COMPARISON OF LANDSAT-8 AND SENTINEL-2 DATA FOR CLASSIFICATION OF RABI CROPS OVER KARNATAKA, INDIA

8 citations as recorded by crossref.

1. Remote sensing satellite data and spectral indices: an initial evaluation for the sustainable development of an urban area A. Prasad et al. https://doi.org/10.1007/s40899-022-00607-2
2. 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
3. Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China L. Han et al. https://doi.org/10.3390/rs14010225
4. Identification of Suitable Machine Learning Classifier for In-Season Crop Mapping in the Absence of Concurrent Ground Truth Data S. Mallya et al. https://doi.org/10.1007/s12524-025-02329-2
5. Comparative analysis of different supervised methods for satellite-based land-use classification: A case study of Reyhanlı M. Özbuldu & Y. Şekerli https://doi.org/10.37908/mkutbd.1485236
6. A hybrid UNet based approach for crop classification using Sentinel-1B synthetic aperture radar images S. Kaur & S. Madaan https://doi.org/10.1007/s11042-024-18849-x
7. Accuracy Assessment of different classifiers for Sustainable Development in Landuse and Landcover mapping using Sentinel SAR and Landsat-8 data K. Kanmani et al. https://doi.org/10.4108/ew.4141
8. Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression S. Paul & D. Nagesh Kumar https://doi.org/10.1109/TGRS.2020.3009290