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A Hybrid 1d-Cnn and Prosail Framework for Brdf Modeling and Correction of Modis Data on Gee to Enhance Crop Biophysical Variable Estimation

https://doi.org/10.2139/ssrn.4823395
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PROSPECT+SAIL models: A review of use for vegetation characterization
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Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning
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An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea
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inferredcites →https://doi.org/10.1109/lgrs.2016.2560799openalex-snapshot
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
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Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images
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An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing
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Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study
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Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
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Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review
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Machine Learning Algorithms for the Retrieval of Canopy Chlorophyll Content and Leaf Area Index of Crops Using the PROSAIL-D Model with the Adjusted Average Leaf Angle
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Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data
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Deep Neural Network Approximation Theory
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Adam: A Method for Stochastic Optimization
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Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
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Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
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