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Prediction of Soil pH Hyperspectral Spectrum in Guanzhong Area of Shaanxi Province Based on PLS

机译:基于PLS的陕西省关中地区土壤pH高光谱预测

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The soil pH of Fufeng County, Yangling County and Wugong County in Shaanxi Province was studied. The spectral reflectance was measured by ASD Field Spec HR portable terrain spectrum, and its spectral characteristics were analyzed. The first deviation of the original spectral reflectance of the soil, the second deviation, the logarithm of the reciprocal logarithm, the first order differential of the reciprocal logarithm and the second order differential of the reciprocal logarithm were used to establish the soil pH Spectral prediction model. The results showed that the correlation between the reflectance spectra after SNV pre-treatment and the soil pH was significantly improved. The optimal prediction model of soil pH established by partial least squares method was a prediction model based on the first order differential of the reciprocal logarithm of spectral reflectance. The principal component factor was 10, the decision coefficient Rc2 = 0.9959, the model root means square error RMSEC — 0.0076, the correction deviation SEC = 0.0077; the verification decision coefficient Rv2 = 0.9893, the predicted root mean square error RMSEP = 0.0157, The deviation of SEP = 0.0160, the model was stable, the fitting ability and the prediction ability were high, and the soil pH can be measured quickly.
机译:研究了福丰县土壤pH值,陕西省陕西省武力县。通过ASD现场规范HR便携式地形频谱测量光谱反射率,分析了其光谱特性。原始光谱反射率的土壤的第一偏差,第二偏差,互易对数的对数,互易对数的第一阶差分和互易对数的二阶差分来建立土壤pH光谱预测模型。结果表明,SNV预处理后反射光谱与土壤pH值之间的相关性显着提高。部分最小二乘法建立的土壤pH的最佳预测模型是基于谱反射率互易对数的第一阶差分预测模型。主成分因子为10,决策系数RC2 = 0.9959,模型根部误差RMSEC-0.0076,校正偏差秒= 0.0077;验证决策系数RV2 = 0.9893,预测的根均方误差RMSEP = 0.0157,SEP = 0.0160的偏差,模型是稳定的,拟合能力和预测能力高,可以快速测量土壤pH值。

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