{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:10:44Z","timestamp":1790035844453,"version":"4.0.1"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005667","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","doi-asserted-by":"publisher","award":["2017TR1762; 2019TR816"],"award-info":[{"award-number":["2017TR1762; 2019TR816"]}],"id":[{"id":"10.13039\/501100005667","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["313887\/2018-7; 436863\/2018-9"],"award-info":[{"award-number":["313887\/2018-7; 436863\/2018-9"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["PPGEF"],"award-info":[{"award-number":["PPGEF"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study\u2019s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite\/Phased Array type L-band Synthetic Aperture Radar (ALOS\/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS\/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS\/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.<\/jats:p>","DOI":"10.3390\/rs13020229","type":"journal-article","created":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T11:36:11Z","timestamp":1610364971000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Benefits of Combining ALOS\/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1947-6386","authenticated-orcid":false,"given":"Jessica da Silva","family":"Costa","sequence":"first","affiliation":[{"name":"Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"},{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-7818","authenticated-orcid":false,"given":"Veraldo","family":"Liesenberg","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-3881","authenticated-orcid":false,"given":"Marcos Benedito","family":"Schimalski","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel Val\u00e9rio de","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3334-4507","authenticated-orcid":false,"given":"Leonardo Joso\u00e9","family":"Biffi","sequence":"additional","affiliation":[{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra Rodrigues","family":"Gomes","sequence":"additional","affiliation":[{"name":"Amazon Regional Center, Brazilian National Institute for Space Research (INPE), Avenida Perimetral 2651, Belem PA 66077-830, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6640-1961","authenticated-orcid":false,"given":"S\u00edlvio Lu\u00eds Rafaeli","family":"Neto","sequence":"additional","affiliation":[{"name":"Department of Environmental and Sanitation Engineering, College of Agriculture and Veterinary, Santa Catarina State University (UDESC), Avenida Luiz de Cam\u00f5es 2090, Lages SC 88520-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1717-7657","authenticated-orcid":false,"given":"Edson","family":"Mitishita","sequence":"additional","affiliation":[{"name":"Graduate Program in Geodetic Sciences, Federal University of Paran\u00e1 (UFPR), Avenida Coronel Francisco Her\u00e1clito dos Santos 210, Curitiba PR 81531-990, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-8449","authenticated-orcid":false,"given":"Polyanna da Concei\u00e7\u00e3o","family":"Bispo","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1080\/17538947.2011.620639","article-title":"Full polarimetric PALSAR-based land cover monitoring in Cambodia for implementation of REDD policies","volume":"6","author":"Avtar","year":"2013","journal-title":"Int. J. Digit. Earth."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1590\/1809-4392201401439","article-title":"Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques","volume":"25","author":"Furtado","year":"2015","journal-title":"Acta Amaz."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1590\/s1982-21702018000200017","article-title":"PALSAR-2\/ALOS-2 and OLI\/LANDSAT-8 data integration for land use and land cover mapping in northern Brazilian Amazon","volume":"24","author":"Pavanelli","year":"2018","journal-title":"Bol. Ci\u00eanc. Geod."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Souza Mendes, F., Baron, D., Gerold, G., Liesenberg, V., and Erasmi, S. (2019). Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado\/Amazon Ecotone of Nova Mutum\u2014Mato Grosso. Remote Sens., 11.","DOI":"10.3390\/rs11101161"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1023\/A:1006790026612","article-title":"Principles of synthetic aperture radar","volume":"21","author":"Bamler","year":"2000","journal-title":"Surv. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, K.-S. (2016). Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach, CRC Press.","DOI":"10.1201\/b19057"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bovenga, F. (2020). Special Issue Synthetic Aperture Radar (SAR) Techniques and Applications. Sensors, 20.","DOI":"10.3390\/s20071851"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2015.12.013","article-title":"Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon v\u00e1rzea wetlands","volume":"174","author":"Furtado","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1080\/01431161.2015.1136448","article-title":"Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data","volume":"37","author":"Varghese","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Camargo, F.F., Sano, E.E., Almeida, C.M., Mura, J.C., and Almeida, T. (2019). A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2\/PALSAR-2 Polarimetric Images. Remote Sens., 11.","DOI":"10.3390\/rs11131600"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2019002","DOI":"10.1590\/s1982-21702019000100002","article-title":"Analysis of the target decomposition technique attributes and polarimetric ratios to discriminate land use and land cover classes of the Tapaj\u00f3s region","volume":"25","author":"Wiederkehr","year":"2019","journal-title":"Bol. Ci\u00eanc. Geod."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s42452-020-2866-1","article-title":"Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India","volume":"2","author":"Parida","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cassol, H.L.G., Carreiras, J.M.B., Moraes, E.C., Arag\u00e3o, L.E.O.C., Silva, C.V.J., Quegan, S., and Shimabukuro, Y.E. (2019). Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon. Remote Sens., 11.","DOI":"10.20944\/preprints201807.0604.v1"},{"key":"ref_14","first-page":"209","article-title":"Potential of multipolarized ALOS\/PALSAR satellite images to discriminate vegetation coverage in the Pantanal biome: A case study in the region of Medio Taquari, MS","volume":"66","author":"Sano","year":"2014","journal-title":"RBC"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wiederkehr, N.C., Gama, F.F., Castro, P.B.N., Bispo, P.C., Balzter, H., Sano, E.E., Liesenberg, V., Santos, J.R., and Mura, J.C. (2020). Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS\/PALSAR-2 Full-Polarimetric Data. Remote Sens., 12.","DOI":"10.3390\/rs12213512"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/S0034-4257(01)00276-0","article-title":"Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions","volume":"79","author":"Moran","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.25249\/0375-7536.200333S2137146","article-title":"Sinergismo entre dados \u00f3pticos e de radar para o estudo geol\u00f3gico na regi\u00e3o de Bezerra-Cabeceiras, Goi\u00e1s","volume":"33","author":"Chaves","year":"2003","journal-title":"RBG"},{"key":"ref_18","first-page":"441","article-title":"An\u00e1lise de imagens do sat\u00e9lite ALOS PALSAR para o mapeamento de uso e cobertura da terra do Distrito Federal","volume":"28","author":"Sano","year":"2009","journal-title":"Geoci\u00eancias"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rse.2009.09.013","article-title":"Fusing optical and radar data to estimate sagebrush, herbaceous, and bare ground cover in Yellowstone","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/15481603.2013.805589","article-title":"Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon","volume":"50","author":"Pereira","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_21","first-page":"5357","article-title":"Evaluating moisture and geometry effects on L-Band SAR classification performance over a tropical rain forest environment","volume":"9","author":"Liesenberg","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"De Magalh\u00e3es, T.L., Schimalski, M.B., Mantovani, A., and Bortoluzzi, R.L.C. (2012, January 7\u20139). Image classification using Landsat TM images to mapping wetlands vegetation (banhados) of the Catarinense Plateau, Southern Brazil. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5007\/2177-5230.2015v30n59p47","article-title":"An\u00e1lise da hidrografia da Coxilha Rica, Sul do munic\u00edpio de Lages\u2013SC Introdu\u00e7\u00e3o","volume":"30","author":"Oliveira","year":"2015","journal-title":"Geosul"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1127\/0941-2948\/2013\/0507","article-title":"K\u00f6ppen\u2019s climate classification map for Brazil","volume":"22","author":"Alvares","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_25","unstructured":"IBGE (2020, March 03). Mapa de Vegeta\u00e7\u00e3o do Brasil, Available online: Ftp:\/\/ftp.ibge.gov.br\/Cartas_e_Mapas\/Mapas_Murais\/."},{"key":"ref_26","unstructured":"ANA-Ag\u00eancia Nacional De \u00c1guas (2020, March 05). HidroWeb: Sistema de Informa\u00e7\u00f5es Hidrol\u00f3gicas, Available online: http:\/\/hidroweb.ana.gov.br."},{"key":"ref_27","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_28","first-page":"26","article-title":"Group theory and polarisation algebra","volume":"75","author":"Cloude","year":"1986","journal-title":"Optik"},{"key":"ref_29","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging from Basics to Applications, CRC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"244","DOI":"10.2747\/1548-1603.41.3.244","article-title":"Speckle Noise Reduction in SAR Imagery Using a Local Adaptive Median Filter","volume":"41","author":"Qiu","year":"2004","journal-title":"GISci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s12665-016-6341-7","article-title":"Dual-polarimetric C-band SAR data for land use\/land cover classification by incorporating textural information","volume":"76","author":"Mishra","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_32","unstructured":"ESA (2020, July 26). PolSARpro v6.0 (Biomass Edition) Toolbox. Available online: https:\/\/step.esa.int\/main\/toolboxes\/polsarpro-v6-0-biomass-edition-toolbox\/."},{"key":"ref_33","unstructured":"Meneses, P.R., and Almeida, T.D. (2012). Introdu\u00e7\u00e3o ao Processamento de Imagens de Sensoriamento Remoto, UNB."},{"key":"ref_34","unstructured":"M\u00fcller-Wilm, U., Devignot, O., and Pessiot, L. (2020, April 11). Sen2Cor Configuration and User Manual-S2-PDGS-MPC-L2A-SUM-V2.4. Available online: https:\/\/step.esa.int\/thirdparties\/sen2cor\/2.4.0\/Sen2Cor_240_Documenation_PDF\/S2-PDGS-MPC-L2A-SUM-V2.4.0.pdf."},{"key":"ref_35","unstructured":"Rstudio Team (2020, April 07). RStudio: Integrated Development for R. RStudio. Available online: http:\/\/www.rstudio.com\/."},{"key":"ref_36","unstructured":"Henderson, F.M., and Lewis, A.J. (1998). Principles and Applications of Imaging Radar, Wiley. [3rd ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3915","DOI":"10.1109\/TGRS.2009.2023909","article-title":"Palsar radiometric and geometric calibration","volume":"47","author":"Shimada","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","unstructured":"Saatchi, S.S., Dubayah, R., Clark, D., Chazdon, R., and Hollinger, D. (2020, April 07). Estimation of forest biomass change from fusion of radar and lidar measurements. Available online: http:\/\/www.slideshare.net\/grssieee\/estimation-offorest-biomass."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"179","DOI":"10.5194\/bg-9-179-2012","article-title":"Mapping tropical forest biomass with radar and spaceborne LiDAR in Lop\u00e9 National Park, Gabon: Overcoming problems of high biomass and persistent cloud","volume":"9","author":"Mitchard","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target scattering decomposition in terms of roll-invariant target parameters","volume":"45","author":"Touzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3624","DOI":"10.3390\/rs6053624","article-title":"Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest","volume":"6","author":"Attarchi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"\u00dcst\u00fcner, M., G\u00f6kda\u011f, \u00dc., Bilgin, G., and \u015eanl\u0131, F.B. (2018, January 2\u20135). Comparing the classification performances of supervised classifiers with balanced and imbalanced SAR data sets. Proceedings of the Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404183"},{"key":"ref_44","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, W., and Du, Q. (2015). Support vector machine with adaptive composite kernel for hyperspectral image classification. Satellite Data Compression, Communications, and Processing XI, International Society for Optics and Photonics.","DOI":"10.1117\/12.2178012"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box\u2014A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TGRS.2003.815238","article-title":"Spectral resolution requirements for mapping urban areas","volume":"41","author":"Herold","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., D\u2019Andrimont, R., and Defourny, P. (2016). Sentinel-2\u2032s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sothe, C., Almeida, C.M., Liesenberg, V., and Schimalski, M.B. (2017). Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil. Remote Sens., 9.","DOI":"10.3390\/rs9080838"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Prieto-Amparan, J.A., Villarreal-Guerrero, F., Martinez-Salvador, M., Manjarrez-Dom\u00ednguez, C., Santellano-Estrada, E., and Pinedo-Alvarez, A. (2018). Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity. Remote Sens., 10.","DOI":"10.3390\/rs10020219"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ettehadi Osgouei, P., Kaya, S., Sertel, E., and Alganci, U. (2019). Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030345"},{"key":"ref_54","first-page":"65","article-title":"Estudos florestais com imagens de radar","volume":"6","author":"Kuplich","year":"2003","journal-title":"Espa\u00e7. Geogr."},{"key":"ref_55","first-page":"281","article-title":"Monitoring South Florida wetlands using ERS-1 SAR imagery","volume":"63","author":"Kasischke","year":"1997","journal-title":"Photogramm Eng. Remote Sens."},{"key":"ref_56","first-page":"435","article-title":"Forest applications using imaging radar","volume":"Volume 2","author":"Henderson","year":"1998","journal-title":"Principles and Applications of Imaging Radar"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1590\/S0100-204X2001000500013","article-title":"Radarsat backscattering from an agricultural scene","volume":"36","author":"Formaggio","year":"2001","journal-title":"Pesq. Agropec. Bras."},{"key":"ref_58","unstructured":"Oliver, C.J., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, Scitech Publishing."},{"key":"ref_59","first-page":"49","article-title":"Sensitivity of ALOS\/PALSAR imagery to forest degradation by fire in northern Amazon","volume":"49","author":"Martins","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in Radar Polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"465612","DOI":"10.1155\/2010\/465612","article-title":"Polarimetric SAR image classification using multifeatures combination and combination and extremely randomized clustering forests","volume":"2010","author":"Zou","year":"2009","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_62","first-page":"1786","article-title":"Assessment of ALOS PALSAR 50 m orthorectified FBD data for regional land cover classification by support vector machines","volume":"262","author":"Longepe","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.1080\/01431161.2020.1723178","article-title":"Extracting impervious surfaces from full polarimetric SAR images in different urban areas","volume":"41","author":"Attarchi","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"106934","DOI":"10.1016\/j.geomorph.2019.106934","article-title":"Cosmo-SkyMed and TerraSAR-X datasets for geomorphological mapping in the eastern of Maraj\u00f3 Island, Amazon coast","volume":"350","author":"Galo","year":"2020","journal-title":"Geomorphology"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Pulella, A., Arag\u00e3o Santos, R., Sica, F., Posovszky, P., and Rizzoli, P. (2020). Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12050847"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Voormansik, K., Zalite, K., S\u00fcnter, I., Tamm, T., Koppel, K., Verro, T., Brauns, A., Jakovels, D., and Praks, J. (2020). Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12223784"},{"key":"ref_67","unstructured":"Almeida, J.A., Albuquerque, J.A., Bortoluzzi, R.L.C., and Mantovani, A. (2020, April 07). Caracteriza\u00e7\u00e3o dos solos e da vegeta\u00e7\u00e3o de \u00e1reas palustres (brejos e banhados) do Planalto Catarinense. (In Portuguese)."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature selection for classification of hyperspectral data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Rabe, A., Van Der Linden, S., and Hostert, P. (2010, January 14\u201316). Simplifying support vector machines for classification of hyperspectral imagery and selection of relevant features. Proceedings of the 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland.","DOI":"10.1109\/WHISPERS.2010.5594937"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2010.2041784","article-title":"Sensitivity of support vector machines to random feature selection in classification of hyperspectral data","volume":"48","author":"Waske","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/17583004.2018.1511383","article-title":"The challenges of using satellite data sets to assess historical land use change and associated greenhouse gas emissions: A case study of three Indonesian provinces","volume":"9","author":"Chatterton","year":"2018","journal-title":"Carbon Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:09:45Z","timestamp":1760159385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,11]]},"references-count":71,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020229"],"URL":"https:\/\/doi.org\/10.3390\/rs13020229","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,11]]}}}