{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:51:11Z","timestamp":1776441071104,"version":"3.51.2"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efficient methodologies for vegetation-type mapping are significant for wetland\u2019s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.<\/jats:p>","DOI":"10.3390\/rs14030501","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1935-973X","authenticated-orcid":false,"given":"Huayu","family":"Li","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanwei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9933-1955","authenticated-orcid":false,"given":"Hui","family":"Sheng","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6758-9863","authenticated-orcid":false,"given":"Mingming","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1109\/JSTARS.2019.2937949","article-title":"Changes of Wiang Nong Lom and Nong Luang wetlands in Chiang Saen Valley (Chiang Rai Province, Thailand) during the period 1988\u20132017","volume":"12","author":"Hempattarasuwan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s11769-013-0641-6","article-title":"Effects of crude oil contamination on soil physical and chemical properties in Momoge wetland of China","volume":"23","author":"Wang","year":"2013","journal-title":"Chin. Geogr. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/10106049.2017.1408699","article-title":"Discrimination and classification of mangrove forests using EO-1 Hyperion data: A case study of Indian Sundarbans","volume":"34","author":"Kumar","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1126\/science.1118160","article-title":"The importance of land-cover change in simulating future climates","volume":"310","author":"Feddema","year":"2005","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10938","DOI":"10.3390\/rs70810938","article-title":"The challenges of remote monitoring of wetlands","volume":"7","author":"Gallant","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, G., Ye, S., Gao, M., Ding, X., Yuan, H., and Wang, J. (2011, January 24\u201326). Change analysis on eco-environment of the estuary wetland reserve area of Yellow River Delta based on RS and GIS. Proceedings of the 19th International Conference on Geoinformatics, Shanghai, China.","DOI":"10.1109\/GeoInformatics.2011.5981074"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/LGRS.2016.2532743","article-title":"Predicting vascular plant richness in a heterogeneous wetland using spectral and textural features and a random forest algorithm","volume":"13","author":"Cabezas","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dang, A.T.N., Kumar, L., Reid, M., and Nguyen, H. (2021). Remote sensing approach for monitoring coastal wetland in the Mekong Delta, Vietnam: Change trends and their driving forces. Remote Sens., 13.","DOI":"10.3390\/rs13173359"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/JSTARS.2013.2240263","article-title":"Multi-sensor monitoring system for forest cover change assessment in central Africa","volume":"6","author":"Desclee","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9688","DOI":"10.1109\/TGRS.2019.2928562","article-title":"Unsupervised cross-temporal classification of hyperspectral images with multiple geodesic flow kernel learning","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7669","DOI":"10.1109\/TGRS.2020.3033266","article-title":"Simple nonlinear iterative temporal clustering","volume":"59","author":"Soares","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Montgomery, J., Mahoney, C., Brisco, B., Boychuk, L., Cobbaert, D., and Hopkinson, C. (2021). Remote sensing of wetlands in the prairie pothole region of North America. Remote Sens., 13.","DOI":"10.3390\/rs13193878"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5424","DOI":"10.1109\/JSTARS.2016.2623567","article-title":"A semi-supervised hybrid approach for multitemporal multi-region multisensor Landsat data classification","volume":"9","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7086","DOI":"10.1109\/TGRS.2014.2307354","article-title":"Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gao, J., Yi, Y., Wei, T., and Zhang, H. (2021). Sentinel-2 cloud removal considering ground changes by fusing multitemporal SAR and optical images. Remote Sens., 13.","DOI":"10.3390\/rs13193998"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.01.011","article-title":"Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM\/ETM plus data","volume":"132","author":"Melaas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"Gomez","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wozniak, E., Kofman, W., Aleksandrowicz, S., Rybicki, M., and Lewinski, S. (2019, January 5\u20137). Multi-temporal indices derived from time series of Sentinel-1 images as a phenological description of plants growing for crop classification. Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MULTITEMP), Shanghai, China.","DOI":"10.1109\/Multi-Temp.2019.8866905"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2006.05.017","article-title":"Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure","volume":"105","author":"Evans","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1109\/TGRS.2012.2183137","article-title":"Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis","volume":"50","author":"Brooks","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/LGRS.2012.2236297","article-title":"Refinement of microwave vegetation index using Fourier analysis for monitoring vegetation dynamics","volume":"10","author":"Du","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1109\/TGRS.2016.2518167","article-title":"A simple method for detecting phenological change from time series of vegetation index","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2016.05.014","article-title":"Automated mapping of soybean and corn using phenology","volume":"119","author":"Zhong","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Granero-Belinchon, C., Adeline, K., Lemonsu, A., and Briottet, X. (2020). Phenological dynamics characterization of alignment trees with Sentinel-2 imagery: A vegetation indices time series reconstruction methodology adapted to urban areas. Remote Sens., 12.","DOI":"10.3390\/rs12040639"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112320","DOI":"10.1016\/j.rse.2021.112320","article-title":"Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series","volume":"256","author":"Sun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1109\/TGRS.2012.2223218","article-title":"Exploring spatiotemporal phenological patterns and trajectories using self-organizing maps","volume":"51","author":"Hamm","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lee, B., Kim, E., Lim, J., Seo, B., and Chung, J. (2018, January 23\u201327). Detecting vegetation phenology in various forest types using long-term MODIS vegetation indices. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518142"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ghaderpour, E., and Vujadinovic, T. (2020). Change detection within remotely sensed satellite image time series via spectral analysis. Remote Sens., 12.","DOI":"10.3390\/rs12234001"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite image time series analysis under time warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Qi, Y., Wang, W.K., Bent, B., Avram, R., Olgin, J., and Dunn, J. (2020). EventDTW: An improved dynamic time warping algorithm for aligning biomedical signals of nonuniform sampling frequencies. Sensors, 20.","DOI":"10.3390\/s20092700"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A time-weighted dynamic time warping method for land-use and land-cover mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, F., Yang, G., Yang, X., Cen, H., Zhu, Y., Han, S., Yang, H., He, Y., and Zhao, C. (2021). Determination of key phenological phases of winter wheat based on the time-weighted dynamic time warping algorithm and MODIS time-series data. Remote Sens., 13.","DOI":"10.3390\/rs13091836"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hao, P., Wang, L., Zhan, Y., Niu, Z., and Wu, M. (2016, January 10\u201315). Using historical NDVI time series to classify crops at 30 m spatial resolution: A case in Southeast Kansas. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730651"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4618","DOI":"10.1109\/JSTARS.2018.2869528","article-title":"Comparing the effects of temporal features derived from synthetic time-series NDVI on fine land cover classification","volume":"11","author":"Huang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Csillik, O., Belgiu, M., Asner, G.P., and Kelly, M. (2019). Object-based time-constrained dynamic time warping classification of crops using Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11101257"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/TCYB.2013.2277664","article-title":"Structural laplacian eigenmaps for modeling sets of multivariate sequences","volume":"44","author":"Lewandowski","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TAI.2020.3027279","article-title":"Approaches and applications of early classification of time series: A review","volume":"1","author":"Gupta","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.knosys.2008.03.014","article-title":"Classification of multivariate time series using two-dimensional singular value decomposition","volume":"21","author":"Weng","year":"2008","journal-title":"Knowl.-Based Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/JSTARS.2020.3040305","article-title":"Joint classification of hyperspectral and multispectral images for mapping coastal wetlands","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Louis, J., Pflug, B., Main-Knorn, M., Debaecker, V., Mueller-Wilm, U., Iannone, R.Q., Cadau, E.G., Boccia, V., and Gascon, F. (August, January 28). Sentinel-2 global surface reflectance level-2a product generated with Sen2cor. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898540"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"1","article-title":"Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice","volume":"10","author":"Zhu","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Qian, L., Zhang-qin, H., Yi-bin, H., and Rui, C. (2010, January 22\u201324). Utterance verification on DTW based speech recognition using likelihood. Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China.","DOI":"10.1109\/ICCASM.2010.5620715"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jambhale, S.S., and Khaparde, A. (2014, January 7\u201310). Gesture recognition using DTW & piecewise DTW. Proceedings of the 2014 21st International Conference on Electronics and Communication Systems (ICECS 2014), Marseille, France.","DOI":"10.1109\/ECS.2014.6892646"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4606","DOI":"10.1109\/JSTARS.2019.2950406","article-title":"Constrained distance-based clustering for satellite image time-series","volume":"12","author":"Lampert","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, X., Jiang, M., Chen, S., Yang, C., Jing, W., and Guo, Z. (2016, January 10\u201311). A hybrid time series matching algorithm based on feature-points and DTW. Proceedings of the 2016 9th International Symposium on Computational Intelligence and Design (ISCID 2016), Hangzhou, China.","DOI":"10.1109\/ISCID.2016.2048"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/JSTARS.2013.2290296","article-title":"A support vector conditional random fields classifier with a Mahalanobis distance boundary constraint for high spatial resolution remote sensing imagery","volume":"7","author":"Zhong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1109\/TNN.2010.2052630","article-title":"Scalable large-margin Mahalanobis distance metric learning","volume":"21","author":"Shen","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4920","DOI":"10.1109\/TIP.2014.2359765","article-title":"LogDet divergence-based metric learning with triplet constraints and its applications","volume":"23","author":"Mei","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TCYB.2015.2426723","article-title":"Learning a Mahalanobis distance-based dynamic time warping measure for multivariate time series classification","volume":"46","author":"Mei","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Davis, J.V., Kulis, B., Jain, P., Sra, S., and Dhillon, I.S. (2007, January 20\u201324). Information-theoretic metric learning. Machine Learning. Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, OR, USA.","DOI":"10.1145\/1273496.1273523"},{"key":"ref_56","unstructured":"Jain, P., Kulis, B., Dhillon, I.S., and Grauman, K. (2008, January 8\u201311). Online metric learning and fast similarity search. Advances in Neural Information Processing Systems 21. Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_57","unstructured":"Bernhard, S., John, P., and Thomas, H. (2007). Differential Entropic Clustering of Multivariate Gaussians. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, MIT Press."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1016\/j.patcog.2010.06.005","article-title":"Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification","volume":"43","author":"Orsenigo","year":"2010","journal-title":"Pattern Recogn."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gravano, L., and Paparrizos, J. (2015, January 27). K-shape: Efficient and accurate clustering of time series. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia.","DOI":"10.1145\/2723372.2737793"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Qu, C., and Li, P. (October, January 26). Winter wheat mapping from Landsat NDVI time series data using time-weighted dynamic time warping and phenological rules. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323243"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/501\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:05:20Z","timestamp":1760133920000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,21]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030501"],"URL":"https:\/\/doi.org\/10.3390\/rs14030501","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,21]]}}}