Assessing cropland disagreement in Tanzania using machine learning methods with Sentinel-2 and Planet Scope imagery-Reference-Cited by-同舟云学术

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Assessing cropland disagreement in Tanzania using machine learning methods with Sentinel-2 and Planet Scope imagery

Published:2023-11-02 Issue:21 Volume:44 Page:6716-6735
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ogweno Vincent Wayuga¹²^ORCID,Moore Nathan¹³^ORCID,Wanyama Dan⁴

Affiliation:

1. Department of Geography Environment and Spatial Sciences, Michigan State University, East Lansing, MI, USA

2. Department of Mathematical Sciences (Climate Option), AIMS Rwanda, Kigali, Rwanda

3. Center for Global Change and Earth Observation, Michigan State University, East Lansing, MI, USA

4. Department of Geography, University of Connecticut, Storrs, CT, USA

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2274320

Reference41 articles.

1. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

2. Climate change adaptation strategies by local farmers in Kilombero District, Tanzania

3. The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets

4. Random forest in remote sensing: A review of applications and future directions

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1. Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects;Agronomy;2024-08-27

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