ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING

8 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
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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
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7. MODIS–Sentinel-2 Data Fusion for Cloud-Robust Crop Evapotranspiration Estimation in a Nitrate-Sensitive Irrigated Maize System: Evaluating Gap-Filling Strategies for Evidence-Based Irrigation Scheduling G. Nxumalo et al. https://doi.org/10.3390/w18131644
8. 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