Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning

Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning

The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (<i>Glycine max</i> L.) as a representative crop, aiming to provide an alternati...

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Bibliographic Details
Main Authors: Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
PROBA-V
Sentinel-3
leaf area index (LAI)
fraction of absorbed photosynthetically active radiation (FAPAR)
random forest
FAO land suitability standard
Online Access:https://www.mdpi.com/2076-3417/15/1/372
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