Estimation of actual evapotranspiration using the simplified-surface energy balance index model on an irrigated agricultural farm
DOI:
https://doi.org/10.54386/jam.v25i3.2254Keywords:
Evapotrasnpiration, S-SEBI, Eddy Covariance, Land Surface TemperatureAbstract
Evapotranspiration (ET) plays a crucial role in the energy and water balance of agricultural ecosystems and is a vital component of the hydrological cycle. Efficient irrigation water management relies on accurate spatiotemporal coverage of crop ET across a farm. Thanks to the availability of multi-temporal high-resolution satellite datasets and remote sensing-based surface energy balance models, near-real-time estimation of ET is now possible. This study utilized Landsat 8/9 data to estimate ET using the simplified surface energy balance index (S-SEBI) model, which was then compared to eddy covariance measurements over a semi-arid agricultural farm in New Delhi, India during the post-monsoon periods of 2021-22 and 2022-23. The S-SEBI model predicted daily ET from Landsat 8/9 data with an average correlation coefficient and RMSE of 0.89 and 0.79 mm/day, respectively. The spatiotemporal map was also used to evaluate the model's performance, and it could accurately differentiate between ET over dryland crops and well-irrigated wheat fields on the farm. Despite underestimating ET (0.51 mm/day) during the initial growing season (Nov-Dec) and overestimating it (0.73 mm/day) during mid-season (Feb-Mar), the S-SEBI model can still be an operational tool for mapping ET with high accuracy and sufficient variation across pixels, making it an ideal option for incorporating into irrigation scheduling.
References
Allen, R.G., Tasumi, M. and Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J. Irrig. Drain. Eng, 133(4): 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
Allen, R.G., Pereira, L.S., Howell, T.A. and Jensen, M.E. (2011). Evapotranspiration information reporting: II. Recommended documentation. Agric. Water Manage., 98(6): 921-929. https://doi.org/10.1016/j.agwat.2010.12.016
Bag, K., Bandyopadhyay, K.K., Sehgal, V.K., Sarangi, A. and Krishnan, P. (2020). Effect of tillage, residue and nitrogen management on radiation interception, radiation use efficiency and evapotranspiration partitioning. J. Agrometeorol, 22(3): 285-294. DOI: https://doi.org/10.54386/jam.v22i3.190
Chattopadhyay, N., Vyas, S.S., Bhattacharya, B.K. and Chandras, S. (2016). Evaluating the potential of rainfall product from Indian geostationary satellite for operational agromet advisory services in India. J. Agrometeorol, 18(1): 29-33. DOI: https://doi.org/10.54386/jam.v18i1.895
Bastiaanssen, W.G., Menenti, M., Feddes, R.A., and Holtslag, A.A.M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol., 212: 198-212. DOI: https://doi.org/10.1016/S0022-1694(98)00253-4
Boretti, A. and Rosa, L. (2019). Reassessing the projections of the world water development report. NPJ Clean Water, 2(1): 15. DOI: https://doi.org/10.1038/s41545-019-0039-9
Chen, H., Zhu, G., Shang, S., Qin, W., Zhang, Y., Su, Y., and Xu, C. (2022). Uncertainties in partitioning evapotranspiration by two remote sensing-based models. J. Hydrol., 604: 127223. DOI: https://doi.org/10.1016/j.jhydrol.2021.127223
Cleverly, J., Thibault, J.R., Teet, S.B., Tashjian, P., Hipps, L.E., Dahm, C.N., and Eamus, D. (2015). Flooding regime impacts on radiation, evapotranspiration, and latent energy fluxes over groundwater-dependent riparian cottonwood and saltcedar forests. Adv. Meteorol., 2015. DOI: https://doi.org/10.1155/2015/935060
Colaizzi, P.D., Kustas, W.P., Anderson, M.C., Agam, N., Tolk, J.A., Evett, S.R. and O’Shaughnessy, S. A. (2012). Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures. Adv. Water Resour., 50: 134-151. DOI: https://doi.org/10.1016/j.advwatres.2012.06.004
Cristóbal, J., Jiménez-Muñoz, J.C., Prakash, A., Mattar, C., Skoković, D. and Sobrino, J.A. (2018). An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sens., 10(3): 431. DOI: https://doi.org/10.3390/rs10030431
Danodia, A., Patel, N.R., Chol, C.W.,Nikam, B.R. and Sehgal, V.K. (2019). Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India. Geocarto Int. 34, (1): 114-131. DOI: https://doi.org/10.1080/10106049.2017.1374473
Ezenne, G.I., Eyibio, N.U., Tanner, J.L., Asoiro, F.U. and Obalum, S.E. (2023). An overview of uncertainties in evapotranspiration estimation techniques. J. Agrometeorol, 25(1): 173-182. DOI: https://doi.org/10.54386/jam.v25i1.2014
French, A.N., Hunsaker, D.J., Sanchez, C.A., Saber, M., Gonzalez, J.R. and Anderson, R. (2020). Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agric. Water Manage., 239: 106266. DOI: https://doi.org/10.1016/j.agwat.2020.106266
Goldblatt, R., Addas, A., Crull, D., Maghrabi, A., Levin, G.G. and Rubinyi, S. (2021). Remotely sensed derived land surface temperature (LST) as a proxy for air temperature and thermal comfort at a small geographical scale. Land, 10(4): 410. DOI: https://doi.org/10.3390/land10040410
Gomez, M., Olioso, A., Sobrino, J.A. and Jacob, F. (2005). Retrieval of evapotranspiration over the Alpilles/ReSeDA experimental site using airborne POLDER sensor and a thermal camera. Remote Sens. Environ., 96(3-4): 399-408. DOI: https://doi.org/10.1016/j.rse.2005.03.006
Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sens. Environ., 25(3): 295-309. DOI: https://doi.org/10.1016/0034-4257(88)90106-X
Jin, Y., He, R., Marino, G., Whiting, M., Kent, E., Sanden, B.L., Culumber, M., Ferguson, L., Little, C., Grattan, S., Tha Paw U, K., Lagos, Luis O., Snyder R. and Zaccaria, D. (2018). Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates. Agric. For. Meteorol., 262: 178-191. DOI: https://doi.org/10.1016/j.agrformet.2018.07.004
Khan, M.S., Baik, J. and Choi, M. (2020). Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia. Adv. Space Res., 66(3): 533-545. DOI: https://doi.org/10.1016/j.asr.2020.04.037
Kisekka, I., Peddinti, S.R., Kustas, W.P., McElrone, A.J., Bambach-Ortiz, N., McKee, L. and Bastiaanssen, W. (2022). Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrig. Sci., 40(4-5): 761-777. DOI: https://doi.org/10.1007/s00271-022-00775-1
Kumar, S., Attri, S.D., Soni, A.K., Vishnoi, L., Singh, K.K., Sharma, G., and Tripathi, J. N. (2019). Satellite derived crop coefficient and crop water stress for soybean in semi-arid region of India. J. Agrometeorol, 21 (special issue): 140-146.
Kumar, U., Sahoo, B., Chatterjee, C., and Raghuwanshi, N.S. (2020). Evaluation of simplified surface energy balance index (S-SEBI) method for estimating actual evapotranspiration in Kangsabati reservoir command using landsat 8 imagery. J. Indian Soc. Remote Sens., 48: 1421-1432. https://doi.org/10.1007/s12524-020-01166-9
Li, Z., Zheng, F.L., and Liu, W.Z. (2012). Spatiotemporal characteristics of reference evapotranspiration during 1961–2009 and its projected changes during 2011–2099 on the Loess Plateau of China. Agric. For. Meteorol., 154 (2012): 147-155. DOI: https://doi.org/10.1016/j.agrformet.2011.10.019
Menenti, M. and Choudhury, B. (1993). Parameterization of Land Surface Evapotranspiration Using a Location Dependent Potential Evapotranspiration and Surface Temperature Range. Exchange Processes at the Land Surface for a Range of Space and Time Scales, IAHS Publication, 212: 561-568.
Mukherjee, J., Sharma, A., Dhakar, R., Sehgal, V.K., Chakraborty, D. and Das, D.K. (2021). Estimation and Validation of Actual Evapotranspiration (ET a) of Maize Wheat Cropping System Using SSEBop Model Over IARI Research Farm, New Delhi, India. J. Indian Soc. Remote Sens., 49: 1823-1837. DOI: https://doi.org/10.1007/s12524-021-01350-5
Nigam, R., Mallick, K., Bhattacharya, B.K., Pandey, V. and Patel, N.K. (2008). Heat flux estimation from MODIS TERRA-AQUA and validation over a semi-arid agroecosystem using scintillometry and model simulation. J. Agrometeorol, 10 (special issue-Part I), 75:81.
Norman, J.M., Kustas, W.P. and Humes, K.S. (1995). Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteorol., 77(3-4): 263-293. DOI: https://doi.org/10.1016/0168-1923(95)02265-Y
Olmedo, G.F., Ortega Farias, S., Fonseca Luengo, D., and Fuentes Peñailillo, F. (2016). Water: tools and functions to estimate actual evapotranspiración using Land Surface Energy Balance Models in R.
Parmar, H.V. and Gontia, N.K. (2016). Remote sensing based vegetation indices and crop coefficient relationship for estimation of crop evapotranspiration in Ozat-II canal command. J. Agrometeorol, 18(1): 137-140. DOI: https://doi.org/10.54386/jam.v18i1.918
Pimpale, A.R., Rajankar, P.B., Wadatkar, S.B., Wanjari, S.S. and Ramteke, I.K. (2015). Estimation of water requirement of wheat using multispectral vegetation indices. J. Agrometeorol, 17(2): 208-212. DOI: https://doi.org/10.54386/jam.v17i2.1008
Roerink, G.J., Su, Z. and Menenti, M. (2000). S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth Part B, 25(2), 147-157. DOI: https://doi.org/10.1016/S1464-1909(99)00128-8
Senay, G.B., Budde, M.E. and Verdin, J.P. (2011). Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model. Agric. Water Manage., 98(4): 606-618. DOI: https://doi.org/10.1016/j.agwat.2010.10.014
Sharma, V., Irmak, S., Kilic, A. and Mutiibwa, D. (2015). Application of remote sensing for quantifying and mapping surface energy fluxes in south central Nebraska: Analyses with respect to field measurements. Trans. ASABE, 58(5): 1265-1285. DOI: https://doi.org/ 10.13031/trans.58.11091
Sholihah, R., Dasanto, B.D. and Hendarti, H. (2016). Water allocation plan on part of Cisadane catchment in the district and city of Bogor. J. Tek. Hidraulik., 7(2): 195-210. DOI: https://doi.org/10.32679/jth.v7i2.568
Sobrino, J.A., Gómez, M., Jiménez-Muñoz, J.C., Olioso, A. and Chehbouni, G. (2005). A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX campaigns. J. Hydrol., 315(1-4): 117-125. DOI: https://doi.org/10.1016/j.jhydrol.2005.03.027
Sobrino, J.A., Souza da Rocha, N., Skoković, D., Suélen Käfer, P., López-Urrea, R., Jiménez-Muñoz, J. C. and Alves Rolim, S. B. (2021). Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain. Remote Sens., 13(18): 3686. DOI: https://doi.org/10.3390/rs13183686
Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 6(1): 85-100. DOI: https://doi.org/10.5194/hess-6-85-2002
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