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This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV\/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel\u03c1\u00a0\u03c1HHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and\/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.<\/jats:p>","DOI":"10.3390\/rs13071394","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T11:48:29Z","timestamp":1617623309000},"page":"1394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-3354","authenticated-orcid":false,"given":"Qinghua","family":"Xie","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunyu","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-0530","authenticated-orcid":false,"given":"Jinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, The University of Western Ontario, London, ON N6A 5C2, Canada"},{"name":"Institute for Earth and Space Exploration, The University of Western Ontario, London, ON N6A 3K7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-5175","authenticated-orcid":false,"given":"Juan M.","family":"Lopez-Sanchez","sequence":"additional","affiliation":[{"name":"Institute for Computer Research (IUII), University of Alicante, E-03080 Alicante, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Shang","sequence":"additional","affiliation":[{"name":"Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5504-206X","authenticated-orcid":false,"given":"Chunhua","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, The University of Western Ontario, London, ON N6A 5C2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiqiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"ref_1","unstructured":"Brown, L.R. (2005). Outgrowing the Earth: The Food Security Challenge in an Age of Falling Water Tables and Rising Temperatures, W. W. Norton & Company."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"773","article-title":"Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?","volume":"78","author":"Thenkabail","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rse.2017.06.022","article-title":"A new method for crop classification combining time series of radar images and crop phenology information","volume":"198","author":"Bargiel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1364\/OE.414050","article-title":"Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model","volume":"29","author":"Yang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_7","first-page":"102032","article-title":"Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm","volume":"87","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2006.04.004","article-title":"Resolution dependent errors in remote sensing of cultivated areas","volume":"103","author":"Ozdogan","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. central great plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1177\/0309133309350121","article-title":"Problems in remote sensing of landscapes and habitats","volume":"33","author":"Wang","year":"2009","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2005.03.010","article-title":"Efficiency of crop identification based on optical and SAR image time series","volume":"96","author":"Blaes","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2015.01.004","article-title":"Tracking the dynamics of paddy rice planting area in 1986\u20132010 through time series Landsat images and phenology-based algorithms","volume":"160","author":"Dong","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3712","DOI":"10.1109\/JSTARS.2015.2454297","article-title":"Efficiency assessment of multitemporal C-band RADARSAT-2 intensity and landsat-8 surface reflectance satellite imagery for crop classification in Ukraine","volume":"9","author":"Skakun","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1109\/TGRS.2012.2208649","article-title":"Multiyear crop monitoring using polarimetric RADARSAT-2 data","volume":"51","author":"Liu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar remote sensing of agricultural canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liao, C., Wang, J., Xie, Q., Baz, A.A., Huang, X., Shang, J., and He, Y. (2020). Synergistic Use of multi-temporal RADARSAT-2 and VEN\u00b5S data for crop classification based on 1D convolutional neural network. Remote Sens., 12.","DOI":"10.3390\/rs12050832"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7317","DOI":"10.1109\/TGRS.2020.2981929","article-title":"Polarimetric SAR time series change analysis over agricultural areas","volume":"58","author":"Papathanassiou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Larra\u00f1aga, A., and \u00c1lvarez-Mozos, J. (2016). On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery. Remote Sens., 8.","DOI":"10.3390\/rs8040335"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2017.02.014","article-title":"Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery","volume":"193","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"78","article-title":"Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring","volume":"74","author":"Homayouni","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Zhang, B., and Liu, M. (2018). Crop classification based on temporal information using sentinel-1 SAR time-series data. Remote Sens., 11.","DOI":"10.3390\/rs11010053"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1109\/TGRS.2014.2372897","article-title":"Dynamical approach for real-time monitoring of agricultural crops","volume":"53","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/JSTARS.2010.2047634","article-title":"First results of rice monitoring practices in Spain by means of time series of TerraSAR-X Dual-Pol Images","volume":"4","author":"Hajnsek","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.rse.2017.07.031","article-title":"Tracking crop phenological development using multi-temporal polarimetric RADARSAT-2 data","volume":"210","author":"Canisius","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"45","article-title":"Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California","volume":"74","author":"Li","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cloude, S.R. (2010). Polarisation: Applications in Remote Sensing Polarisation, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"},{"key":"ref_29","unstructured":"Lee, J., and Pottier, E. (2009). Polarimetric Radar Imaging: From basics to applications, CRC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/36.739083","article-title":"A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees","volume":"37","author":"Lin","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TGRS.2011.2176740","article-title":"Rice phenology monitoring by means of SAR polarimetry at X-band","volume":"50","author":"Cloude","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1109\/TGRS.2013.2268319","article-title":"Polarimetric response of rice fields at C-band: Analysis and phenology retrieval","volume":"52","author":"Cloude","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Busquier, M., Lopez-Sanchez, J.M., Mestre-Quereda, A., Navarro, E., Gonz\u00e1lez-Dugo, M.P., and Mateos, L. (2020). Exploring TanDEM-X interferometric products for crop-type mapping. Remote Sens., 12.","DOI":"10.3390\/rs12111774"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/LGRS.2019.2933738","article-title":"Added value of coherent copolar polarimetry at X-band for crop-type mapping","volume":"17","author":"Busquier","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1080\/01431161.2018.1528021","article-title":"Investigation of the effect of the incidence angle on land cover classification using fully polarimetric SAR images","volume":"40","author":"Xu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/LGRS.2014.2334371","article-title":"Influence of incidence angle on the coherent copolar polarimetric response of rice at X-band","volume":"12","author":"Cloude","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","first-page":"643","article-title":"Influence of incidence angle in the correlation of C-band polarimetric parameters with biophysical variables of rain-fed crops","volume":"44","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xie, Q., Wang, J., Lopez-Sanchez, J.M., Peng, X., Liao, C., Shang, J., Zhu, J., Fu, H., and Ballester-Berman, J.D. (2021). Crop height estimation of corn from multi-year RADARSAT-2 polarimetric observables using machine learning. Remote Sens., 13.","DOI":"10.3390\/rs13030392"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/07038992.2018.1481737","article-title":"Contribution of minimum noise fraction transformation of multi-temporal RADARSAT-2 polarimetric SAR data to cropland classification","volume":"44","author":"Liao","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1109\/LGRS.2019.2951805","article-title":"A LiDAR-aided multibaseline polInSAR method for forest height estimation: With emphasis on dual-baseline selection","volume":"17","author":"Xie","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xie, Q., Wang, J., Liao, C., Shang, J., Lopez-Sanchez, J.M., Fu, H., and Liu, X. (2019). On the use of Neumann decomposition for crop classification using multi-temporal RADARSAT-2 polarimetric SAR data. Remote Sens., 11.","DOI":"10.3390\/rs11070776"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5589\/m11-023","article-title":"The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index","volume":"37","author":"Jiao","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","first-page":"1","article-title":"Selection of PolSAR observables for crop biophysical variable estimation with global sensitivity analysis","volume":"13","author":"Erten","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1080\/01431161.2017.1407046","article-title":"Sensitivity study of RADARSAT-2 polarimetric SAR to crop height and fractional vegetation cover of corn and wheat","volume":"39","author":"Liao","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/36.739146","article-title":"Coherence estimation for SAR imagery","volume":"37","author":"Touzi","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Neumann, M., Ferro-Famil, L., Jager, M., Reigber, A., and Pottier, E. (2009, January 12\u201317). A Polarimetric Vegetation Model to Retrieve Particle and Orientation Distribution Characteristics. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417351"},{"key":"ref_49","unstructured":"Kim, Y., and van Zyl, J. (2001, January 9\u201313). Comparison of Forest Parameter Estimation Techniques Using SAR data. Proceedings of the IGARSS 2001. Scanning the Present and Resolving the Future. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, NSW, Australia."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1109\/TGRS.2009.2014944","article-title":"A time-series approach to estimate soil moisture using polarimetric radar data","volume":"47","author":"Kim","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","first-page":"1","article-title":"A generalized volume scattering model-based vegetation index from polarimetric SAR data","volume":"10","author":"Ratha","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/LGRS.2013.2279255","article-title":"Retrieval of wheat growth parameters with radar vegetation indices","volume":"11","author":"Kim","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TGRS.2010.2046331","article-title":"A general characterization for polarimetric scattering from vegetation canopies","volume":"48","author":"Arii","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"60","DOI":"10.5589\/m12-012","article-title":"Towards operational radar-only crop type classification: Comparison of a traditional decision tree with a random forest classifier","volume":"38","author":"Deschamps","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2014.889863","article-title":"Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data","volume":"5","author":"Sonobe","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4244","DOI":"10.1109\/JSTARS.2018.2866407","article-title":"A novel phenology based feature subset selection technique using random forest for multitemporal PolSAR crop classification","volume":"11","author":"Hariharan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6321","DOI":"10.1109\/TGRS.2020.2976661","article-title":"A radar vegetation index for crop monitoring using compact polarimetric SAR data","volume":"58","author":"Mandal","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4070","DOI":"10.1109\/JSTARS.2020.3008096","article-title":"Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping","volume":"13","author":"Jacob","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/JSTARS.2019.2958847","article-title":"Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers","volume":"13","author":"Jacob","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, C., Zhang, H., Tang, Y., and Liu, X. (2018). Analysis of Permafrost Region Coherence Variation in the Qinghai\u2013Tibet Plateau with a High-Resolution TerraSAR-X Image. Remote Sens., 10.","DOI":"10.3390\/rs10020298"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:21Z","timestamp":1760363901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":62,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071394"],"URL":"https:\/\/doi.org\/10.3390\/rs13071394","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,5]]}}}