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.

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 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.

Potential plant species distribution in the Yellow River Delta under the influence of groundwater level and soil salinity

Ecohydrology ◽  
2010 ◽  
Vol 4 (6) ◽  
pp. 744-756 ◽  
Author(s):  
Xiaomei Fan ◽  
Bas Pedroli ◽  
Gaohuan Liu ◽  
Hongguang Liu ◽  
Chuangye Song ◽  
...  
Keyword(s):  
Plant Species ◽  
Species Distribution ◽  
Soil Salinity ◽  
Groundwater Level ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta

scholarly journals Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities

2018 ◽  
Vol 10 (9) ◽  
pp. 1387 ◽  
Author(s):  
Chengbiao Fu ◽  
Shu Gan ◽  
Xiping Yuan ◽  
Heigang Xiong ◽  
Anhong Tian
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Human Activities ◽  
Salt Content ◽  
Particle Swarm ◽  
Wave Band ◽  
Swarm Optimization ◽  
Affected Area ◽  
Soil Salt Content ◽  
Soil Salt

Traditional partial least squares regression (PLSR) and artificial neural networks (ANN) have been widely applied to estimate salt content from spectral reflectance in many different saline environments around the world. However, these methods entail a great amount of calculation, and their accuracy is low. To overcome these problems, a probability neural network (PNN) model based on particle swarm optimization was used in this study to build soil salt content models. Furthermore, there is a clear correlation between the level of human activities and the degree of salinization of an environment. This paper is the first to discuss this matter. Here, the performance of the PNN model to estimate soil salt content from reflectance data was investigated in areas non-affected (Area A) and affected (Area B) by human activities. The study area is located in Xingjinag, China. Different mathematical procedures, five wave band intervals, and two types of signal input sources were used for cross analysis. The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) index values were compared to verify the reliability of the model. Particle swarm optimization was used to adjust the optimal smoothing parameters of the PNN model and to avoid the long training processes required by the traditional ANN. The results show that the optimal wave band interval of the PNN is between 1000 nm and 1350 nm in Area A and between 400 nm and 700 nm in Area B. The reciprocal (1/R) transformation after Savitzky-Golay (SG) smoothing of the signal source is optimal for both areas. The RPD for both is greater than 30, which shows that the PNN model is applicable to areas with and without human activities and the prediction results are very good. The results indicated that the optimal wave band intervals for PNN modeling differed in areas affected and non-affected by human activities. The optimal interval of the artificial activities region falls in the visible light portion of the spectrum, and the optimized wave band region without human activities falls in the near-infrared short-wave portion of the spectrum.

Functions of Cotton Yield Response to Soil Salinity in Akesu River Irrigation District

2012 ◽  
Vol 535-537 ◽  
pp. 486-494
Author(s):  
Yu Zhang ◽  
Pei Tong Cong ◽  
Shun Jun Hu ◽  
Li Hong Wang ◽  
Feng Qing Guo ◽  
...  
Keyword(s):  
Salt Tolerance ◽  
Salt Content ◽  
Soil Layer ◽  
Relative Yield ◽  
Irrigation District ◽  
Yield Response ◽  
Yield Reduction ◽  
Soil Salt Content ◽  
Soil Salt ◽  
Salinized Soil

Based on experimental data from the five observation points during the three years, the linear subsected functions and the nonlinear s-shaped functions between the cotton relative yield and soil salt content on the salinized soil about the 0-20cm soil layer and the 0-40cm soil layer in Akesu River Irrigation District were constructed by linear regression and nonlinear least square approximation. Their applicabilities were analyzed and compared and it was found the nonlinear s-shaped function of the 0-20cm soil layer to fit better with the response relationship between the cotton relative yield and the soil salt content on the salinity soil than others in Akesu River Irrigation District.which and the indexes of cotton salt tolerance were definited, and then the indexes of cotton salt tolerance were drawn on with the function with better applicability. From the function, some indexes of salt tolerance，which contained the cotton critical soil salt content, the cotton threshold soil salt content, the soil salt content at the fastest rate of cotton relative yield reduction, and the soil salt content at the 50% cotton relative yield reduction, and so on, were determined, which can be provide as the important references for the agricultural planting, improvement of salinized soil and irrigation with saline water in Akesu River Irrigation District.

Sign in / Sign up

Export Citation Format

Share Document