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基于近红外光谱信息的土壤电导率预测模型研究

Prediction Model of Bulk Soil Electrical Conductivity Based on Near-infrared Spectral Information

  • 摘要: 利用土壤含水率与近红外光谱土壤反射率和土壤电导率三者之间的关系,以土壤含水率为中间变量,间接表达土壤光谱反射率和土壤电导率之间的关系。土壤含水率与土壤光谱反射率存在指数关系,土壤含水率与土壤电导率存在线性关系,消除中间变量(土壤含水率),得到土壤光谱反射率和土壤电导率之间的关系。以土壤水分敏感波段1 450 nm作为研究对象,研究土壤电导率的预测模型,分别建立指数预测模型和对数预测模型,并分别对两种模型进行验证。本文实验建模集样本72个,验证集样本48个,土壤电导率对数预测模型R2达0.80,土壤电导率指数预测模型R2达0.85,预测效果均可满足农田电导率估算,但对数模型在土壤电导率较低区间预测效果不理想,因此土壤电导率指数预测模型预测效果优于对数模型的预测效果。研究结果表明,土壤光谱反射率预测土壤电导率的方案可行,并为光谱信息预测土壤电导率提供了新思路。

     

    Abstract: The relationship between soil spectral reflectance and soil electrical conductivity was expressed indirectly by using the relationship between soil water content and NIR spectral soil reflectance and soil electrical conductivity with soil water content as an intermediate variable. There was an exponential relationship between soil water content and soil spectral reflectance, and a linear relationship between soil water content and soil electrical conductivity, and the relationship between soil spectral reflectance and soil electrical conductivity was obtained by eliminating the intermediate variable(soil water content). The exponential prediction model and the logarithmic prediction model were established and validated respectively by taking the soil moisture sensitive band 1 450 nm as the research object to study the prediction model of soil electrical conductivity. There were 72 samples in the experimental modeling set and 48 samples in the validation set, and the R~2 of the logarithmic prediction model of soil electrical conductivity reached 0.80, and the R~2 of the exponential prediction model of soil conductivity reached 0.85, both of which can satisfy the estimation of farmland conductivity, but the prediction effect of the logarithmic model was not satisfactory in the lower range of soil conductivity, so the prediction effect of the exponential prediction model of soil conductivity was better than the prediction effect of the logarithmic model. The results showed that the scheme of soil spectral reflectance prediction of soil conductivity was feasible, which provided an idea for the prediction of soil electrical conductivity by spectral information.

     

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