ASSESSING CROP MONITORING POTENTIAL OF SENTINEL-2 IN A SPATIO-TEMPORAL SCALE

23 citations as recorded by crossref.

1. Enhancing Crop Mapping Precision through Multi-Temporal Sentinel-2 Image and Spatial-Temporal Neural Networks in Northern Slopes of Tianshan Mountain X. Zhang et al. https://doi.org/10.3390/agronomy13112800
2. Early cereal yield prediction using machine learning and Sentinel-1 & Sentinel-2 satellite data A. Chahbi-Bellakanji et al. https://doi.org/10.1080/22797254.2025.2558614
3. Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran) Y. Afrasiabian et al. https://doi.org/10.1007/s11119-020-09749-9
4. Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification A. Ghosh et al. https://doi.org/10.1007/s10668-021-01885-0
5. Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data A. Kushwaha et al. https://doi.org/10.1016/j.asr.2022.02.021
6. Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images O. Narin & S. Abdikan https://doi.org/10.1080/10106049.2020.1765886
7. Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks Z. Yao et al. https://doi.org/10.3390/agriculture13010010
8. Yozgat İli NDVI Yersel ve Zamansal Değişiminin Uydu Görüntüleri Yardımıyla Tespit Edilmesi Ö. Karaca https://doi.org/10.59128/bojans.1576006
9. Faulting and Lithological Features in Vegetation Distribution: A Remote Sensing Asisted Case Study from SE Turkiye Y. Öztürk et al. https://doi.org/10.30897/ijegeo.1138059
10. Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system D. Demirkan et al. https://doi.org/10.1117/1.JRS.14.026524
11. Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany P. Ghosh et al. https://doi.org/10.1080/10106049.2022.2039306
12. The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2 R. Burger et al. https://doi.org/10.3390/rs16050835
13. Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability M. Sellami et al. https://doi.org/10.3390/agronomy12020489
14. Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation M. Waleed et al. https://doi.org/10.1038/s41598-022-17454-y
15. Assessment of red-edge based vegetation indices for crop yield prediction at the field scale across large regions in Australia D. Al-Shammari et al. https://doi.org/10.1016/j.eja.2024.127479
16. Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm A. Uribeetxebarria et al. https://doi.org/10.3390/rs15061640
17. Monitoring rice lodging grade via Sentinel-2A images based on change vector analysis Q. Sun et al. https://doi.org/10.1080/01431161.2021.2012293
18. Exploitation of Sentinel-2 Spectral Bands and Vegetation Indices in Potato Yield Estimation M. Amin et al. https://doi.org/10.1007/s12524-025-02236-6
19. Improving Yield Prediction and Decision-Making in Mediterranean Vineyards Using Machine Learning on Multisource Remote Sensing and Soil Data J. González-Vivar et al. https://doi.org/10.1177/11786221261432379
20. A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt S. Khan et al. https://doi.org/10.3390/rs14122843
21. Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series E. Perry et al. https://doi.org/10.3390/rs14133071
22. Bartın’daki Sahil Çamı (Pinus pinaster Ait.) Ağaçlandırma Alanlarında Sentinel-1 ve Sentinel-2 Uydu Görüntüleri Kullanılarak Toprak Üstü Biyokütlenin Kestirilmesi E. ÖZDEMİR et al. https://doi.org/10.48123/rsgis.1327406
23. Impact assessment of TPLF invasion on irrigated agriculture by using remote sensing approach in the Eastern Amhara, Ethiopia D. Mebrie & Y. Kassie https://doi.org/10.1080/23311932.2025.2562476