Comparison of remote sensing methods for estimating actual daily evapotranspiration using Landsat 8 multispectral images

Document Type : Original Article

Authors

1 MSc student of Remote sensing, Department of photogrammetry and remote sensing, College of geodesy and geomatics, K.N.Toosi University, Tehran, Iran

2 Professor in Department of photogrammetry and remote sensing, College of geodesy and geomatics, K.N.Toosi University, Tehran, Iran

3 Ph.D. student of photogrammetry Department of photogrammetry and remote sensing, College of geodesy and geomatics, K.N.Toosi University, Tehran, Iran

Abstract

Background and Purpose: Agriculture serves as the cornerstone of the global economy, providing the main source of food and raw materials for various industries. However, the rising demand for food as a consequence of population growth represents a considerable threat to food security, particularly in light of the limited access to freshwater resources. It is noteworthy that agriculture alone consumes about 70% of the world's freshwater resources, thereby emphasizing the critical need to manage and enhance irrigation efficiency to ensure sustainable food production. Therefore, the management and enhancement of irrigation efficiency are essential. At the core of determining irrigation water requirements lies the concept of actual crop evapotranspiration (ETa), which represents the combined water loss from soil evaporation and plant transpiration. Accurate estimation of ETa is crucial in optimizing irrigation methods, maximizing crop yield, and minimizing water consumption. Various models and tools have been developed to estimate ETa, aiming to provide more user-friendly and efficient methods for farmers and researchers. Given the extensive application of ET estimation models, there is a clear need to focus on the development of accurate and efficient methods for determining this parameter. Thus, this study aims to compare user-friendly ETa estimation methods, including the EEFLUX system, the METRICTOOL tool, and the automatic hot and cold pixel selection method of the SEBAL and METRIC models.
Materials and Methods: The Earth Engine Evapotranspiration Flux (EEFLUX) is a version of the METRIC model that operates on the Google Earth Engine platform. METRICTOOL is a new tool in ArcGIS based on the METRIC model, offering enhanced pre-processing capabilities and automatic data identification. This tool reduces computation time by 50% and provides a user-friendly alternative to other existing METRIC model implementation platforms. The automatic hot and cold pixel selection method involves creating a binary map of eligible pixels using a rule-based classifier and a comprehensive search algorithm to identify hot and cold pixels based on defined criteria. To estimate ET using these methods, six Landsat 8 satellite images were utilized during the winter wheat crop planting period at Tehran University farms in Mohammadshahr Karaj. The evaluation of these methods was conducted using alfalfa reference evapotranspiration (ETr) calculated with the FAO-Penman-Monteith method as reference data.
Results and Discussion: The Root Mean Square Error (RMSE) values for the EEFLUX system, METRICTOOL, SEBAL, and automatic METRIC tools were determined as 2.45, 0.33, 0.39, and 2.76, respectively. Despite numerical differences, the evaporation and transpiration product of the EEFLUX system showed significant correlations with other methods. For instance, the R2 between ETa estimates from the EEFLUX system and the METRICTOOL tool was found to be 0.91. Although the data from the EEFLUX system may not be precise enough for local studies due to the use of CFSV2 global meteorological data in Iran, they yield acceptable results in large or global-scale studies. The METRICTOOL tool and automatic METRIC model exhibited the highest correlation (R2=0.99) and numerical agreement with each other, with RMSE values of 0.33 and 0.39, respectively, indicating higher accuracy compared to the automatic SEBAL model.
Conclusion: The results of the numerical analysis indicate that the automatic hot and cold pixel selection approach can achieve similar accuracy to that of the METRICTOOL tool. This automated approach enhances the efficiency of the model in terms of time and effectiveness, reducing the potential for human error in estimating evapotranspiration for new or inexperienced users, and making these models accessible to the public. Furthermore, EEFLUX data can be utilised for the implementation of management measures in large-scale studies.

Keywords


Allen, R. G., Morton, C., Kamble, B., Kilic, A., Huntington, J., Thau, D., . . . Robison, C. (2015, November). EEFlux: A Landsat-based Evapotranspiration mapping tool on the Google Earth Engine. 2015 ASABE / IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation - A Tribute to the Career of Terry Howell, Sr. Conference Proceedings. American Society of Agricultural and Biological Engineers. doi:10.13031/irrig.20152143511
Allen, R. G., Pereira, L. S., Raes, D., Smith, M., & others. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300, D05109.
Allen, R. G., Morton, C., Kamble, B., Kilic, A., Huntington, J., Thau, D., . . . Robison, C. (2015, November).
Allen, R. G., Tasumi, M., & Trezza, R. (2007, August). Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133, 380–394. doi:10.1061/(asce)0733-9437(2007)133:4(380)
Allen, R. G., Tasumi, M., Morse, A., Trezza, R., Wright, J. L., Bastiaanssen, W., . . . Robison, C. W. (2007, August). Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications. Journal of Irrigation and Drainage Engineering, 133, 395–406. doi:10.1061/(asce)0733-9437(2007)133:4(395)
Allen, R. G., Walter, I. A., Elliott, R., Howell, R., Itenfisu, D., & Jensen, M. (2005). RL Snyder, the ASCE standardized reference evapotranspiration equation. Environmental and Water Resources Institute of the American Society of Civil Engineers, 57.
Bastiaanssen, W. G. (2000, March). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229, 87–100. doi:10.1016/s0022-1694(99)00202-4
Bastiaanssen, W. G., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J., & van der Wal, T. (1998, December). A remote sensing surface energy balance algorithm for land (SEBAL). Journal of Hydrology, 212–213, 213–229. doi:10.1016/s0022-1694(98)00254-6
Bhattarai, N., Quackenbush, L. J., Im, J., & Shaw, S. B. (2017, July). A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models. Remote Sensing of Environment, 196, 178–192. doi:10.1016/j.rse.2017.05.009
Brutsaert, W., & Sugita, M. (1992, November). Application of self‐preservation in the diurnal evolution of the surface energy budget to determine daily evaporation. Journal of Geophysical Research: Atmospheres, 97, 18377–18382. doi:10.1029/92jd00255
Calzadilla, A., Rehdanz, K., Betts, R., Falloon, P., Wiltshire, A., & Tol, R. S. (2013, July). Climate change impacts on global agriculture. Climatic Change, 120, 357–374. doi:10.1007/s10584-013-0822-4
Chakraborty, S., & Newton, A. C. (2011, January). Climate change, plant diseases and food security: an overview. Plant Pathology, 60, 2–14. doi:10.1111/j.1365-3059.2010.02411.x
Crago, R. D. (1996, May). Conservation and variability of the evaporative fraction during the daytime. Journal of Hydrology, 180, 173–194. doi:10.1016/0022-1694(95)02903-6
Dang, C., Liu, Y., Yue, H., Qian, J., & Zhu, R. (2020, October). Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods. Canadian Journal of Remote Sensing, 47, 162–181. doi:10.1080/07038992.2020.1833186
Derakhshandeh, M., & Tombul, M. (2021, November). Calibration of METRIC Modeling for Evapotranspiration Estimation Using Landsat 8 Imagery Data. Water Resources Management, 36, 315–339. doi:10.1007/s11269-021-03029-5
Eswar, R., Sekhar, M., & Bhattacharya, B. K. (2017, December). Comparison of three remote sensing based models for the estimation of latent heat flux over India. Hydrological Sciences Journal, 62, 2705–2719. doi:10.1080/02626667.2017.1404067
Farah, H. O. (2001). Estimation of regional evaporation using a detailed agro-hydrological model. Journal of Hydrology, 229, 50–58.
Farah, H. O., Bastiaanssen, W. G., & Feddes, R. A. (2004, May). Evaluation of the temporal variability of the evaporative fraction in a tropical watershed. International Journal of Applied Earth Observation and Geoinformation, 5, 129–140. doi:10.1016/j.jag.2004.01.003
Fitzgerald, R. W., & Lees, B. G. (1994, March). Assessing the classification accuracy of multisource remote sensing data. Remote Sensing of Environment, 47, 362–368. doi:10.1016/0034-4257(94)90103-1
Ghaderi, A., Dasineh, M., Shokri, M., & Abraham, J. (2020, June). Estimation of Actual Evapotranspiration Using the Remote Sensing Method and SEBAL Algorithm: A Case Study in Ein Khosh Plain, Iran. Hydrology, 7, 36. doi:10.3390/hydrology7020036
Hodgson, M. E., Li, X., & Cheng, Y. (2004, December). A Parameterization Model for Transportation Feature Extraction. Photogrammetric Engineering & Remote Sensing, 70, 1399–1404. doi:10.14358/pers.70.12.1399
Im, J., & Hodgson, M. E. (2009, July). Characteristics of Search Spaces for Identifying Optimum Thresholds in Change Detection Studies. GIScience & Remote Sensing, 46, 249–272. doi:10.2747/1548-1603.46.3.249
Jawad, L. A., & Mohamed, H. A. (2020). Integrative Use of Penman-Monteith Equation with Remote Sensing and Geographical Information System Techniques to Estimate Evapotranspiration Vriances in Iraq. The Iraqi Journal of Agricultural Science, 51, 530–541.
Kamali, M. I., & Nazari, R. (2018, October). Determination of maize water requirement using remote sensing data and SEBAL algorithm. Agricultural Water Management, 209, 197–205. doi:10.1016/j.agwat.2018.07.035
Khatibi, A., & Krauter, S. (2021, February). Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications. Energies, 14, 882. doi:10.3390/en14040882
Knipper, K. R., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Prueger, J. H., Hain, C. R., . . . Sanchez, L. (2018, October). Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science, 37, 431–449. doi:10.1007/s00271-018-0591-y
Kumar, L., & Mutanga, O. (2018, September). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sensing, 10, 1509. doi:10.3390/rs10101509
Laipelt, L., Henrique Bloedow Kayser, R., Santos Fleischmann, A., Ruhoff, A., Bastiaanssen, W., Erickson, T. A., & Melton, F. (2021, August). Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 81–96. doi:10.1016/j.isprsjprs.2021.05.018
Mondal, I., Thakur, S., De, A., & De, T. K. (2022, March). Application of the METRIC model for mapping evapotranspiration over the Sundarban Biosphere Reserve, India. Ecological Indicators, 136, 108553. doi:10.1016/j.ecolind.2022.108553
Mountrakis, G., Im, J., & Ogole, C. (2011, May). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247–259. doi:10.1016/j.isprsjprs.2010.11.001
Mutanga, O., & Kumar, L. (2019, March). Google Earth Engine Applications. Remote Sensing, 11, 591. doi:10.3390/rs11050591
Nisa, Z., Khan, M. S., Govind, A., Marchetti, M., Lasserre, B., Magliulo, E., & Manco, A. (2021, February). Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy. Agronomy, 11, 345. doi:10.3390/agronomy11020345
Norman, J. M., Kustas, W. P., & Humes, K. S. (1995, December). Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77, 263–293. doi:10.1016/0168-1923(95)02265-y
Ramírez-Cuesta, J. M., Allen, R. G., Intrigliolo, D. S., Kilic, A., Robison, C. W., Trezza, R., . . . Lorite, I. J. (2020, September). METRIC-GIS: An advanced energy balance model for computing crop evapotranspiration in a GIS environment. Environmental Modelling & Software, 131, 104770. doi:10.1016/j.envsoft.2020.104770
Ramírez-Cuesta, J. M., Allen, R. G., Zarco-Tejada, P. J., Kilic, A., Santos, C., & Lorite, I. J. (2019, February). Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard. International Journal of Applied Earth Observation and Geoinformation, 74, 88–102. doi:10.1016/j.jag.2018.09.001
azagui, A., Abdeladim, K., Bouchouicha, K., Bachari, N., Semaoui, S., & Hadj Arab, A. (2021, June). A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures. Solar Energy, 221, 148–161. doi:10.1016/j.solener.2021.04.024
Roerink, G. J., Su, Z., & Menenti, M. (2000, January). S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25, 147–157. doi:10.1016/s1464-1909(99)00128-8
Saha, S. K., Ahmmed, R., & Jahan, N. (2022). Actual Evapotranspiration Estimation Using Remote Sensing: Comparison of Sebal and Metric Models. In Water Management: A View from Multidisciplinary Perspectives (pp. 365–383). Springer International Publishing. doi:10.1007/978-3-030-95722-3_18
Santos, C., Lorite, I. J., Tasumi, M., Allen, R. G., & Fereres, E. (2007, October). Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level. Irrigation Science, 26, 277–288. doi:10.1007/s00271-007-0093-9
Senay, G., Budde, M., Verdin, J., & Melesse, A. (2007, June). A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields. Sensors, 7, 979–1000. doi:10.3390/s7060979
Shamloo, N., Taghi Sattari, M., Apaydin, H., Valizadeh Kamran, K., & Prasad, R. (2021, August). Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods. International Journal of Digital Earth, 14, 1638–1658. doi:10.1080/17538947.2021.1962996
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325. doi:10.1109/jstars.2020.3026724
Sobrino, J. A., Souza da Rocha, N., Skoković, D., Suélen Käfer, P., López-Urrea, R., Jiménez-Muñoz, J. C., & Alves Rolim, S. B. (2021, September). Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain. Remote Sensing, 13, 3686. doi:10.3390/rs13183686
Su, Z. (2002, February). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6, 85–100. doi:10.5194/hess-6-85-2002
Sun, Z., Wei, B., Su, W., Shen, W., Wang, C., You, D., & Liu, Z. (2011, August). Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling, 54, 1086–1092. doi:10.1016/j.mcm.2010.11.039
Tasumi, M. (2003). Progress in operational estimation of regional evapotranspiration using satellite imagery. University of Idaho.
Tasumi, M., Trezza, R., Allen, R. G., & Wright, J. L. (2005, November). Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S. Irrigation and Drainage Systems, 19, 355–376. doi:10.1007/s10795-005-8138-9
 Thorp, K. R., Marek, G. W., DeJonge, K. C., Evett, S. R., & Lascano, R. J. (2019, September). Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model. Environmental Modelling & Software, 119, 214–227. doi:10.1016/j.envsoft.2019.06.007
Tian, D., Asadi, P., Medina, H., Ortiz, B., & Kesikka, I. (2020, March). A Climate Smart Framework for Forecasting Field-level Potential Evapotranspiration and Irrigation Requirement with Numerical Weather Predictions and Satellite Remote Sensing. doi:10.5194/egusphere-egu2020-11756
Wagle, P., Bhattarai, N., Gowda, P. H., & Kakani, V. G. (2017, June). Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 192–203. doi:10.1016/j.isprsjprs.2017.03.022
Wang, J., Li, H., & Lu, H. (2021, December). An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model. Journal of Water and Climate Change, 13, 926–942. doi:10.2166/wcc.2021.432
Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., & Wade, T. G. (2013, March). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment, 130, 294–304. doi:10.1016/j.rse.2012.12.001
Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., & Kycko, M. (2021, July). Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing, 13, 2581. doi:10.3390/rs13132581
Zhang, H., Anderson, R. G., & Wang, D. (2015, August). Satellite-based crop coefficient and regional water use estimates for Hawaiian sugarcane. Field Crops Research, 180, 143–154. doi:10.1016/j.fcr.2015.05.023