Estimation Of Soil Salt Content Research Articles

Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and the sustainable development of agriculture. Due to the spatial and temporal heterogeneity of soil properties and environmental conditions in a large-scale region, the monitoring accuracy of soil salinization can be challenging. This study investigated whether the classification of diverse crop types on a time series can improve the prediction accuracy of regional soil salinity levels. Specifically, we evaluated the changes in soil salt content (SSC) under diverse vegetation cover over time in the Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery and ground-truth data collected from June to September 2021 and 2022. Focused on sunflower and maize fields, this study analyzed the impact of classifying these two crop types and examining four distinct time series on the accuracy of SSC estimation. Five indices were selected as characteristic parameters from a pool of 17 vegetation indices (VIs) and 13 soil salinity indices (SIs) derived from satellite images. Moreover, three machine learning algorithms were used to establish SSC estimation models. The findings underscored the efficacy of classifying crop types and considering different time series in enhancing the response sensitivity of spectral indices to SSC and improving modeling accuracy. Among the spectral indices, VIs made more contributions to the SSC estimation model than SIs, achieving the highest coefficient of determination (R2) of 0.71. The artificial neural networks algorithm outperformed the other two machine learning algorithms in terms of accuracy and stability, yielding an optimal R2 of 0.72 and a Root Mean Square Error (RMSE) of 0.15%. This study proposed a modeling and mapping approach that considers crop types and various time series, offering valuable insights for accurately assessing soil salinization, guiding strategies for its prevention and remediation.

Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms

Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing

Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.

Estimation of soil salt content in the Bosten Lake watershed, Northwest China based on a support vector machine model and optimal spectral indices.

Low-cost and efficient dynamic monitoring of surface salinization information is critical in arid and semi-arid regions, we conducted a remote sensing inversion exercise for soil salinity in the Bosten Lake watershed in Xinjiang, Northwest China, with a total area of about 43,930 km2, a typical watershed in an arid area. Sentinel MSI and Landsat OLI data were combined with measured soil salinity data in July 2020, and optimal combination bands were selected based on characteristic bands to create a grid search-support vector machine (GS-SVM) inversion model of soil salt content. The maximum value of soil salt content in the Bosten Lake watershed was 11.8 g/kg. The minimum value was 0.41 g/kg, and the average value was 4.77 g/kg, soil salinization is serious. The results of previous studies were applied to the estimation of salt content in Bosten Lake watershed and could not meet the monitoring requirements of the study area, R2 < 0.3. The GS-SVM soil salinity monitoring model was established based on the optimal DI, RI, and NDI remote sensing indexes for the Bosten Lake watershed. After model verification, it was found that the optimal model of image data was the Landsat OLI first-derivative model with R2 of 0.64, RMSE of 3.12, and RPD of 1.64, indicating that the prediction ability of the model was high. We used the first-order derivative model of Landsat OLI data to map the soil salt content in the Bosten Lake watershed in arid area, and found that soil salt content in most of the study area was between 10 and 20 g/kg, indicating severe salinization. This study not only reveals the distribution characteristics of salinization in Bosten Lake watershed, but also provides a scientific basis for soil salinization monitoring in Central Asia to lay a foundation for further soil salinization monitoring in arid areas.

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm.

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg-1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg-1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on unmanned aerial vehicle remote sensing

AbstractSoil salinization is one of the important factors limiting the sustainable development of agriculture and causing land degradation. In order to explore the effect of growth stage division on the estimation accuracy of soil salt content (SSC) in fields cropped with sunflower, multiple time‐series unmanned aerial vehicle (UAV) were used to monitor SSC in the Hetao Irrigation District, Inner Mongolia, China. Aerial and field campaigns for four growth stages of sunflowers in six study areas were conducted from July to September in 2021. The ground samplings of electrical conductivity (EC), leaf area index (LAI), plant height (H), and leaf chlorophyll content (CHL) were taken simultaneously with (UAV) multispectral images. The correlation between six vegetation indices (VIs), four salinity indices (SIs), three crop parameters (LAI, CHL, H) and SSC was investigated. The optimal parameters were determined and used as input variables to establish SSC estimation model using artificial neural network (ANN), random Forest (RF), multiple linear regression (MLR) algorithm, respectively. The results show that the division of growth stages could improve the correlation between spectral index, growth parameters and SSC, and the estimation model by each growth stage was more accurate than that of the whole growth period. Among the spectral indices, the VIs showed a higher correlation with SSC than the SIs. Among the crop parameters, the LAI was the most sensitive to the degree of soil salinization. The nonlinear regression algorithm (ANN, RF) performed better than the linear regression model (MLR) in the application of SSC estimation, and the best estimation models for the four growth stages of sunflower were the ANN_SSC models. This study proposed a fast and low‐cost method to monitor the soil salinization of sunflower‐cropped fields in time‐series and provided a reference for the quick perception and prevention of soil salinization.

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery.

Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.

Improving Estimates of Soil Salt Content by Using Two-Date Image Spectral Changes in Yinbei, China

Soil salt content (SSC) is normally featured with obvious spatiotemporal variations in arid and semi-arid regions. Space factors such as elevation, temperature, and spatial locations are usually used as input variables for a model to estimate the SSC. However, whether temporal patterns of salt-affected soils (identified as temporal spectral patterns) can indicate the SSC level and be applied as a covariate in a model to estimate the SSC remains unclear. Hence, temporal changes in soil spectral patterns need to be characterized and explored as to their use as an input variable to improve SSC estimates. In this study, a total of 54 field samples and a time-series of Sentinel-2 multispectral images taken at monthly intervals (from October 2017 to April 2018) were collected in the Yinbei area of western China. Then, two-date satellite images were used to quantify significant spectral changes over time using spectral change vector analysis, and four two-date-based index methods were used to characterize soil spectral changes. Lastly, the optimal two-date-based spectral indices and multispectral bands were used as input variables to build the estimation models using a random forest algorithm. Results showed that the two-date-based spectral index could be applied as an input variable to improve the accuracy of SSC estimation at a regional scale. Temporal changes in salt-induced spectral patterns can be indicated by the band difference in the wavelength range from 400 nm to 900 nm. Three two-date-based indices designated as D28a (i.e., the band difference between band 2 from an image acquired in April 2018 and band 8a from an image acquired in December 2017), D22, and D28 were the optimal parameters for characterizing salt-induced spectral changes, which were dominated by the total brightness, chloride, and sulfate accumulation of the soils. The model did not yield satisfactory estimation results (RPD = 1.49) when multispectral bands were used as the input variables. Multispectral bands coupled with two two-date-based indices (D22 and D28a) used as the input variables produced the best estimation result (R2 = 0.92, RPD = 3.27). Incorporating multispectral bands and two-date-based indices into the random forest model provides a remotely-sensed strategy that effectively supports the monitoring of soil salt content.

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.

Soil salinization is an important factor affecting winter wheat growth in coastal areas. The rapid, accurate and efficient estimation of soil salt content is of great significance for agricultural production. The Kenli area in the Yellow River Delta was taken as the research area. Three machine learning inversion models, namely, BP neural network (BPNN), support vector machine (SVM) and random forest (RF) were constructed using ground-measured data and UAV images, and the optimal model is applied to UAV images to obtain the salinity inversion result, which is used as the true salt value of the Sentinel-2A image to establish BPNN, SVM and RF collaborative inversion models, and apply the optimal model to the study area. The results showed that the RF collaborative inversion model is optimal, R2 = 0.885. The inversion results are verified by using the measured soil salt data in the study area, which is significantly better than the directly satellite remote sensing inversion method. This study integrates the advantages of multi-scale data and proposes an effective “Satellite-UAV-Ground” collaborative inversion method for soil salinity, so as to obtain more accurate soil information, and provide more effective technical support for agricultural production.

Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China

AbstractSoil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms.

Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0–20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination (R2), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy (Rc2 = 0.835, RP2 = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.

A digital camera as an alternative tool for estimating soil salinity and soil surface roughness

Determining soil salt content (SSC) and soil surface roughness (SSR) rapidly and simply is important for various environmental and agricultural applications. Recently, increasing attention has been focused on the color analysis to detect soil properties. Based on the fact that soil attributes affect soil color, this study investigated the feasibility of utilizing digital camera observations as an alternative for SSC and SSR estimation. In this study, field measurements were first conducted in the farmlands and wastelands in the inland river basin of Northwest China, and soil photographs were captured within the random-selected spots that were sampled for SSC and SSR measurements. Then, four color components, red (R), green (G), blue (B) and gray (Gr), were extracted from the raw photograph, and the digital number (DN) value of each pixel in each color component was calculated and counted. Each number of DN value was differently correlated with SSC and SSR. To find out certain DN ranges that are mostly sensitive to the variation of SSC and SSR, the whole DN value range (0–255) was separated equably into several partitions (from 2 to 10, 16, and 32), and the pixel number of each partition were recounted while the portion of every partition was computed for all pixels in each color component. After that, soil properties and these portions of four color components were regressed by partial least squares regression to find the best partitions according to the best determining coefficient (R2) and the ratio of performance to deviation (RPD). To obtain a better fit, we trained the models based on random training dataset for 100 times. Finally, SSC inversion model was built and evaluated with the excellent accuracy (R2 = 0.90 and RPD = 3.11), and SSR model with comparatively satisfactory accuracy (R2 = 0.71 and RPD = 1.87). We conclude that it is viable to apply digital cameras to estimate SSC and SSR. Findings from this study provide a base for research that can maximize the potential of digital cameras in estimating soil attributes under field conditions.

Estimating Soil Salt Content Using Fractional Derivatives and Optional Spectral Indices in the Ebinur Lake Oasis, Northwestern China

Derivative spectroscopy is a powerful mathematical tool that provides more useful information of spectral data than untreated data. Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. The VIS-NIR spectrum is one of the most important data acquisition technologies for digital soil mapping, precision agriculture, and soil resource surveys. The objective of this study was to develop a soil salt content model using the fractional-order derivatives of field-measured spectral data paired with ground measurements. The results showed the following: There was a significant correlation between the single-band reflectance spectra and the soil salt content, and as the derivative order increased, reflectance values first increased and then decreased, with a peak value, R = 0.525, in the 1.2-order derivative. Strong correlations between the estimation of soil salt content and the derivative spectral data of the difference index (DI), ratio index (RI), and normalized difference index (NDI) were also produced by the 1.2-order derivative. R values for the DI, RI, and NDI were 0.818, 0.8624, and 0.8297, respectively. Selected bands with the highest correlation values from the 1.2-order derivative spectral data of the R1428, DI (R1426 / R2151), RI (R1429 − R2024), and NDI (R1526 − R2470)/(R1526 + R2470)] were used to build a model to estimate soil salt content, which showed an R2 value of 0.53 and low RMSE and SD values. The 15 samples of the data were then used to validate the single-band model, R1428, and the combination models, DI (R1426 / R2151), RI (R1429 − R2024), and NDI [(R1526 − R2470)/(R1526 + R2470)], which demonstrated that the R2 values were greater than 0.8. The RMSE and SD values were low, and the value of the RPD was greater than 1.4. This study demonstrated the potential of using fractional derivatives rather than integer derivatives for soil salt content estimation. By making full use of hyperspectral data, fractional derivatives could enrich data preprocessing methods and unearth information about spectral emissions that are lost by integer derivatives. Although this study is a straightforward application of the fractional derivative method, it provides a reference for the estimation of other parameters with hyperspectral technology.

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

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid regions. Producers and decision-makers thus require updated and accurate maps of salinity in agronomical and environmentally relevant regions. The goals of this study were to test various regression models for estimating soil salt content based on hyperspectral data, HJ-CCD images, and Landsat OLI data using; develop optimal band Difference Index (DI), Ratio Index (RI), and Normalization Index (NDI) algorithms for monitoring soil salt content using image and spectral data; and to compare the performances of the proposed models using a Bootstrap-BP neural network model (Bootstrap-BPNN) from different data sources. The results showed that previously published optimal remote sensing parameters can be applied to estimate the soil salt content in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). Optimal band combination indices based on DI, RI, and NDI were developed for different data sources. Then, the Bootstrap-BP neural network model was built using 1000 groups of Bootstrap samples of remote sensing indices (DI, RI and NDI) and soil salt content. When verifying the accuracy of hyperspectral data, the model yields an R2 value of 0.95, a root mean square error (RMSE) of 4.38g/kg, and a residual predictive deviation (RPD) of 3.36. The optimal model for remote sensing images was the first derivative model of Landsat OLI, which yielded R2 value of 0.91, RMSE of 4.82g/kg, and RPD of 3.32; these data indicated that this model has a high predictive ability. When comparing the salinization monitoring accuracy of satellite images to that of ground hyperspectral data, the accuracy of the first derivative of the Landsat OLI model was close to that of the hyperspectral parameter model. Soil salt content was inverted using the first derivative of the Landsat OLI model in the study area.

Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta

This study explored hyperspectral field and satellite-based remote sensing of soil salt content. Using Kenli County in the Yellow River Delta as the study area, in situ soil field spectra and satellite-based remote-sensing images were integrated with laboratory measurements of soil sample salinity to improve remote sensing-based soil salt estimation and inversion procedures. First, the narrow-band hyperspectral reflectance field data were used to model the wide-band reflectance data from Landsat 7. Second, the bands and spectral features sensitive to soil salt content were identified through correlation analysis and band combination. Stepwise multiple linear regression was used to find a best model, which was then inverted to predict soil salt content using remote-sensing images from Landsat 7 and Landsat 8. The applicability of the model was verified by ground-checking the inversion results. The results show that the bands sensitive to soil salinity are mainly in the visible and near-infrared (NIR) regions. Combining information from these bands can eliminate some background effects and significantly improve the correlation with salinity. The best model of soil salinity is y = 1.345 − 25.898 × gSWIR1 − 245.440 × gRed × (gRed − gNIR) − 0.252 × (gRed + gNIR)/(gRed − gNIR) − 19.563 × (gRed − gSWIR1). This model has a coefficient of determination (R2) of 0.896, a verification R2 of 0.867, a relative prediction deviation (RPD) of 2.135, and a root mean square error (RMSE) of 0.264. The model fits well and is highly stable. The inversion results based on Landsat 7 and Landsat 8 images are consistent with the actual situation of soil salinity in the study area. This study provides an effective and feasible method for the estimation of soil salt content in coastal regions based on field spectral measurements and remote-sensing inversion.

Retrieval of Soil Salt Content From an Integrated Approach of Combining Inversed Reflectance Model and Regressions: An Experimental Study

Monitoring soil salinization has been a difficult process in arid lands due their large spatial and temporal variability. Hyperspectral remote sensing has offered a potential for faster detection of salinization process but mostly from empirical approaches. In this paper, an integrated approach combining model inversion and empirical regressions has been proposed for soil salt content (SSC) estimation from hyperspectral information obtained from controlled laboratory experiments. All soil samples were artificially salinized using Na2SO4, NaCl, and Na2CO3 (99% purity) salts to different levels and to different soil-moisture conditions, since soil moisture often jointly affects reflectance spectra with SSC. Hapke model was calibrated and validated for its simulation on soil reflectance and showed good agreements with measured data. The optimal values of single scattering albedo that was inversely retrieved from the Hapke model had good relationships with SSC at 2000-2200 nm for each treatment even under various soil-moisture conditions. Taking usage of these findings, the integrated approach obtained high accuracies on SSC estimations with R2's of 0.90, 0.86, and 0.72 and slightly dropped R2 's of 0.89, 0.81, and 0.67 for NaCl-, Na2SO4-, and Na2CO3-type saline soils under respective dry and wet conditions. The R2 decreased to 0.55 and 0.53 for dry and wet soils when salt types were ignored. The integrated approach provides a novel as well as an efficient way for SSC estimation from reflected spectra, and hence, we foresee its potential applications for large-scale SSC mapping from reflectance measurements.

A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2 052 and 2 203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation ( r =0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R 2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R 2 =0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

Soil salt content estimation in the Yellow River delta with satellite hyperspectral data

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area.