Estimation of Soil Organic Matter, Total Nitrogen and Total Carbon in Sustainable Coastal Wetlands

Sen Zhang, Xia Lu, Yuanzhi Zhang, Gege Nie and Yurong Li
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Sen Zhang: School of Geomatics and Marine Information, Huaihai Institute of Technology, Lianyungang 222005, China
Xia Lu: School of Geomatics and Marine Information, Huaihai Institute of Technology, Lianyungang 222005, China
Yuanzhi Zhang: National Astronomical Observatories, Key Lab of Lunar Science and Deep-exploration, Chinese Academy of Sciences, Beijing 100101, China
Gege Nie: School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450002, China
Yurong Li: School of Geomatics and Marine Information, Huaihai Institute of Technology, Lianyungang 222005, China

Sustainability, 2019, vol. 11, issue 3, 1-18

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 (R 2 ) 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 R 2 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.

Keywords: soil organic matter; sustainable coastal wetland; estimate model; support vector machine; neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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