Exploring cropping intensity dynamics by integrating crop phenology information using Bayesian networks

文献类型： 外文期刊

第一作者： Tao, Jianbin

作者： Tao, Jianbin;Wang, Yun;Qiu, Bingwen;Wu, Wenbin

作者机构：

关键词： Cropping intensity dynamics; Bayesian network; Semantic information; MODIS time-series

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCI

摘要： The demand for agricultural products continues to increase while there is little room for cropland expansion. Agricultural intensification on existing croplands may provide a promising solution to increase agricultural production and alleviate the human-land conflict. To achieve this, the spatio-temporal dynamics of cropping intensity are essential information for agricultural production. However, existing methods are usually based on machine learning and are highly reliant on training samples. There are relatively few studies on automatic change detection of cropping intensity. In this study, a knowledge-based dynamic Bayesian network model for change analysis (DBN-CA) was developed for automatic detection of cropping intensity dynamics as well as the overall trends. Four statuses of cropping intensity dynamics were designed by referencing crop phenology to delineate the temporal dynamics of cropping intensity, and the overall trends from 2001 to 2019 were extracted based on their combinations. The conclusions were as follows: (1) the DBN-CA model based on crop phenology knowledge has a remarkable ability to effectively detect cropping intensity dynamics as well as its overall trends. (2) The cropping intensity in Hubei Province has shown a significant decreasing trend during the early 21st century.

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