地球信息科学学报

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (11): 1811-1822.doi: 10.12082/dqxxkx.2019.180666

• 遥感科学与应用技术 • 上一篇    下一篇

基于多源数据的农业干旱监测模型构建

刘高鸣1,2, 谢传节1,*(), 何天乐1,2, 刘高焕1   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学资源与环境学院,北京 100049
  • 收稿日期:2018-12-18 修回日期:2019-09-16 出版日期:2019-10-25 发布日期:2019-12-11
  • 作者简介:刘高鸣（1994-）,河南濮阳,女,硕士生,主要从事农业遥感研究。E-mail: liugm.16s@igsnrr.ac.cn
  • 基金资助:
    国家重点研发计划资助项目(No.2017YFD0300403);资源与环境信息系统国家重点实验室自主创新项目

Agricultural Drought Monitoring Model Constructing based on Multi-Source Data

LIU Gaoming1,2, XIE Chuanjie1,*(), HE Tianle1,2, LIU Gaohuan1   

  1. 1. State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences, Beijing 100101, China
    2. School of Resources and Environmental Information, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-12-18 Revised:2019-09-16 Online:2019-10-25 Published:2019-12-11
  • Contact: XIE Chuanjie
  • Supported by:
    National Key Research and Development Program of China, No.2017YFD0300403(No.2017YFD0300403);National Key Laboratory of Resources and Environmental Information System Independent Innovation Project

RichHTML

PDF (PC)

赞

摘要：

在全球气候变暖的大背景下,干旱事件发生越来越频繁,严重危害我国的粮食生产安全。构建准确的干旱监测模型不仅能够及时地反映出干旱事件的发生,同时可以为地方政府制定减灾保产措施提供科学支撑和保障。传统的气象干旱监测方法因为缺乏对植被本身需水状态和土壤供水信息的考虑旱情判定结果往往比实际情况偏重,而遥感监测指标大多只考虑了植被或土壤等单方面因素具有局限性,目前已有的综合干旱监测模型大多以气象指标为因变量,一方面需要数据资料较多参数复杂,另一方面模型准确度依赖于气象指标对当地农业干旱的响应能力,而同一气象指标在不同区域适应性存在差异,因此同样存在局限性。本文以河南省的冬小麦为研究对象,利用2001-2011年的EOS-MODIS数据产品以及气象站点监测数据,计算了标准化降水蒸散指数SPEI、植被状态指数VCI、温度状态指数TCI、温度植被状态指数TVDI,同时结合河南省农业气象灾害旬报对冬小麦受灾的记录,构建了基于决策树的定性农业干旱监测模型。测试集结果表明,模型综合了大气异常信息、植被状态信息以及土壤水分信息,优于单个指标的监测结果。另外,基于此模型监测了河南省2009年4-5月的干旱事件,结果与实情相符,能够较好地反映农业旱情的发生和空间演变情况。

关键词: 农业干旱, SPI, SPEI, TVDI, 决策树, 河南省

Abstract:

In the context of global warming, drought has become increasingly frequent and have seriously jeopardized the safety of food production in China. Constructing an accurate drought monitoring model can not only reflect the occurrence of drought events in a timely manner, but also provide scientific support and guarantee for local governments to formulate measures for disaster reduction and production. Traditional meteorological monitoring methods may overestimate drought severity, because of the lack of information on vegetation requirements of water and soil water supply. However, most relevant remote sensing indicators also have the limitation of considering one single factor such as vegetation status or soil moisture. In addition, most of the existing comprehensive drought monitoring models use meteorological indicators as the dependent variables. On the one hand, more data materials and parameters for calculation are required. On the other hand, the accuracy of the model depends on the meteorological indicators' ability of responding to local agricultural drought. At the same time, the same meteorological indicators also have different adaptability in different regions, which causes limitations. Focusing on winter wheat in Henan Province, this study used the EOS-MODIS data products and meteorological station monitoring data from 2001 to 2011 to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and the Temperature Vegetation Dryness Index (TVDI). Based on the records of agricultural meteorological disasters of winter wheat in Henan Province, an agricultural drought monitoring model was developed using the decision tree algorithm. Results show that the model which integrates atmospheric anomaly information, vegetation state information, and soil moisture information, is better than models that consider only individual indicators. Moreover, drought monitoring results of wheat based on the decision tree model reflect the actual drought situation and its spatial variation in Henan province from April to July in 2009. We conclude that the proposed method can be used to monitor agricultural droughts in Henan province.

Key words: agricultural drought, SPI, SPEI, TVDI, decision tree, Henan Province