基于综合干旱数据库与随机森林算法的草地干旱监测模型构建

doi:10.7523/j.ucas.2021.0066

中国科学院大学学报 ›› 2023, Vol. 40 ›› Issue (3): 351-361.DOI: 10.7523/j.ucas.2021.0066

• 电子信息与计算机科学 • 上一篇下一篇

基于综合干旱数据库与随机森林算法的草地干旱监测模型构建

袁雪琪^1,2, 李静¹, 朱欣然¹, 张召星¹, 柳钦火¹

1. 中国科学院空天信息创新研究院遥感科学国家重点实验室, 北京 100094;
2. 中国科学院大学电子电气与通信工程学院, 北京 100049

收稿日期:2021-02-22 修回日期:2021-09-10 发布日期:2021-10-13
通讯作者: 李静,E-mail:llijing01@radi.ac.cn
基金资助:
国家重点研发计划（2017YFA0603001）资助

Construction of grassland drought monitoring model based on comprehensive drought database and random forest algorithm

YUAN Xueqi^1,2, LI Jing¹, ZHU Xinran¹, ZHANG Zhaoxing¹, LIU Qinhuo¹

1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2021-02-22 Revised:2021-09-10 Published:2021-10-13

摘要/Abstract

摘要： 干旱是草地主要的自然灾害之一，建立有效的草地干旱监测模型，具有重要的现实意义。传统的干旱监测基于单一的气象或遥感数据，无法全面地刻画复杂的干旱事件。而已有的综合干旱监测模型多依赖于标准化降水蒸散指数(SPEI)等传统气象指标，传统气象指标的计算较为复杂，且对农业干旱的表征能力有限，因此应考虑应用更可靠稳定的数据源构建草地干旱监测模型。目前已形成一些耦合气候指标、水文指数和遥感信息来描述干旱过程的监测模型，如美国干旱监测系统USDM。以USDM干旱类别为先验干旱知识，采用随机森林方法，构建耦合多源遥感和气象数据的草地综合干旱监测模型，并考虑气候和地理条件的适用性在内蒙古草地区进行适用性验证。模型相较USDM具有1 km尺度更高的空间分辨率，在区域尺度上具有更优的监测能力；在内蒙古地区的验证结果表明，相较于单变量干旱指标，模型与土壤墒情具有更高的相关性，且能够以较高的时间分辨率监测出草地干旱的时空演变情况。

关键词: 干旱监测, 草地, USDM, 内蒙古

Abstract: Drought is one of the major natural disasters in the grassland. Therefore developing an effective drought monitoring method for grassland has important practical significance. Traditional drought monitoring model is based on single meteorological or remote sensing data, unable to fully describe complex drought event. The construction of the existing comprehensive drought monitoring models mostly relies on the standardized precipitation evapotranspiration index and other traditional meteorological indicators. However, traditional meteorological index calculation is more complicated, and has certain limitation in terms of agricultural drought monitoring. The United States Drought Monitor (USDM) combines various drought-causing factors with the help of expert's knowledge, which is a more reliable drought indicator. Therefore, in this study, USDM drought categories were used as the prior drought knowledge, and the random forest method was adopted to build a comprehensive grassland drought monitoring model based on multi-source remote sensing and meteorological data. Meanwhile, the applicability of climate and geographical conditions was considered for application verification in Inner Mongolia. Compared with USDM, the model has higher spatial resolution at 1 km scale and better monitoring capability at regional scale. The results of application in Inner Mongolia show that the model has a higher correlation with soil moisture compared with single drought index, and can monitor the spatial and temporal evolution of drought in grassy areas with a higher temporal resolution.

Key words: drought monitoring, grassland, USDM, Inner Mongolia

中图分类号:

S127
TP79

袁雪琪, 李静, 朱欣然, 张召星, 柳钦火. 基于综合干旱数据库与随机森林算法的草地干旱监测模型构建[J]. 中国科学院大学学报, 2023, 40(3): 351-361.

YUAN Xueqi, LI Jing, ZHU Xinran, ZHANG Zhaoxing, LIU Qinhuo. Construction of grassland drought monitoring model based on comprehensive drought database and random forest algorithm[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(3): 351-361.

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基于综合干旱数据库与随机森林算法的草地干旱监测模型构建

Construction of grassland drought monitoring model based on comprehensive drought database and random forest algorithm

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