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

Domaine:

agriculturegeospatial

Type de record:

dataset
Créateur:
Mur
Éditeur:
Zenodo
Hôte:avatar
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.

Visit

doi.orgzenodo.org

Tags

Remote sensingMachine LearningRandom ForestSupport Vector MachineRice

Licenses

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

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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