{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:51:38Z","timestamp":1767423098141,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28010500","42271396","57457"],"award-info":[{"award-number":["XDA28010500","42271396","57457"]}]},{"name":"National Natural Science Foundation of China","award":["XDA28010500","42271396","57457"],"award-info":[{"award-number":["XDA28010500","42271396","57457"]}]},{"name":"European Space Agency (ESA) and Ministry of Science and Technology of China (MOST) Dragon","award":["XDA28010500","42271396","57457"],"award-info":[{"award-number":["XDA28010500","42271396","57457"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is \u201cfrom scientific rice to world rice\u201d, is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development.<\/jats:p>","DOI":"10.3390\/rs16234406","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T08:38:24Z","timestamp":1732523904000},"page":"4406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Accurate Paddy Rice Mapping Based on Phenology-Based Features and Object-Based Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiayi","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Lixin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Miao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Yingying","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chongwen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-7680","authenticated-orcid":false,"given":"Raffaele","family":"Casa","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Forestry scieNcEs (DAFNE), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-8926","authenticated-orcid":false,"given":"Stefano","family":"Pignatti","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis (IMAA), National Council of Research (CNR), C. da S. Loja, 85050 Tito Scalo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3274","authenticated-orcid":false,"given":"Zhenhai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Tingting","family":"Tian","sequence":"additional","affiliation":[{"name":"Surveying and Mapping Geographic Information Center of Inner Mongolia Autonomous Region, Hohhot City 010010, China"}]},{"given":"Richa","family":"Hu","sequence":"additional","affiliation":[{"name":"Surveying and Mapping Geographic Information Center of Inner Mongolia Autonomous Region, Hohhot City 010010, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"ref_1","first-page":"100","article-title":"The development of rice science, technology and industry in China","volume":"8","author":"Fuping","year":"2018","journal-title":"J. Agric."},{"key":"ref_2","first-page":"9","article-title":"Progress and prospect of application of remote sensing to rice spatial distribution","volume":"35","author":"Li","year":"2014","journal-title":"Chin. J. Agric. Resour. Reg. Plan."},{"key":"ref_3","first-page":"166","article-title":"Global crop growth condition monitoring and yield trend prediction with remote sensing","volume":"28","author":"Qian","year":"2012","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_4","first-page":"4269","article-title":"A new index for delineating built-up land features in satellite imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Sci. Geogr. Sin."},{"key":"ref_5","first-page":"208","article-title":"Comparation of rice yield estimation model combining spectral index screening method and statistical regression algorithm","volume":"37","author":"Wang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/S2095-3119(20)63458-X","article-title":"Delineating the rice crop activities in Northeast China through regional parametric synthesis using satellite remote sensing time-series data from 2000 to 2015","volume":"20","author":"Dan","year":"2021","journal-title":"J. Integr. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining earliest identifiable timing of crops using all available Sentinel 1\/2 imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","first-page":"177","article-title":"Extraction of rice planting structure in tropical region based on Sentinel-1 temporal features integration","volume":"36","author":"Huang","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Costa, J.d.S., Liesenberg, V., Schimalski, M.B., Sousa, R.V.d., Biffi, L.J., Gomes, A.R., Neto, S.L.R., Mitishita, E., and Bispo, P.d.C. (2021). Benefits of combining ALOS\/PALSAR-2 and Sentinel-2A data in the classification of land cover classes in the Santa Catarina southern Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13020229"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mizuochi, H., Iijima, Y., Nagano, H., Kotani, A., and Hiyama, T. (2021). Dynamic mapping of subarctic surface water by fusion of microwave and optical satellite data using conditional adversarial networks. Remote Sens., 13.","DOI":"10.3390\/rs13020175"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1080\/22797254.2021.2018667","article-title":"Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region","volume":"55","author":"Modica","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151585","DOI":"10.1016\/j.scitotenv.2021.151585","article-title":"Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method","volume":"816","author":"Tavus","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tavares, P.A., Beltr\u00e3o, N.E.S., Guimar\u00e3es, U.S., and Teodoro, A. (2019). Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Bel\u00e9m, eastern Brazilian Amazon. Sensors, 19.","DOI":"10.3390\/s19051140"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1080\/2150704X.2016.1225172","article-title":"Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data","volume":"7","author":"Nguyen","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12789","DOI":"10.3390\/rs61212789","article-title":"Complementarity of two rice mapping approaches: Characterizing strata mapped by hypertemporal MODIS and rice paddy identification using multitemporal SAR","volume":"6","author":"Asilo","year":"2014","journal-title":"Asian Conf. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ. Interdiscip. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Minasny, B., Shah, R.M., Che Soh, N., Arif, C., and Indra Setiawan, B. (2019). Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform. Remote Sens., 11.","DOI":"10.3390\/rs11141666"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.06.018","article-title":"An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine","volume":"178","author":"Ni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1080\/01431160802575653","article-title":"Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product","volume":"31","author":"Chandrasekar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113374","DOI":"10.1016\/j.rse.2022.113374","article-title":"A robust index to extract paddy fields in cloudy regions from SAR time series","volume":"285","author":"Xu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","first-page":"1140","article-title":"A Paddy Rice Planting Area Extraction Method Using Random Forest Based on Multi-Temporal Differences","volume":"31","author":"Lei","year":"2016","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops","volume":"114","author":"Pena","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1080\/01431160152558332","article-title":"A generalized confusion matrix for assessing area estimates from remotely sensed data","volume":"22","author":"Lewis","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Djerriri, K., Safia, A., and Adjoudj, R. (2020, January 9\u201311). Object-based classification of Sentinel-2 imagery using compact texture unit descriptors through Google Earth Engine. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105181"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 data integration for object-based land cover classification in Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14112628"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"441","DOI":"10.5589\/m03-006","article-title":"Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction","volume":"29","author":"Flanders","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M.J.R.S. (2020). Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.3390\/rs70505057","article-title":"Mapping above-ground biomass in a tropical forest in Cambodia using canopy textures derived from Google Earth","volume":"7","author":"Singh","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1080\/01431161.2016.1278314","article-title":"Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales","volume":"38","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mananze, S., P\u00f4\u00e7as, I., and Cunha, M. (2020). Mapping and assessing the dynamics of shifting agricultural landscapes using google earth engine cloud computing, a case study in Mozambique. Remote Sens., 12.","DOI":"10.3390\/rs12081279"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sarzynski, T., Giam, X., Carrasco, L., and Lee, J. (2020). Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12071220"},{"key":"ref_36","unstructured":"(2022, January 21). Hinggan League Bureau of Statistics, Available online: http:\/\/tjj.xam.gov.cn\/xamtj\/2022-01\/21\/article_2024041403290955218.html."},{"key":"ref_37","first-page":"103178","article-title":"Mapping crop type in Northeast China during 2013\u20132021 using automatic sampling and tile-based image classification","volume":"117","author":"Xuan","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","unstructured":"(2023, March 15). Google Developers Sentinel-1Algorithm. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/36.964973","article-title":"Filtering of multichannel SAR images","volume":"39","author":"Quegan","year":"2001","journal-title":"Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"725","DOI":"10.11834\/jrs.20100408","article-title":"Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter","volume":"14","author":"Bian","year":"2010","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_42","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near-and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_48","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, Z., Li, S., Wang, Y., Dai, L., and Lin, S. (2018). Monitoring rice phenology based on backscattering characteristics of multi-temporal RADARSAT-2 datasets. Remote Sens., 10.","DOI":"10.3390\/rs10020340"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Jim\u00e9nez-Brenes, F.M., Csillik, O., and L\u00f3pez-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020285"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sensing."},{"key":"ref_52","first-page":"100351","article-title":"Environment. Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use\/land cover mapping using sentinel 2 bands","volume":"19","author":"Rana","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bhatt, P., Maclean, A., Dickinson, Y., and Kumar, C. (2022). Fine-scale mapping of natural ecological communities using machine learning approaches. Remote Sens., 14.","DOI":"10.3390\/rs14030563"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wei, M., Wang, H., Zhang, Y., Li, Q., Du, X., Shi, G., and Ren, Y. (2022). Investigating the potential of Sentinel-2 MSI in early crop identification in Northeast China. Remote Sens., 14.","DOI":"10.3390\/rs14081928"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, L., Mansaray, L.R., Huang, J., and Wang, L. (2019). Optimal segmentation scale parameter, feature subset and classification algorithm for geographic object-based crop recognition using multisource satellite imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050514"},{"key":"ref_57","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"103437","DOI":"10.1016\/j.agsy.2022.103437","article-title":"Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020","volume":"200","author":"Han","year":"2022","journal-title":"Agric. Syst."},{"key":"ref_60","unstructured":"(2023, November 22). Hinggan League Bureau of Agriculture and Animal Husbandry, Available online: http:\/\/nmj.xam.gov.cn\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4406\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:39:01Z","timestamp":1760114341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":60,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234406"],"URL":"https:\/\/doi.org\/10.3390\/rs16234406","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,25]]}}}