基于涡动相关通量观测的农田蒸散发产品精度验证

刘萌; 彭中; 黄凌霄; 李召良; 段四波; 唐荣林

doi:10.11834/jrs.20222008

卫星定标应用验证 | 浏览量 : 0 下载量: 225 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

基于涡动相关通量观测的农田蒸散发产品精度验证
Validation of crop evapotranspiration products based on eddy-covariance flux observations
2023年27卷第5期页码：1238-1253
纸质出版日期： 2023-05-07 ，
DOI： 10.11834/jrs.20222008

扫描看全文

刘萌，彭中，黄凌霄，李召良，段四波，唐荣林.2023.基于涡动相关通量观测的农田蒸散发产品精度验证.遥感学报，27（5）： 1238-1253

Liu M，Peng Z，Huang L X，Li Z L，Duan S B and Tang R L. 2023. Validation of crop evapotranspiration products based on eddy-covariance flux observations. National Remote Sensing Bulletin， 27（5）：1238-1253
刘萌，彭中，黄凌霄，李召良，段四波，唐荣林.2023.基于涡动相关通量观测的农田蒸散发产品精度验证.遥感学报，27（5）： 1238-1253 DOI： 10.11834/jrs.20222008.

Liu M，Peng Z，Huang L X，Li Z L，Duan S B and Tang R L. 2023. Validation of crop evapotranspiration products based on eddy-covariance flux observations. National Remote Sensing Bulletin， 27（5）：1238-1253 DOI： 10.11834/jrs.20222008.

摘要

高精度的农田蒸散发（ET）对于田间尺度精准的水分平衡量化和水分亏缺研究具有重要意义，对农业精准灌溉和农田水分利用效率提高具有实用价值。本研究利用全球共计28个农田站点的涡动相关系统（EC）通量观测数据，对500 m空间分辨率8 d时间分辨率的MOD16和PML-V2两种遥感蒸散发产品，进行了对比分析与精度评估。评估结果表明，PML-V2 ET产品与EC观测ET相比，均方根误差（RMSE）变化范围为3.3—22.4 mm/8 d，平均偏差（bias）变化范围为-15.98—13.27 mm/8 d；MOD16 ET产品与EC观测ET相比，RMSE变化范围为3.81—21.47 mm/8 d，bias变化范围为-16.42—15.05 mm/8 d。整体而言，两种产品精度相当，MOD16产品低估8 d ET（bias：-2.31 mm/8 d，

：0.452，RMSE：8.82 mm/8 d），PML-V2产品略高估8 d ET（bias：0.51 mm/8 d，

：0.455，RMSE：8.81 mm/8 d）；PML-V2产品在18个站点（64%）上表现更优，但是部分站点上MOD16产品对时序变化细节（如到达波峰的季节、年中的下降/上升趋势）的刻画优于PML-V2产品；PML-V2产品未能捕捉到冬小麦和夏玉米轮种而引起的ET年中先下降后上升的变化趋势，而MOD16产品虽然成功捕捉到ET时序曲线上冬小麦和夏玉米两种作物各自生长期内的波峰，但仍较大程度上低估了冬小麦ET（如栾城站点和禹城站点）；另外，MOD16产品和PML-V2产品均严重低估了水稻ET（如US-Twt站点）。本研究可为农田蒸散发算法发展及其精度验证提供参考。

Abstract

Crop evapotranspiration (ET) with high precision is of great significance for the accurate quantification of water balance and the study of water deficit in the field-scale

and it has practical value for the precision irrigation of farmland and the improvement of agricultural water use efficiency. It is essential to validate ET before a remotely sensed ET product being used. This study evaluated crop ETs from two remotely sensed products (MOD16 and PML-V2) with 500 m spatial resolution and 8-day temporal resolution by using Eddy-Covariance (EC) flux observations from 28 flux tower sites cover with crop globally. The results showed that

compared with the observed ET

the Root Mean Square Error (RMSE) and bias of the PML-V2 ET products varied from 3.3 to 22.4 mm/8 d and from 15.98 to 13.27 mm/8 d

respectively

while the RMSE and bias of the MOD16 ET product varied from 3.81 to 21.47 mm/8 d and from -16.42 to 15.05 mm/8 d

respectively. On the whole

the overall accuracies of these two products were similar

the MOD16 product underestimated the 8-day ET with a bias of -2.31 mm/8 d

of 0.452 and a RMSE of 8.82 mm/8 d

while the PML-V2 product slightly overestimated the 8-day ET with a bias of 0.51 mm/8 d

of 0.455 and a RMSE of 8.81 mm/8 d. The PML-V2 product performed better at 18 tower sites (almost 64%)

but the MOD16 product performed better than the PML-V2 products at some sites in the depiction of details on time-series change (such as the season of reaching the peak during the year

the decreasing and increasing trend in the middle of year). The results showed that the PML-V2 product failed to capture the gradual decrease then increase ET trend in the middle of year which caused by the rotation of winter wheat and summer maize

while the MOD16 product successfully captured the hitting of the two peaks in ET time-series during the two growth seasons of winter wheat and summer maize (such as the Luancheng and Yucheng sites). However

the MOD16 product still underestimated the 8-day ET of winter wheat to a certain degree. Moreover

the results showed that both the MOD16 product and the PML-V2 product seriously underestimated the 8-day ET of paddy with a RMSE of 21.47—22.4 mm/8 d and a bias of -16.42—-15.98 mm/8 d (such as the US-Twt site). This study could provide reference for the development and validation of ET models for cropland. In the future

more detailed evaluation of land surface heterogeneity needs to be carried out and more products should be taken into consideration. Further detailed evaluation of ET in different crop types is also required.

关键词

遥感MOD16产品PML-V2产品农田蒸散发产品验证

Keywords

remote sensingMOD16 productPML-V2 productcrop evapotranspirationvalidation

references

Bai J, Jia L, Liu S M, Xu Z W, Hu G C, Zhu M J and Song L S. 2015. Characterizing the footprint of eddy covariance system and large aperture scintillometer measurements to validate satellite-based surface fluxes. IEEE Geoscience and Remote Sensing Letters, 12(5): 943-947 [DOI: 10.1109/LGRS.2014.2368580http://dx.doi.org/10.1109/LGRS.2014.2368580]

Ghilain N, Arboleda A and Gellens-Meulenberghs F. 2011. Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data. Hydrology and Earth System Sciences, 15(3): 771-786 [DOI: 10.5194/hess-15-771-2011http://dx.doi.org/10.5194/hess-15-771-2011]

Hu G C and Jia L. 2015. Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations. Remote Sensing, 7(3): 3056-3087 [DOI: 10.3390/rs70303056http://dx.doi.org/10.3390/rs70303056]

Jia Z Z, Liu S M, Xu Z W, Chen Y J and Zhu M J. 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research: Atmospheres, 117(D13): D13113 [DOI: 10.1029/2011JD017037http://dx.doi.org/10.1029/2011JD017037]

Jiang C Y and Ryu Y. 2016. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sensing of Environment, 186: 528-547 [DOI: 10.1016/j.rse.2016.08.030http://dx.doi.org/10.1016/j.rse.2016.08.030]

Jung M, Koirala S, Weber U, Ichii K, Gans F, Camps-Valls G, Papale D, Schwalm C, Tramontana G and Reichstein M. 2019. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Scientific Data, 6(1): 74 [DOI: 10.1038/s41597-019-0076-8http://dx.doi.org/10.1038/s41597-019-0076-8]

Jung M, Reichstein M, Ciais P, Seneviratne S I, Sheffield J, Goulden M L, Bonan G, Cescatti A, Chen J Q, De Jeu R, Dolman A J, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law B E, Montagnani L, Mu Q Z, Mueller B, Oleson K, Papale D, Richardson A D, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S and Zhang K. 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318): 951-954 [DOI: 10.1038/nature09396http://dx.doi.org/10.1038/nature09396]

Li J, Xin X Z, Peng Z Q and Li X J. 2021. Remote sensing products of terrestrial evapotranspiration: comparison and outlook. Remote Sensing Technology and Application, 36(1): 103-120

李佳, 辛晓洲, 彭志晴, 李小军. 2021. 地表蒸散发遥感产品比较与分析. 遥感技术与应用, 36(1): 103-120 [DOI: 10.11873/j.issn.1004-0323.2021.1.0103http://dx.doi.org/10.11873/j.issn.1004-0323.2021.1.0103]

Li X, Liu S M, Li H X, Ma Y F, Wang J H, Zhang Y, Xu Z W, Xu T R, Song L S, Yang X F, Lu Z, Wang Z Y and Guo Z X. 2018. Intercomparison of six upscaling evapotranspiration methods: from site to the satellite pixel. Journal of Geophysical Research: Atmospheres, 123(13): 6777-6803 [DOI: 10.1029/2018JD028422http://dx.doi.org/10.1029/2018JD028422]

Li Z L, Tang R L, Wan Z M, Bi Y Y, Zhou C H, Tang B H, Yan G J and Zhang X Y. 2009. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5): 3801-3853 [DOI: 10.3390/s90503801http://dx.doi.org/10.3390/s90503801]

Liu M, Tang R L, Li Z L, Gao M F and Yao Y J. 2021. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration. National Remote Sensing Bulletin, 25(8): 1517-1537

刘萌, 唐荣林, 李召良, 高懋芳, 姚云军. 2021. 数据驱动的蒸散发遥感反演方法及产品研究进展. 遥感学报, 25(8): 1517-1537 [DOI: 10.11834/jrs.20211310http://dx.doi.org/10.11834/jrs.20211310]

Liu S M, Xu Z W, Song L S, Zhao Q Y, Ge Y, Xu T R, Ma Y F, Zhu Z L, Jia Z Z and Zhang F. 2016. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteorology, 230-231: 97-113 [DOI: 10.1016/j.agrformet.2016.04.008http://dx.doi.org/10.1016/j.agrformet.2016.04.008]

Liu S M, Xu Z W, Wang W Z, Jia Z Z, Zhu M J, Bai J and Wang J M. 2011. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15(4): 1291-1306 [DOI: 10.5194/hess-15-1291-2011http://dx.doi.org/10.5194/hess-15-1291-2011]

Ma N, Szilagyi J, Zhang Y S and Liu W B. 2019. Complementary-relationship-based modeling of terrestrial evapotranspiration across China during 1982-2012: validations and spatiotemporal analyses. Journal of Geophysical Research: Atmospheres, 124(8): 4326-4351 [DOI: 10.1029/2018JD029850http://dx.doi.org/10.1029/2018JD029850]

Martens B, Miralles D G, Lievens H, van der Schalie R, de Jeu R A M, Fernández-Prieto D, Beck H E, Dorigo W A and Verhoest N E C. 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5): 1903-1925 [DOI: 10.5194/gmd-10-1903-2017http://dx.doi.org/10.5194/gmd-10-1903-2017]

Mu Q Z, Zhao M S and Running S W. 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8): 1781-1800 [DOI: 10.1016/j.rse.2011.02.019http://dx.doi.org/10.1016/j.rse.2011.02.019]

Pastorello G, Trotta C, Canfora E, Chu H S, Christianson D, Cheah Y W, Poindexter C, Chen J Q, Elbashandy A, Humphrey M, Isaac P, Polidori D, Reichstein M, Ribeca A, Van Ingen C, Vuichard N, Zhang L M, Amiro B, Ammann C, Arain M A, Ardö J, Arkebauer T, Arndt S K, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini L B, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black T A, Blanken P D, Bohrer G, Boike J, Bolstad P V, Bonal D, Bonnefond J M, Bowling D R, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns S P, Buysse P, Cale P, Cavagna M, Cellier P, Chen S P, Chini I, Christensen T R, Cleverly J, Collalti A, Consalvo C, Cook B D, Cook D, Coursolle C, Cremonese E, Curtis P S, D’andrea E, Da Rocha H, Dai X Q, Davis K J, Cinti B D, Grandcourt A D, Ligne A D, De Oliveira R C, Delpierre N, Desai A R, Di Bella C M, Tommasi P D, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, Elkhidir H A M, Eugster W, Ewenz C M, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein A H, Gough C M, Goulden M L, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S J, Han X G, Hansen B U, Hanson C, Hatakka J, He Y T, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S C, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch W L, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y N, Liddell M, Limousin J M, Lion M, Liska A J, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S Y, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis H A, Marras S, Massman W, Mastepanov M, Matamala R, Matthes J H, Mazzenga F, McCaughey H, McHugh I, McMillan A M S, Merbold L, Meyer W, Meyers T, Miller S D, Minerbi S, Moderow U, Monson R K, Montagnani L, Moore C E, Moors E, Moreaux V, Moureaux C, Munger J W, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen J E, Ourcival J M, Papuga S A, Parmentier F J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips R P, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober S M, Rambal S, Rannik Ü, Raz-Yaseef N, Rebmann C, Reed D, de Dios V R, Restrepo-Coupe N, Reverter B R, Roland M, Sabbatini S, Sachs T, Saleska S R, Sánchez-Cañete E P, Sanchez-Mejia Z M, Schmid H P, Schmidt M, Schneider K, Schrader F, Schroder I, Scott R L, Sedlák P, Serrano-Ortíz P, Shao C L, Shi P L, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens R W, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y H, Tapper N, Thom J, Tomassucci M, Tuovinen J P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker J P, Walter-Shea E, Wang H M, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Li Y L, Zampedri R, Zhang J H, Zhou G Y, Zona D, Agarwal D, Biraud S, Torn M and Papale D. 2020. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, 7: 225 [DOI: 10.1038/s41597-020-0534-3http://dx.doi.org/10.1038/s41597-020-0534-3]

Running S, Mu Q and Zhao M. 2021. MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V061 [Data set] NASA EOSDIS Land Processes DAAC [DOI: 10.5067/MODIS/MOD16A2.061http://dx.doi.org/10.5067/MODIS/MOD16A2.061]

Senay G B, Bohms S, Singh R K, Gowda P H, Velpuri N M, Alemu H and Verdin J P. 2013. Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. Journal of the American Water Resources Association, 49(3): 577-591 [DOI: 10.1111/jawr.12057http://dx.doi.org/10.1111/jawr.12057]

Tang R L and Li Z L. 2017. An end-member-based two-source approach for estimating land surface evapotranspiration from remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5818-5832 [DOI: 10.1109/TGRS.2017.2715361http://dx.doi.org/10.1109/TGRS.2017.2715361]

Tang R L, Shao K, Li Z L, Wu H, Tang B H, Zhou G Q and Zhang L. 2015. Multiscale validation of the 8-day MOD16 evapotranspiration product using flux data collected in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1478-1486 [DOI: 10.1109/JSTARS.2015.2420105http://dx.doi.org/10.1109/JSTARS.2015.2420105]

Wang K C and Dickinson R E. 2012. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2): RG2005 [DOI: 10.1029/2011RG000373http://dx.doi.org/10.1029/2011RG000373]

Wang Y H. 2010. Flux observations analysis at Shouxian National Climate Observatory. Nanjing: Nanjing University of Information Science and Technology

王有恒. 2010. 寿县国家气候观象台通量观测分析. 南京: 南京信息工程大学

Xiong Y J, Feng F G, Fang Y Z, Qiu G Y, Zhao S H and Yao Y J. 2021. Critical problems when applying remotely sensed evapotranspiration products. Remote Sensing Technology and Application, 36(1): 121-131

熊育久, 冯房观, 方奕舟, 邱国玉, 赵少华, 姚云军. 2021. 蒸散发遥感反演产品应用关键问题浅议. 遥感技术与应用, 36(1): 121-131 [DOI: 10.11873/j.issn.1004-0323.2021.1.0121http://dx.doi.org/10.11873/j.issn.1004-0323.2021.1.0121]

Xu T R, Guo Z X, Liu S M, He X L, Meng Y F Y, Xu Z W, Xia Y L, Xiao J F, Zhang Y, Ma Y F and Song L S. 2018. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. Journal of Geophysical Research: Atmospheres, 123(16): 8674-8690 [DOI: 10.1029/2018JD028447http://dx.doi.org/10.1029/2018JD028447]

Yao Y J, Cheng J, Zhao S H, Jia K, Xie X H and Sun L. 2012. Estimation of farmland evapotranspiration: a review of methods using thermal infrared remote sensing data. Advances in Earth Sciences, 27(12): 1308-1318

姚云军, 程洁, 赵少华, 贾坤, 谢先红, 孙亮. 2012. 基于热红外遥感的农田蒸散估算方法研究综述. 地球科学进展, 27(12): 1308-1318 [DOI: 10.11867/j.issn.1001-8166.2012.12.1308http://dx.doi.org/10.11867/j.issn.1001-8166.2012.12.1308]

Yao Y J, Liang S L, Li X L, Hong Y, Fisher J B, Zhang N N, Chen J Q, Cheng J, Zhao S H, Zhang X T, Jiang B, Sun L, Jia K, Wang K C, Chen Y, Mu Q Z and Feng F. 2014. Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119(8): 4521-4545 [DOI: 10.1002/2013JD020864http://dx.doi.org/10.1002/2013JD020864]

Zhang Y, Jia Z Z, Liu S M, Xu Z W, Xu T R, Yao Y J, Ma Y F, Song L S, Li X, Hu X, Wang Z Y, Guo Z X and Zhou J. 2020. Advances in validation of remotely sensed land surface evapotranspiration. Journal of Remote Sensing (Chinese), 24(8): 975-999

张圆, 贾贞贞, 刘绍民, 徐自为, 徐同仁, 姚云军, 马燕飞, 宋立生, 李相, 胡骁, 王泽宇, 郭枝虾, 周纪. 2020. 遥感估算地表蒸散发真实性检验研究进展. 遥感学报, 24(8): 975-999 [DOI: 10.11834/jrs.20209099http://dx.doi.org/10.11834/jrs.20209099]

Zhang Y Q, Kong D D, Gan R, Chiew F H S, McVicar T R, Zhang Q and Yang Y T. 2019. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sensing of Environment, 222: 165-182 [DOI: 10.1016/j.rse.2018.12.031http://dx.doi.org/10.1016/j.rse.2018.12.031]

Zhang Y Q, Kong D D, Zhang X Z, Tian J and Li C C. 2021. Impacts of vegetation changes on global evapotranspiration in the period 2003-2017. Acta Geographica Sinica, 76(3): 584-594

张永强, 孔冬冬, 张选泽, 田静, 李聪聪. 2021. 2003—2017年植被变化对全球陆面蒸散发的影响. 地理学报, 76(3): 584-594 [DOI: 10.11821/dlxb202103007http://dx.doi.org/10.11821/dlxb202103007]

Zhao K G, Wulder M A, Hu T X, Bright R, Wu Q S, Qin H M, Li Y, Toman E, Mallick B, Zhang X S and Brown M. 2019. Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: a Bayesian ensemble algorithm. Remote Sensing of Environment, 232: 111181 [DOI: 10.1016/j.rse.2019.04.034http://dx.doi.org/10.1016/j.rse.2019.04.034]

文章被引用时，请邮件提醒。

提交

透视地球——新一代对地观测技术

迁移深度卷积神经网络模型秋粮作物泛化识别

Grid A*：面向野外空地协同应急处置的快速路径规划

基于高光谱卫星影像的生长期互花米草指数构建