The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey (original) (raw)
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Diffused Reflectance Spectroscopy for Characterization of Salt-Affected Soil (SAS) Attributes
Agropedology, 2019
Identification of soil salinity based on traditional methods (measurement in saturation extract) required time, labour and capital, whereas, ground based non-imaging hyperspectral remote sensing estimates the soil salinity and alkalinity parameters within limited resources and can be used for real time monitoring purpose. Laboratory experiment was conducted to study the spectral properties using VNIR spectroscopy in silt loam and silty clay loam soil saturated with different levels of chloride, sulphate and carbonate of sodium salts. Salinity absorption features were more pronounced around 1900 nm, followed by 1400 and 2200 nm. The salt concentration was inversely related to reflectance values in saline soils. Wavelength was shifted from 1900 nm to higher wavelength value and this shifting feature was also correlated with the increase in salt concentration. Relatively high correlation coefficients of ECe, saturated extract Na+ and Cl- with soil reflectance values were found in betwe...
Desert, 2020
Soil salinity undergoes significant spatial and temporal variations; therefore, salinity mapping is difficult, expensive, and time consuming. However, researchers have mainly focused on arid soils (bare) and less attention has been paid to halophyte plants and their role as salinity indicators. Accordingly, this paper aimed to investigate the relationship between soil properties, such as electrical conductivity of the saturation extract (ECe) and the spectral reflectance of vegetation species and bare soil, to offer a method for providing salinity map using remote sensing. Various vegetation species and bare soil reflectance were measured. Spectral Response Index (SRI) for bare soil and soil with vegetation was measured via the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and salinity indexes. The electrical conductivity of the saturated extract, texture, and organic matter of soil samples were determined. The correlation coefficient of soil salinity with SRI, SAVI, and salinity indexes were obtained, and a model was presented for soil salinity prediction. EC map was estimated using the proposed model. The correlation between SRI and EC was higher than other models (0.97). The results showed that the salinity map obtained from the model had the highest compliance (0.96) with field findings. In general, in this area and similar areas, the SRI index is an acceptable indicator of salinity and soil salinity mapping.
Using hyperspectral remote sensing to monitor the properties of salt-affected soils
Authorea
The aim of the study was to estimate the properties of the salt-affected soils (SAS) using hyperspectral remote sensing. The study was carried out on typical SAS from 372 locations covering 17 coastal districts from west coast region of India. The spectral reflectance of processed soil samples was recorded in the wavelength range of 350-2500 nm. The full data set (n=372) was split into two as calibration dataset (n=260, 70%) to develop the model and validation dataset (n=112, 30%) to evaluate the performance of the model independently. The spectral data were calibrated using the laboratory estimated soil properties with five different multivariate techniques: (a) linear-partial component regression (PCR) and partial least square regression (PLSR) and (b) non-linear-multivariate adaptive regression spline (MARS), random forest (RF) and support vector regression (SVR). In general, the spectral reflectance from the soils decreased with increasing levels of salinity (electrical conductivity,
2021
The study aims to analyze the ability of the most popular and widely used vegetation indices (VI's), including NDVI, SAVI, EVI and TDVI, to discriminate and map soil salt contents compared to the potential of evaporite mineral indices such as SSSI and NDGI. The proposed methodology leverages on two complementary parts exploiting simulated and imagery data acquired over two study areas, i.e. Kuwait-State and Omongwa salt-pan in Namibia. In the first part, a field survey was conducted on the Kuwait site and 100 soil samples with various salinity levels and contents were collected; as well as, herbaceous vegetation cover canopy (alfalfa and forage plants) with various LAI coverage rates. In a Goniometric-Laboratory, the spectral signatures of all samples were measured and transformed using the continuum removed reflectance spectrum (CRRS) approach. Subsequently, they were resampled and convolved in the solar-reflective spectral bands of Landsat-OLI, and converted to the considered indices. Meanwhile, soil laboratory analyses were accomplished to measure pHs, electrical conductivity (EC-Lab), the major soluble cations and anions; thereby the sodium adsorption ratio was calculated. These elements support the investigation of the relationship between the spectral signature of each soil sample and its salt content. Furthermore, on the Omongwa salt-pan site, a Landsat-OLI image was acquired, pre-processed and converted to the investigated indices. Mineralogical ground-truth information collected during previous field work and an accurate Lidar DEM were used for the characterization and validation procedures on this second site. The obtained results demonstrated that regardless of the data source (simulation or image), the study site and the applied analysis methods, it is impossible for VI's to discriminate or to predict soil salinity. In fact, the spectral analysis revealed strong confusion between signals resulting from salt-crust and soil optical properties in the VNIR wavebands. The CRRS transformation highlighted the complete absence of salt absorption features in the blue, red and NIR wavelengths. As well as the analysis in 2D spectral-space pointed-out how VI's compress and completely remove the signal fraction emitted by the soil background. Moreover, statistical regressions (p ˂ 0.05) between VI's and EC-Lab showed insignificant fits for SAVI, EVI and TDVI (R 2 ≤ 0.06), and for NDVI (R 2 of 0.35). Although the Omongwa is a natural flat salt playa, the four derived VI's from OLI image are completely unable to detect the slightest grain of salt in the soil. Contrariwise, analyses of spectral signatures and CRRS highlighted the potential of the SWIR spectral domain to distinguish salt content in soil regardless of its optical properties. Likewise, according to Kuwait spectral data and EC-Lab analysis, NDGI and SSSI incorporating SWIR wavebands have performed very well and similarly (R 2 of 0.72) for the differentiation of salt-affected soil classes. These statistical results were also corroborated visually by the maps derived from these evaporite indices over the salt-pan site, as well as by their
Journal of Geographic Information System, 2020
Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it's Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.
The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe) and laboratory soil reflectance spectra resampled to Landsat sensor's resolution. The models used were partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R 2 = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), which performed better than the PLSR model (R 2 = 0.70, RMSE = 6.95, and RPD = 1.82). The models were subsequently applied on a pixel-by-pixel basis to the reflectance values derived from two Landsat images (2006 and 2012) to generate quantitative maps of the soil salinity. The resulting maps were validated successfully for 37 and 26 sampling points for 2006 and 2012, respectively, with R 2 = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R 2 = 0.71 and 0.73 for 2006 and 2012, respectively, for the PLSR model. The results indicated that MARS is a more suitable technique than PLSR for the estimation and mapping of soil salinity, especially in areas with high levels of
International Journal of Remote Sensing, 2002
The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil eld moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis ( VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a signi cant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the re ectance image data. Applying this equation on a pixel-bypixel basis revealed images that described spatially and quantitatively the surface distribution of each property. The VNIRA results were validated successfully from a priori knowledge of the area characteristics and from data collected from several sampling points. Following these examinations, a procedure was developed in order to create a soil property map of the entire area, including soils under vegetated areas. This procedure employed a random selection of more than 80 points along nonvegetated areas from the quantitative soil property images and interpolation of the points to yield an isocontour map for each property. It is concluded that the VNIRA method is a promising strategy for quantitative soil surface mapping, furthermore, the method could even be improved if a better quality of HSR data were used.
Mapping soil salinity in irrigated land using optical remote sensing data
EURASIAN JOURNAL OF SOIL SCIENCE (EJSS), 2014
Soil salinity caused by natural or human-induced processes is certainly a severe environmental problem that already affects 400 million hectares and seriously threatens an equivalent surface. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. In semi-arid and arid areas, 21% of irrigated lands suffer from waterlogging, salinity and/or sodicity that reduce their yields. 77 million hectares are saline soils induced by human activity, including 58% in the irrigated areas. In the irrigated perimeter of Tadla plain (central Morocco), the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality. Experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in terms of spatial coverage. Several studies have described the usefulness of remote sensing for mapping salinity by its synoptic coverage and the sensitivity of the electromagnetic signal to surface soil parameters. In this study, we used an image of the TM Landsat sensor and field measurements of electrical conductivity (EC), the correlation between the image data and field measurements allowed us to develop a semi-empirical model allowing the mapping of soil salinity in the irrigated perimeter of Tadla plain. The validation of this model by the ground truth provides a correlation coefficient r² = 0.90. Map obtained from this model allows the identification of different salinization classes in the study area.
Remote Sensing, 2017
Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS•m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS•m −1) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.
Soil salinity expansion is an environmental challenge particularly in arid and semi arid regions. In order to evaluate the progressing extent of soil salinity in relation with natural and human-induced conditions, a study was conducted using the Landsat TM imagery. The present study was conducted in the Garmsar area to the East of Tehran. A total of 288 soil samples were analyzed to determine the relationship between the spectral reflectance and Electrical Conductivity (EC), as salinity indicator. Multiple regression analysis and Ordinary Least Square regression (OLS) were used to examine the relationships between EC and derived spectral to generate several models. In the case of derived spectral, mid-infrared band (TM Band-7), visible band (Band-1), Tasseled cap3 (Wetness index) and PCA2 (Principal Component Analysis) were found to be most correlated with the observed EC values of the surface layer of the soil, at 99% confidence level. The accuracy of the prediction model was tested using a validation set of 52 soil samples in Eyvanekey plain, close to study area where the environmental circumstance consist of similar properties. RMSE and MAE were used to evaluate the performance of the map prediction quality. Results showed that the appropriate model could predict the soil salinity with precision of 4.1 and 0.49 dS m -1 , respectively. The predicted salinity ranged from 0dS/m to 110dS/m. Therefore, the EC estimations were suitable to generate soil salinity map. Sensitivity analysis was tested on applied parameters that showed Band-1 and Band-7 were 3 and 2 times more than sensitive rather than other parameters respectively. The results are promising and certainly useful for soil salinity prediction.