Abstract
Abstract. The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance and TN based on spectral reflectance curves of soil samples collected from subsided land determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, P < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided land caused by the extraction of natural resources including groundwater, oil and coal.
Highlights
In recent years, land subsidence caused by the extraction of natural resources such as groundwater (Pacheco-Martinez et al, 2013; Bakr, 2015) oil (Moghaddam et al, 2013) and coal (Xu et al, 2014; Demirel et al, 2011) has created a severe and widespread hazard in China, resulting in new ecological and environmental issues such as soil degradation and a loss of biodiversity
Several issues should be considered to provide satisfactory prediction accuracy, such as whether the existing total nitrogen (TN) estimation models are suitable for used in this type of area experiencing subsidence, how to reduce noise while retaining as much useful information as possible in remotely sensed hyperspectral data, and how to realize the complementary superiority of partial least squares regression (PLS) and Adaptive Neuro-fuzzy Inference System (ANFIS) to further improve the accuracy of estimates of TN
To develop an ideal prediction model, this paper tried to solve several issues such as whether the existing TN estimation models were suitable for use with land that had subsided as a result of the excessive extraction of various resources such as groundwater, oil and coal, how to reduce noise while retaining as much useful information as possible, and how to realize the complementary superiority between PLS and ANFIS to further improve the estimation accuracy of models
Summary
Land subsidence caused by the extraction of natural resources such as groundwater (Pacheco-Martinez et al, 2013; Bakr, 2015) oil (Moghaddam et al, 2013) and coal (Xu et al, 2014; Demirel et al, 2011) has created a severe and widespread hazard in China, resulting in new ecological and environmental issues such as soil degradation and a loss of biodiversity. Hyperspectral remote sensing provides an abundance of spectral information suggesting a potential method for estimating TN (Dematte et al, 2004). Shi et al (Shi et al, 2013) compared three methods for estimating TN content with visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils, and the PLS model performed best. Several issues should be considered to provide satisfactory prediction accuracy, such as whether the existing TN estimation models are suitable for used in this type of area experiencing subsidence, how to reduce noise while retaining as much useful information as possible in remotely sensed hyperspectral data, and how to realize the complementary superiority of PLS and ANFIS to further improve the accuracy of estimates of TN. China superiority of PLS and ANFIS to each other, the LCMCS models were built and assessed
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