Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Multisource Remote Sensing Data and Ensemble Machine Learning-Reference-Cited by-同舟云学术

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Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Multisource Remote Sensing Data and Ensemble Machine Learning

Published:2024 Issue: Volume:17 Page:14437-14453
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Pei Jie¹^ORCID,Zou Yaopeng¹,Liu Yibo¹,He Yinan²^ORCID,Tan Shaofeng¹^ORCID,Wang Tianxing¹^ORCID,Huang Jianxi³^ORCID

Affiliation:

1. School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China

2. Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

3. College of Land Science and Technology, China Agricultural University, Beijing, China

Funder

Unveiling the List of Hanging

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10632574.pdf?arnumber=10632574

Reference62 articles.

1. Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

2. Massive soybean expansion in South America since 2000 and implications for conservation

3. A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering

4. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

5. FAOSTAT,2018

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