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Dryland cropping in different Land uses of Senegal using Sentinel-2 and hybrid ML method

OAR@ICRISAT

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Relation http://oar.icrisat.org/12793/
https://www.tandfonline.com/doi/full/10.1080/17538947.2024.2378815
https://doi.org/10.1080/17538947.2024.2378815
 
Title Dryland cropping in different Land uses of Senegal using Sentinel-2 and hybrid ML method
 
Creator Gumma, M K
Panjala, P
Teluguntla, P
 
Subject GIS Techniques/Remote Sensing
 
Description In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–temporal distribution of crops. Monitoring and mapping crops is crucial for food security and implementing agricultural support programs. This study aims to map crop types across Senegal using Sentinel-2 satellite imagery and the limited ground reference data available, which has been increasing recently. The study compares conventional supervised classification algorithms to unsupervised classification algorithms using high-resolution satellite imagery. Crop type classification for 2020 in Senegal employed supervised machine learning algorithms, including Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on the Google Earth Engine (GEE) cloud platform, and the unsupervised Iso-clustering classification algorithm with Spectral Matching Techniques (SMTs). Due to limited ground data, supervised classifiers achieved 45-55% accuracy, whereas the unsupervised semi-automatic approach achieved over 75% accuracy. The study indicates that supervised classifiers' performance depends on ground data quantity, while SMT shows good performance even with limited ground data. This SMT approach is valuable for classifying crop types in dryland areas with smallholder farms and diverse cropping patterns.
 
Publisher Taylor & Francis
 
Date 2024-07-18
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_by_nc
 
Identifier http://oar.icrisat.org/12793/1/International%20Journal%20of%20Digital%20Earth_17_1_1-18_2024.pdf
Gumma, M K and Panjala, P and Teluguntla, P (2024) Dryland cropping in different Land uses of Senegal using Sentinel-2 and hybrid ML method. International Journal of Digital Earth, 17 (1). pp. 1-18. ISSN 1753-8947