Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

doi:10.3390/s150817990

Cited by 19 publications

(13 citation statements)

References 77 publications

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“…Figure 3 illustrates the spectral absorption features of the reflectance curves for each of the 75 soil samples. The characteristics of the waveform and absorption peak of these spectral curves are consistent with the current research [30][31][32], indicating that the collected soil spectral data are of high quality.…”

Section: Collection and Chemical Analysis Of Soil Samplessupporting

confidence: 85%

Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

Peng

Zhao

et al. 2019

IJGI

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Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents.

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Section: Collection and Chemical Analysis Of Soil Samplessupporting

confidence: 85%

Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

Peng

Zhao

et al. 2019

IJGI

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“…BPNN is a multi-layer feed forward network trained by the error back propagation algorithm, and is suitable for various types of nonlinear relationship analysis methods. It consists of an input layer, an output layer, and several hidden layers [34,35]. Trainlm and Purelin were selected as the training function and the transfer function of the output layer in the BPNN, respectively.…”

Section: Methodsmentioning

confidence: 99%

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Liu

Peng

Xia

et al. 2019

Sensors

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Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

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“…This was probably due to the high amount of quartz in the sand fraction, which raised the intensity of spectral reflectance. Lin et al (2015), studied that soil reflectance generally decreases with increasing total N (TN) content ( Figure 6). In the entire visible-nearinfrared spectrum, three remarkable water absorption peaks were observed at 1400, 1905 and 2200 nm.…”

Section: Proximal Soil Sensingmentioning

confidence: 99%

“…Mean Reflectance Spectra for Different Soil Texture Classes(Conforti et al, 2015) Figure 6. Reflectance Curve of Soil Samples with Different TN Contents(Lin et al, 2015) …”

mentioning

confidence: 99%

Explore the Character of Soil Spectral Reflectance Relate to the Soil Organic Matter Content

Virgawati

Mawardi²,

Sutiarso³

et al. 2019

JTEP-L

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The visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to explore how significant the relationship between the soil spectral reflectance and soil organic matter (SOM) content. Some soil samples in Yogyakarta were taken for SOM content and spectroscopy measurement. The SOM was analyzed using Walkley and Black method, while the spectral reflectance was determined using ASD Field-spectrophotometer by scanned the sample with Vis-NIR spectrum. Pearson’s coefficient showed that there was a strong negative correlation between SOM and soil spectral of certain wavelengths. Soil with less organic matter content performed high reflectance. Keywords: Soil organic matter; Vis-NIR spectroscopy; soil reflectance; Pearson’s correlation coefficient.

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Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

Cited by 19 publications

References 77 publications

Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Explore the Character of Soil Spectral Reflectance Relate to the Soil Organic Matter Content

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