院报 ›› 2016, Vol. 33 ›› Issue (11): 5-11.DOI: 10.11988/ckyyb.20160819

• 遥感技术应用 • 上一篇    下一篇

基于时序数据分析的云参数法背景场构建

向大享,李 喆,文雄飞   

  1. 空间信息技术应用研究所,武汉 430010
  • 收稿日期:2016-08-11 出版日期:2016-11-20 发布日期:2016-11-08
  • 作者简介:向大享(1984-),男,湖北巴东人,高级工程师,博士,主要研究方向为遥感数据处理,(电话)027-82926550(电子信箱)daxiangx@163.com。
  • 基金资助:

    国家自然科学基金青年基金项目(41401487);中央级公益性科研院所基本科研业务费项目(CKSF2016036/KJ,CKSF2015019/KJ)

Background Field Construction of Cloud ParametersBased on Time-series Data Analysis

XIANG Da-xiang, LI Zhe,WEN Xiong-fei   

  1. Spatial Information Technology Application Department, Yangtze River Scientific Research Institute,Wuhan 430010, China
  • Received:2016-08-11 Online:2016-11-20 Published:2016-11-08

摘要:

为了减少云参数法受年际变化的影响,以内蒙古自治区作为研究对象,计算多年云参数干旱指数;结合地面实地观测数据,选取合适的特征值作为背景场计算方法,构建云参数法背景场并修正云参数法干旱指数。研究结果表明经云参数背景场修订后,2006年和2007年内蒙古中西部沙漠区以及东南部农田区的风险等级有所降低,高风险区范围明显减少;2008年西部偏北地区及东南地区高风险等级有所降低,范围有所减少;从2009—2011年2月份监测结果来看,本方法监测结果在空间分布上更具有连续性,风险等级更加符合实际情况。

关键词: 云参数法, 遥感监测, 时序数据, 干旱指数, 背景场, 修正函数

Abstract:

In order to reduce the effect of inter-annual variations on cloud parameter, we computed the cloud parameter drought index with Inner Mongolia Autonomous Region as a case study. According to ground observation data, we selected appropriate characteristic value to calculate the background field data, and then modified the cloud parameter drought index. Results revealed that modified through background field data, the risk level of desert in middle and west Inner Mongolia and farmland in southeast Inner Mongolia reduced in 2006 and 2007, and the area of high risk level decreased apparently. In 2008, the high risk level in the north part of west Inner Mongolia and the southeast Inner Mongolia reduced as well, so did the high risk area. According to the monitoring results in every February from 2009-2011, we can conclude that the spatial distribution of monitoring results obtained by the present method are of good continuity, and the risk levels are closer to the real situation.

Key words: cloud parameters method, remote sensing monitoring, time-series data, drought index, background field, modified function

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