Validation of a Simple MODIS Land Surface Temperature-Based Model for Potential Evapotranspiration (PET) using Long-Term Global Dataset
DOI:
https://doi.org/10.54386/jam.v28i1.3285Keywords:
Potential evapotranspiration (PET), MODIS LST, FAO Penman–Monteith, Global validation, Climate zones, Remote sensing, Google earth engine (GEE)Abstract
Accurate and usable potential evapotranspiration (PET) estimation is important for managing water resources around the world, planning agriculture, and adapting to climate change. Complex energy balance models yield valuable insights; yet practical applications necessitate straightforward, resilient, and simply implementable long-term monitoring methodologies. This study confirms a more straightforward empirical model that estimates monthly PET only utilizing MODIS land surface temperature (LST) data of 25 years (2000–2024), addressing a deficiency in the comprehension of simple model transferability across global climatic regimes. The LST products (MOD11A1/MYD11A1) processed in Google Earth Engine to confirm the accuracy of PET predictions against the FAO-56 Penman–Monteith (FAO-PM) technique, which was based on data from 58 ground-based meteorological stations in 5 Major Köppen–Geiger climate zones. The model was very accurate (R² = 0.76, RMSE = 30.02 mm/month); however, it was completely unique in different areas because of environmental controls. The model worked well in the Continental and Mediterranean climate zones (R² = 0.93, NSE = 0.88), but it had trouble in the Tropical Wet (R² = 0.39, NSE = -6.15) and Polar (R² = 0.64, NSE = -2.64) regions because of the moisture in the air and the complicated way energy is divided. The initial comprehensive analysis of basic LST-based model constraints sets essential standards for operational implementation and underscores the necessity for climate-zone-specific parameterization in this global, long-term validation. The results enhance the comprehension of environmental influences on remote sensing-derived PET estimation and inform water resource management in a dynamic climate.
References
Afify, N. M., El-Shirbeny, M. A., El-Wesemy, A. F., & Nabil, M. (2023). Analyzing satellite data time-series for agricultural expansion and its water consumption in arid region: a case study of the Farafra oasis in Egypt’s Western Desert. Euro-Mediterranean Journal for Environmental Integration, 8(1), 129-142.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, 56.
Beck, H. E., McVicar, T. R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., ... & Miralles, D. G. (2023). High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Scientific data, 10(1), 724.
Caguiat, L. S., Saludes, R. B., Castro, M. L. Y., & Lampanyan, R. M. L. (2025). Machine learning modeling of reference evapotranspiration in Central Luzon, Philippines. Journal of Agrometeorology, 27(4), 464–469. https://doi.org/10.54386/jam.v27i4.2909
Chen, R., Ersi, K., Yang, J., Lu, S., & Zhao, W. (2004). Validation of five satellite-based precipitation products over a mountainous watershed in China. International Journal of Remote Sensing, 25(4), 683-700.
Danodia, A., Patel, N., Suresh Kumar, Singh, R., Satpathi, A., Chauhan, P., & A. S. Nain. (2024). Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing. Journal of Agrometeorology, 26(3), 271–278. https://doi.org/10.54386/jam.v26i3.2618
El-Shirbeny, M. A., Abdellatif, B., Ali, A. E. M., & Saleh, N. H. (2016). Evaluation of Hargreaves based on remote sensing method to estimate potential crop evapotranspiration. GEOMATE Journal, 11(23), 2143-2149.
El-Shirbeny, M. A., Ali, A. M., Savin, I., Poddubskiy, A., & Dokukin, P. (2021). Agricultural water monitoring for water management under pivot irrigation system using spatial techniques. Earth Systems and Environment, 5(2), 341-351.
El-Shirbeny, M. A., Biradar, C., Amer, K., & Paul, S. (2022). Evapotranspiration and vegetation cover classifications maps based on cloud computing at the Arab countries scale. Earth Systems and Environment, 6(4), 837-849.
El-Shirbeny, M., Saleh, S. M., Baioumy, E., Badr, M., Selim, A., Hendawy, E., & Abdelraouf, R. E. (2025). Impact of climate classification on evapotranspiration variability in arid regions of Saudi Arabia. Journal of Arid land Agriculture, 11, 121-130.
Ezenne, G. I., Eyibio, N. U., Tanner, J. L., Asoiro, F. U., & Obalum, S. E. (2023). An overview of uncertainties in evapotranspiration estimation techniques. Journal of Agrometeorology, 25(1), 173–182. https://doi.org/10.54386/jam.v25i1.2014
Faseyiku, O. O., Obiora-Okeke, O. A., Olowoselu, A. S., Olafusi, O. R., & Adewumi, J. R. (2024). Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria. Arabian Journal of Geosciences, 17(5), 153.
Gamal, R., El-Shirbeny, M., Abou-Hadid, A., Swelam, A., El-Gindy, A. G., Arafa, Y., & Nangia, V. (2022). Identification and quantification of actual evapotranspiration using integrated satellite data for sustainable water management in dry areas. Agronomy, 12(9), 2143.
Goldblatt, R., Addas, A., Crull, D., Maghrabi, A., Levin, G. G., & 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.
Jin, K., Liu, N., Tang, R., Sun, G., & Hao, L. (2025). Quantifying Long Term (2000–2020) Water Balances Across Nepal by Integrating Remote Sensing and an Ecohydrological Model. Remote Sensing. 17(11), 1819; https://doi.org/10.3390/rs17111819
Lee, S. J., Kim, N., & Lee, Y. (2021). Development of integrated crop drought index by combining rainfall, land surface temperature, evapotranspiration, soil moisture, and vegetation index for agricultural drought monitoring. Remote Sensing, 13(9), 1778.
Mehta, R., & Pandey, V. (2016). Crop water requirement (ETc) of different crops of middle Gujarat. Journal of Agrometeorology, 18(1), 78–82. https://doi.org/10.54386/jam.v18i1.906
Nousu, J.-P., Lafaysse, M., Mazzotti, G., Ala-aho, P., Marttila, H., Cluzet, B., Aurela, M., Lohila, A., Kolari, P., Boone, A., Fructus, M., & Launiainen, S. (2024). Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests. The Cryosphere, 18(1), 231–263. https://doi.org/10.5194/tc-18-231-2024
Ouyang, W., Tan, Z., Chen, Y. C., & Ren, G. (2025). Comparative evaluation of PET and mPET models for outdoor walking in hot-humid climates. Building and Environment, 271, 112636.
Peng, L., Sheffield, J., Wei, Z., Ek, M., & Wood, E. F. (2024). An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics. Earth System Dynamics, 15(5), 1277-1300.
Pimentel, R., Arheimer, B., Crochemore, L., Andersson, J. C. M., Pechlivanidis, I. G., & Gustafsson, D. (2023). Which potential evapotranspiration formula to use in hydrological modeling world‐wide? Water Resources Research, 59(5), e2022WR033447.
Salam, M. A., & Al Mazrooe, S. (2006). Evapotranspiration estimates and water balance of Kuwait. Journal of Agrometeorology, 8(2), 243–247. https://doi.org/10.54386/jam.v8i2.1055
Salmoiraghi, A., Zamboni, A., Toffaletti, S., Di Mauro, M., Malgieri, M., Fiorello, C., ... & Oss, S. (2025). Core of Sustainability Education: Bridging Theory and Practice in Teaching Climate Science to Future Mathematics and Physics Teachers. Sustainability, 17(11), 5120.
Singh, R. N., Mukherjee, J., Chaudhary, A., Banerjee, A., Singh, A. K., & Reddy, K. S. (2023). Exploring the landscape of contemporary crop micrometeorology: A bibliometric investigation. Journal of Agrometeorology, 25(3), 333–342. https://doi.org/10.54386/jam.v25i3.2320
Sun, Z., Wei, B., Su, W., Shen, W., Wang, C., You, D., & Liu, Z. (2011). Evapotranspiration estimation based on the SEBAL model in the Nansi Lake wetland of China. Mathematical and Computer Modelling, 54(3-4), 1086-1092.
Tang, R., Li, Z. L., & Sun, X. (2013). Temporal upscaling of instantaneous evapotranspiration on clear-sky days using the constant evapotranspiration assumption. Remote Sensing, 5(2), 434-453.
Wan, Z., Hook, S., & Hulley, G. (2021). MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes DAAC.
Zhao, Q., Sun, T., Guo, H., Li, Z., Chang, L., Yin, J., & Yao, Y. (2024). Two-step method of surface-based high-precision PET retrieval. Atmospheric Research, 304, 107406.
Zhou, S., & Yu, B. (2024). Physical basis of the potential evapotranspiration and its estimation over land. Journal of Hydrology, 641, 131825.
Zhu, B., Xie, X., Lu, C., Lei, T., Wang, Y., Jia, K., & Yao, Y. (2021). Extensive evaluation of a continental-scale high-resolution hydrological model using remote sensing and ground-based observations. Remote Sensing, 13(7), 1247.
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