Rice Crop Biophysical Parameters Retrieval from Sentinel-2 Imagery Using Parsimonious Multioutput Machine Learning Techniques

Domain:

agriculturegeospatial

Record type:

dataset

Creator:

Mur

Publisher:

Host:

This dataset provides a Jupyter notebook used in training novel parsimonious multioutput machine learning models for simultaneous retrieval of the parameters from Sentinel-2 imagery, ~200 in-situ rice LAI and LCC measurements, study area shapefiles, prediction maps and SHAP feature importance plots output. Field campaigns were conducted in 2023 at Ahero Irrigation Scheme, Kenya, as part of Afri4CAst project.

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Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode

Dataset for: Retrieval of Rice Crop Biophysical Parameters from Sentinel-2 Imagery Using Parsimonious Multioutput Machine Learning Techniques Project

This dataset contains Jupyter notebook code for training novel parsimonious multioutput mac

Retrieval of rice biophysical parameters from Sentinel‑2 using parsimonious multi‑output machine learning

Abstract Rice ( Oryza sativa