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Remote Sensing
Machine Learning-Based Estimation of Canopy Chlorophyll Content in Crops from Multiple Satellite Images with Various Spatial Resolutions
Mir Md Tasnim Alam and Dr. Anita Simic Milas This study examines how various machine learning methods perform when combined with data fusion and a radiative transfer model (RTM). Three distinct sensors, namely Landsat-7, RapidEye, and PlanetScope, each offering varying levels of spatial and spectral resolution were employed to assess and quantify canopy chlorophyll content (CCC) from a cornfield. The.. Read more
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