Zone Research,2020,37(1):26-36.
11 李晓媛, 于德永. 蒸散发估算方法及其驱动力研究进展[J]. 干旱区研究,2020,37(1):26-36.
12 Mu Q, Zhao M, Running S W. MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) Collection 5[DB]. NASA Headquarters. 2013.
13 Hu Chen, Ge Jiwen, Xu Xingnan, et al. Estimation of evapotrans-piration and crop coefficient in Dajiuhupeatland of Shennongjia based on FA056 Penman-JMonteith[J]. Chinese Journal of Applied Ecology,2020,31(5):1699-1706.
13 胡晨,葛继稳,许向南,等. 基于FAO56 Penman-Moneith公式估算神农架大九湖泥炭湿地蒸散农作物系数[J]. 应用生态学报,2020,31(5):1699-1706.
14 Adam J P, Joshua B F, Michael L G,et al. SMAP soil moisture improves global evapotranspiration[J]. Remote Sensing of Environment,2018,219:1-14.
15 Li Xiaolaing, Yang Lixiao, Xu Xuefeng, et al. Analysis of evapotranspiration pattern by SEBAL model during the growing season in the agro-pastoral ecotone in Northwest China[J]. Acta Ecologica Sinica,2020,40(7):2175-2185.
15 李旭亮, 杨礼箫, 胥学峰, 等. 基于SEBAL模型的西北农牧交错带生长季蒸散发估算及变化特征分析[J]. 生态学报,2020, 40(7):2175-2185.
16 Bao Yongzhi, Liu Yanxi, Duan Limin, et al. Simulation of evapo-transpiration for the mobile and semimobile dunes in the Horqin Sandy Land using the Shuttleworth-Wallace model[J]. Chinese Journal of Applied Ecology, 2019, 30(3):867-876.
16 包永志, 刘廷玺, 段利民,等. 基于Shuttleworth-Wallace模型的科尔沁沙地流动半流动沙丘蒸散发模拟[J]. 应用生态学报, 2019, 30(3):867-876.
17 Sanjay S N C, Nandagiri L. A Penman-Monteith evapotranspiration model with bulk surface conductance derived from remotely sensed spatial contextual information[J]. International Journal of Remote Sensing, 2019,41(4):1-26.
18 Zhang Baozhong, Xu Di, Liu Yu, et al. Review of multi-scale evapo-transpiration estimation and spatio-temporal scale expansion[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(6):8-16.
18 张宝忠, 许迪, 刘钰, 等. 多尺度蒸散发估测与时空尺度拓展方法研究进展[J]. 农业工程学报, 2015, 31(6):8-16.
19 Tang Ronglin, Wang Shengli, Jiang Yazhen, et al. A review of retrieval of land surface evapotranspiration based on remotely sensed surface temperature versus vegetation index triangular/trapezoidal characteristic space[J]. National Remote Sensing Bulletin, 2021, 25(1):65-82.
19 唐荣林, 王晟力, 姜亚珍,等. 基于地表温度——植被指数三角/梯形特征空间的地表蒸散发遥感反演综述[J]. 遥感学报, 2021, 25(1):65-82.
20 Wei Jing, Guo Yamin, Sun Lin, et al. Evaluation of ecological environment vulnerability for Sanjiangyuan area[J]. Chinese Journal of Ecology, 2015,34(7):1968-1975.
20 韦晶, 郭亚敏, 孙林, 等. 三江源地区生态环境脆弱性评价[J]生态学杂志,2015,34(7):1968-1975.
21 Zhang Jiping, Liu Chunlan, Hao Haiguang,et al. Spatial-temporal change of carbon storage and carbon sink of grassland eco-system in the Three-River Headwaters Region based on MODIS GPP/NPP Data[J]. Ecology and Environmental Sciences,2015,24(1):8-13.
21 张继平,刘春兰,郝海广,等.基于MODIS GPP/NPP数据的三江源地区草地生态系统碳储量及碳汇量时空变化研究[J]生态环境学报,2015,24(1):8-13.
22 Liu Chuang, Ge Chenghui. Features and applications of remote sensing data from the Moderate Resolution Imaging Spectro-radiometer (MODIS) in the American Earth Observation System (EOS)[J]. Remote Sensing Information, 2000, 15(3):45-48.
22 刘闯, 葛成辉. 美国对地观测系统(EOS)中分辨率成像光谱仪(MODIS)遥感数据的特点与应用[J]. 遥感信息,2000,15(3):45-48.
23 Yang K, He J. China meteorological forcing dataset (1979-2018). National Tibetan Plateau Data Center[DB/OL]. 2019. DOI: 10.11888/ AtmosphericPhysics.tpe.249369 .
doi: 10.11888/ AtmosphericPhysics.tpe.249369
24 Martens B, Miralles D G, Lievens H, et al. GLEAM v3:Satellite-based land evaporation and root-zone soil moisture[J]. Geoscientific Model Development, 2017, 10(5):1903–1925.
25 Miralles D G, Holmes T, De J, et al Global land-surface evaporation estimated from satellite-based observations[J]. Hydrology and Earth System Sciences Discussions, 2010, 7(5):453-469.
26 Yang Xiuqin, Wang Guojie, Pan Xin, et al. Spatio-temporal vari-ability of terrestrial evapotranspiration in China from 1980 to 2011 based on GLEAM data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(21):132-141.
26 杨秀芹, 王国杰, 潘欣, 等. 基于GLEAM遥感模型的中国1980~2011年地表蒸散发时空变化[J]. 农业工程学报, 2015, 31(21):132-141.
27 Li Jia, Xin Xiaozhou, Peng Zhiqing,et al.Remote sensing pro-ducts of terrestrial evapotranspiration: comparison and outlook[J]. Remote Sensing Technology and Application,2021,36(1):103-120.
27 李佳,辛晓洲,彭志晴,等. 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.
28 Bisht G, Venturini V, Islam S, et al. Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days[J]. Remote Sensing of Environment,2005,97(1):52-67. DOI:10.1016/j.rse. 2005. 03.014 .
doi: 10.1016/j.rse. 2005. 03.014
29 Bisht G, Bras R L. Estimation of net radiation from the MODIS data under all sky conditions:Southern Great Plains case study[J]. Remote Sensing of Environment, 2010, 114(7):1522-1534. DOI: 10.1016/j.rse.2010.02.007 .
doi: 10.1016/j.rse.2010.02.007
30 Yu Xiaoyu, Jia Shaofeng, Zhu Wenbin. Estimation of land surface net radiation flux based on remote sensing and analysis of its spatial-temporal characteristics in Qinghai Province[J]. Plateau Meteorology, 2022,41(8):1-13).[余晓雨, 贾绍凤, 朱文彬. 青海省地表净辐射通量的遥感估算方法及时空特征分析[J]. 高原气象, 2022,41(8):1-13.]
31 Zhu W, Jia S, Lü A. A universal Ts-VI triangle method for the continuous retrieval of evaporative fraction from MODIS products[J]. Journal of Geophysical Research:Atmospheres, 2017, 122(19):10206-10227.
32 Sun Liang, Sun Rui, Li Xiaowen, et al. Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information[J]. Agricultural and Forest Meteorology, 2012, 166-167,175-187.
33 Long D, Singh V. A Two-source Trapezoid Model for Evapotrans-piration (TTME) from satellite imagery[J]. Remote Sensing of Environment, 2012, 121:370-388. DOI: 10.1016/j.rse.2012.02.015 .
doi: 10.1016/j.rse.2012.02.015
34 Zhang R, Tian J, Su H, et al. Two improvements of an operational Two-Layer Model for terrestrial surface heat flux Retrieval[J]. Sensors, 2008, 8(10):6165-6187. DOI: 10.3390/s8106165 .
doi: 10.3390/s8106165
35 Kustas W P. Estimation of the soil heat flux/net radiation ratio from spectral data[J]. Agricultural and Forest Meteorology, 1990, 49(3): 205-223.
36 Brutsaert W. Evaporation into the atmosphere, theory, history, and applications[M]. The Netherlands:D Reidel, Dordrecht, 1982.
37 Szilagyi J, Crago R, Qualls R. A calibration‐free formulation of the complementary relationship of evaporation for continental‐scale hydrology[J]. Journal of Geophysical Research:Atmospheres, 2017, 122(1):264-278.
38 Zare M, Drastig K, Zude-Sasse M. Tree water status in apple orchards measured by means of Land Surface Temperature and Vegetation Index (LST-NDVI) trapezoidal space derived from landsat 8 satellite images[J]. Sustainability,2019,2(1):70. DOI:10.3390/su12010070 .
doi: 10.3390/su12010070
39 Hu X, Shi L, Lin L, et al. Nonlinear boundaries of Land Surface Temperature-Vegetation Index space to estimate water deficit index and evaporation fraction[J]. Agricultural and Forest Meteorology,2019,279:107736. DOI:10.1016/j.agrformet. 2019.107736 .
doi: 10.1016/j.agrformet. 2019.107736
40 Szilagyi J. Temperature corrections in the Priestley–Taylor equation of evaporation[J]. Journal of Hydrology, 2014, 519:455-464.
41 Zhu W, Lv A, Jia S, et al. Retrievals of all-weather daytime air temperature from MODIS products[J]. Remote Sensing of Environment,2017,189:152-163. DOI:10.1016/j.rse. 2016. 11.011 .
doi: 10.1016/j.rse. 2016. 11.011
42 Zhu W, Jia S, Lall U, et al. An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas[J]. Remote Sensing of Environment,2020,247:111887. DOI:10.1016/j.rse.2020.111887 .
doi: 10.1016/j.rse.2020.111887
43 Gillies R R, Kustas W P, Humes K S. A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface[J]. International Journal of Remote Sensing,1997,18(15):3145-3166. DOI: 10.1080/014311697217026 .
doi: 10.1080/014311697217026
44 Zhu W, Lü A, Jia S. Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products[J] Remote Sensing of Environment, 2013, 130:62-73. DOI: 10.1016/j.rse.2012.10.034 .
doi: 10.1016/j.rse.2012.10.034
45 Cui Y, Ma S, Yao Z, et al. Developing a gap-filling algorithm using DNN for the Ts-VI triangle model to obtain temporally continuous daily actual evapotranspiration in an arid area of China[J]. Remote Sensing,2020,12(7):1121. DOI: 10.3390/rs12071121 .
doi: 10.3390/rs12071121
46 Tang R, Li Z. An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instan-taneous observations[J]. Geophysical Research Letters, 2017, 44(5):2319-2326.
47 Rivas R E, Carmona F. Evapotranspiration in the Pampean Region using field measurements and satellite data[J]. Physics and Chemistry of the Earth, 2013, 55-57:27-34.
48 Gan Haihong. Temporal and spatial distribution of evapotrans-piration in the Three-Rivers Headwaters Region[D]. Beijing:China University of Geosciences (Beijing), 2020.
48 甘海洪. 三江源区区域蒸散发的分布特征[D]. 北京:中国地质大学(北京), 2020.
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