{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:32:04Z","timestamp":1775230324479,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFC3002701"],"award-info":[{"award-number":["2022YFC3002701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1\u00b0 to 0.01\u00b0 based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01\u00b0 over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.<\/jats:p>","DOI":"10.3390\/rs15184601","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T23:17:20Z","timestamp":1695165440000},"page":"4601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Downscaling\u2013Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM"],"prefix":"10.3390","volume":"15","author":[{"given":"Bingru","family":"Tian","sequence":"first","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2320-3228","authenticated-orcid":false,"given":"Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-9542","authenticated-orcid":false,"given":"Xin","family":"Yan","sequence":"additional","affiliation":[{"name":"Anhui Survey and Design Institute of Water Resources and Hydropower Co., Ltd., Hefei 230088, China"}]},{"given":"Sheng","family":"Sheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}]},{"given":"Kangling","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2974","DOI":"10.1002\/2015WR016935","article-title":"Point rainfall statistics for ecohydrological analyses derived from satellite integrated rainfall measurements","volume":"51","author":"Rinaldo","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111606","DOI":"10.1016\/j.rse.2019.111606","article-title":"RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements","volume":"239","author":"Beck","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Long, Y.P., Zhang, Y.N., and Ma, Q.M. (2016). A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area. Remote Sens., 8.","DOI":"10.3390\/rs8070599"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s00376-016-5042-1","article-title":"Numerical simulation of the impact of urban non-uniformity on precipitation","volume":"33","author":"Song","year":"2016","journal-title":"Adv. Atmos. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.5194\/hess-15-1895-2011","article-title":"Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression","volume":"15","author":"Saenz","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1175\/1525-7541(2001)002<0140:UPORCM>2.0.CO;2","article-title":"Uncertainty Propagation of Regional Climate Model Precipitation Forecasts to Hydrologic Impact Assessment","volume":"2","author":"Kyriakidis","year":"2001","journal-title":"J. Hydrometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The Global Precipitation Measurement Mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1175\/JTECH-D-19-0114.1","article-title":"IMERG V06: Changes to the Morphing Algorithm","volume":"36","author":"Tan","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TGRS.2007.895337","article-title":"Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation","volume":"45","author":"Kubota","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.5194\/nhess-13-1959-2013","article-title":"Precipitation products from the hydrology SAF","volume":"13","author":"Mugnai","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wu, H., Yong, B., and Shen, Z. (2023). Research on the Monitoring Ability of Fengyun-Based Quantitative Precipitation Estimates for Capturing Heavy Precipitation: A Case Study of the \u201c7\u00b720\u201d Rainstorm in Henan Province, China. Remote Sens., 15.","DOI":"10.3390\/rs15112726"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"D02114","DOI":"10.1029\/2009JD012097","article-title":"Performance of high-resolution satellite precipitation products over China","volume":"115","author":"Shen","year":"2010","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1175\/BAMS-D-15-00306.1","article-title":"The Global Precipitation Measurement (GPM) Mission for Science and Society","volume":"98","author":"Petersen","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_15","first-page":"106","article-title":"Downscaling in remote sensing","volume":"22","author":"Atkinson","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jhydrol.2010.01.023","article-title":"An improved statistical approach to merge satellite rainfall estimates and raingauge data","volume":"385","author":"Li","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1080\/01431161.2013.876118","article-title":"Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges","volume":"35","author":"Hu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1175\/JHM574.1","article-title":"Evaluation of PERSIANN-CCS Rainfall Measurement Using the NAME Event Rain Gauge Network","volume":"8","author":"Hong","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.12.002","article-title":"First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling\u2013calibration procedure","volume":"131","author":"Duan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.5194\/essd-13-5455-2021","article-title":"A high-accuracy rainfall dataset by merging multiple satellites and dense gauges over the southern Tibetan Plateau for 2014\u20132019 warm seasons","volume":"13","author":"Li","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"125156","DOI":"10.1016\/j.jhydrol.2020.125156","article-title":"Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method","volume":"589","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1175\/BAMS-D-17-0138.1","article-title":"MSWEP V2 Global 3-Hourly 0.1 degrees Precipitation: Methodology and Quantitative Assessment","volume":"100","author":"Beck","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1002\/2015JD023788","article-title":"High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes","volume":"121","author":"Manz","year":"2016","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jhydrol.2018.01.042","article-title":"Geographically weighted regression based methods for merging satellite and gauge precipitation","volume":"558","author":"Chao","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5849","DOI":"10.3390\/rs70505849","article-title":"Mapping Annual Precipitation across Mainland China in the Period 2001\u20132010 from TRMM3B43 Product Using Spatial Downscaling Approach","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jing, W.L., Yang, Y.P., Yue, X.F., and Zhao, X.D. (2016). A Spatial Downscaling Algorithm for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI, DEM, and Land Surface Temperature. Remote Sens., 8.","DOI":"10.3390\/rs8080655"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"124664","DOI":"10.1016\/j.jhydrol.2020.124664","article-title":"A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China","volume":"584","author":"Wu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_29","unstructured":"Shi, X.J., Chen, Z.R., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"124296","DOI":"10.1016\/j.jhydrol.2019.124296","article-title":"Streamflow and rainfall forecasting by two long short-term memory-based models","volume":"583","author":"Ni","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13281","DOI":"10.1007\/s00521-021-06877-9","article-title":"Two-stream convolutional LSTM for precipitation nowcasting","volume":"34","author":"Chen","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/nrn755","article-title":"Control of goal-directed and stimulus-driven attention in the brain","volume":"3","author":"Corbetta","year":"2002","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M.Q., Qian, C., Yang, S., Li, C., Zhang, H.G., Wang, X.G., and Tang, X.O. (2017, January 21\u201326). Residual Attention Network for Image Classification. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module, Springer International Publishing.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shen, J.M., Liu, P., Xia, J., Zhao, Y.J., and Dong, Y. (2022). Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network. Remote Sens., 14.","DOI":"10.3390\/rs14163939"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_38","first-page":"1","article-title":"1977 Rietz Lecture\u2014Bootstrap Methods\u2014Another Look at the Jackknife","volume":"7","author":"Efron","year":"1979","journal-title":"Ann. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1002\/rra.1247","article-title":"Predicting the natural flow regime: Models for assessing hydrological alteration in streams","volume":"26","author":"Carlisle","year":"2009","journal-title":"River Res. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.geoderma.2016.03.025","article-title":"POLARIS: A 30-meter probabilistic soil series map of the contiguous United States","volume":"274","author":"Chaney","year":"2016","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, Z.Q., He, K., Tan, X., Xu, J.T., Fang, W.Z., He, Y., and Hong, Y. (2018). Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau. Remote Sens., 10.","DOI":"10.3390\/rs10121883"},{"key":"ref_43","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.renene.2021.05.095","article-title":"Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models","volume":"177","author":"Agga","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"126698","DOI":"10.1016\/j.jhydrol.2021.126698","article-title":"Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning","volume":"600","author":"Li","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"50982","DOI":"10.1109\/ACCESS.2021.3065939","article-title":"Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm","volume":"9","author":"Moishin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1506017","DOI":"10.1155\/2018\/1506017","article-title":"Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area","volume":"2018","author":"Zhan","year":"2018","journal-title":"Adv. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.2002.tb01078.x","article-title":"Three-dimensional neurointerpolation of annual mean precipitation and temperature surfaces for China","volume":"34","author":"Bryan","year":"2002","journal-title":"Geogr. Anal."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/S0168-1923(99)00169-0","article-title":"A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data","volume":"101","author":"Price","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2015.02.024","article-title":"A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics","volume":"162","author":"Xu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"126803","DOI":"10.1016\/j.jhydrol.2021.126803","article-title":"Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China","volume":"602","author":"Shen","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2018.05.021","article-title":"A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data","volume":"214","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"124414","DOI":"10.1016\/j.jhydrol.2019.124414","article-title":"A downscaling-merging method for high-resolution daily precipitation estimation","volume":"581","author":"Chen","year":"2020","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4601\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:53:35Z","timestamp":1760129615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4601"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,19]]},"references-count":54,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184601"],"URL":"https:\/\/doi.org\/10.3390\/rs15184601","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,19]]}}}