基于多光谱遥感的裸土土壤含水量反演研究

Inversion of soil moisture in bare soil based on multi-spectral remote sensing

矿区排土场的土壤含水量监测研究在矿产资源开发、生态恢复及干旱预警等方面具有重要意义。以我国东部草原区胜利露天矿北排土场土壤为试验材料,使用Spequoia多光谱相机和ECH2O土壤水分传感器对4种不同深度(1 cm、3 cm、5 cm、10 cm)的土柱样本每天10:00至14:00持续监测,采集到4个波段(550 nm、660 nm、735 nm、790 nm)处的土壤光谱反射率和土壤含水量数据,分别使用偏最小二乘回归法、岭回归法、反向传播(BP)神经网络三种方法建立单波段或多波段光谱反射率组合作为反演因子的土壤含水量反演模型。结果表明,偏最小二乘回归法、岭回归法反演精度较低,R2最高仅为0.606。以绿(550 nm)、红边(735 nm)、近红外(790 nm)三波段组合作为反演因子的反向传播神经网络(G-R-N-BP)模型反演效果最佳,其对1 cm、3 cm、5 cm、10 cm深度土壤含水量反演模型的决定系数(R2)分别为0.866、0.800、0.975、0.911,均方根误差(RMSE)分别为0.333、0.361、0.103、0.315,最佳反演深度为5 cm。本研究为矿区地表水分监测提供了重要的理论依据与实践应用价值。

The monitoring of soil water content in the dumping area of mining area is of great significance to the development of mineral resources,ecological restoration and early warning of drought in China.In this paper,four different depths(1 cm,3 cm,5 cm,10 cm)of soil samples were continuously monitored from 10:00 to 14:00 using Spequoia multispectral camera and ECH2O soil moisture sensor.Soil spectral reflectance of four bands(550 nm,660 nm,735 nm,790 nm)and soil water content data were collected.The soil water content inversion model with single-band or multi-band spectral reflectance combination as the inversion factor was established by using the methods of partial least squares regression,ridge regression and back propagation(BP)neural network.The results show that the inversion accuracy of the two regression methods is low,and the highest R2 is only 0.606.The G-R-N-BP model with the combination of green(550 nm),red edge(735 nm)and near-infrared(790 nm)as the inversion factor has the best inversion effect,corresponding to the soil water content inversion model of 1 cm,3 cm,5 cm and 10 cm depth.The coefficient of determination(R2)is 0.866,0.8,0.975,0.911,and the root mean square error(RMSE)is 0.333,0.361,0.103,0.315.In our research,a variety of methods are used to invert the soil water content at different depths.The optimal inversion model is G-R-N-BP model.The best inversion depth is 5 cm.The method has high precision and can further develop the surface moisture monitoring of the mining area.