ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING

7 citations as recorded by crossref.

1. Monitoring of the State of Saint Petersburg Green Spaces by Remote Sensing Data M. Skachkova & O. Guryeva https://doi.org/10.18412/1816-0395-2023-5-51-57
2. Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach F. Mesas-Carrascosa et al. https://doi.org/10.3390/rs17081412
3. Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning A. Danilov & E. Serdiukova https://doi.org/10.3390/s24165089
4. Development of a methodology for inventorying forest lands using an unmanned aerial vehicle; the example of the Magdagachinsky forestry, the Amur Oblast, RF V. Kovyazin et al. https://doi.org/10.22389/0016-7126-2024-1009-7-34-45
5. Mapping Rice Phenology Using MODIS Products in An Giang Province, Mekong River Delta, Vietnam S. Chiang & M. Ton https://doi.org/10.3390/rs17091583
6. An Innovative Method for NDVI Correction and Cloud Removal in Sugarcane Remote Sensing M. Kambli & B. Palkar https://doi.org/10.1007/s40031-025-01276-3
7. RDNRnet: A Reconstruction Solution of NDVI Based on SAR and Optical Images by Residual-in-Residual Dense Blocks Y. Han et al. https://doi.org/10.1109/TGRS.2024.3354255