{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T03:06:22Z","timestamp":1774839982771,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T00:00:00Z","timestamp":1659225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Western Weather and Water Extremes (CW3E) at the Scripps Institution of Oceanography","award":["4600013361"],"award-info":[{"award-number":["4600013361"]}]},{"name":"Center for Western Weather and Water Extremes (CW3E) at the Scripps Institution of Oceanography","award":["80NSSC 21K1668"],"award-info":[{"award-number":["80NSSC 21K1668"]}]},{"DOI":"10.13039\/100000104","name":"California Department of Water Resources and NASA","doi-asserted-by":"publisher","award":["4600013361"],"award-info":[{"award-number":["4600013361"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"California Department of Water Resources and NASA","doi-asserted-by":"publisher","award":["80NSSC 21K1668"],"award-info":[{"award-number":["80NSSC 21K1668"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System\u2013Climate Data Record (CCS\u2013CDR), a climatology dataset, and PERSIANN\u2013Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS\u2013CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, EKGE values (Kling\u2013Gupta efficiency) for CCS\u2013CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (EKGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS\u2013CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.<\/jats:p>","DOI":"10.3390\/rs14153675","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T04:04:00Z","timestamp":1659326640000},"page":"3675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["The Application of PERSIANN Family Datasets for Hydrological Modeling"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9908-9794","authenticated-orcid":false,"given":"Hossein","family":"Salehi","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Semnan, Iran"}]},{"given":"Mojtaba","family":"Sadeghi","sequence":"additional","affiliation":[{"name":"Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA"},{"name":"Our Kettle Inc., New York, NY 10041, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-3977","authenticated-orcid":false,"given":"Saeed","family":"Golian","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Semnan, Iran"},{"name":"Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9055-2583","authenticated-orcid":false,"given":"Phu","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA"}]},{"given":"Conor","family":"Murphy","sequence":"additional","affiliation":[{"name":"Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland"}]},{"given":"Soroosh","family":"Sorooshian","sequence":"additional","affiliation":[{"name":"Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA"},{"name":"Department of Earth System Science, University of California, Irvine, CA 92697, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s40710-016-0128-4","article-title":"Hydrologic simulation for water balance improvement in an outcrop area of the Guarani Aquifer system","volume":"3","author":"Machado","year":"2016","journal-title":"Environ. Processes"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sorooshian, S., Hsu, K.-L., Coppola, E., Tomassetti, B., Verdecchia, M., and Visconti, G. (2008). Hydrological Modelling and the Water Cycle: Coupling the Atmospheric and Hydrological Models, Springer Science & Business Media.","DOI":"10.1007\/978-3-540-77843-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1175\/JHM-D-15-0192.1","article-title":"Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow","volume":"17","author":"Ashouri","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1175\/1525-7541(2003)004<1088:SREUCP>2.0.CO;2","article-title":"Satellite rainfall estimation using combined passive microwave and infrared algorithms","volume":"4","author":"Kidd","year":"2003","journal-title":"J. Hydrometeorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1175\/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2","article-title":"Evaluation of PERSIANN system satellite-based estimates of tropical rainfall","volume":"81","author":"Sorooshian","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1175\/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2","article-title":"Precipitation estimation from remotely sensed information using artificial neural networks","volume":"36","author":"Hsu","year":"1997","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_7","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_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":"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_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-13-00068.1","article-title":"PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies","volume":"96","author":"Ashouri","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.296","article-title":"The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data","volume":"6","author":"Nguyen","year":"2019","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-021-00940-9","article-title":"PERSIANN-CCS-CDR, a 3-hourly 0.04\u00b0 global precipitation climate data record for heavy precipitation studies","volume":"8","author":"Sadeghi","year":"2021","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hossain, F., and Anagnostou, E.N. (2004). Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction. J. Geophys. Res. Atmos., 109.","DOI":"10.1029\/2003JD003986"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1175\/JHM437.1","article-title":"Analysis of multiple precipitation products and preliminary assessment of their impact on global land data assimilation system land surface states","volume":"6","author":"Gottschalck","year":"2005","journal-title":"J. Hydrometeorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1175\/JAM2173.1","article-title":"Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system","volume":"43","author":"Hong","year":"2004","journal-title":"J. Appl. Meteorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1175\/BAMS-88-1-47","article-title":"Comparison of near-real-time precipitation estimates from satellite observations and numerical models","volume":"88","author":"Ebert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.jhydrol.2010.11.043","article-title":"Hydrologic evaluation of satellite precipitation products over a mid-size basin","volume":"397","author":"Behrangi","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1080\/22797254.2020.1819169","article-title":"Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: A case study in the United States","volume":"54","author":"Feng","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"26527","DOI":"10.1029\/96JD01655","article-title":"Hydrologic applications of satellite data: 2. Flow simulation and soil water estimates","volume":"101","author":"Guetter","year":"1996","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.jhydrol.2013.07.012","article-title":"Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin","volume":"499","author":"Thiemig","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1175\/BAMS-D-11-00116.1","article-title":"Advancing the remote sensing of precipitation","volume":"92","author":"Sorooshian","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1175\/JHM-D-20-0177.1","article-title":"Persiann dynamic infrared\u2013rain rate (PDIR-now): A near-real-time, quasi-global satellite precipitation dataset","volume":"21","author":"Nguyen","year":"2020","journal-title":"J. Hydrometeorol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sadeghi, M., Lee, J., Nguyen, P., Hsu, K., Sorooshian, S., and Braithwaite, D. (2019, January 9\u201313). Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR). Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA.","DOI":"10.1175\/JHM-D-19-0110.1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1029\/94JD00483","article-title":"A simple hydrologically based model of land surface water and energy fluxes for general circulation models","volume":"99","author":"Liang","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1002\/(SICI)1099-1085(20000415)14:5<867::AID-HYP975>3.0.CO;2-5","article-title":"Effects of land cover change on streamflow in the interior Columbia River Basin (USA and Canada)","volume":"14","author":"Matheussen","year":"2000","journal-title":"Hydrol. Processes"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"680","DOI":"10.5589\/m04-032","article-title":"An application of the VIC-3L land surface model and remote sensing data in simulating streamflow for the Hanjiang River basin","volume":"30","author":"Yuan","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_28","unstructured":"Yulin, C., Zhifeng, G., and Li, Y. (July, January 30). A macro hydrologic model simulation based on remote sensing data. Proceedings of the 2008 International Workshop on Earth Observation and Remote Sensing Applications, Beijing, China."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0921-8181(95)00046-1","article-title":"Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification","volume":"13","author":"Liang","year":"1996","journal-title":"Glob. Planet. Chang."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/02626669809492107","article-title":"Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model","volume":"43","author":"Lohmann","year":"1998","journal-title":"Hydrol. Sci. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"397","DOI":"10.5194\/hess-24-397-2020","article-title":"On the representation of water reservoir storage and operations in large-scale hydrological models: Implications on model parameterization and climate change impact assessments","volume":"24","author":"Dang","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.advwatres.2012.01.005","article-title":"Evolutionary multiobjective optimization in water resources: The past, present, and future","volume":"51","author":"Reed","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11831-017-9224-5","article-title":"Computer aided numerical methods for hydrological model calibration: An overview and recent development","volume":"26","author":"Kan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Wagener, T., Van Werkhoven, K., Reed, P., and Tang, Y. (2009). Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modeling. Water Resour. Res., 45.","DOI":"10.1029\/2008WR007347"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Nachtergaele, F., Velthuizen, H., Verelst, L., and Wiberg, D. (2009). Harmonized World Soil Database (HWSD), Food and Agriculture Organization of the United Nations."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1175\/2010BAMS3001.1","article-title":"The NCEP climate forecast system reanalysis","volume":"91","author":"Saha","year":"2010","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1175\/1520-0442(1997)010<2690:FBHPIT>2.0.CO;2","article-title":"Feedbacks between hydrological processes in tropical South America and large-scale ocean\u2013atmospheric phenomena","volume":"10","author":"Poveda","year":"1997","journal-title":"J. Clim."},{"key":"ref_42","first-page":"146","article-title":"Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture","volume":"48","author":"Martens","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.agrformet.2018.01.022","article-title":"Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach","volume":"252","author":"Khan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.jhydrol.2018.09.065","article-title":"Intercomparison and evaluation of three global high-resolution evapotranspiration products across China","volume":"566","author":"Bai","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asr.2020.04.037","article-title":"Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia","volume":"66","author":"Khan","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"453","DOI":"10.5194\/hess-15-453-2011","article-title":"Global land-surface evaporation estimated from satellite-based observations","volume":"15","author":"Miralles","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"425","DOI":"10.5194\/hess-15-425-2011","article-title":"Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals","volume":"15","author":"Liu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, T., Zhou, P., Shao, Y., and Gao, S. (2017). Validation analysis of SMAP and AMSR2 soil moisture products over the United States using ground-based measurements. Remote Sens., 9.","DOI":"10.3390\/rs9020104"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112248","DOI":"10.1016\/j.rse.2020.112248","article-title":"In-situ and triple-collocation based evaluations of eight global root zone soil moisture products","volume":"254","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5194\/hess-25-17-2021","article-title":"Evaluation of 18 satellite-and model-based soil moisture products using in situ measurements from 826 sensors","volume":"25","author":"Beck","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1175\/JHM-D-19-0030.1","article-title":"The role of hydrological initial conditions on Atmospheric River floods in the Russian River basin","volume":"20","author":"Cao","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_52","first-page":"102521","article-title":"Multiple timescale assessment of wet season precipitation estimation over Taiwan using the PERSIANN family products","volume":"103","author":"Huang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"127054","DOI":"10.1016\/j.jhydrol.2021.127054","article-title":"Comprehensive evaluation of precipitation datasets over Iran","volume":"603","author":"Saemian","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Moges, E., Demissie, Y., Larsen, L., and Yassin, F. (2020). Sources of hydrological model uncertainties and advances in their analysis. Water, 13.","DOI":"10.3390\/w13010028"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.atmosres.2018.05.016","article-title":"Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China","volume":"212","author":"Gao","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1002\/hyp.446","article-title":"On the need for benchmarks in hydrological modelling","volume":"15","author":"Seibert","year":"2001","journal-title":"Hydrol. Processes"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4815","DOI":"10.5194\/hess-22-4815-2018","article-title":"Toward continental hydrologic-hydrodynamic modeling in South America","volume":"22","author":"Siqueira","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.5194\/gmd-11-2429-2018","article-title":"PCR-GLOBWB 2: A 5 arcmin global hydrological and water resources model","volume":"11","author":"Sutanudjaja","year":"2018","journal-title":"Geosci. Model Dev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3675\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:30Z","timestamp":1760140830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,31]]},"references-count":58,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153675"],"URL":"https:\/\/doi.org\/10.3390\/rs14153675","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,31]]}}}