Abstract
Soil plays an important role in coastal wetland ecosystems. The estimation of soil organic matter (SOM), total nitrogen (TN), and total carbon (TC) was investigated at the topsoil (0–20 cm) in the coastal wetlands of Dafeng Elk National Nature Reserve in Yancheng, Jiangsu province (China) using hyperspectral remote sensing data. The sensitive bands corresponding to SOM, TN, and TC content were retrieved based on the correlation coefficient after Savitzky–Golay (S–G) filtering and four differential transformations of the first derivative (R′), first derivative of reciprocal (1/R)′, second derivative of reciprocal (1/R)″, and first derivative of logarithm (lgR)′ by spectral reflectance (R) as R′, (1/R)′, (1/R)″, (lgR)′ of soil samples. The estimation models of SOM, TN, and TC by support vector machine (SVM) and back propagation (BP) neural network were applied. The results indicated that the effective bands can be identified by S–G filtering, differential transformation, and the correlation coefficient methods based on the original spectra of soil samples. The estimation accuracy of SVM is better than that of the BP neural network for SOM, TN, and TC in the Yancheng coastal wetland. The estimation model of SOM by SVM based on (1/R)′ spectra had the highest accuracy, with the determination coefficients (R2) and root mean square error (RMSE) of 0.93 and 0.23, respectively. However, the estimation models of TN and TC by using the (1/R)″ differential transformations of spectra were also high, with determination coefficients R2 of 0.88 and 0.85, RMSE of 0.17 and 0.26, respectively. The results also show that it is possible to estimate the nutrient contents of topsoil from hyperspectral data in sustainable coastal wetlands.
Highlights
The organic matter in wetland soil is an important source of surface soil organic carbon, and an important indicator for judging the soil fertility of wetlands [1]
In MATLAB2014b, Savitzky–Golay (S–G) filter is applied to being smoothened the original spectra of the soil in the coastal wetland by five-order polynomial filter to improve the smoothness of the spectra and reduce the noise interference
The results of this study indicate that the accuracy of soil organic matter (SOM), total nitrogen (TN), and total carbon (TC) in soil by SVM is better than that of the back propagation (BP) neural network, which is consistent with the results of the estimation model of nitrogen, phosphorus, and potassium in Zhangzhou red soil and Haining green purple mud constructed by Jiang et al [50] through least squares support vector machine and BP neural network
Summary
The organic matter in wetland soil is an important source of surface soil organic carbon, and an important indicator for judging the soil fertility of wetlands [1]. Nitrogen is the most important limiting nutrient in wetland soils and a sensitive indicator for measuring the soil nutrient levels in wetlands [2]. Determining the contents of soil organic matter (SOM), total nitrogen (TN) and total carbon (TC) in wetland soil is of great significance for protecting the wetland ecological environment [4]. Traditional methods for the analysis of nutrient contents in soil are mainly based on chemical analysis, which is time consuming and labor-intensive. The emergence of the hyper-spectral remote sensing technique makes up for the shortcomings of traditional laboratory methods, and can provide a strong technical support for the estimation of soil nutrients
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