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

IF 3.3 4区地球科学 Q2 ENVIRONMENTAL SCIENCES

Geocarto International Pub Date : 2024-08-04 DOI:10.1080/10106049.2024.2384473

Xiaomi Wang, Qi Tang, Kang Yang

引用次数: 0

Abstract

Accurate and timely classification of crop rotations is essential to confront the issues of agricultural management and food crisis. Crop growth conditions generally exhibit a strong spatial hetero...

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利用包含空间异质性的随机森林模型改进轮作分类

要解决农业管理和粮食危机问题，就必须准确及时地对作物轮作进行分类。作物生长条件通常表现出很强的空间异质性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY

CiteScore

6.30

自引率

13.20%

发文量

407

审稿时长

>12 weeks

期刊介绍： Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.