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Works cited by this work

50 works

Work: Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning

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  2. A soil-adjusted vegetation index (SAVI)

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  3. Red and photographic infrared linear combinations for monitoring vegetation

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  4. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features

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  5. Use of a green channel in remote sensing of global vegetation from EOS-MODIS

    Anatoly A. Gitelson, Yoram J. Kaufman, Mark N. Merzlyak

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  6. Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology

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  7. Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

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  8. Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation

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  9. A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States

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  10. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data

    Yingxin Gu, Eric Hunt, Brian Wardlow +3

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  11. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

    Douglas K. Bolton, M. A. Friedl

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  12. The Control of Errors in Momentary Yield Data from Combine Harvesters

    Lars Thylén, Donal Patrick Murphy

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  13. Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

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  14. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

    Yang Shao, James B. Campbell, Gregory N. Taff +1

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  15. Ecosystem carbon fluxes and canopy spectral reflectance of a mountain meadow

    Damiano Gianelle, Loris Vescovo, Barbara Marcolla +2

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  16. Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

    Joel Segarra, Ma. Luisa Buchaillot, J. L. Araus +1

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  17. Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

    Branislav Pejak, Predrag Lugonja, Aleksandar Antić +7

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  18. Random decision forests

    Tin Kam Ho

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  19. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

    Amanda Veloso, Stéphane Mermoz, Alexandre Bouvet +4

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  20. Crop Yield Prediction using Machine Learning Techniques

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    Other1 citations
    ABI

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    Other1 citations
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    Other1 citations
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    Other1 citations
    ABI