基于神经网络的土壤含水量高光谱估测

Hyper Spectral Estimation of Soil Water Content Based on Neural Network

摘要: 高光谱遥感凭其极高的光谱分辨率在岩石矿物、土壤、植被等领域的应用越来越广泛。本文使用某地84个土壤样本的光谱反射数据，通过分析反射率光谱与土壤含水量之间的关系，对光谱数据进行一系列的数学变换，利用相关分析法获取的反演因子建立BP神经网络反演模型。结果表明，在土壤含水量预测研究中，利用BP神经网络建立的模型有效，且光谱变换为平方根的一阶微分时模型精度较高。

Abstract: Hyperspectral remote sensing is widely used in rock minerals, soil, vegetation and other fields due to its extremely high spectral resolution. Using the spectral reflectance data of 84 soil samples in a certain place, by analyzing the relationship between reflectance spectrum and soil water content, the BP (back propagation) neural network inversion model was established by mathematically transforming the spectral data and using the inversion factors obtained by the correlation analysis method. The results show that in the prediction of soil water content, the model established by BP neural network is effective, and the first-order differential time model with spectral transformation to square root has higher precision.