سابِق|تال

Validation of crop evapotranspiration products based on eddy-covariance flux observations

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northen China, Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences, Beijing 100081, China

  • Email：liumeng01@caas.cn
  • Introduction：刘萌，研究方向为地表蒸散发遥感反演。E-mail： liumeng01@caas.cn
LIU Meng1,  
  • Affiliation：

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

PENG Zhong23,  
  • Affiliation：

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

HUANG Lingxiao23,  
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northen China, Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences, Beijing 100081, China

LI Zhaoliang1,  
  • Affiliation：

    State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northen China, Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences, Beijing 100081, China

DUAN Sibo1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：tangrl@lreis.ac.cn
  • Introduction：唐荣林，研究方向为地表蒸散发遥感反演、遥感大数据分析。E-mail：tangrl@lreis.ac.cn
TANG Ronglin23*

ملخص

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, a R2 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, a R2 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.

مفهوم

remote sensing;MOD16 product;PML-V2 product;crop evapotranspiration;Validation

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Wang Y H. 2010. Flux observations analysis at Shouxian National Climate Observatory. Nanjing: Nanjing University of Information Science and Technology
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website