摘要
目前遥感监测土壤含水率的方法较多,本次研究选取了稳定性较好、应用较为广泛且所需气象资料少的3种监测方法——SWEPDI指数法、能量指数法与TVDI指数法。以2016年4月和9月两期中高分辨Landsat8数据为数据源,分别将SWEPDI光谱法、能量指数法与TVDI指数法按不同时间、不同土层深度与对应时间的土壤含水率野外实测数据进行线性拟合,并进行各模型之间的比较,选择出更加适当的模型。同时利用景观指数在斑块类型上分析2时相土壤水分的变化趋势。结果表明:TVDI法效果明显优于SWEPDI光谱法和能量指数法,同时该方法还解决了其他方法不能连续监测土壤含水量的问题,适用于各种植被覆盖条件下以及各种土层深度的土壤水分反演。此外,选用6个景观指数分析了2016年4月与9月不同旱情等级干旱等级的景观格局变化,发现4月份PLAND指数达到了58. 76%,而9月份轻旱等级的PLAND指数达到了44. 16%,都占据优势地位,其中LPI、AREA_CV、AI指数的值也都达到了最大,其旱情状况有了明显的改善。
At present the methods using remote sensing technology to monitor soil moisture content become popular. This paper selected three monitoring methods which are widely applied with better stability and require less meteorological data, namely the SWEPDI index method, the energy index method and the TVDI index method, to better understand the characteristics of the landscape changes under the hinterland drought conditions in Mu Us Sandland, Inner Mongolia, China. Using Landsat8 data in April 2016 and September 2016 as data source, each of the three methods was conducted a linear fitting with the field survey data according to the time, the soil depth and corresponding soil moisture content. A comparison of the results from the models determined the more suitable model. At the same time, the change trend of soil moisture in 2 time phases was analyzed based on the plaque type by using the landscape index. The results showed that the effect of TVDI index method is superior to both the SWEPDI index method and the energy index method,and the TVDI index method also solved the problem that existed in continuously monitoring the soil moisture content. The TVDI index method is suitable for all kinds of vegetation conditions and various soil depth in drought monitoring. In addition, six landscape indexes were chosen to analyze the landscape pattern changes in April 2016 and September 2016 at different levels of drought, and it is found that the PLAND reached 58.76% in April, and it became 44.16% in September when it is at the light drought grade. Both were dominant as other indexes like LPI, AREA_CV and AI index all reached the maximum value. The drought condition was improved obviously.
作者
王思楠
李瑞平
韩刚
王耀强
胡勇平
田鑫
WANG Si-nan;LI Rui-ping;HAN Gang;WANG Yao-qiang;HU Yong-ping;TIAN Xin(Inner Mongolia Agricultural University,Inner Mongolia,Hohhot 010018,Neimengu,China)
出处
《干旱区地理》
CSCD
北大核心
2018年第5期1080-1087,共8页
Arid Land Geography
基金
国家自然科学基金(51769021
51169016)
内蒙古自然科学基金(2015MS0513)
关键词
SWEPDI指数法
能量指数法
温度植被干旱指数
景观格局
SWEPDI index method
energy index method
drought index of temperature vegetation
landscape pattern
