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基于神东矿区TM/ETM+影像波段3、波段4、波段5和波段7反射率数据,建立二维光谱特征空间,根据土壤湿度在此空间中的分布规律,利用特征空间中任意一点到原点的距离大小表征土壤水分变化状况,提出了不依赖土壤背景线而变化的TM/ETM+土壤湿度监测指数(SMMI),并与实测土壤湿度数据进行相关性分析.结果表明:SMMI能更好地反映地表0～5cm深度土壤湿度,且基于SWIR(波段7)-Nir(波段4)特征空间的SMMI(7,4)(R2=0.541 0)是最优的,而在监测10cm深度土壤湿度方面,基于SWIR(波段7)-SWIR(波段5)特征空间的SMMI(7,5)更有优势.以SMMI(7,4)作为土壤湿度监测指标,评估了神东矿区20a来土壤湿度时空变化特点,结果表明土壤湿度20a来呈上升趋势,这与矿区植被环境改善紧密相关.
Based on TM / ETM + image band 3, band 4, band 5 and band 7 reflectance data in Shendong Mining Area, a two-dimensional spectral feature space was established. According to the distribution of soil moisture in this space and using any point in the feature space to the origin (TMMI + ETM + Soil Moisture Sensitivity Index (SMMI)), which is independent of the soil background, is proposed and the correlation between soil moisture and measured soil moisture is analyzed.The results show that SMMI can better reflect the surface 0-5 cm depth soil moisture and is optimal based on the SMMI (7,4) (R2 = 0.541 0) of the SWIR (Band 4) feature space, whereas for monitoring the 10 cm depth soil moisture, SWMI (Band 7) -SWIR (Band 5) feature space SMMI (7,5) is more advantageous.With SMMI (7,4) as the indicator of soil moisture monitoring, the spatio-temporal variations of soil moisture in Shendong Mine Area The results showed that the soil moisture increased to 20a, which is closely related to the improvement of vegetation environment in the mining area.