Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta

2015 ◽  
Vol 738-739 ◽  
pp. 197-203 ◽  
Author(s):  
Hong Yan Chen ◽  
Geng Xing Zhao ◽  
Ya Qiu Liu ◽  
Jing Chun Chen ◽  
Hong Zhang
Keyword(s):  
Soil Salinity ◽  
Yellow River ◽  
Saline Soil ◽  
Salt Content ◽  
Back Propagation ◽  
Yellow River Delta ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Soil Salt Content ◽  
Input Variables

Quantitative identification of the saline soil salinity content is a necessary precondition for the reasonable improvement and utilization of saline land, the article aimed at comparing the different quantitative analysis methods and achieving fast estimation of the saline soil salt content in the Yellow River Delta based on the visible-near infrared spectroscopy. Kenli County in Shandong Province was selected as the experimental area, firstly, the representative soil samples were selected, hyperspectral reflectance of the soil samples were measured in situ and transformed to the first deviation. Secondly the correlate spectra, the characteristic spectra and indices were firstly filtered using correlation analysis. Finally, the estimation models of soil salinity content were built using the multiple linear regression (MLR), back propagation neural network (BPNN) and support vector machine (SVM) respectively. The results indicated that the characteristic wave bands of soil salinity were 684 nm and 2058 nm. On the condition of the same input variables, the prediction precision of the SVM models was the highest, followed by the BPNN, the MLR was the lowest. The SVM model based on the first deviation of the reflectance at 684 and 2058nm had the highest precision, with the calibration R2 of 0.91 and RMSE as 0.11%, the validation R2 of 0.93, RMSE as 0.26% and RPD as 2.61, which had very good prediction accuracy of soil salt content, and was very stable and reliable. Different input variables had a great impact on the model accuracy, among of the MLR models, only the precision of the model based on characteristic spectral indices was slightly higher and could be used to estimate salt content, among of the BPNN and SVM models, the precision of the models based on characteristic spectra and indices was more high and stable significantly than the models on the correlate spectra. Therefore, for the three modeling methods of multiple linear regression, back propagation neural network and support vector machine, building the estimation model of saline soil salinity content based on characteristic spectra indices was effective.

scholarly journals Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 546
Author(s):  
Xinyang Yu ◽  
Chunyan Chang ◽  
Jiaxuan Song ◽  
Yuping Zhuge ◽  
Ailing Wang
Keyword(s):  
Soil Salinity ◽  
Yellow River ◽  
Pearson Correlation ◽  
Arable Land ◽  
Salt Content ◽  
Yellow River Delta ◽  
River Delta ◽  
Support Vector ◽  
Optimal Method ◽  
Sensitive Parameter

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

scholarly journals Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1163
Author(s):  
Wei Peng ◽  
Qing Wang ◽  
Xudong Zhang ◽  
Xiaohui Sun ◽  
Yongchao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Saline Soil ◽  
Salt Content ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Soil Particles ◽  
Western Jilin ◽  
Bpnn Model

With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na+. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil’s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (rPCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R2, RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content > compactness > salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.

scholarly journals SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions

2019 ◽  
Vol 11 (8) ◽  
pp. 967 ◽  
Author(s):  
Sijia Wang ◽  
Yunhao Chen ◽  
Mingguo Wang ◽  
Yifei Zhao ◽  
Jing Li
Keyword(s):  
Soil Salinity ◽  
Yellow River ◽  
Salt Content ◽  
The Yellow River ◽  
Mean Square ◽  
Estimation Model ◽  
Soil Salt Content ◽  
Maximum Value ◽  
Soil Salt ◽  
Plsr Model

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.

scholarly journals Spatiotemporal Changes of Soil Salinization in the Yellow River Delta of China from 2015 to 2019

2021 ◽  
Vol 13 (2) ◽  
pp. 822
Author(s):  
Lingling Bian ◽  
Juanle Wang ◽  
Jing Liu ◽  
Baomin Han
Keyword(s):  
Vegetation Index ◽  
Yellow River ◽  
Salt Content ◽  
Feature Space ◽  
Yellow River Delta ◽  
Soil Salinization ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta ◽  
Soil Salt Content

Soil salinization poses a significant challenge for achieving sustainable utilization of land resources, especially in coastal, arid, and semi-arid areas. Timely monitoring of soil salt content and its spatial distribution is conducive to secure efficient agricultural development in these regions. In this study, to address the persistent problem of soil salinization in the Yellow River Delta in China, the feature space method was used to construct multiple feature spaces of surface albedo (Albedo)–modified soil-adjusted vegetation index (MSAVI), salinity index (SI)–Albedo, and SI–normalized difference vegetation index (NDVI), and an optimal inversion model of soil salinity was developed. Based on Landsat 8 Operational Land Imager (OLI) image data and simultaneous field-measured sampling data, an optimal model from 2015 to 2019 was used to obtain the soil salt content in the region at a 30 m resolution. The results show that the proportion of soil salinization in 2015 and 2019 was approximately 76% and 70%, respectively, and overall soil salinization showed a downward trend. The salinization-mitigated areas are primarily distributed in the southwest of the Yellow River Delta, and the aggravated areas are distributed in the northeast and southeast. In general, the spatial variation characteristics show an increasing trend from the southwest to the eastern coastal areas, corresponding to the formation mechanism of salt accumulation in the region. Further, corresponding sustainable development countermeasures and suggestions were proposed for different salinity levels. Meanwhile, this study revealed that the SI–Albedo feature space model is the most suitable for inversion of salinization in coastal areas.

scholarly journals Monthly runoff prediction using modified CEEMD-based weighted integrated model

2020 ◽  
Author(s):  
Xinqing Yan ◽  
Yuan Chang ◽  
Yang Yang ◽  
Xuemei Liu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Yellow River ◽  
Back Propagation ◽  
Poor Performance ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Integrated Model ◽  
Support Vector ◽  
Monthly Runoff

Abstract Due to the nonlinear characteristics of runoff data and the poor performance of the single prediction model, a weighted integrated modified complementary ensemble empirical mode decomposition (MCEEMD)-based model was proposed to predict the monthly runoff of three hydrological stations in the lower reaches of the Yellow River. In this model, particle swarm optimization (PSO) was used to optimize the parameters of support vector regression (SVR), back propagation neural network (BP), long short-term memory neural network (LSTM) that constitute it. The weight coefficients and frequency terms decomposed by MCEEMD were used to obtain the final prediction results. Results indicated that this model performs better than other models, with the Nash–Sutcliffe efficiency (NSE) reaching above 0.92, qualification rate (QR) reaching above 75% and all error indicators being minimal. In addition, considering the influence of extreme weather and climate anomalies, the integrated model combined the atmospheric circulation anomalies factors and the results can still be improved. It can be verified that this weighted integrated model can be used for the stable and accurate predication of medium- and long-term runoff.

scholarly journals Monitoring the seasonal dynamics of soil salinization in the Yellow River delta of China using Landsat data

2019 ◽  
Vol 19 (7) ◽  
pp. 1499-1508 ◽  
Author(s):  
Hongyan Chen ◽  
Gengxing Zhao ◽  
Yuhuan Li ◽  
Danyang Wang ◽  
Ying Ma
Keyword(s):  
Soil Salinity ◽  
Seasonal Dynamics ◽  
Yellow River ◽  
Yellow River Delta ◽  
Soil Salinization ◽  
Support Vector ◽  
The Yellow River ◽  
Landsat Data ◽  
The Yellow River Delta ◽  
The Yrd

Abstract. In regions with distinct seasons, soil salinity usually varies greatly by season. Thus, the seasonal dynamics of soil salinization must be monitored to prevent and control soil salinity hazards and to reduce ecological risk. This article took the Kenli District in the Yellow River delta (YRD) of China as the experimental area. Based on Landsat data from spring and autumn, improved vegetation indices (IVIs) were created and then applied to inversion modeling of the soil salinity content (SSC) by employing stepwise multiple linear regression, back propagation neural network and support vector machine methods. Finally, the optimal SSC model in each season was extracted, and the spatial distributions and seasonal dynamics of SSC within a year were analyzed. The results indicated that the SSC varied by season in the YRD, and the support vector machine method offered the best SSC inversion models for the precision of the calibration set (R2>0.72, RMSE < 6.34 g kg−1) and the validation set (R2>0.71, RMSE < 6.00 g kg−1 and RPD > 1.66). The best SSC inversion model for spring could be applied to the SSC inversion in winter (R2 of 0.66), and the best model for autumn could be applied to the SSC inversion in summer (R2 of 0.65). The SSC exhibited a gradual increasing trend from the southwest to northeast in the Kenli District. The SSC also underwent the following seasonal dynamics: soil salinity accumulated in spring, decreased in summer, increased in autumn and reached its peak at the end of winter. This work provides data support for the control of soil salinity hazards and utilization of saline–alkali soil in the YRD.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

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