Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States-Reference-Cited by-同舟云学术

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

Published:2024-05-09 Issue:1 Volume:61 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Li Yuanchao¹²³,Zeng Hongwei¹²,Zhang Miao¹,Wu Bingfang¹²,Qin Xingli¹

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

3. Department of Marketing Management, China Siwei Surveying and Mapping Technology Co. Ltd, Beijing, China

Funder

National Key Research and Development Project of China

Natural Science Foundation of China

Agricultural Remote Sensing Innovation Team Project of AIRCAS

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2024.2349341

Reference61 articles.

1. Development of gridded surface meteorological data for ecological applications and modelling

2. The Effects of Drought and Nitrogen on Light Interception, Growth and Yield of Winter Oilseed Rape

3. A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data

4. Genetic strategies for improving crop yields

5. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

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