地理科学进展 ›› 2006, Vol. 25 ›› Issue (2): 123-130.doi: 10.11820/dlkxjz.2006.02.014

• 土地利用 • 上一篇    下一篇

土壤墒情遥感反演与旱情诊断

李玉环1,2, 王静1, 曹银贵1   

  1. 恋乜辈夤婊?国土资源部土地利用重点实验室, 北京100035|2 山东农业大学资源与环境学院, 泰安 271018
  • 收稿日期:2006-01-01 修回日期:2006-02-01 出版日期:2006-03-25 发布日期:2006-03-25
  • 作者简介:李玉环(1965-),山东诸城人,博士,副教授,主要从事"3S"技术在土地资源与环境应用方面的教学与研究工作.
  • 基金资助:

    "国土资源部百名优秀青年科技人才计划"项目资助.

Retrieved Deduction of Soil Moisture Spatial Distribution and Drought Discrimination Based on Remote Sensing

LI Yuhuan11,2, 21, WANG Jing11   

  1. 1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying &|Planning Institute, Beijing,100035|
    2. Shandong Agricultural University College of Resources and Environment, Taian 271018
  • Received:2006-01-01 Revised:2006-02-01 Online:2006-03-25 Published:2006-03-25

摘要:

土壤墒情与植被生长状况和地表温度之间存在密切联系?贑OST模型算法和单窗算法,开展了TM/ETM+多光谱数据的地表反射率、地表温度(LST)和土壤调整植被指数反演(MSAVI),分析了地表温度和植被指数的线性关系,提出了土壤墒情几何特征指数和旱情诊断函数,结合土壤含水量实测数据,建立了横山县土壤墒情遥感反演模型。实证结果表明,基于TM/ETM+数据反演的长度指数可进行旱情诊断;对土壤含水量的反演模型进行T检验,差异不显著,而基于地面温度的土壤墒情反演模型优于土壤调整植被指数反演模型。

关键词: 地表温度, 旱情诊断函数, 土壤调整植被指数, 土壤墒情

Abstract:

Soil moisture monitoring acts as an important role in reasonable water resource utilization and scientific management and decision-making of drought-fight. Soil moisture has relation with vegetation growth index(NDVI) and land surface temperature(LST). The NDVI was derived from the red and the NIR bands and the LST from the one or two thermal bands. The paper adopted LST retrieved from TM/ETM+ by mono-window algorithm and the modified soil adjustment vegetation index(MSAVI) based on ground soil spectrum line parameters and the earth reflection by COST model from the same satellite data. By analyzing the linear relation between LST and MSAVI, soil moisture indicators were put forward in terms of three geometrical expressions based on the two extreme points of the LST-MSAVI scatterplots, and drought discrimination function(DF) which was be used to discriminate the drought years or areas from the wet ones based on the DFij received either positive or negative values or the regress between the DFij values of the respective geometrical index and soil moisture component by Laboratory. a clear trend was exhibited between the drought-year cluster (negative values) and the wet-year cluster (positive values). The crossing point between the regression line and the DF= 0 line could be used to quantify the threshold between wet and drought regions in terms of soil moisture component. The retrieved model was built to illuminate soil moisture spatial distribution depending on linear regression analysis between soil moisture and LST or MSAVI. Results of the discrimination function for each of the drought indicators are presented. The “length” indicator is able to successfully separate the two drought years(1990, 2001) from the wet years(1991, 2002). The drought-land was discriminated by the DFj values as a function of the soil moisture of each region with DFj=0 and soil moisture=10~11%. Therefore the result showed that more information was mined by combining LST and MSAVI; length indicator could present valuable information for drought based on TM/ETM+; It wasn’t remarkable for the retrieved soil moisture to be tested by T-test; and the retrieved model from LST was better and more valuable than that from MSAVI.

Key words: drought discrimination function, LST, MSAVI, soil moisture

中图分类号: 

  • P934