{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:44:38Z","timestamp":1778694278077,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41975066"],"award-info":[{"award-number":["41975066"]}]},{"name":"National Natural Science Foundation of China","award":["42005053"],"award-info":[{"award-number":["42005053"]}]},{"name":"National Natural Science Foundation of China","award":["ZK21-46"],"award-info":[{"award-number":["ZK21-46"]}]},{"name":"National University of Defense Technology","award":["41975066"],"award-info":[{"award-number":["41975066"]}]},{"name":"National University of Defense Technology","award":["42005053"],"award-info":[{"award-number":["42005053"]}]},{"name":"National University of Defense Technology","award":["ZK21-46"],"award-info":[{"award-number":["ZK21-46"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products.<\/jats:p>","DOI":"10.3390\/rs14122925","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"2925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries"],"prefix":"10.3390","volume":"14","author":[{"given":"Yanbo","family":"Gao","sequence":"first","affiliation":[{"name":"School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiping","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Long","sequence":"additional","affiliation":[{"name":"School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,18]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"044037","DOI":"10.1088\/1748-9326\/7\/4\/044037","article-title":"A near real-time satellite-based global drought climate data record","volume":"7","author":"AghaKouchak","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10584-007-9353-1","article-title":"Progress on incorporating climate change into management of California\u2019s water resources","volume":"87","author":"Anderson","year":"2007","journal-title":"Clim. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"W11406","DOI":"10.1029\/2007WR006736","article-title":"Sustainable water resource management under hydrological uncertainty","volume":"44","author":"Ajami","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.atmosres.2016.02.020","article-title":"Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China","volume":"176\u2013177","author":"Guo","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1175\/JHM431.1","article-title":"Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting","volume":"6","author":"Yilmaz","year":"2005","journal-title":"J. Hydrometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/2017RG000574","article-title":"A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons","volume":"56","author":"Sun","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1175\/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2","article-title":"Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs","volume":"78","author":"Xie","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.jhydrol.2014.05.064","article-title":"Spatial estimation of daily precipitation in regions with complex relief and scarce data using terrain orientation","volume":"517","author":"Castro","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1175\/JAMC-D-14-0082.1","article-title":"Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests","volume":"53","author":"Appelhans","year":"2014","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jhydrol.2015.12.008","article-title":"Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales","volume":"533","author":"Tang","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_12","unstructured":"Yang, H. (2012). Multiscale Hydrologic Remote Sensing Perspectives and Applications, CRC Press. [1st ed.]."},{"key":"ref_13","first-page":"28","article-title":"Progress of the Satellite Remote Sensing Retrieval of Precipitation","volume":"11","author":"Shaojun","year":"2021","journal-title":"Adv. Meteorol. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1175\/JHM-D-15-0051.1","article-title":"Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme","volume":"17","author":"Chern","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1109\/TGRS.2003.820312","article-title":"Multivariate statistical integration of Satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale","volume":"42","author":"Marzano","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1175\/1520-0450(2001)040<1500:GMRAG>2.0.CO;2","article-title":"GOES Multispectral Rainfall Algorithm (GMSRA)","volume":"40","author":"Ba","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1175\/2009JHM1139.1","article-title":"PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis","volume":"10","author":"Behrangi","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1175\/2010JHM1248.1","article-title":"REFAME: Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates","volume":"11","author":"Behrangi","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1175\/2009JHM1077.1","article-title":"Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation","volume":"10","author":"Sorooshian","year":"2009","journal-title":"J. Hydrometeorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1175\/2007JAMC1525.1","article-title":"Over-Ocean Validation of the Global Convective Diagnostic","volume":"47","author":"Kohrs","year":"2008","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_21","first-page":"15","article-title":"Warm water vapour pixels over high clouds as observed by METEOSAT","volume":"70","author":"Tjemkes","year":"1997","journal-title":"Contrib. Atmos. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1175\/2011BAMS3158.1","article-title":"Advanced Concepts on Remote Sensing of Precipitation at Multiple Scales","volume":"92","author":"Sorooshian","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_23","unstructured":"Canziani, A., Paszke, A., and Culurciello, E. (2016). An Analysis of Deep Neural Network Models for Practical Applications. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding Structure in Time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_26","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the NIPS\u201914: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_27","first-page":"12543","article-title":"Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks","volume":"123","author":"Yang","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_28","unstructured":"Liu, Y., Racah, E., Correa, J., Khosrowshahi, A., Lavers, D., Kunkel, K., Wehner, M., and Collins, W. (2016). Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. arXiv."},{"key":"ref_29","unstructured":"Shi, X., Chen, Z., Wang, H., and Yeung, D.-Y. (2015, January 11\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":"1271","DOI":"10.1175\/JHM-D-16-0176.1","article-title":"Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches","volume":"18","author":"Gao","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1175\/JHM-D-17-0077.1","article-title":"A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information","volume":"19","author":"Tao","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1175\/JHM-D-19-0110.1","article-title":"PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks\u2013Convolutional Neural Networks","volume":"20","author":"Sadeghi","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8612","DOI":"10.1109\/TGRS.2020.2989183","article-title":"Infrared Precipitation Estimation Using Convolutional Neural Network","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hayatbini, N., Kong, B., Hsu, K.-L., Nguyen, P., Sorooshian, S., Stephens, G., Fowlkes, C., and Nemani, R. (2019). Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries\u2014PERSIANN-cGAN. Remote Sens., 11.","DOI":"10.3390\/rs11192193"},{"key":"ref_35","unstructured":"Ganquan, W. (2004, January 22). The Multiple Channel Scanning Imager of \u201cFY-4\u201d Meteorological Satellite. Proceedings of the 2004 Academic Conference of Chinese Optical Society, Hangzhou, China."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1175\/1520-0450(2001)040<2115:REFACO>2.0.CO;2","article-title":"Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network","volume":"39","author":"Bellerby","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_37","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\u20131984","volume":"115","author":"Arkin","year":"1987","journal-title":"Mon. Weather. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1175\/1520-0442(1999)012<2503:TRAPCT>2.0.CO;2","article-title":"The Relationship among Precipitation, Cloud-Top Temperature, and Precipitable Water over the Tropics","volume":"12","author":"Zeng","year":"1999","journal-title":"J. Clim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7437","DOI":"10.1080\/01431161.2018.1471246","article-title":"Evaluation of multi-satellite precipitation products in Xinjiang, China","volume":"39","author":"Lu","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1175\/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2","article-title":"CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution","volume":"5","author":"Joyce","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_41","first-page":"P96","article-title":"Evaluation and Verification of FY-4A Satellite Quantitative Precipitation Estimation Product","volume":"11","year":"2021","journal-title":"J. Agric. Catastropholgy"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_43","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., and Heinrich, M. (2018, January 21). Attention-unet: Learning Where to Look for the Pancreas. Proceedings of the 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands."},{"key":"ref_44","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.atmosres.2015.02.010","article-title":"Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran","volume":"158\u2013159","author":"Ghajarnia","year":"2015","journal-title":"Atmos. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2925\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:34:51Z","timestamp":1760139291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,18]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122925"],"URL":"https:\/\/doi.org\/10.3390\/rs14122925","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,18]]}}}