1. Allen, R., et al., FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998. 56: p. 26-40.

2. McKenney, M.S. and N.J. Rosenberg, Sensitivity of some potential evapotranspiration estimation methods to climate change. Agricultural and Forest Meteorology, 1993. 64(1): p. 81-110. [DOI:10.1016/0168-1923(93)90095-Y]

3. Fisher, J.B., R.J. Whittaker, and Y. Malhi, ET come home: potential evapotranspiration in geographical ecology. Global Ecology and Biogeography, 2011. 20(1): p. 1-18. [DOI:10.1111/j.1466-8238.2010.00578.x]

4. Thornthwaite, C.W., An Approach toward a Rational Classification of Climate. Geographical Review, 1948. 38(1): p. 55-94. [DOI:10.2307/210739]

5. Hao, L., et al., A Comprehensive Study on Factors Affecting the Calibration of Potential Evapotranspiration Derived from the Thornthwaite Model. Remote sensing, 2022. 14(18): p. 4644-4644. [DOI:10.3390/rs14184644]

6. Hsin-Fu, Y., Comparison of Evapotranspiration Methods Under Limited Data, in Current Perspective to Predict Actual Evapotranspiration, B. Daniel, Editor. 2017, IntechOpen: Rijeka. p. Ch. 1.

7. Li, H., et al., Estimation of diurnal-provided potential evapotranspiration using GNSS and meteorological products. Atmospheric Research, 2022. 280: p. 106424. [DOI:10.1016/j.atmosres.2022.106424]

8. Sheffield, J., E. Wood, and M. Roderick, Little Change in Global Drought over the Past 60 Years. Nature, 2012. 491: p. 435-438. [DOI:10.1038/nature11575]

9. Pereira, A. and W. Pruitt, Adaptation of the Thornthwaite Scheme for Estimating Daily Reference Evapotranspiration. Agricultural Water Management, 2004. 66: p. 251-257. [DOI:10.1016/j.agwat.2003.11.003]

10. Zhao, Q., et al., High-Precision Potential Evapotranspiration Model Using GNSS Observation. Remote Sensing, 2021. 13: p. 4848. [DOI:10.3390/rs13234848]

11. H. Hargreaves, G. and Z. A. Samani, Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1985. 1(2): p. 96-99. [DOI:10.13031/2013.26773]

12. Zotarelli, L., et al., Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida, 2010. 8. [DOI:10.32473/edis-ae459-2010]

13. Vicente-Serrano, S., S. Beguería, and J.I. López-Moreno, A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 2010. 23: p. 1696-1718. [DOI:10.1175/2009JCLI2909.1]

14. Zhao, Q., et al., Retrieval of a High-Precision Drought Monitoring Index by Using GNSS-Derived ZTD and Temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. PP: p. 1-1. [DOI:10.1109/JSTARS.2021.3106703]

15. Mori, M., K. Hiramatsu, and M. Harada, Estimating Daily Potential Evapotranspiration using the Relation between GPS-derived Precipitable Water Vapor and Surface Temperature. Transactions of The Japanese Society of Irrigation, Drainage and Rural Engineering, 2007. 2007(250): p. 347-352,a1.

16. Zhao, Q., et al., Improved Drought Monitoring Index Using GNSS- Derived Precipitable Water Vapor over the Loess Plateau Area. Sensors, 2019. 19: p. 5566. [DOI:10.3390/s19245566]

17. Ma, X., et al., A novel method of retrieving potential ET in China. Journal of Hydrology, 2021. 598: p. 126271. [DOI:10.1016/j.jhydrol.2021.126271]

18. Pipatsitee, P., et al., Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor. Heliyon, 2023. 9(7): p. e17747. [DOI:10.1016/j.heliyon.2023.e17747]

19. Aschonitis, V., et al., Correcting Thornthwaite potential evapotranspiration using a global grid of local coefficients to support temperature-based estimations of reference evapotranspiration and aridity indices. 2021. [DOI:10.5194/essd-2021-115]

20. Bevis, M., et al., GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System. Journal of Geophysical Research, 1992. 97. [DOI:10.1029/92JD01517]

21. Abdellaoui, H., N. Zaourar, and S. Kahlouche, Contribution of permanent stations GPS data to estimate the water vapor content over Algeria. Arabian Journal of Geosciences, 2019. 12. [DOI:10.1007/s12517-019-4226-2]

22. Saastamoinen, J., Contributions to the theory of atmospheric refraction. Bulletin Géodésique (1946-1975), 1972. 105(1): p. 279-298. [DOI:10.1007/BF02521844]

23. Adavi, Z., R. Weber, and M.F. Glaner, Assessment of regularization techniques in GNSS tropospheric tomography based on single- and dual-frequency observations. GPS Solutions, 2021. 26(1): p. 21. [DOI:10.1007/s10291-021-01202-2]

24. Hastie, T., et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Math. Intell., 2004. 27: p. 83-85. [DOI:10.1007/BF02985802]

25. James, G., et al., An Introduction to Statistical Learning. 2013. 426. [DOI:10.1007/978-1-4614-7138-7]

26. Bruno, D., et al., Climate of Los Angeles, California. 2000.