摘要
Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer (Analytical Spectral Devices Inc., Boulder, USA). Raw spectral reflectance data were transformed using four methods: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA + FD), NWMA with standard normal variate (NWMA + SNV), and NWMA with min-max standardization (NWMA + MS). These data were analyzed and correlated with SOM content. The evaluation model was established using support vector machine regression (SVM) with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991, 1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant ( R 2 = 0.875, root mean square error (RMSE) = 0.107 g kg −1 for calibration set;R 2 = 0.853, RMSE = 0.097 g kg −1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients.
Soil organic matter（SOM） is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer（Analytical Spectral Devices Inc., Boulder, USA）. Raw spectral reflectance data were transformed using four methods: nine points weighted moving average（NWMA）, NWMA with first derivative（NWMA + FD）, NWMA with standard normal variate（NWMA+ SNV）, and NWMA with min-max standardization（NWMA + MS）. These data were analyzed and correlated with SOM content.The evaluation model was established using support vector machine regression（SVM） with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991,1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant(R2=0.875, root mean square error（RMSE） = 0.107 g kg-1 for calibration set; R2= 0.853, RMSE = 0.097 g kg-1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients.
基金
supported by the National Nature Science Foundation of China(Nos.41671346 and41301482)
the Shandong Province Natural Science Fund of China(No.ZR2012DM007)
