Comparison of Daily Evapotranspiration and Reference Evapotranspiration Fraction Values Calculated using METRIC Model and Google Earth Engine FLux

Abstract

Accurate evapotranspiration (ET) calculation is crucial in water resources management. In recent years, various remote sensing techniques based models have been developed to determine ET. Among these models, Mapping EvapoTranspiration at High Resolution with Internalized Calibration (METRIC) is one of the most widely used. In the METRIC model, anchor pixels must be selected to represent extreme conditions where ET is at potential level and ET is zero in the internal calibration process. These choices should be made by experienced users in both remote sensing and ET. However, this limits the use of the METRIC model. In order to fill this gap, the Earth Engine Evapotranspiration Flux (EEFlux) application was developed in the Google Earth Engine platform. With this application, the METRIC model is calibrated automatically. The aim of this study was to compare the daily ET (ETd) and Reference ET fraction (ETrF) values obtained from the METRIC and EEFlux model. The study was carried out in the agricultural fields of barley, sunflower, wheat, corn, sugar beet, potato and onion cultivation in Uzunyazı, Çayırözü and Yeşilören villages in Merzifon district of Amasya province. Landsat 8 satellite images of three different dates were used in this research. According to the results obtained from this study, although the ETd values calculated with METRIC and EEFlux were compatible (R2=0.87), the EEFlux-ETd values were generally lower than the METRIC- ETd values (RMSE=2.5 mm day-1 and MAE=2.38 mm day-1). Calculated ETrF values were calculated with similar agreement to ETd values (R2=0.88, RMSE=0.11 and MAE=0.09). Accordingly, although there are certain differences between ETd and ETrF values calculated with EEFlux and METRIC, ETd values can be determined very quickly with EEFlux, without the need for local climate data and an experienced user.

Keywords

• Evapotranspiration
• Remote Sensing
• METRIC
• GEEFLUX
• ETrF.

METRIC Modeli ve Google Earth Engine Flux ile Hesaplanan Günlük Bitki Su Tüketimi ve Referans Bitki Su Tüketimi Fraksiyonu Değerlerinin Karşılaştırılması

Öz

Evapotranspirasyonun (ET) hassas bir biçimde belirlenmesi su kaynakları yönetiminde oldukça önemlidir. Son yıllarda ET’nin uzaktan algılama teknikleri yardımıyla belirlenmesinde çeşitli modeller geliştirilmiştir. Bu modeller arasında Evapotranspirasyon Haritalamasında Yüksek Çözünürlük ve İçsel Kalibrasyon Modeli (METRIC) en yaygın kullanılanlardan biridir. METRIC modelinde içsel kalibrasyon amacıyla ET’nin potansiyel seviyede ve ET’nin sıfır olduğu iki ekstrem koşulu temsilen seçimler yapılması gerekmektedir. Bu seçimleri hem uzaktan algılama hem de ET üzerine deneyimli kişilerin yapması gerekmektedir. Ancak bu durum METRIC modelinin kullanımını kısıtlamaktadır. Oluşan bu boşluğun doldurulması için Google Earth Engine platformunda Earth Engine Evapotranspiration Flux (EEFlux) uygulaması geliştirilmiştir. Bu uygulamayla METRIC modeli otomatik olarak kalibre edilmektedir. Bu çalışmanın amacı elle METRIC modeli ile EEFlux modelinden elde edilen günlük ET (ETd) ve Referans ET fraksiyonu (ETrF) değerlerinin karşılaştırılmasıdır. Çalışma Amasya ili Merzifon ilçesinde yer alan Uzunyazı, Çayırözü ve Yeşilören köylerindeki arpa, ayçiçeği, buğday, mısır, şeker pancarı, patates ve soğan yetiştiriciliği yapılan tarım alanlarında gerçekleştirilmiştir. Araştırmada üç farklı tarihe ait Landsat 8 uydu görüntüleri kullanılmıştır. Çalışmadan elde edilen sonuçlara göre METRIC ve EEFLUX ile hesaplanan ETd değerleri uyumlu olmasına rağmen (R2=0,87), genel olarak EEFlux-ETd değerleri METRIC-ETd değerlerinden daha düşük gerçekleşmiştir (RMSE=2,5 mm gün-1 ve MAE=2,38 mm gün-1). ETrF değerleri ise ETd değerlerine benzer uyumla hesaplanmıştır (R2=0,88, RMSE=0,11 ve MAE=0,09). Buna göre EEFLUX ve METRIC ile hesaplanan ETd ve ETrF değerleri arasında belirli farklılıklar olmasına rağmen, EEFLUX ile oldukça hızlı, yerel iklim verileri ve deneyimli bir kullanıcı ihtiyacı olmadan ETd değerleri belirlenebilmektedir.

Anahtar Kelimeler

• Bitki su tüketimi
• uzaktan algılama
• METRIC
• GEEFLUX
• ETrF.

Kaynakça

1. Al-Gaadi, K. A., Patil, V. C., Tola, E., Madugundu, R., & Gowda, P. H. (2016). Evaluation of METRIC-derived ET fluxes over irrigated alfalfa crop in desert conditions using scintillometer measurements. Arabian Journal of Geosciences, 9(6), 1-12. https://doi.org/10.1007/s12517-016-2469-8
2. Allen, R. G., Pereira, L. S., Howell, T. A., & Jensen, M. E. (2011). Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water Management, 98(6), 899-920. https://doi.org/10.1016/j.agwat.2010.12.015
3. Allen, R. G., Tasumi, M., Morse, A., Trezza, R., Wright, J. L., Bastiaanssen, W., Kramber, W., Lorite, I., & Robison, C. W. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Applications. Journal of Irrigation and Drainage Engineering, 133(4), 395-406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395).
4. Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
5. Allen, R. G., Morton, C., Kamble, B., Kilic, A., Huntington, J., Thau, D., Gorelick, N., Erickson, T., Moore, R., Trezza, R., Ratcliffe, I., & Clarence, R. (2015). EEFlux: A Landsat-based evapotranspiration mapping tool on the Google Earth Engine. ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation Proceedings of the 10-12 November 2015 Symposium, Long Beach, California, USA.
6. ASCE-EWRI, (2005). The ASCE Standardized Reference Evapotranspiration Equation. Technical Committee report to the Environmental and Water Resources Institute of the American Society of Civil Engineers from the Task Committee on Standardization of Reference Evapotranspiration, American Society of Civil Engineers Press, USA. https://doi.org/10.13031/irrig.20152143511
7. Bastiaanssen, W. G., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212, 198-212. https://doi.org/10.1016/S0022-1694(98)00253-4.
8. Burchard-Levine, V., Nieto, H., Riaño, D., Migliavacca, M., El-Madany, T. S., Guzinski, R., Carrara, A., & Martín, M. P. (2021). The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem. Remote Sensing of Environment, 260, 112440. https://doi.org/10.1016/j.rse.2021.112440

9. Carrasco-Benavides, M., Ortega-Farías, S., Gil, P. M., Knopp, D., Morales-Salinas, L., Lagos, L. O., de la Fuente, D., López-Olivari, R., & Fuentes, S. (2021). Assessment of the vineyard water footprint by using ancillary data and EEFlux satellite images. Examples in the Chilean central zone. Science of The Total Environment, 811, 152452. https://doi.org/10.1016/j.scitotenv.2021.152452
10. Çetin, S., Köksal, E. S., & Tunca, E. (2017). Enerji dengesine dayalı evapotranspirasyon haritalamada içsel kalibrasyon için temel hücrelerin görüntü filtreleme yaklaşımı ile seçilmesi. Anadolu Tarım Bilimleri Dergisi, 32(3), 330-339. https://doi.org/10.7161/omuanajas.319335
11. ÇKS (2015). Çiftçi kayıt sistemi. https://tbs.tarbil.gov.tr/. [Erişim Tarihi: 10 Haziran 2016]
12. Courault, D., Seguin, B., & Olioso, A. (2005). Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. Irrigation and Drainage systems, 19(3), 223-249. https://doi.org/10.1007/s10795-005-5186-0.
13. de Oliveira Costa, J., José, J. V., Wolff, W., de Oliveira, N. P. R., Oliveira, R. C., Ribeiro, N. L., Coelho, R.D., da Silva, T.J.A., Bonfim-Silva, E.M., & Schlichting, A. F. (2020). Spatial variability quantification of maize water consumption based on Google EEflux tool. Agricultural Water Management, 232, 106037. https://doi.org/10.1016/j.agwat.2020.106037.
14. Filgueiras, R., Mantovani, E. C., Althoff, D., Ribeiro, R. B., Venancio, L. P., & dos Santos, R. A. (2019). Dynamics of actual crop evapotranspiration based in the comparative analysis of sebal and metric-eeflux. Irriga, 1(1), 72-80. https://doi.org/10.15809/irriga.2019v1n1p72-80
15. Foolad, F., Blankenau, P., Kilic, A., Allen, R. G., Huntington, J. L., Erickson, T. A., Ozturk, D., Morton, C. G., Ortega, S., Ratcliffe, I., Franz, T. E., Thau, D., Moore, R., Gorelick, N., Kamble, B., Revelle, P., Trezza, R., Zhao, W., & Robison, C. W. (2018). Comparison of the automatically calibrated Google evapotranspiration application—EEFlux and the manually calibrated METRIC application. Preprints. https://doi.org/10.20944/preprints201807.0040.v1
16. Kalma, J. D., McVicar, T. R., & McCabe, M. F. (2008). Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4), 421-469. https://doi.org/10.1007/s10712-008-9037-z.
17. Khan, M. S., Baik, J., & Choi, M. (2021). A physical-based two-source evapotranspiration model with Monin–Obukhov similarity theory. GIScience & Remote Sensing, 58(1), 88-119. https://doi.org/10.1080/15481603.2020.1857625.
18. Li, C., Li, Z., Gao, Z., & Sun, B. (2021). Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model. Remote Sensing, 13(7), 1344. https://doi.org/10.3390/rs13071344.
19. Morton, C. G., Huntington, J. L., Pohll, G. M., Allen, R. G., McGwire, K. C., & Bassett, S. D. (2013). Assessing calibration uncertainty and automation for estimating evapotranspiration from agricultural areas using METRIC. Journal of the American Water Resources Association, 49(3), 549-562. https://doi.org/10.1111/jawr.12054.
20. Nassar, A., Torres-Rua, A., Kustas, W., Alfieri, J., Hipps, L., Prueger, J., Nieto, H., Alsina, M. M., White, W., McKee, L., Coopmans, C., Sanchez, L., & Dokoozlian, N. (2021). Assessing daily evapotranspiration methodologies from one-time-of-day sUAS and EC information in the GRAPEX project. Remote Sensing, 13(15), 2887. https://doi.org/10.3390/rs13152887.
21. Nisa, Z., Khan, M.S., Govind, A., Marchetti, M., Lasserre, B., Magliulo, E., & Manco, A. (2021). Evaluation of SEBS, METRIC-EEFlux, and QWaterModel actual evapotranspiration for a Mediterranean cropping system in southern Italy. Agronomy, 11(2), 345. https://doi.org/10.3390/agronomy11020345.
22. Nouri, H., Beecham, S., Anderson, S., Hassanli, A. M., & Kazemi, F. (2015). Remote sensing techniques for predicting evapotranspiration from mixed vegetated surfaces. Urban Water Journal, 12(5), 380–393. https://doi.org/10.5194/hessd-10-3897-2013.
23. Ortega-Salazar, S., Ortega-Farías, S., Kilic, A., & Allen, R. (2021). Performance of the METRIC model for mapping energy balance components and actual evapotranspiration over a superintensive drip-irrigated olive orchard. Agricultural Water Managemen. 251, 106861. https://doi.org/10.1016/j.agwat.2021.106861.
24. Park, S., Ryu, D., Fuentes, S., Chung, H., O’Connell, M., & Kim, J. (2021). Mapping very-high-resolution evapotranspiration from unmanned aerial vehicle (UAV) imagery. ISPRS International Journal of Geo-Information, 10(4), 211. https://doi.org/10.3390/ijgi10040211.
25. Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y. T., Chuang, H. Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., & Becker, E. (2014). The NCEP Climate Forecast System version 2. Journal of Climate, 27(6), 2185-2208. https://doi.org/10.1175/JCLI-D-12-00823.1.
26. Tasumi, M., Trezza, R., Allen, R. G., & Wright, J. L. (2005). Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S. Irrigation and Drainage Systems, 19(3), 355-376. https://doi.org/10.1007/s10795-005-8138-9.
27. USGS (2022). United States Geological Survey, Landsat 8 Mission. https://www.usgs.gov/landsat-missions/landsat-8. [Erişim Tarihi: 22.04.2022].
28. Venancio, L. P., Eugenio, F. C., Filgueiras, R., França da Cunha, F., Argolo Dos Santos, R., Ribeiro, W. R., & Mantovani, E. C. (2020). Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application. Plos One, 15(7), e0235620. https://doi.org/10.1371/journal.pone.0235620.
29. Wolff, W., Francisco, J. P., Flumignan, D. L., Marin, F. R., & Folegatti, M. V. (2022). Optimized algorithm for evapotranspiration retrieval via remote sensing. Agricultural Water Management, 262, 107390. https://doi.org/10.1016/j.agwat.2021.107390.
30. Yuan, X., Wood, E. F., Luo, L., & Pan, M. (2011). A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. Geophysical Research Letters, 38(13), L13402. https://doi.org/10.1029/2011GL047792.