Authors

Abstract

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

Abstract in Chinese

利用遥感数据进行大面积干旱监测是目前干旱监测的重要方法之一。然而，传统的遥感干旱监测方法主要侧重于监测土壤水分或植被状况等单一干旱响应因子，而对多因子干旱综合监测的研究却十分有限。为了提高抗旱能力，本文以河南省为例，以多源遥感数据为数据源，考虑各种致灾因素，采用随机森林法进行建模，探讨了利用各种遥感数据源进行区域遥感综合干旱监测的方法。与神经网络、分类回归树和线性回归相比，随机森林的性能更稳定，对噪声和离群点的容忍度更高。为了为区域干旱综合评价提供一种新的方法，综合考虑土壤水分胁迫等干旱因素，建立了基于多源遥感数据的干旱综合监测模型，干旱发生发展过程中植被生长状况与气象降水盈亏。