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.

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.

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

Study on Microbial Community Characteristics in the Northern Wetland Reserve Area of the Modern Yellow River Delta

2013 ◽  
Vol 726-731 ◽  
pp. 463-469
Author(s):  
Guang Ming Zhao ◽  
Si Yuan Ye ◽  
Yuan Zheng Xin ◽  
Xi Gui Ding ◽  
Hong Ming Yuan ◽  
...  
Keyword(s):  
Microbial Community ◽  
Coastal Wetland ◽  
Yellow River ◽  
Salt Content ◽  
Yellow River Delta ◽  
River Delta ◽  
Community Characteristics ◽  
Different Depths ◽  
Negative Linear Relationship ◽  
Modern Yellow River Delta

Yellow River Delta has a special status of coastal wetland research in China. The microbial community characteristics such as community structure, activity and size in the wetland were investigated in the modern Yellow River Delta of Shandong Province. The aim was to find the effect of salinity on the microbial community. There was a significant negative linear relationship between soluble salt content and the total number of microbes, overall microbial activity, and diversity of culturally viable microbes. Differences of the soil bacterial community in different depths were monitored using the terminal restriction fragment length polymorphism (T-RFLP) and clone library analyses. In a word, these results indicate that higher salinity and deeper depth resulted in a smaller, more stressed microbial community which was less active and diverse .

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