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Large-scale mapping of the spatial distribution and cutting intensity of cultivated alfalfa based on a sample generation algorithm and random forest

文献类型： 外文期刊

作者： Miao, Chunli 1 ; Fu, Shuai 1 ; Sun, Wei 3 ; Feng, Senyao 4 ; Hu, Yongjia 1 ; Liu, Jie 1 ; Feng, Qisheng 1 ; Li, Yunhao 1 ; Liang, Tiangang 1 ;

作者机构： 1.Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Herbage Improvement & Grassland Agro, Engn Res Ctr Grassland Ind,Minist Agr & Rural Affa, Lanzhou 730000, Peoples R China

2.Lanzhou Univ, Ctr Remote Sensing Ecol Environm Cold & Arid Reg, Lanzhou, Peoples R China

3.Ningxia Acad Agr & Forestry Sci, Inst Plant Protect, Yinchuan, Peoples R China

4.Tsinghua Univ, Inst Environm & Ecol, Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China

关键词： Time series; Phenology; Random forest; Alfalfa; Automatic labeling; Cutting intensity

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： Accurate and timely information describing the spatial distribution of alfalfa grasslands is essential for food security and the sustainable development of grass-livestock husbandry. However, the accuracy of remote sensing-based mapping of alfalfa grasslands is typically limited by a lack of ground observation samples and the low spatiotemporal resolution of widely used satellite remote sensing data. In this study, we propose a knowledge-based method to extract high-quality alfalfa training data using integrated multi-source time series satellite remote sensing data. Using the Google Earth Engine platform and machine learning classifiers, we conducted research on mapping the spatial distribution and cutting intensity of alfalfa grasslands in the Ningxia region. The results are as follows: (1) the random forest classification model was constructed by combining automatically generated knowledge-based training sample data, measured sample data, and Sentinel-1, Sentinel-2, and Landsat 8 satellite data, in which achieved good classification accuracy, with an F1 score of 0.97, and an overall accuracy of 0.97; (2) the top three variables with the highest importance are all NIRv (near-infrared reflectance of terrestrial vegetation); (3) spring is the best time to identify alfalfa using multi-source remote sensing data, and (4) through the proposed novel mowing intensity framework for remote sensing mapping of alfalfa cutting intensity here are pronounced differences in cutting intensity in different regions. Overall, this study's results indicate that the proposed method has important application potential for accurate mapping of alfalfa grassland spatial distribution and cutting intensity, as well as alfalfa yield estimation.

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