{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:06:34Z","timestamp":1788836794079,"version":"build-2803163510"},"reference-count":123,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971340"],"award-info":[{"award-number":["41971340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41271410"],"award-info":[{"award-number":["41271410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["RSP2023R296"],"award-info":[{"award-number":["RSP2023R296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["41971340"],"award-info":[{"award-number":["41971340"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["41271410"],"award-info":[{"award-number":["41271410"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2023R296"],"award-info":[{"award-number":["RSP2023R296"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The inclusion of physiographic and atmospheric influences is critical for spatial modeling of orographic precipitation in complex terrains. However, attempts to incorporate cloud cover frequency (CCF) data when interpolating precipitation are limited. CCF considers the rain shadow effect during interpolation to avoid an overly strong relationship between elevation and precipitation in areas at equivalent altitudes across rain shadows. Conventional multivariate regression or geostatistical methods assume the precipitation\u2013explanatory variable relationship to be steady, even though this relation is often non-stationarity in complex terrains. This study proposed a novel spatial mapping approach for precipitation that combines regression-kriging (RK) to leverage its advantages over conventional multivariate regression and the spatial autocorrelation structure of residuals via kriging. The proposed hybrid model, RK (GT + CCF), utilized CCF and other physiographic factors to enhance the accuracy of precipitation interpolation. The implementation of this approach was examined in a mountainous region of southern Syria using in situ monthly precipitation data from 57 rain gauges. The RK model\u2019s performance was compared with conventional multivariate regression models (CMRMs) that used geographical and topographical (GT) factors and CCF as predictors. The results indicated that the RK model outperformed the CMRMs with a root mean square error of &lt;8 mm, a mean absolute percentage error range of 5\u201315%, and an R2 range of 0.75\u20130.96. The findings of this study showed that the incorporation of MODIS\u2013CCF with physiographic variables as covariates significantly improved the interpolation accuracy by 5\u201320%, with the largest improvement in modeling precipitation in March.<\/jats:p>","DOI":"10.3390\/rs15092435","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:03:31Z","timestamp":1683511411000},"page":"2435","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8925-7918","authenticated-orcid":false,"given":"Karam","family":"Alsafadi","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuoben","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0384-9061","authenticated-orcid":false,"given":"Bashar","family":"Bashir","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-8406","authenticated-orcid":false,"given":"Ehsan","family":"Sharifi","sequence":"additional","affiliation":[{"name":"Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alsalman","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6073-0860","authenticated-orcid":false,"given":"Amit","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-6452","authenticated-orcid":false,"given":"Shamsuddin","family":"Shahid","sequence":"additional","affiliation":[{"name":"Department of Water & Environmental Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1080\/10106049.2019.1583291","article-title":"An Integration of Bioclimatic, Soil, and Topographic Indicators for Viticulture Suitability Using Multi-Criteria Evaluation: A Case Study in the Western Slopes of Jabal Al Arab\u2014Syria","volume":"35","author":"Alsafadi","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moreira, R.M., dos Santos, B.C., Sanches, R.G., Bourscheidt, V., de Sales, F., Sieber, S., and de Souza, P.H. (2023). Precipitation Variability for Protected Areas of Primary Forest and Pastureland in Southwestern Amaz\u00f4nia. Climate, 11.","DOI":"10.3390\/cli11020027"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ray, R.L., Sishodia, R.P., and Tefera, G.W. (2022). Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas. Remote Sens., 14.","DOI":"10.3390\/rs14163860"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.5194\/hess-15-2259-2011","article-title":"Geostatistical Interpolation of Daily Rainfall at Catchment Scale: The Use of Several Variogram Models in the Ourthe and Ambleve Catchments, Belgium","volume":"15","author":"Ly","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1175\/JTECH-D-22-0024.1","article-title":"Daily High-Resolution Temperature and Precipitation Fields for the Contiguous United States from 1951 to Present","volume":"39","author":"Durre","year":"2022","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1659\/mrd.0944","article-title":"A Multivariate Regression Model for Predicting Precipitation in the Daqing Mountains","volume":"28","author":"Sun","year":"2008","journal-title":"Mt. Res. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1002\/joc.4777","article-title":"Geographically Weighted Regression Based Quantification of Rainfall-Topography Relationship and Rainfall Gradient in Central Himalayas","volume":"37","author":"Kumari","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1002\/joc.4990","article-title":"Comparison of spatial interpolation techniques to generate high-resolution climate surfaces for Nigeria","volume":"37","author":"Arowolo","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, Q., Li, Z., Wang, L., Huang, Y., Wang, Y., and Li, L. (2019). Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging. Water, 11.","DOI":"10.3390\/w11030579"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/cr021001","article-title":"A high-resolution data set of surface climate over global land areas","volume":"21","author":"New","year":"2002","journal-title":"Clim. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/joc.3711","article-title":"Updated high-resolution grids of monthly climatic observations\u2013the CRU TS3. 10 Dataset","volume":"34","author":"Harris","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/j.2041-210X.2011.00134.x","article-title":"CliMond: Global High-Resolution Historical and Future Scenario Climate Surfaces for Bioclimatic Modelling","volume":"3","author":"Kriticos","year":"2012","journal-title":"Methods Ecol. Evol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karger, D.N., Conrad, O., B\u00f6hner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., and Kessler, M. (2017). Climatologies at high resolution for the earth\u2019s land surface areas. Sci. Data, 4.","DOI":"10.1038\/sdata.2017.122"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., and Hegewisch, K.C. (2018). TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958\u20132015. Sci. Data, 5.","DOI":"10.1038\/sdata.2017.191"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1002\/joc.3848","article-title":"An update of high-resolution monthly climate surfaces for Mexico","volume":"34","author":"Manjarrez","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1002\/joc.1688","article-title":"Physiographically Sensitive Mapping of Climatological Temperature and Precipitation across the Conterminous United States","volume":"28","author":"Daly","year":"2008","journal-title":"Int. J. Climatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1002\/joc.4986","article-title":"High-Resolution Precipitation Mapping in a Mountainous Watershed: Ground Truth for Evaluating Uncertainty in a National Precipitation Dataset","volume":"37","author":"Daly","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s00704-006-0264-2","article-title":"Monthly Precipitation Mapping of the Iberian Peninsula Using Spatial Interpolation Tools Implemented in a Geographic Information System","volume":"89","author":"Ninyerola","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s40641-014-0001-3","article-title":"The Regional Downscaling Approach: A Brief History and Recent Advances","volume":"1","author":"Rockel","year":"2015","journal-title":"Curr. Clim. Change Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1002\/joc.5933","article-title":"High-resolution monthly precipitation climatologies over Norway (1981\u20132010): Joining numerical model data sets and in situ observations","volume":"39","author":"Crespi","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1038\/s41597-021-01084-6","article-title":"Global Daily 1 Km Land Surface Precipitation Based on Cloud Cover-Informed Downscaling","volume":"8","author":"Karger","year":"2021","journal-title":"Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Qin, B., Zhao, S., and Duan, Z. (2020). Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area. Front. Earth Sci., 8.","DOI":"10.3389\/feart.2020.536337"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1175\/2011JCLI4109.1","article-title":"A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain","volume":"25","author":"Gutmann","year":"2012","journal-title":"J. Clim."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5859","DOI":"10.5194\/hess-26-5859-2022","article-title":"Machine-Learning-Based Downscaling of Modelled Climate Change Impacts on Groundwater Table Depth","volume":"26","author":"Schneider","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Serifi, A., G\u00fcnther, T., and Ban, N. (2021). Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks. Front. Clim., 3.","DOI":"10.3389\/fclim.2021.656479"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2020WR029308","DOI":"10.1029\/2020WR029308","article-title":"Deep Learning for Daily Precipitation and Temperature Downscaling","volume":"57","author":"Wang","year":"2021","journal-title":"Water Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Salimi, A.H., Masoompour Samakosh, J., Sharifi, E., Hassanvand, M.R., Noori, A., and von Rautenkranz, H. (2019). Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data. Water, 11.","DOI":"10.3390\/w11081653"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1080\/02508060708692227","article-title":"Spatial Interpolation of Annual Precipitation in South Africa-Comparison and Evaluation of Methods","volume":"32","author":"Coulibaly","year":"2007","journal-title":"Water Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s00704-022-03927-7","article-title":"Spatial Interpolation Methods for Estimating Monthly Rainfall Distribution in Thailand","volume":"148","author":"Chutsagulprom","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"ref_31","unstructured":"Vargas-Guzman, J.A., and Warrick, A.W. (1997). Geostatistics for Natural Resources Evaluation: Pierre Goovaerts, Oxford University Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.5194\/hess-10-197-2006","article-title":"Distance in Spatial Interpolation of Daily Rain Gauge Data","volume":"10","author":"Ahrens","year":"2006","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0022-1694(00)00144-X","article-title":"Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall","volume":"228","author":"Goovaerts","year":"2000","journal-title":"J. Hydrol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1080\/02626667.2012.710334","article-title":"Incorporating elevation in rainfall interpolation in Tunisia using geostatistical methods","volume":"57","author":"Feki","year":"2012","journal-title":"Hydrol. Sci. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1002\/hyp.11163","article-title":"Cokriging for Enhanced Spatial Interpolation of Rainfall in Two Australian Catchments","volume":"31","author":"Adhikary","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ecoinf.2010.12.003","article-title":"A Review of Comparative Studies of Spatial Interpolation Methods in Environmental Sciences: Performance and Impact Factors","volume":"6","author":"Li","year":"2011","journal-title":"Ecol. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1002\/joc.5878","article-title":"Uncertainty in Gridded Precipitation Products: Influence of Station Density, Interpolation Method and Grid Resolution","volume":"39","author":"Herrera","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_38","first-page":"115","article-title":"Comparison of Regression and Kriging Techniques for Mapping the Average Annual Precipitation of Turkey","volume":"19","author":"Bostan","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/joc.614","article-title":"Spatial Variations in the Average Rainfall-Altitude Relationship in Great Britain: An Approach Using Geographically Weighted Regression","volume":"21","author":"Brunsdon","year":"2001","journal-title":"Int. J. Climatol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1002\/joc.1892","article-title":"Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom","volume":"30","author":"Lloyd","year":"2010","journal-title":"Int. J. Climatol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4171","DOI":"10.1002\/joc.5057","article-title":"Nonstationary modelling framework for rainfall interpolation in complex terrain","volume":"37","author":"Kumari","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s00704-012-0716-9","article-title":"Spatial Interpolation of Monthly Climate Data for Finland: Comparing the Performance of Kriging and Generalized Additive Models","volume":"112","author":"Aalto","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0198-9715(01)00009-6","article-title":"Geographically Weighted Summary Statistics\u2014A Framework for Localised Exploratory Data Analysis","volume":"26","author":"Brunsdon","year":"2002","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Papacharalampous, G., Tyralis, H., Doulamis, A., and Doulamis, N. (2023). Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data. Water, 15.","DOI":"10.5194\/egusphere-egu23-3296"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/13658810110038951","article-title":"Artificial Neural Networks as a Tool for Spatial Interpolation","volume":"15","author":"Rigol","year":"2001","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1623\/hysj.52.2.305","article-title":"Interpolating Monthly Precipitation by Self-Organizing Map (SOM) and Multilayer Perceptron (MLP)","volume":"52","author":"Kalteh","year":"2007","journal-title":"Hydrol. Sci. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4631","DOI":"10.1007\/s00500-014-1456-9","article-title":"An Interpretable Fuzzy Monthly Rainfall Spatial Interpolation System for the Construction of Aerial Rainfall Maps","volume":"20","author":"Kajornrit","year":"2016","journal-title":"Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sekuli\u0107, A., Kilibarda, M., Heuvelink, G.B.M., Nikoli\u0107, M., and Bajat, B. (2020). Random Forest Spatial Interpolation. Remote Sens., 12.","DOI":"10.3390\/rs12101687"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5667","DOI":"10.5194\/hess-25-5667-2021","article-title":"Easy-to-Use Spatial Random-Forest-Based Downscaling-Calibration Method for Producing Precipitation Data with High Resolution and High Accuracy","volume":"25","author":"Chen","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.proeng.2016.07.514","article-title":"A hybrid model for statistical downscaling of daily rainfall","volume":"154","author":"Pour","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.jhydrol.2019.01.035","article-title":"Copula-Based Stochastic Uncertainty Analysis of Satellite Precipitation Products","volume":"570","author":"Sharifi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3962","DOI":"10.1002\/joc.6051","article-title":"Development of a Hybrid Model to Interpolate Monthly Precipitation Maps Incorporating the Orographic Influence","volume":"39","author":"Llasat","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, D., Zheng, S., Liu, S., Lo\u00e1iciga, H.A., and Li, W. (2020). Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging. Remote Sens., 12.","DOI":"10.3390\/rs12162547"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Alsafadi, K., Mohammed, S., Mokhtar, A., Sharaf, M., and He, H. (2021). Fine-Resolution Precipitation Mapping over Syria Using Local Regression and Spatial Interpolation. Atmos. Res., 256.","DOI":"10.1016\/j.atmosres.2021.105524"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1002\/joc.1762","article-title":"Examination of Selected Atmospheric and Orographic Effects on Monthly Precipitation of Taiwan Using the ASOADeK Model","volume":"29","author":"Guan","year":"2009","journal-title":"Int. J. Climatol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1175\/JHM448.1","article-title":"Geostatistical Mapping of Mountain Precipitation Incorporating Autosearched Effects of Terrain and Climatic Characteristics","volume":"6","author":"Guan","year":"2005","journal-title":"J. Hydrometeorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0016-7061(95)00007-B","article-title":"Further Results on Prediction of Soil Properties from Terrain Attributes: Heterotopic Cokriging and Regression-Kriging","volume":"67","author":"Odeh","year":"1995","journal-title":"Geoderma"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.cageo.2007.05.001","article-title":"About Regression-Kriging: From Equations to Case Studies","volume":"33","author":"Hengl","year":"2007","journal-title":"Comput. Geosci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s00704-014-1105-3","article-title":"Mapping the Mean Annual Precipitation of China Using Local Interpolation Techniques","volume":"119","author":"Sun","year":"2014","journal-title":"Theor. Appl. Climatol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1007\/s11269-015-0935-9","article-title":"Estimating spatial precipitation using regression kriging and artificial neural network residual kriging (RKNNRK) hybrid approach","volume":"29","author":"Seo","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1002\/met.1797","article-title":"Modelling long-term monthly rainfall using geographical inputs: Assessing heuristic and geostatistical models","volume":"26","author":"Kisi","year":"2019","journal-title":"Meteorol. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lei, J., Quan, Q., Li, P., and Yan, D. (2021). Research on monthly precipitation prediction based on the least square support vector machine with multi-factor integration. Atmosphere, 12.","DOI":"10.3390\/atmos12081076"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tan, J., Xie, X., Zuo, J., Xing, X., Liu, B., Xia, Q., and Zhang, Y. (2021). Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with multiple-covariates. J. Hydrol., 598.","DOI":"10.1016\/j.jhydrol.2021.126270"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1002\/joc.3764","article-title":"High-resolution temperature climatology for Italy: Interpolation method intercomparison","volume":"34","author":"Brunetti","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, M., He, G., Zhang, Z., Wang, G., Zhang, Z., Cao, X., Wu, Z., and Liu, X. (2017). Comparison of spatial interpolation and regression analysis models for an estimation of monthly near surface air temperature in China. Remote Sens., 9.","DOI":"10.3390\/rs9121278"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"D13106","DOI":"10.1029\/2007JD009318","article-title":"Modeling air temperature through a combination of remote sensing and GIS data J","volume":"113","author":"Ninyerola","year":"2008","journal-title":"Geophys. Res. Atmos."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s00704-021-03675-0","article-title":"Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal","volume":"145","author":"Antal","year":"2021","journal-title":"Theor. Appl. Climatol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.rse.2008.10.004","article-title":"Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula","volume":"113","author":"Immerzeel","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1016\/j.rse.2011.06.009","article-title":"A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China","volume":"115","author":"Jia","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lu, X., Tang, G., Wang, X., Liu, Y., Wei, M., and Zhang, Y. (2020). The development of a two-step merging and downscaling method for satellite precipitation products. Remote Sens., 12.","DOI":"10.3390\/rs12030398"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jhydrol.2011.05.016","article-title":"Validation of surface soil moisture from AMSR-E using auxiliary spatial data in the transboundary Indus Basin","volume":"405","author":"Cheema","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1175\/1520-0493(1981)109<1081:OTRBSO>2.0.CO;2","article-title":"On the relationship between satellite-observed cloud cover and precipitation","volume":"109","author":"Richards","year":"1981","journal-title":"Mon. Weather Rev."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1175\/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2","article-title":"The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982\u201384","volume":"115","author":"Arkin","year":"1987","journal-title":"Mon. Weather Rev."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1175\/JHM-D-16-0203.1","article-title":"Revisiting Hydrometeorology Using Cloud and Climate Observations","volume":"18","author":"Betts","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s40562-023-00273-y","article-title":"Modeling the Impacts of Projected Climate Change on Wheat Crop Suitability in Semi-Arid Regions Using the AHP-Based Weighted Climatic Suitability Index and CMIP6","volume":"10","author":"Alsafadi","year":"2023","journal-title":"Geosci. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wilson, A.M., and Jetz, W. (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol., 14.","DOI":"10.1371\/journal.pbio.1002415"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Danielson, J.J., and Gesch, D.B. (2011). Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), US Department of the Interior, US Geological Survey.","DOI":"10.3133\/ofr20111073"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/joc.3370060607","article-title":"A homogeneity test applied to precipitation data","volume":"6","author":"Alexandersson","year":"1986","journal-title":"J. Climatol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5194\/asr-3-23-2009","article-title":"Data quality control and homogenization of air temperature and precipitation series in the area of the Czech Republic in the period 1961\u20132007","volume":"3","year":"2009","journal-title":"Adv. Sci. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1002\/joc.5162","article-title":"High-resolution precipitation downscaling in mountainous areas over China: Development and application of a statistical mapping approach","volume":"38","author":"Zhu","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1175\/JAM2548.1","article-title":"High-Resolution Spatial Modeling of Daily Weather Elements for a Catchment in the Oregon Cascade Mountains, United States","volume":"46","author":"Daly","year":"2007","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1002\/joc.1988","article-title":"Seasonal precipitation interpolation at the Valencia region with multivariate methods using geographic and topographic information","volume":"30","author":"Boronat","year":"2010","journal-title":"Int. J. Climatol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s11629-015-3522-x","article-title":"A topographical model for precipitation pattern in the Tibetan Plateau","volume":"13","author":"Qi","year":"2016","journal-title":"J. Mt. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1002\/joc.671","article-title":"A statistical\u2013topographic model using an omnidirectional parameterization of the relief for mapping orographic rainfall","volume":"22","author":"Drogue","year":"2002","journal-title":"Int. J. Climatol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.jhydrol.2004.10.026","article-title":"Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain","volume":"308","author":"Lloyd","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1029\/2018JD028795","article-title":"Downscaling Satellite Precipitation Estimates with Multiple Linear Regression, Artificial Neural Networks, and Spline Interpolation Techniques","volume":"124","author":"Sharifi","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Zandi, O., Zahraie, B., Nasseri, M., and Behrangi, A. (2022). Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area. Atmos. Res., 272.","DOI":"10.1016\/j.atmosres.2022.106159"},{"key":"ref_91","unstructured":"Peterson, P., Baker, E., and McGaw, B. (2010). Principal Components Analysis, Elsevier. [3rd ed.]."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"WIREs Comput. Stat."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1080\/09715010.2019.1653799","article-title":"Assessing and solving multicollinearity in sediment transport prediction models using principal component analysis","volume":"27","author":"Sulaiman","year":"2021","journal-title":"ISH J. Hydraul. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Chen, T., Ren, L., Yuan, F., Yang, X., Jiang, S., Tang, T., Liu, Y., Zhao, C., and Zhang, L. (2017). Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling. Water, 9.","DOI":"10.3390\/w9050342"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"E2445","DOI":"10.1002\/joc.6857","article-title":"Analysis of Monthly and Annual Rainfall Variability Using Linear Models in the State of Mato Grosso Do Sul, Midwest of Brazil","volume":"41","author":"Abreu","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_96","unstructured":"Tveito, O.E., Wegehenkel, M., and Wel, F.V.D. (2008). Cost Action 719, EU Publications."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/0022-1694(95)02704-S","article-title":"Impact of small-scale spatial rainfall variability on runoff modeling","volume":"173","author":"Goodrich","year":"1995","journal-title":"J. Hydrol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.atmosres.2009.04.008","article-title":"Spatial and temporal characteristics of the annual rainfall frequency distribution in Cyprus","volume":"94","author":"Michaelides","year":"2009","journal-title":"Atmos. Res."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Elbeltagi, A., Kumari, N., Dharpure, J.K., Mokhtar, A., Alsafadi, K., Kumar, M., Mehdinejadiani, B., Ramezani Etedali, H., Brouziyne, Y., and Towfiqul Islam, A.R.M. (2021). Prediction of combined terrestrial evapotranspiration index (CTEI) over large river basin based on machine learning approaches. Water, 13.","DOI":"10.3390\/w13040547"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"65503","DOI":"10.1109\/ACCESS.2021.3074305","article-title":"Estimation of SPEI meteorological drought using machine learning algorithms","volume":"9","author":"Mokhtar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Li, X., He, X., Li, X., Du, Y., Yang, G., Tian, H., and Yu, J. (2023). Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China. Front. Earth Sci., 11.","DOI":"10.3389\/feart.2023.1128990"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Hong, Y., Adler, R., and Huffman, G. (2006). Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL028010"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ceron, W.L., Andreoli, R.V., Kayano, M.T., Canchala, T., Carvajal-Escobar, Y., and Souza, R.A. (2021). Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America. An. Acad. Bras. Cienc., 93.","DOI":"10.1590\/0001-3765202120190674"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Alsafadi, K., Bi, S., Abdo, H.G., Al Sayah, M.J., Ratonyi, T., Harsanyi, E., and Mohammed, S. (2022). Spatial\u2013temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean. Environ. Sci. Pollut. Res., 1\u201319.","DOI":"10.1007\/s11356-022-24012-6"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Huang, Z., Derin, Y., Kirstetter, P.E., and Li, Y. (2022). Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain. EEE Geosci. Remote Sens. Lett., 19.","DOI":"10.1109\/LGRS.2022.3212644"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Kumar, A., Kumar, M., Pandey, R., ZhiGuo, Y., and Cabral-Pinto, M. (2021). Forest Soil Nutrient Stocks along Altitudinal Range of Uttarakhand Himalayas: An Aid to Nature Based Climate Solutions. Catena, 207.","DOI":"10.1016\/j.catena.2021.105667"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Bhardwaj, D.R., Tahiry, H., Sharma, P., Pala, N.A., Kumar, D., Kumar, A. (2021). Influence of Aspect and Elevational Gradient on Vegetation Pattern, Tree Characteristics and Ecosystem Carbon Density in Northwestern Himalayas. Land, 10.","DOI":"10.3390\/land10111109"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1002\/2013JD020611","article-title":"Evaluation of interpolation techniques for the creation of gridded daily precipitation (1\u00d7 1 km2); Cyprus, 1980\u20132010","volume":"119","author":"Camera","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"7729","DOI":"10.5194\/amt-14-7729-2021","article-title":"Inpainting Radar Missing Data Regions with Deep Learning","volume":"14","author":"Geiss","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1175\/1520-0442(2001)014<1864:RCICTF>2.0.CO;2","article-title":"Recent changes in cloud-type frequency and inferred increases in convection over the United States and the former USSR","volume":"14","author":"Sun","year":"2001","journal-title":"J. Clim."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/met.1745","article-title":"Investigating changes in cloud cover using the long-term record of precipitation extremes","volume":"26","author":"Mishra","year":"2019","journal-title":"Meteorol. Appl."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"4899","DOI":"10.5194\/acp-21-4899-2021","article-title":"Observed trends in clouds and precipitation (1983\u20132009): Implications for their cause(s)","volume":"21","author":"Zhong","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/joc.1276","article-title":"Very high resolution interpolated climate surfaces for global land areas","volume":"25","author":"Hijmans","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1038\/s41597-022-01568-z","article-title":"Developing a high-resolution gridded rainfall product for Bangladesh during 1901\u20132018","volume":"9","author":"Dewan","year":"2022","journal-title":"Sci. Data"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"3651","DOI":"10.1002\/joc.5525","article-title":"Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy)","volume":"38","author":"Pellicone","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"ACL-5","DOI":"10.1029\/2001JD000819","article-title":"A comparison of cloud properties at a coastal and inland site at the North Slope of Alaska","volume":"107","author":"Doran","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_117","first-page":"35","article-title":"Marine Geographic and Geological Environment of China","volume":"10","author":"Ye","year":"2017","journal-title":"Mar. Geo-Hazards China"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"16985","DOI":"10.1038\/s41598-021-96246-2","article-title":"Intra-seasonal contrasting trends in clouds due to warming induced circulation changes","volume":"11","author":"Prijith","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1111\/j.1600-0889.2007.00251.x","article-title":"An overview of regional land-use and land-cover impacts on rainfall","volume":"59","author":"Adegoke","year":"2007","journal-title":"Tellus B Chem. Phys. Meteorol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosres.2018.05.025","article-title":"Temporal and spatial variation of precipitation in the Hengduan Mountains region in China and its relationship with elevation and latitude","volume":"213","author":"Yu","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/15230406.2013.762138","article-title":"Assessment of Regression Kriging for Spatial Interpolation\u2014Comparisons of Seven GIS Interpolation Methods","volume":"40","author":"Meng","year":"2013","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1007\/s10596-018-9747-3","article-title":"Hybrid Gaussian-cubic radial basis functions for scattered data interpolation","volume":"22","author":"Mishra","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"329","DOI":"10.5194\/npg-29-329-2022","article-title":"Using a hybrid optimal interpolation\u2013ensemble Kalman filter for the Canadian Precipitation Analysis","volume":"29","author":"Khedhaouiria","year":"2022","journal-title":"Nonlinear Process. Geophys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2435\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:04Z","timestamp":1760124604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":123,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092435"],"URL":"https:\/\/doi.org\/10.3390\/rs15092435","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}