Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices

Wang, Xiaoping; Fei, Zhang; Ding, Jianli; Kung, Hsiang‐te; Latif, Aamir; Johnson, Verner Carl

doi:10.1016/j.scitotenv.2017.10.025

Cited by 135 publications

(58 citation statements)

References 32 publications

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“…Based on self-adjustment of internal control parameters in the human brain, the ANN model includes three layers (an input layer, hidden layer and output layer) [ 43 , 44 ]. A back-propagation neural network (BPNN), given its structural simplicity and robustness in simulation [ 45 ], was applied to estimate soil nutrient contents in the study. The BPNN model can also incorporates the need to adjust activation functions (i.e., the sigmoid function) (Equation (7)), number of neurons ( n ) and weights (i.e., input weights

and output weights

).…”

Section: Methodsmentioning

confidence: 99%

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Song

Zhao

et al. 2018

Sensors

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Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg−1, R2 = 44.24%), soil AP (RMSE = 40.808 mg kg−1, R2 = 42.91%) and soil AK (RMSE = 67.464 mg kg−1, R2 = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg−1, R2 = 68.51%), soil AP (RMSE = 29.62 mg kg−1, R2 = 69.30%) and soil AK (RMSE = 49.67 mg kg−1 and R2 = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

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and output weights

).…”

Section: Methodsmentioning

confidence: 99%

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Song

Zhao

et al. 2018

Sensors

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“…The optimal band combination algorithm has the advantage of enhancing the correlation between the specific properties and spectral characteristics of a target, minimizing the influence of other irrelevant variables, and is widely applied in soil evaluation [18,[20][21][22]. Previous studies have mostly explored the performance of indices in the form of two bands [1,23,24], while the potential of the three-band index (TBI) form has remained unknown.…”

Section: Introductionmentioning

confidence: 99%

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

Zhu

Zhang

Wang

et al. 2020

Sensors

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Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

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“…Similarly, due to the low vegetation coverage, the differences in the vegetation indexes are not as obvious as those in the soil salinity in the study area, so the spectral characteristics of soil salinity cannot be expressed. In addition, previous studies have explored the relationship between vegetation indexes and soil salinity and showed that the R values are low [49][50][51]. Further research has shown that vegetation indexes are not efficient in soil salinity monitoring and has recommended the construction of spectral parameters via band combinations [52,53].…”

mentioning

confidence: 99%

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

Zhang¹,

Zhao²

2019

Remote Sensing

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Soil salinization adversely impacts crop growth and production, especially in coastal areas which experience serious soil salinization. Therefore, rapid and accurate monitoring of the salinity and distribution of coastal saline soil is crucial. Representative areas of the Yellow River Delta (YRD)—the Hekou District (the core test area with 140 sampling points) and the Kenli District (the verification area with 69 sampling points)—were investigated. Ground measurement data, unmanned aerial vehicle (UAV) multispectral imagery and Sentinel-2A multispectral imagery were used as the data sources and a satellite-UAV-ground integrated inversion of the coastal soil salinity was performed. Correlation analyses and multiple regression methods were used to construct an accurate model. Then, a UAV-based inversion model was applied to the satellite imagery with reflectance normalization. Finally, the spatial and temporal universality of the UAV-based inversion model was verified and the soil salinity inversion results were obtained. The results showed that the green, red, red-edge and near-infrared bands were significantly correlated with soil salinity and the spectral parameters significantly improved this correlation; hence, the model is more effective upon combining spectral parameters with sensitive bands, with modeling precision and verification precision of the best model being 0.743 and 0.809, respectively. The reflectance normalization yielded good results. These findings proved that applying the UAV-based model to reflectance normalized Sentinel-2A images produces results that are consistent with the actual situation. Moreover, the inversion results effectively reflect the distributions characteristic of the soil salinity in the core test area and the study area. This study integrated the advantages of satellite, UAV and ground methods and then proposed a method for the inversion of the salinity of coastal saline soils at different scales, which is of great value for real-time, rapid and accurate soil salinity monitoring applications.

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Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices

Cited by 135 publications

References 32 publications

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

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