Stratified Machine Learning Models for Wheat Yield Estimation Using Remote Sensing Data-Reference-Cited by-同舟云学术

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Stratified Machine Learning Models for Wheat Yield Estimation Using Remote Sensing Data

Published:2024-07-07 Issue: Volume:67 Page:1946-1949
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Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Khechba Keltoum¹,Belgiu Mariana²,Laamrani Ahmed¹,Dong Qi²,Stein Alfred²,Chehbouni Abdelghani¹

Affiliation:

1. Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P),Benguerir,Morocco,43150

2. Digital Society Institute UT-I-ITC-ACQUAL Faculty of Geo-Information Science and Earth Observation,Department of Earth Observation Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641044.pdf?arnumber=10641044

Reference20 articles.

1. A Systematic National Stocktake of Crop Models in Morocco

2. The use of satellite data for crop yield gap analysis

3. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

4. Challenges and opportunities in remote sensing-based crop monitoring: a review

5. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally

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