CROP CLASSIFICATION ON SINGLE DATE SENTINEL-2 IMAGERY USING RANDOM FOREST AND SUPPOR VECTOR MACHINE

40 citations as recorded by crossref.

1. Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery V. Syrris et al. 10.3390/rs11080907
2. Enhanced Convolutional-Neural-Network Architecture for Crop Classification M. Moreno-Revelo et al. 10.3390/app11094292
3. A Highland Barley Crop Extraction Method Based on Optimized Feature Combination of Multiple Phenological Sentinel-2 Images X. Wu et al. 10.3390/agriculture14091466
4. Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers Y. Chabalala et al. 10.3390/geomatics3010004
5. Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition J. Pluto-Kossakowska 10.3390/agriculture11100999
6. Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images B. Hejmanowska et al. 10.3390/rs13163176
7. Land use land cover mapping and snow cover detection in Himalayan region using machine learning and multispectral Sentinel-2 satellite imagery R. Saini & S. Singh 10.1007/s41870-023-01673-1
8. Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling G. Meraj et al. 10.3390/rs14133005
9. Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping P. Sivaraj et al. 10.3390/geomatics2010007
10. Classification Ensembles for Beach Cast and Drifting Vegetation Mapping with Sentinel-2 and PlanetScope F. Uhl et al. 10.3390/geosciences12010015
11. Remote sensing and machine learning for crop water stress determination in various crops: a critical review S. Virnodkar et al. 10.1007/s11119-020-09711-9
12. Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia B. Tuvdendorj et al. 10.3390/rs14081830
13. How to combine socioeconomic assessment and remote sensing methods to recover and group farm plots at risk of abandonment C. Calafat-Marzal et al. 10.1080/1747423X.2023.2234921
14. Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data W. Luan et al. 10.3390/su152316343
15. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France G. Xie & S. Niculescu 10.3390/rs14184437
16. 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
17. Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images M. Tuğaç et al. 10.30897/ijegeo.1479116
18. Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information M. Gumma et al. 10.1080/10106049.2020.1805029
19. Remote Sensing from Different Sources for Crop Growth Monitoring in the Area of the Lower Northern Mississippi Y. Huang et al. 10.3390/challe14010012
20. An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery Y. Wang et al. 10.1016/j.isprsjprs.2023.04.002
21. Greenery change and its impact on human-elephant conflict in Sri Lanka: a model-based assessment using Sentinel-2 imagery T. Gunawansa et al. 10.1080/01431161.2023.2244644
22. Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine X. Xiao et al. 10.3390/rs15041112
23. Cross Domain Early Crop Mapping Using CropSTGAN Y. Wang et al. 10.1109/ACCESS.2024.3436620
24. Urban expansion and its impacts on local temperature in San Salvador, El Salvador N. Son et al. 10.1016/j.uclim.2020.100617
25. Evaluation of the Use of the 12 Bands vs. NDVI from Sentinel-2 Images for Crop Identification A. Lozano-Tello et al. 10.3390/s23167132
26. Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms M. Gumma et al. 10.3390/agriengineering6010045
27. A simple rule-based algorithm in Google Earth Engine for operational discrimination of rice paddies in Sefidroud Irrigation Network P. Esmaeili et al. 10.1007/s12517-023-11770-x
28. Characterising maize and intercropped maize spectral signatures for cropping pattern classification M. Mahlayeye et al. 10.1016/j.jag.2024.103699
29. Classification of tobacco using remote sensing and deep learning techniques U. Qazi et al. 10.1002/agj2.21382
30. AI can empower agriculture for global food security: challenges and prospects in developing nations A. Ahmad et al. 10.3389/frai.2024.1328530
31. FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms S. Sadri et al. 10.5194/hess-26-5373-2022
32. Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study F. López-Andreu et al. 10.3390/agronomy11040621
33. Multidecadal evaluation of changes in coffee-growing areas using Landsat data in Central Highlands, Vietnam N. Son et al. 10.1080/10106049.2023.2204099
34. Evaluating Performance of Community-based Irrigation Schemes Using Remote-sensing Technologies to Enhance Sustainable Irrigation Water Management T. Bashe et al. 10.1007/s41101-023-00222-y
35. Walnut Acreage Extraction and Growth Monitoring Based on the NDVI Time Series and Google Earth Engine Z. Shi et al. 10.3390/app13095666
36. Recognition of Maize Phenology in Sentinel Images with Machine Learning A. Murguia-Cozar et al. 10.3390/s22010094
37. Optimizing crop classification in precision agriculture using AlexNet and high resolution UAV imagery O. Ajayi et al. 10.48130/tia-0024-0009
38. Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms J. Espinosa-Herrera et al. 10.3390/s22166106
39. Assessment of multi-date Sentinel-2 data for field-level monitoring of isabgol (Plantago ovata Forsk) cropping practices in India P. Hirapara et al. 10.1016/j.asr.2024.08.001
40. Crop Classification and Cropping Intensity Estimation Using Geospatial Technology in the Upper Gangetic Plains of Uttarakhand A. Hegde et al. 10.1016/j.heliyon.2024.e36364