ASSESSMENTS OF SENTINEL-2 VEGETATION RED-EDGE SPECTRAL BANDS FOR IMPROVING LAND COVER CLASSIFICATION

10 citations as recorded by crossref.

1. Winter remote sensing images are more suitable for forest mapping in Jiangxi Province R. Wang et al. https://doi.org/10.1080/22797254.2023.2237655
2. Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain N. Georgopoulos et al. https://doi.org/10.3390/f13122157
3. Multi-Source Remote Sensing Data for Wetland Information Extraction: A Case Study of the Nanweng River National Wetland Reserve H. Yu et al. https://doi.org/10.3390/s24206664
4. Integrating Sentinel-2 Derivatives to Map Land Use/Land Cover in an Avocado Agro-Ecological System in Kenya E. King’ori et al. https://doi.org/10.1007/s41976-023-00090-z
5. Machine learning for asymptomatic Ratoon stunting disease detection with freely available satellite based multispectral imaging E. Waters et al. https://doi.org/10.1016/j.inpa.2025.12.001
6. Use of hyperspectral sensor and random forest for broadleaf and narrowleaf weed differentiation P. Fernandes et al. https://doi.org/10.1007/s11056-025-10113-w
7. Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models L. Alonso et al. https://doi.org/10.3390/rs13122237
8. Comparison of Tree Typologies Mapping Using Random Forest Classifier Algorithm of PRISMA and Sentinel-2 Products in Different Areas of Central Italy E. Caputi et al. https://doi.org/10.3390/rs17030356
9. Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data L. Suarez et al. https://doi.org/10.1007/s11119-020-09722-6
10. Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier J. Šandera & P. Štych https://doi.org/10.3390/land9110420