Aghelpour, P., Varshavian, V., Khodamorad Pour, M., and Hamedi, Z. 2022. Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions. Scientific Reports, 12(1), 17363.##
Amani, M., Ghorbanian, A., Ahmadi, S.A., Kakooei, M., Moghimi, A., Mirmazloumi, S.M., Moghaddam, S.H.A., Mahdavi, S., Ghahremanloo, M., Parsian, S. and Wu, Q. 2020. Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350.##
Anderson, K., Fawcett, D., Cugulliere, A., Benford, S., Jones, D. and Leng, R. 2020. Vegetation expansion in the subnival Hindu Kush Himalaya. Global Change Biology, 26(3), 1608-1625.##
Arshad, M., Ma, X., Yin, J., Ullah, W., Ali, G., Ullah, S., and Ullah, I. 2021. Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan. Atmospheric Research, 249, 105341.##
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., Van Dijk, A. I., Weedon, G. P., and Wood, E. F. 2017. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12), 6201-6217.##
Chen, C., Chen, Q., Duan, Z., Zhang, J., Mo, K., Li, Z. and Tang, G. 2018. Multiscale comparative evaluation of the GPM IMERG v5 and TRMM 3B42 v7 precipitation products from 2015 to 2017 over a climate transition area of China. Remote Sensing, 10(6), 944.##
Chicco, D., Warrens, M. J., and Jurman, G. 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.##
Du, H., Tan, M. L., Zhang, F., Chun, K. P., Li, L., and Kabir, M. H. 2024. Evaluating the effectiveness of CHIRPS data for hydroclimatic studies. Theoretical and Applied Climatology, 155(3), 1519-1539.##
Et-Takaouy, C., Aqnouy, M., Boukholla, A., and Stitou El Messari, J. E. 2024. Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions. Mediterranean Geoscience Reviews, 6(2), 123-144##
Faghih H, Behmanesh J, Khalili K.2018. Simulation of Spatiotemporal Annual Precipitation Using Stochastic Models. Journal of Water and Soil Science, 22(1), 367–386. (In Farsi)##
Fang, J., Du, J., Xu, W., Shi, P., Li, M. and Ming, X. 2013. Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area. Advances in Water Resources, 61, 42-50.##
Firpi, O.A.A., 2016. Satellite data for all? Review of Google Earth Engine for archaeological remote sensing. Internet Archaeology, (42). https://doi.org/10.11141/ia.42.10##
Gao, F., Zhang, Y., Chen, Q., Wang, P., Yang, H., Yao, Y. and Cai, W. 2018. Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China. Atmospheric Research, 212, 150-157.##
Helmi, A.M., Abdelhamed, M.S. 2022. Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2. 0, TMPA 3B42 V7, and GPM IMERG V6 satellite precipitation datasets in Arabian arid regions. Water, 15(1), 92.##
Islam, M.N., Das, S. and Uyeda, H. 2010. Calibration of TRMM derived rainfall over Nepal during 1998-2007. The Open Atmospheric Science Journal, 4(1), 4(1), 12-23.##
Javanmard, S., Yatagai, A., Nodzu, M.I., BodaghJamali, J. and Kawamoto, H. 2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences, 25, 119-125.##
Kheimi, M.M., Gutub, S.2015. Assessment of Remotely-Sensed Precipitation Products Across the Saudi Arabia Region. Water Resources and Arid Environments, 4(1), 76–88.##
Kidd C, Levizzani V. 2011. Status of satellite precipitation retrievals. Hydrology and Earth System Sciences, 15(4), 1109-1116.##
Kim, K., Park, J., Baik, J. and Choi, M. 2017. Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmospheric Research, 187, 95-105.##
Lamptey, B. L. 2008. Comparison of gridded multisatellite rainfall estimates with gridded gauge rainfall over West Africa. Journal of Applied Meteorology and Climatology, 47(1), 185-205.##
Li, N., Wang, Z., Chen, X. and Austin, G. 2019. Studies of general precipitation features with TRMM PR data: An extensive overview. Remote Sensing, 11(1), 80.##
Li, X., Zhang, Q. and Xu, C.Y. 2014. Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin. Theoretical and applied climatology, 115, 713-729.##
Lockhoff, M., Zolina, O., Simmer, C., and Schulz, J. 2019. Representation of precipitation characteristics and extremes in regional reanalyses and satellite-and gauge-based estimates over western and central Europe. Journal of Hydrometeorology, 20(6), 1123-1145.##
Maúre, E.D.R., Ilyushchenko, S. and Terauchi, G. 2022. A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine. Remote Sensing, 14(19), 4906.##
Nwachukwu, P.N., Satge, F., Yacoubi, S.E., Pinel, S. and Bonnet, M.P. 2020. From trmm to gpm: how reliable are satellite-based precipitation data across Nigeria? Remote Sensing, 12(23), 3964.##
Partarini, N. M. C., Sujono, J., and Pratiwi, E. P. A. 2024. Correction and Validation of GPM-IMERG and CHIRPS Satellite Precipitation Data in the Lahor River Basin. ARPHA Proceedings, 7, 236-245.##
Prat, O. P., Nelson, B. R., Nickl, E., and Leeper, R. D. 2021. Global evaluation of gridded satellite precipitation products from the NOAA Climate Data Record program. Journal of Hydrometeorology, 22(9), 2291-2310.##
Sahour, H., Sultan, M., Vazifedan, M., Abdelmohsen, K., Karki, S., Yellich, J. A. and Elbayoumi, T. M. 2020. Statistical applications to downscale GRACE-derived terrestrial water storage data and to fill temporal gaps. Remote Sensing, 12(3), 533.##
Saicharan, V. and Rangaswamy, S.H. 2023. A comparison and ranking study of monthly average rainfall datasets with IMD gridded data in India. Sustainability, 15(7), 5758.##
Shalishe, A., Berihu, T., and Arba, Y. 2024. Performance evaluation of multi-satellite rainfall products for analyzing rainfall variability in Abaya–Chamo basin: Southern Ethiopia. Journal of Earth System Science, 133(3), 139.##
Shirvani, A. Fakhari Zade Shirazi, E. 2014. Comparison of ground based observation of precipitation with TRMM satellite estimations in Fars Province. Journal of Agricultural Meteorology, 2(2), 1-15. (In Farsi)##
Tang, G., Clark, M.P., Papalexiou, S.M., Ma, Z. and Hong, Y. 2020. Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote sensing of environment, 240, 111697.##
Turk, F.J. and Miller, S.D. 2005. Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Transactions on Geoscience and Remote Sensing, 43(5), 1059-1069.##
Varga, Á. J., and Breuer, H. 2024. Evaluation of multiple surface-, satellite-, reanalysis-, and WRF model-based gridded precipitation datasets over south-east Central Europe. Atmospheric Research, 298, 107138.##
Wei, L., Jiang, S., Ren, L., Wang, M., Zhang, L., Liu, Y., and Yang, X. 2021. Evaluation of seventeen satellite-, reanalysis-, and gauge-based precipitation products for drought monitoring across mainland China. Atmospheric Research, 263, 105813.##
Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K. and Gorelick, N. 2017. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sensing, 9(10), 1065.##
Xu, R., Tian, F., Yang, L., Hu, H., Lu, H. and Hou, A. 2017. Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high‐density rain gauge network. Journal of Geophysical Research: Atmospheres, 122(2), 910-924.##
Yang, Q., Dai, Q., Han, D., Chen, Y., and Zhang, S. 2019. Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation. Atmospheric Research, 228, 1-13.##
Zhang, Y., Li, X., Wang, K. 2023. A robust approach for multi-temporal remote sensing analysis using spectral indices in hydrological modeling. Journal of Hydrology, 625, 130115.##
Zhao, Y., Feng, D., Yu, L. 2022. Empirical approaches for bathymetry estimation from Sentinel-2 imagery in inland waters. Remote Sensing of Environment, 272, 112956.##
