Improving crop rotation classification using a random forest model incorporating spatial heterogeneity-Reference-Cited by-同舟云学术

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Improving crop rotation classification using a random forest model incorporating spatial heterogeneity

Published:2024-01 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Wang Xiaomi¹²,Tang Qi¹²,Yang Kang³

Affiliation:

1. School of Geographical Sciences, Hunan Normal University, Changsha, China

2. Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha, China

3. Technical Department, Guizhou Zhiyuan Engineering Technology Consulting Co., Ltd, Guiyang, China

Funder

National Natural Science Foundation of China

Guizhou Provincial Science and Technology Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2384473

Reference60 articles.

1. Preseason crop type prediction using crop sequence boundaries

2. Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

3. eXtreme Gradient Boosting Algorithm with Machine Learning: a Review

4. Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle

5. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

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