草业学报2017，Vol.26Issue(11)：1-11,11.DOI:10.11686/cyxb2017024
锡林郭勒草原土壤含水量遥感反演模型及干旱监测
Soil moisture modelling and drought monitoring using remote sensing in Xilingol grassland
摘要
Abstract
Soil moisture is an important factor for grassland vegetation growth and its measurement is a critical task for drought monitoring systems.In order to monitor moisture in Xilingol grassland,monthly evapotranspiration (ET) data from MODIS MOD16A2 and measured soil moisture data from ground monitoring stations were collected.Correlation and regression analyses were employed to establish a retrieval model for soil volumetric moisture (SVM) based on the evapotranspiration deficit index (ETDI):SVM=-48.851 × ETDI+ 54.669.The root mean square error (RMSE) of this model was 3.27％.This model can be used to retrieve soil moisture at regional scale.The thresholds of drought grades were established based on the national standard and used to analyze drought dynamics in Xilingol grassland over the past 15 years (2000-2014).The results showed that SVM fluctuated in 14％ of the meadow steppe,approximating normal levels except for the slight droughts in 2007 and 2009.SVM fluctuated in 11％ of the typical steppe and the sandy vegetation areas,as well as in the Xilingol grassland as a whole,approaching slight drought conditions except for the moderate droughts recorded in 2007 and 2009.SVM fluctuated in 8％ of desert steppe,approaching severe drought conditions except for the moderate droughts recorded in 2002,2003 and 2012.On average,over the past 15 years some 66％ of the Xilingol grassland has experienced drought conditions,though to varying degrees.Nondrought and severe drought areas increased,while slight and moderate drought areas decreased,but significant tests indicate that none of the changes were significant (P＞0.05).关键词
锡林郭勒草原/土壤体积含水量/干旱监测/蒸散发Key words
Xilingol grassland/soil volumetric moisture (SVM)/drought monitoring/evapotranspiration (ET)引用本文复制引用
张巧凤,刘桂香,于红博,玉山,包玉海..锡林郭勒草原土壤含水量遥感反演模型及干旱监测[J].草业学报,2017,26(11):1-11,11.基金项目
中国农业科学院创新工程“草原非生物灾害防灾减灾团队”(CAAS-ASTIP-IGR2015-04),内蒙古自治区自然科学基金(2017MS0408),内蒙古师范大学高层次人才科研启动项目(2016YJRC012)和国家自然科学基金(41661009)资助. （CAAS-ASTIP-IGR2015-04）
