Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images

Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) ti...

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Bibliographic Details
Main Authors: Saeideh Maleki, Nicolas Baghdadi, Hassan Bazzi, Cassio Fraga Dantas, Dino Ienco, Yasser Nasrallah, Sami Najem
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
Random Forest
Multi-Layer Perceptron
XGBoost
InceptionTime
S1 backscattering coefficient
S1 polarimetric parameters
Online Access:https://www.mdpi.com/2072-4292/16/23/4548
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