Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic...

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Bibliographic Details
Main Authors: Ghada Sahbeni, Balázs Székely, Peter K. Musyimi, Gábor Timár, Ritvik Sahajpal
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:AgriEngineering
Subjects:
crop yield
machine learning
remote sensing
Sentinel-3 SLSTR
SDGs
Online Access:https://www.mdpi.com/2624-7402/5/4/109

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