| 引用本文: | 尹 涛,王瑞燕,杜文鹏,等.黄河三角洲地区植被生长旺盛期地下水埋深遥感反演[J].灌溉排水学报,2018,37(2):95-100. |
| YIN Tao,WANG Ruiyan,DU Wenpeng,et al.黄河三角洲地区植被生长旺盛期地下水埋深遥感反演[J].灌溉排水学报,2018,37(2):95-100. |
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| 摘要: |
| 【目的】快速准确地获得大面积的黄河三角洲地区地下水埋深。【方法】利用2004年18个站点的植被生长旺盛时期（7—9月）的地下水埋深数据，采用一元和多元线性回归建模方法，确定反演指标，比较了遥感指标反演法与地学和遥感相结合的2种反演模型。【结果】对数变换后的NDVI、指数变换后的晚上LST和指数运算后的晚上TVDI是地下水埋深反演的敏感遥感指标，观测点距黄河的距离（H1）、观测点周围水体密度（ρ）、对数变换后的观测点距海岸线的距离（H2）和DEM是地下水埋深反演的敏感地学指标；只用遥感指标建立的地下水埋深预测模型的决定系数R2为0.496，引入地学参数后模型R2平均值增加到0.791。遥感和地学指标相结合的方法可以更准确地反演植被生长旺盛期研究区的地下水埋深分布状况。【结论】将遥感指标和地学指标相结合进行模拟更合理。 |
| 关键词: 黄河三角洲； 地下水埋深； 模型 |
| DOI：10.13522/j.cnki.ggps.2016.0006 |
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| Remote Sensing Inversion of Groundwater Level in the Yellow River Delta during Plants Thrive |
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YIN Tao, WANG Ruiyan, DU Wenpeng, WANG Jingwei, REN Tao, CAO Guangshan
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College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China;2.Rizhao Municipal Land and Resources Bureau, Rizhao 276800, China; 3. Tai’an Agricultral Bureau,Tai’an 271018, China; 4. Zhanhua District Land and Resources Bureau of Binzhou Municipal, Binzhou 256800, China
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| Abstract: |
| 【Objective】 Obtain the groundwater level of the Yellow River Delta rapidly and accurately. 【Method】The groundwater level data of the 18 sites in 2004 (July to September) and the method of univariate and multivariate linear regression were used to select the inversion indices, then the two inversion models of remote sensing indices inversion and geoscience and remote sensing indices inversion were compared. 【Result】The logarithmic transformed NDVI, exponential transformed LST at night and the exponential transformed TVDI at night were the sensitive remote sensing indices for the inversion of groundwater level. The distance from the Yellow River (H1), the water density around the observation site (H2) and DEM were the sensitive geographical indices for the inversion of groundwater level. The determination coefficient of the groundwater level prediction model was increased from 0.496 to 0.791 when the geographical indices were introduced. The data from other years showed that the method of combination the remote sensing with the geo-referenced indices could accurately predict the distribution of groundwater depth in the study area during the vegetative growth period.【Conclusion】The combination of remote sensing index and geoscience index is more reasonable. |
| Key words: Yellow River Delta; groundwater level; model |
