SENTINEL-2 IMAGERY FOR MAPPING AND MONITORING IMPERVIOUSNESS IN URBAN AREAS

34 citations as recorded by crossref.

1. What would be better for urban mapping in the Klang Valley? SPOT or Sentinel-1 M. Wahab et al. https://doi.org/10.1088/1755-1315/1064/1/012021
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4. Characterization of water hyacinth ( Eichhornia crassipes (Mart.) Solms) biomass in Lake Tana, Ethiopia Y. Damtie et al. https://doi.org/10.1080/26895293.2022.2134933
5. Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification C. Ramezan https://doi.org/10.3390/rs14246218
6. Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment K. Onačillová et al. https://doi.org/10.3390/rs14164076
7. Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier B. Shrestha et al. https://doi.org/10.3390/rs13153040
8. Radiometric Cross-Calibration and Validation of KOMPSAT-3/AEISS Using Sentinel-2A/MSI J. Choi et al. https://doi.org/10.3390/rs17193280
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12. The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland I. Cieślak et al. https://doi.org/10.3390/ijgi9060358
13. Country-wide assessment of biodiversity, naturalness and old-growth status using national forest inventory data C. Borghi et al. https://doi.org/10.1007/s10342-023-01620-6
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16. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. https://doi.org/10.3390/rs13163135
17. Evaluating Small-Scale Urban Regeneration Using Nighttime Lights and Sentinel-2: Evidence from Republic of Korea D. Jin & S. Choi https://doi.org/10.3390/urbansci10010036
18. Neural Network Fusion Processing and Inverse Mapping to Combine Multisensor Satellite Data and Analyze the Prominent Features G. Joshi et al. https://doi.org/10.1109/JSTARS.2023.3247788
19. Mapping the heat stress vulnerability landscape of Agartala City, India R. Neog https://doi.org/10.1007/s10708-024-11072-z
20. Identification and Location of a Transitional Zone between an Urban and a Rural Area Using Fuzzy Set Theory, CLC, and HRL Data A. Biłozor et al. https://doi.org/10.3390/su11247014
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23. Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam N. Quang et al. https://doi.org/10.3390/su15021410
24. BİTKİ ÖRTÜSÜ İNDEKSLERİ KULLANILARAK BİTKİ ÖRTÜSÜ YOĞUNLUKLARININ ARAŞTIRILMASI: KARATEPE-ASLANTAŞ MİLLİ PARKI ÖRNEĞİ S. Oturanç https://doi.org/10.17780/ksujes.1521564
25. Investigating sensitivity to extreme weather of a mid-size global south city: an integrated MCDA (AHP)-GIS based modeling approach N. Ankhi & A. Khan https://doi.org/10.1007/s11069-025-07546-z
26. Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake J. Wang et al. https://doi.org/10.1080/20964471.2025.2597590
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29. City Boundaries—Utilizing Fuzzy Set Theory for the Identification and Localization of the Urban–Rural Transition Zone A. Biłozor et al. https://doi.org/10.3390/su16219490
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31. Kentsel Alanlarda Arazi Kullanım ve Arazi Örtüsü Belirleme Çalışmalarında Spektral İndekslerin Kullanılması: Ceyhan İlçe Merkezi Örneği S. Çiçekli https://doi.org/10.55213/kmujens.1446772
32. Ensemble Learning for Urban Flood Segmentation Through the Fusion of Multi-Spectral Satellite Data with Water Spectral Indices Using Row-Wise Cross Attention H. Xu & A. Woodley https://doi.org/10.3390/rs17010090
33. Kudzu invasion and its influential factors in the southeastern United States M. Shen et al. https://doi.org/10.1016/j.jag.2024.103872
34. Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale B. Shrestha et al. https://doi.org/10.1007/s10661-021-09321-6