EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

21 citations as recorded by crossref.

1. Sentinel SAR-optical fusion for improving in-season wheat crop mapping at a large scale using machine learning and the Google Earth engine platform L. Zoungrana et al. 10.1007/s12518-023-00545-4
2. Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review L. Yang et al. 10.3390/rs14143253
3. Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring M. Rousi et al. 10.1109/JSTARS.2020.3038152
4. A hybrid UNet based approach for crop classification using Sentinel-1B synthetic aperture radar images S. Kaur & S. Madaan 10.1007/s11042-024-18849-x
5. Cross-Regional Crop Classification Based on Sentinel-2 J. He et al. 10.3390/agronomy14051084
6. Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images B. Hejmanowska et al. 10.3390/rs13163176
7. Improving Crop Classification Accuracy with Integrated Sentinel-1 and Sentinel-2 Data: a Case Study of Barley and Wheat G. Faqe Ibrahim et al. 10.1007/s41651-023-00152-2
8. Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal Crop Classification Using Big Data Reduction Strategy B. Al-Awar et al. 10.1007/s41976-022-00072-7
9. Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons M. Hudait & P. Patel 10.1016/j.ejrs.2022.01.004
10. Bi-stage feature selection for crop mapping using grey wolf metaheuristic optimization M. Moustafa et al. 10.1016/j.asr.2024.02.037
11. Detecting and distinguishing between apicultural plants using UAV multispectral imaging A. Papachristoforou et al. 10.7717/peerj.15065
12. Multi-Year Time Series Transfer Learning: Application of Early Crop Classification M. Račič et al. 10.3390/rs16020270
13. Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation J. Cheng et al. 10.1016/j.jag.2024.103871
14. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review M. Amani et al. 10.1109/JSTARS.2020.3021052
15. Post-earthquake recovery monitoring and driving factors analysis of the 2014 Ludian Ms6.5 earthquake in Yunnan, China based on LUCC H. Mi et al. 10.1007/s00477-023-02555-5
16. Crop Types Discrimination and Yield Prediction Using Sentinel-2 Data and AquaCrop Model in Hazaribagh District, Jharkhand B. Parida et al. 10.1007/s42489-021-00073-4
17. Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India J. Niraimathi & S. Saravanan 10.1007/s10661-024-13487-0
18. Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops C. Hyland et al. 10.1038/s41370-022-00467-0
19. Sunflower mapping using machine learning algorithm in Google Earth Engine platform A. Kumar et al. 10.1007/s10661-024-13369-5
20. Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia H. Belay et al. 10.3390/land13030396
21. A Novel Method for Identification and Classification of Indian Vegetables Using Random Forest Algorithm A. K Talawar et al. 10.46632/daai/3/2/1