Mapping contaminated soils: using remotely‐sensed hyperspectral data to predict pH

Abstract

Summary This study assessed the feasibility of remote mapping and, thus, monitoring of soils contaminated by acid mine drainage. We report on the use of laboratory and airborne spectroscopy to determine pH. Reflectance spectra were obtained for rock and soil samples collected at our test site, the abandoned Brukunga Pyrite Mine in South Australia, using a laboratory‐based Analytical Spectral Devices Inc. (ASD) spectroradiometer. A partial least squares (PLS) regression was used to develop a predictive equation for pH based on the reflectance spectra. The validation results indicated that it is possible to generate satisfactory predictions of pH from spectral data, as demonstrated by the ratio of performance to deviation (RPD) of 1.53, a relatively small root mean square error of prediction (RMSE) of 0.91 and an R2 value of 0.58. Evaluation of the predictive equation indicated that it depends on diagnostic spectral features related to secondary iron minerals resulting from acid mine drainage (AMD). The presence of these minerals was validated independently using X‐ray diffraction (XRD). The predictive equation was applied to airborne hyperspectral data, using a form of remote sensing that simultaneously acquires spatially co‐registered images in many spectrally contiguous bands (> 50). Hyperspectral data acquired by the HyMap sensor between 1998 and 2001 were used to produce multi‐temporal pH maps. Despite the inaccuracy of the global positioning system (GPS), locations of the validation samples and geographical inaccuracy of the airborne imagery, validation of the maps indicated that pH can be generated reliably from airborne hyperspectral data, as indicated by the relatively small RMSE of 0.57 and the R2 value of 0.72. These maps demonstrate the potential to provide environmental practitioners, including soil scientists, with a spatially comprehensive view of pH related to AMD conditions. This has implications for the application of remotely sensed hyperspectral data for monitoring soil pH related to AMD conditions, especially in the near future when such data will be available from satellite sensors such EnMap (Stuffler et al., 2009). Some examples of their use include a better understanding of the progress of restoration efforts and or to pinpoint areas where future efforts should be concentrated and to evaluate the extent of downstream impacts, including contamination of soil. More generically, soil pH data are routinely required by soil scientists as part of the suite of data for understanding soil characteristics and the ability to provide spatially comprehensive soil pH data, as demonstrated by this study, would be valuable.

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

“…As a result, the PLSR models based on the HSI-resampled spectra from 505 nm to 956 nm to estimate soil pH exhibited good performance in this study. Similar results are reported in other studies [ 34 , 51 , 52 ]. This case can be attributed to (1) the good correlation between the soil alkalinity and soil color; and (2) a decrease in the soil reflectance with increasing soil alkalinity in the VIS-NIR wavelengths.…”

“…Using HSI images to estimate soil alkalinity and salinity can be attributed to the sensitivity of spectral reflectance to these soil properties, especially in certain wavelengths. Past studies [ 53 ] have used the PLSR regression coefficients to approximate the strength of sensitivity for each soil factor, such as soil pH [ 35 , 51 , 54 ] and salts [ 27 ]. In this study, three HSI bands in green, red, and NIR regions had the most important contribution to soil pH and EC estimation.…”

Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China

“…The correlation coefficients in VIS and NIR bands were higher than those in SWIR bands. Our findings were in agreement with Ong and Cudahy [31], Lopez-Granados et al [50], and Stoner [51] who found that spectral variations in 350-1200 nm were highly related to the changes of the soil pH, and the relationships were much higher in shorter wavelengths. According to Bear [52], organic matter, water, and oxidation were influencing factors of pH and color association.…”

“…As shown in Table 2 commonly accepted as the boundary between saline and non-saline soils [31]. Most soils in the study area were characterized as non-saline soils.…”

Sensing techniques for soil characterization and monitoring

“…Articles by Rivera Villarreyes et al (), Eliran et al () and Ong & Cudahy () tested the application of proximal sensing by using emerging techniques such as cosmic rays, sub‐millimetre wavelengths and optical spectroscopy. Cosmic rays were used to estimate root‐zone soil hydraulic properties.…”