Remote Sensing Inversion for Simulation of Soil Salinization Based on Hyperspectral Data and Ground Analysis in Yinchuan, China

Wu, Dan; Jia, Keli; Zhang, Xiaodong; Zhang, Junhua; El-Hamid, Hazem T. Abd

doi:10.1007/s11053-021-09925-2

Cited by 27 publications

(16 citation statements)

References 16 publications

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“…Correlation analysis of the values of the B1∼B7 at the sampling points and the corresponding pH indicated that there was an internal relationship between the pH and the reflectance ( Table 1 ). Curve estimation was carried out using B1∼B7 as the independent variable and pH as the dependent variable of the calibration set [ 31 ]. The primary model types for the soil pH are listed in Table 2 , the performance of the models was compared and selected based on the coefficient of determination ( R 2 ) and root mean square error ( RMSE ).…”

Section: Methodsmentioning

confidence: 99%

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

Zhang

Nian

Liu

et al. 2022

IJERPH

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In recent years, ecological concerns such as vegetation destruction, permafrost deterioration, and river drying have been paid much more attention to on the Yellow River Basin in China. Soil pH is regarded to be the fundamental variable among soil properties for vegetation growth, while net primary productivity (NPP) is also an essential indicator to reflect the healthy growth of vegetation. Due to the limitation of on-site samples, the spatial–temporal variations in soil pH and NPP, as well as their intrinsic mechanisms, remain unknown, especially in the Yellow River source area, China. Therefore, it is imperative to investigate the coupling relationship between soil pH and NPP of the area. The study coupled MODIS reflectance data (MOD09A1) with on-site soil pH to estimate spatial–temporal variations in soil pH, explore the response of NPP to soil pH, and assess the extent to which they contribute to grassland ecosystems, thus helping to fill knowledge gaps. Results indicated that the surface spectral reflectance for seven bands could express the geographic pattern of soil pH by applying a multiple linear regression equation; NPP exhibited an increasing trend while soil pH was the contrary in summer from 2000 to 2021. In summer, NPP was negatively correlated with soil pH and there was a lag effect in the response of NPP to soil pH, revealing a correlation between temperate steppes > montane meadows > alpine meadows > swamps in different grassland ecosystems. In addition, contribution indices for temperate steppes and montane meadows were positive whereas they were negative for swamps and alpine meadows, which are apparent findings. The contribution index of montane and alpine meadows was greater than that of temperate steppes and swamps. The approach of the study can enable managers to easily identify and rehabilitate alkaline soil and provides an important reference and practical value for ecological restoration and sustainable development of grassland ecosystems in alpine regions.

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Section: Methodsmentioning

confidence: 99%

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

Zhang

Nian

Liu

et al. 2022

IJERPH

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“…A total of 130 soil samples were collected randomly throughout the research region from July to August 2021, according to the land-use type, landscape features, and soil type, with sample locations more than 1 km apart (Figure 2). Two quadrants were taken from each sampling site more than 100 m apart, located and recorded using GPS with latitude and longitude, and numbered sequentially according to the order of sampling sites (Wu et al, 2021a). The weeds on the surface were removed, and the soil was extracted using stainless cutting rings (100 cm 3 ) and used to determine the water content of the soil using the oven-drying method at 105 °C and the bulk density of the surface soil (0-10 cm) (Wang et al, 2015;Ahmed et al, 2020).…”

Section: Soil Sample Data Acquisition and Processingmentioning

confidence: 99%

“…We sorted the soil attribute data in the study by sample number and removed outliers and then evenly distributed two- Frontiers in Environmental Science frontiersin.org thirds of the data to select as the calibration set and the rest as the validation set (Madonsela et al, 2018). The results of the Histograms for Descriptive in IBM SPSS statistics showed that the soil property data had a significant normal distribution, which allowed multiple stepwise regression analyses and curve estimation (Wu et al, 2021a). Soil water content and soil salinity were modeled separately using vegetation index as the independent variable and observed soil properties as the dependent variable (Wu et al, 2021a).…”

Section: Regression Analysismentioning

confidence: 99%

“…The results of the Histograms for Descriptive in IBM SPSS statistics showed that the soil property data had a significant normal distribution, which allowed multiple stepwise regression analyses and curve estimation (Wu et al, 2021a). Soil water content and soil salinity were modeled separately using vegetation index as the independent variable and observed soil properties as the dependent variable (Wu et al, 2021a). The optimal model was determined based on the higher coefficient of determination (R 2 ) and lower root mean square error (RMSE) of the regression equation (Madonsela et al, 2018;Mngadi et al, 2022).…”

Section: Regression Analysismentioning

confidence: 99%

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Spatiotemporal dynamics of soil water-salt and its topographic gradient effects on Alpine area over the Northeastern Tibetan Plateau, China

Zhang

Nian

Adingo

et al. 2022

Front. Environ. Sci.

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Soil water–salt variations at different spatiotemporal scales and their influencing factors are essential for regional vegetation restoration and management. However, the study of soil water–salt of the alpine area has not taken into account the effect of topography, especially in the long time series. This work investigated the spatiotemporal characteristics of water and salt in topsoil from 2000 to 2021, using regression analysis of on-site data and MODIS products. The heterogeneity and trends of soil water–salt in various topographic factors (elevation, slope, and aspect) were also explored by selecting the maximum area of each gradient factor as the constrained condition with the help of ArcGIS software. The results revealed that 1) the models applied to SWC (soil water content), EC (electrical conductivity), and pH utilizing vegetation indices were suitable for estimating the spatiotemporal variations of soil water–salt. 2) Soil water–salt exhibited local aggregation characteristics with an upward trend in SWC and a downward trend in EC and pH as a whole. 3) Mean annual SWC peaked at 3,400–3,800 m with increasing elevation and trended downward with increasing slope. The aspect of mean annual SWC was characterized by lower values in the southwest and south and higher values in the north, while the results for pH were reversed. The slope had a greater influence on EC than on SWC and pH. 4) The trend change of SWC, EC, and pH fluctuated at 2000–2,400 m and >4,600 m with a clear inflection point at 4°–8° and 20°–24°, respectively. In summary, the effects of topography responded to the distribution and trends of soil water–salt. The results have significant implications for implementing ecological improvements in regions with complicated topography and can serve as a reference for formulating future ecological policies.

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“…The traditional method is field sampling and laboratory analysis, which is slow, expensive, and only provides localized point‐like information. Although the accuracy is high, the number of soil sampling points is limited, which is not conducive to the monitoring of soil salinity on a large scale (Wu et al, 2021). Satellite remote sensing has the characteristics of rich spectral information, a wide range of spatial coverage and long time‐series, which can detect the dynamic changes of soil salinity in a large area, and has been widely used in the qualitative, quantitative, and dynamic analysis of soil salinisation (Seifi et al, 2020; Touhami et al, 2020; Wang, Yang, et al, 2021; Wang, Zhang, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Chang

Yang

et al. 2022

Land Degrad Dev

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Soil salinity is a crucial factor in agriculture, rising salinity undermines cotton (Gossypium spp.) production in coastal areas of China and damages crops in other countries. In this study, we propose an effective integration method using satellite‐ground spectral fusion and satellite‐unmanned arial vehicle (UAV) collaboration for soil salinity monitoring in cotton growing areas. Firstly, an extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost) models were constructed based on UAV images from test areas. The optimal model was selected for soil salinity inversion. Meanwhile, ground imaging hyperspectrum and SENTINEL‐2A multispectral images were differentially fused by nonnegative matrix factorization (NMF). Then, taking the inversion results of UAV as the training sample to build convolutional neural network (CNN) model of the fused SENTINEL‐2A, the soil salinity distribution map of cotton fields in the study area was obtained by inversion, and the satellite‐UAV‐ground integrated inversion of soil salinity in coastal cotton fields was realized. The results showed that the spectrum after satellite‐ground fusion was closer to the original ground hyperspectrum, the fusion improved the correlation between spectrum and soil salinity, and UAV inversion data possessed great potential for the reference data of satellite inversion. The soil salinity obtained by satellite‐UAV‐ground integration approach was highly consistent with the measured salinity in the study area (R2 = 0.805), and the integration approach is suitable for soil salinity inversion in the cotton seedling stage in the coastal area. The satellite‐UAV‐ground integration approach proposed in this study fully tap the advantages of remote sensing data from different platforms and improved the ability to obtain soil salinisation information in large‐scale quantitatively, accurately, and quickly.

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Remote Sensing Inversion for Simulation of Soil Salinization Based on Hyperspectral Data and Ground Analysis in Yinchuan, China

Cited by 27 publications

References 16 publications

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

Spatiotemporal dynamics of soil water-salt and its topographic gradient effects on Alpine area over the Northeastern Tibetan Plateau, China

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

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