{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:56:42Z","timestamp":1772301402878,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Basic Research Operating Expenses of the Central Level Nonprofit Research Institutes","award":["IDM2020006"],"award-info":[{"award-number":["IDM2020006"]}]},{"name":"the Basic Research Operating Expenses of the Central Level Nonprofit Research Institutes","award":["SKLCS-OP-2021-10"],"award-info":[{"award-number":["SKLCS-OP-2021-10"]}]},{"name":"the Basic Research Operating Expenses of the Central Level Nonprofit Research 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IDM","award":["SKLCS-OP-2021-10"],"award-info":[{"award-number":["SKLCS-OP-2021-10"]}]},{"name":"the S&amp;T Development Fund of IDM","award":["42071075"],"award-info":[{"award-number":["42071075"]}]},{"name":"the S&amp;T Development Fund of IDM","award":["FY-APP-2022.0401"],"award-info":[{"award-number":["FY-APP-2022.0401"]}]},{"name":"the S&amp;T Development Fund of IDM","award":["KJFZ202305"],"award-info":[{"award-number":["KJFZ202305"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precipitation in the Tianshan Mountains is abundant, and the quantitative estimation of precipitation in mountainous areas is important to the application and evaluation of regional water resources. With remote sensing technology, satellite inversion of precipitation can estimate precipitation in mountainous areas. However, the Tianshan Mountain terrain is complex, and the spatiotemporal variation in precipitation is large, so the accuracy of satellite precipitation inversion is not high. Here, precipitation data from around 1000 automatic weather stations in the Tianshan Mountains are used to study the correction technology of the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (GPM) mission\u2019s (IMERG) monthly precipitation products using stepwise regression (STEP), geographically weighted regression (GWR), and random forest (RF). First, geographic information system technology was used to extract topographic variables from a digital elevation model, and vegetation indexes, which are important precipitation indicators, were introduced as explanatory factors to correct satellite precipitation data. Second, GPM IMERG precipitation was corrected by establishing the stepwise regression, the geographically weighted regression model, and the random forest model. The three correction methods can improve the GPM IMERG in terms of relative bias, root mean square error, correlation coefficient, and Nash\u2013Sutcliffe efficiency, while the random forest method shows better corrections than the two traditional methods. For dense rainfall stations, the geographically weighted regression method is as effective as random forest. For different altitudes, the results show that RF has the best correction effect in the first three zones, but the correction effect in the last zone (over 3000 m) is worse than STEP. This study provides a practical reference method for estimating precipitation data in the non-rainfall observation area, which helps to deepen the scientific understanding of the water resource distribution in the Tianshan Mountains and provide scientific data support for regional hydrological and meteorological research.<\/jats:p>","DOI":"10.3390\/rs15163962","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"3962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Quantitative Precipitation Estimation in the Tianshan Mountains Based on Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5874-3163","authenticated-orcid":false,"given":"Xinyu","family":"Lu","sequence":"first","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"},{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"National Field Science Observation and Research Station of Yulong Snow Mountain Cryosphere and Sustainable Development, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"}]},{"given":"Hong","family":"Huo","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1175\/JHM-D-22-0081.1","article-title":"Precipitation Estimation Based on Infrared Data with a Spherical Convolutional Neural Network","volume":"24","author":"Yi","year":"2023","journal-title":"J. Hydrometeor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/2150704X.2023.2189030","article-title":"Spatio-temporal patterns of snow cover in the Tien Shan, China from 2000 to 2019 based on cloudfree data supported by Google Earth Engine","volume":"14","author":"Ji","year":"2023","journal-title":"Remote Sens. Lett."},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"E1146","DOI":"10.1175\/BAMS-D-20-0328.1","article-title":"AERA5-Asia: A Long-Term Asian Precipitation Dataset (0.1\u00b0, 1-Hourly, 1951\u20132015, Asia) Anchoring the ERA5-Land Under the Total Volume Control by APHRODITE","volume":"103","author":"Ma","year":"2022","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","first-page":"326","article-title":"Characteristics of area precipitation in Xinjiang region with its variations","volume":"19","author":"Shi","year":"2008","journal-title":"J. Appl. Meteorol. Sci."},{"key":"ref_7","first-page":"18","article-title":"Impact of climate change on water resources in the Tianshan Mountians, Central Asia","volume":"1","author":"Chen","year":"2017","journal-title":"Acta Geo. Sin."},{"key":"ref_8","unstructured":"Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E.J., and Xie, P. (2022, August 01). NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 4.5, Available online: http:\/\/pmm.nasa.gov\/sites\/default\/files\/document_files\/IMERG_ATBD_V5.1b."},{"key":"ref_9","unstructured":"Huffman, G.J., Bolvin, D.T., and Nelkin, E.J. (2022, August 01). Integrated Multi-Satellite Retrievals for GPM (IMERG) Technical Documentation, Available online: http:\/\/pmm.nasa.gov\/sites\/default\/files\/document_files\/IMERG_doc_171117b.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.rse.2008.10.004","article-title":"Spatial downscaling of TRMM precipitation using vegetation response on the Iberian Peninsula","volume":"113","author":"Immerzeel","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.catena.2004.05.001","article-title":"An evaluation of methods to determine slope using digital elevation data","volume":"58","author":"Warren","year":"2004","journal-title":"Catena"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"D03110","DOI":"10.1029\/2003JD003749","article-title":"Using a geographic information system to improve Special Sensor Microwave Imager precipitation estimates over the Tibetan Plateau","volume":"109","author":"Yin","year":"2004","journal-title":"J. Geophys. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13060","DOI":"10.1002\/2013JD019964","article-title":"Similarity and difference of the two successive v6 and v7 TRMM multisatellite precipitation analysis performance over China","volume":"118","author":"Chen","year":"2013","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jhydrol.2007.09.001","article-title":"Effects of DEM resolution on the calculation of topographical indices: TWI and its components","volume":"347","author":"Seibert","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1175\/2007JHM903.1","article-title":"An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis","volume":"9","author":"Yin","year":"2008","journal-title":"J. Hydrometeor."},{"key":"ref_18","first-page":"37","article-title":"Quality assessment of hourly merged precipitation product over China","volume":"36","author":"Shen","year":"2013","journal-title":"Trans. Atmos. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"D09105","DOI":"10.1029\/2008JD011178","article-title":"Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 reanalysis and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China","volume":"114","author":"Ma","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.advwatres.2011.05.007","article-title":"Evaluation of precipitation products over complex mountainous terrain: A water resources perspective","volume":"34","author":"Ward","year":"2011","journal-title":"Adv. Water Resour."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.atmosres.2015.02.002","article-title":"Precipitation comparison for the CFSR, MERRA, TRMM3B42 and combined scheme datasets in Bolivia","volume":"163","author":"Blacutt","year":"2015","journal-title":"Atmos. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5648","DOI":"10.1002\/2016JD024781","article-title":"Evaluation of reanalysis, spatially-interpolated and satellite remotely-sensed precipitation datasets in Central Asia","volume":"121","author":"Hu","year":"2016","journal-title":"J. Geophys. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1093\/biomet\/54.3-4.357","article-title":"The discarding of variables in multivariate analysis","volume":"54","author":"Beale","year":"1967","journal-title":"Biometrika"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"51","DOI":"10.2307\/2985237","article-title":"A development of multiple regression for the analysis of routine data","volume":"16","author":"Newton","year":"1967","journal-title":"Appl. Stat."},{"key":"ref_25","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_26","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Wiley."},{"key":"ref_27","first-page":"73","article-title":"Application of geographically weighted regression to investigate the impact of scale on prediction uncertainty by modelling relationship between vegetation and climate","volume":"3","author":"Propastin","year":"2008","journal-title":"Int. J. Spat. Data Infra. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1029\/2011WR010667","article-title":"Estimating annual precipitation for the Colorado River Basin using oceanic-atmospheric oscillations","volume":"48","author":"Kalra","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.envsoft.2016.06.014","article-title":"A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications","volume":"84","author":"Gupta","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1175\/JHM-D-13-041.1","article-title":"Using the back propagation neural network approach to bias correct TMPA data in the arid region of North-west China","volume":"15","author":"Yang","year":"2014","journal-title":"J. Hydrometeor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jhydrol.2019.06.019","article-title":"Correcting GPM IMERG precipitation data over the Tianshan Mountains in China","volume":"575","author":"Lu","year":"2019","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3962\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:30:22Z","timestamp":1760128222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":31,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15163962"],"URL":"https:\/\/doi.org\/10.3390\/rs15163962","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}