Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

Yuanchao Lia State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China;c Department of Marketing Management, China Siwei Surveying and Mapping Technology Co. Ltd, Beijing, ChinaView further author information

Hongwei Zenga State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
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Miao Zhanga State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaView further author information

Bingfang Wua State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China;b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, ChinaCorrespondence[email protected]
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Xingli Qina State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaView further author information

Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

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Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

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