{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T03:08:45Z","timestamp":1772852925875,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"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>This study develops a structure for mapping native vegetation in a transition area between the Brazilian Cerrado and the Atlantic Forest from integrated spatial information of Sentinel-1 and Sentinel-2 satellites. Most studies use integrated data to improve classification accuracy in adverse atmospheric conditions, in which optical data have many errors. However, this method can also improve classifications carried out in landscapes with favorable atmospheric conditions. The use of Sentinel-1 and Sentinel-2 data can increase the accuracy of mapping algorithms and facilitate visual interpretation during sampling by providing more parameters that can be explored to differentiate land use classes with complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near-infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings: SAR and optical data in separately settings (C1 and C2, respectively) and in an integrated setting (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlands in C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the integrated use of SAR and optical data can improve LULC mapping in tropical regions where occurs biomes interface, as in the transitional Brazilian Cerrado and Atlantic Forest.<\/jats:p>","DOI":"10.3390\/rs16142559","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T11:28:03Z","timestamp":1720783683000},"page":"2559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Integrated Use of Synthetic Aperture Radar and Optical Data in Mapping Native Vegetation: A Study in a Transitional Brazilian Cerrado\u2013Atlantic Forest Interface"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5394-4035","authenticated-orcid":false,"given":"Allita R.","family":"Santos","sequence":"first","affiliation":[{"name":"Center for Water Resources and Environmental Studies (CRHEA), University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"given":"Mariana A. G. A.","family":"Barbosa","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environmental Studies (CRHEA), University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"given":"Phelipe S.","family":"Anjinho","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environmental Studies (CRHEA), University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"given":"Denise","family":"Parizotto","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environmental Studies (CRHEA), University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2477-2019","authenticated-orcid":false,"given":"Frederico F.","family":"Mauad","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environmental Studies (CRHEA), University of S\u00e3o Paulo, S\u00e3o Carlos 13566-590, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.ecolind.2018.11.035","article-title":"Evaluating Land Suitability for Spatial Planning in Arid Regions of Eastern Iran Using Fuzzy Logic and Multi-Criteria Analysis","volume":"98","author":"Akbari","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e20210072","DOI":"10.1590\/0001-3765202120210072","article-title":"Spatial and Seasonal Assessment of Water Quality in the Lobo Stream River Basin, Brazil Using Multivariate Statistical Techniques","volume":"93","author":"Neves","year":"2021","journal-title":"An. Acad. Bras. Cienc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104946","DOI":"10.1016\/j.landusepol.2020.104946","article-title":"Environmental Fragility Analysis in Reservoir Drainage Basin Land Use Planning: A Brazilian Basin Case Study","volume":"100","author":"Barbosa","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Santos, A.R., Barbosa, M.A.G.A., Bolleli, T., Anjinho, P.S., Roque, R., and Mauad, F.F. (2023). Assessment of Water Ecosystem Integrity (WEI) in a Transitional Brazilian Cerrado\u2013Atlantic Forest Interface. Water, 15.","DOI":"10.3390\/w15040775"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Souza, C.M., Shimbo, J.Z., Rosa, M.R., Parente, L.L., Alencar, A.A., Rudorff, B.F.T., Hasenack, H., Matsumoto, M., Ferreira, L.G., and Souza-Filho, P.W.M. (2020). Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12172735"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104783","DOI":"10.1016\/j.landusepol.2020.104783","article-title":"Suitability Evaluation of Urban Construction Land in Pendik District of Istanbul, Turkey","volume":"99","author":"Ustaoglu","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1890\/1540-9295(2004)002[0249:LCBHNA]2.0.CO;2","article-title":"Land-Use Choices: Balancing Human Needs and Ecosystem Function","volume":"2","author":"DeFries","year":"2004","journal-title":"Front. Ecol. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1608\/FRJ-6.2.727","article-title":"The Ecology of UHE Carlos Botelho (Lobo-Broa Reservoir) and Its Watershed, S\u00e3o Paulo, Brazil","volume":"6","author":"Tundisi","year":"2013","journal-title":"Freshw. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ecohyd.2016.03.006","article-title":"Integrating Ecohydrology, Water Management, and Watershed Economy: Case Studies from Brazil","volume":"16","author":"Tundisi","year":"2016","journal-title":"Ecohydrol. Hydrobiol."},{"key":"ref_10","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":"Silva","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","unstructured":"WWF (2022, September 19). Cerrado, the Brazilian Savanna. Available online: https:\/\/wwf.panda.org\/discover\/knowledge_hub\/where_we_work\/cerrado\/."},{"key":"ref_12","unstructured":"Projeto MapBiomas (2022). Projeto MapBiomas\u2014Mapeamento Anual de Cobertura e Uso da Terra No Cerrado\u2014Cole\u00e7\u00e3o 7, Projeto MapBiomas Brasil."},{"key":"ref_13","unstructured":"Projeto MapBiomas (2022). Projeto MapBiomas\u2014Mapeamento Anual de Cobertura e Uso da Terra Na Mata Atl\u00e2ntica\u2014Cole\u00e7\u00e3o 7, Projeto MapBiomas Brasil."},{"key":"ref_14","first-page":"1","article-title":"Defining Environmental Conservation Levels Considering Anthropic Activity in the Uberaba River Basin Protected Area","volume":"14","author":"Mauad","year":"2019","journal-title":"Ambient. Agua-Interdiscip. J. Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wen, D., Ma, S., Zhang, A., and Ke, X. (2021). Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method. Sustainability, 13.","DOI":"10.3390\/su13137044"},{"key":"ref_17","first-page":"1482","article-title":"Evaluation of Polarimetry and Interferometry of Sentinel-1A SAR Data for Land Use and Land Cover of the Brazilian Amazon Region","volume":"37","author":"Gama","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Eibedingil, I.G., Gill, T.E., Scott Van Pelt, R., and Tong, D.Q. (2021). Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA. Remote Sens., 13.","DOI":"10.3390\/rs13173402"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.09.016","article-title":"Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for Land Cover Mapping via a Multi-Source Deep Learning Architecture","volume":"158","author":"Ienco","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Morin, D., Planells, M., Guyon, D., Villard, L., Mermoz, S., Bouvet, A., Thevenon, H., Dejoux, J.F., Le Toan, T., and Dedieu, G. (2019). Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation. Remote Sens., 11.","DOI":"10.3390\/rs11111275"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nikaein, T., Iannini, L., Molijn, R.A., and Lopez-Dekker, P. (2021). On the Value of Sentinel-1 InSAR Coherence Time-Series for Vegetation Classification. Remote Sens., 13.","DOI":"10.3390\/rs13163300"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Numbisi, F.N., Van Coillie, F.M.B., and De Wulf, R. (2019). Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.20944\/preprints201901.0050.v1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111465","DOI":"10.1016\/j.rse.2019.111465","article-title":"From Woody Cover to Woody Canopies: How Sentinel-1 and Sentinel-2 Data Advance the Mapping of Woody Plants in Savannas","volume":"234","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.isprsjprs.2020.01.015","article-title":"Collaborative Learning of Lightweight Convolutional Neural Network and Deep Clustering for Hyperspectral Image Semi-Supervised Classification with Limited Training Samples","volume":"161","author":"Fang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"141","DOI":"10.5380\/dma.v40i0.49243","article-title":"O \u201cModelo Broa\u201d e a Produ\u00e7\u00e3o de Conhecimento Cient\u00edfico Sobre o Meio Ambiente","volume":"40","author":"Campregher","year":"2017","journal-title":"Desenvolv. Meio Ambient."},{"key":"ref_26","first-page":"102452","article-title":"Dynamics of Environmental Conservation: Evaluating the Past for a Sustainable Future","volume":"102","author":"Neves","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","unstructured":"Governo do Estado de S\u00e3o Paulo (1983). Declara \u00c1rea de Prote\u00e7\u00e3o Ambiental Regi\u00f5es Situadas Em Diversos Munic\u00edpios, Dentre Os Quais Corumbatai, Botucatu e Tejupa, Di\u00e1rio Oficial do Estado."},{"key":"ref_28","unstructured":"Intituto Federal do Estado de S\u00e3o Paulo (2006). Plano de Manejo Integrado Das Esta\u00e7\u00f5es Ecol\u00f3gica e Experimetnal de Itirapina\/SP, Di\u00e1rio Oficial do Estado."},{"key":"ref_29","unstructured":"ESA (2023, April 24). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_30","unstructured":"United States Geological Survey (2023, September 04). Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emis-Sion and Re-flec-Tion Radiometer (ASTER). Copernicus Open Access Hub Eur. Sp. Agency. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s12145-018-0369-z","article-title":"Performance Evaluation of Textural Features in Improving Land Use\/Land Cover Classification Accuracy of Heterogeneous Landscape Using Multi-Sensor Remote Sensing Data","volume":"12","author":"Mishra","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_32","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_33","first-page":"2","article-title":"The Dual Polarization Entropy\/Alpha Decomposition: A PALSAR Case Study","volume":"644","author":"Cloude","year":"2007","journal-title":"ESASP"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Sen Digital Image Enhancement and Noise Filtering by Use of Local Statistics","volume":"PAMI-2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of Land Cover Classification Accuracy Assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the Unsuitability of the Kappa Coefficient in the Assessment and Comparison of the Accuracy of Thematic Maps Obtained by Image Classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_39","first-page":"397","article-title":"Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.landusepol.2018.10.036","article-title":"Effects of Preservation Policy on Land Use Changes in Iranian Northern Zagros Forests","volume":"81","author":"Heidarlou","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5373","DOI":"10.1007\/s12665-015-4550-0","article-title":"Impact of Land Use\/Land Cover Changes on Water Quality and Hydrological Behavior of an Agricultural Subwatershed","volume":"74","author":"Calijuri","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_42","first-page":"133","article-title":"Land Use and Land Cover Change Detection Using Geospatial Techniques in the Sikkim Himalaya, India","volume":"23","author":"Mishra","year":"2020","journal-title":"Egypt. J. Remote Sens. Sp. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1590\/S1519-69842013000300003","article-title":"Servi\u0109os Ecossist\u00eamicos Do Reservat\u00f3rio Da UHE Carlos Botelho (Lobo-Broa): Uma Nova Abordagem Para o Gerenciamento e Planejamento Dos M\u00faltiplos Usos de Represas","volume":"73","author":"Periotto","year":"2013","journal-title":"Braz. J. Biol."},{"key":"ref_44","first-page":"102007","article-title":"Discriminating Treed and Non-Treed Wetlands in Boreal Ecosystems Using Time Series Sentinel-1 Data","volume":"85","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.046003","article-title":"Knowledge-Based Decision Tree Approach for Mapping Spatial Distribution of Rice Crop Using C-Band Synthetic Aperture Radar-Derived Information","volume":"11","author":"Mishra","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_47","first-page":"102","article-title":"Potential of TerraSAR-X and Sentinel 1 Imagery to Map Deforested Areas and Derive Degradation Status in Complex Rain Forests of Ecuador","volume":"19","author":"Fassnacht","year":"2017","journal-title":"Int. For. Rev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:15:49Z","timestamp":1760109349000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142559"],"URL":"https:\/\/doi.org\/10.3390\/rs16142559","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,12]]}}}