Selenium-enriched soil mapping using airborne SASI images

Geoderma 363 (2020) 114133

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Selenium-enriched soil mapping using airborne SASI images

T

Zhizhong Li Shenyang Geological Survey Center, China Geological Survey

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Alex McBratney

Selenium (Se) is an important trace element that is essential to human beings. In the past, the Se concentration has mostly been obtained by field sampling and analysed under laboratory conditions. Unfortunately, this process is expensive, and the number of available samples is usually relatively small. A soil geochemical survey was conducted in conjunction with an airborne survey via hyperspectral remote sensing in the Chuangye Farm area, China. Twenty-five elements/oxides including Se were analysed in the samples, and the results showed that Se has a highly negative correlation with K. Using hyperspectral Shortwave Infrared Airborne Spectrographic Imager (SASI) data, the abundances of clay minerals were obtained through the sequential maximum angle convex cone (SMACC) classification operation. According to the abundances of clay minerals, the reflectance of clay minerals was obtained using the spectral retrieval method. Due to the correlation among K, Se, clay minerals and their spectral characteristics, a stepwise regression model was established using the results from the geochemical survey data and retrieved hyperspectral SASI data; then, the K and Se concentrations were predicted. The results of this study show that predicting the Se content in soil by using SASI images through the spectral retrieval of clay minerals in soil in conjunction with actual geochemical analysis results boasts a higher prediction accuracy than the use of the raw SASI images, and this prediction approach has been proven to be feasible.

Keywords: Selenium SMACC SASI Geochemical data Clay mineral Spectral retrieval Multiple linear regression

1. Introduction Selenium (Se) is an essential trace element for humans and animals (Fordyce, 2013; Winkel et al., 2015; Xu, et al., 2018). The role of Se in the human body has been studied from various perspectives, such as immunomodulation (Combs and Combs, 1986; Levander, 1986; WHO, 1987; Papp et al., 2007; Hawkes et al., 2001) and cancer prevention (Lippman et al., 2009; Vinceti et al., 2000; Clark et al., 1996). Selenium in humans is mainly derived from the soil, and soil selenium mainly comes from soil-forming parent materials (Taylor and McLennan, 1985; Zhang, 1994). However, Se is not uniformly distributed throughout the Earth's crust. In certain areas, soil selenium concentrations are between 0.01 and 2 mg/kg, and the world average is 0.4 mg/kg (Fordyce, 2007). Small-scale experiments on the soil selenium concentrations of the soil columns or profiles provided the result that with decreases in the pH and reduction potential (Eh) or increases in the content of clay and soil organic carbon (SOC), the sorption increases (Winkel et al., 2015; Alejandro and Charlet, 2009). On regional scales, climatic conditions and the organic matter content could control the soil selenium to some extent (Jones et al., 2017). The correlation coefficient between the water-soluble Se and the total Se in soils was statistically analysed in China, and the correlation coefficient between the water-soluble

selenium and total selenium in 18 meadow soils was very significant (0.72; P less than 0.001). The distribution of soil selenium in the north-eastern plains of China revealed that the selenium content was the highest in black soil, paddy soil, and albic soil and the lowest in sandy soil (Dai et al., 2015; Xu et al., 2018). Clay minerals are the main components of black soil, which has the highest selenium content in Northeast China. Previous results suggest that clay minerals in black soil are dominated by montmorillonite and illite (Jiang et al., 1982). However, other studies have reported that the clay minerals in black soil are mainly illite-mixed types and that montmorillonites are not prevalent (Zhang et al., 2006). In the Daqing area, the clay minerals in saline-alkaline soil and chernozem are dominated by illite and chlorite (Chen et al., 2004). Previous studies have shown a correlation between the clay minerals and spectroscopic features. Soil moisture has obvious absorption characteristics near 1400 and 1900 nm, and the most common sensitive bands associated with clay minerals are 1400–1410 and 2160–2200 nm (Hunt and Salisbury, 1976). The absorption characteristics of clay minerals are used for research on reflectance spectroscopy. Reflectance spectroscopy is a rapid and non-destructive method of measuring soil properties (Hively et al., 2015), although most studies have used laboratory instruments to measure visible (400–700 nm), near-infrared

E-mail address: [email protected]. https://doi.org/10.1016/j.geoderma.2019.114133 Received 5 July 2018; Received in revised form 11 October 2019; Accepted 11 December 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.

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growing rice, beans, and other cash crops. The area is within the national standard of land with selenium-enriched soil. The area was listed as a Se strongly and extremely strongly enriched area based on a statistical analysis of the Se distribution of soil on the north-eastern plain (Dai et al., 2015).

(700–2500 nm) and mid-infrared (2500–25,000 nm) wavelengths (Brown et al., 2006; Reeves et al., 2010; Rossel et al., 2006). Aerial and airborne hyperspectral remote sensing represents a useful tool for measuring surface soil properties (Selige et al., 2006; Bendor., 2009; Gomez et al., 2008), such as the application of hyperspectral VNIR data for soil organic matter and carbon fractions (e.g., Adar et al., 2014; Jung Et al., 2015; O'Rourke and Holden, 2012), soil biochar (e.g., Tong et al., 2013), and total nitrogen (e.g., Buddenbaum and Steffens, 2012; Li et al., 2015). The soil nutrient elements N, P, and K have been analysed (Confalonieri et al., 2001; Viscarra Rossel et al., 2006; Mouazen et al., 2010; Shao and He, 2011; Gholizade et al., 2013; Wu et al., 2014; Ji et al., 2014 Paz-Kagan et al., 2015, Paz-Kagan et al., 2015; Hu et al., 2016; Sarathjith et al., 2016; Yu et al., 2016; Shaddad et al., 2016), and the results show that it is feasible to predict the soil N, P, and K using spectroscopy in the visible near-infrared to shortwave infrared (VNIR-SWIR, 400–2500 nm) regions. The Shortwave Infrared Airborne Spectrographic Imager (SASI) sensor is one part of the Flying Laboratory of Imaging Systems (FLIS) developed by Itres Ltd. The FLIS consists of the Compact Airborne Spectrographic Imager (CASI), SASI and Thermal Airborne Spectrographic Imager (TASI) sensors. The spectral range of the CASI sensor is 370–1040 nm, that of the SASI is 960–2440 nm, and that of the TASI is 8–11.5 µm. CASI/SASI/TASI hyperspectral data have been used in many fields in the past; for example, CASI/SASI data have been applied in the sparse graph regularization method to map crop types (Xue et al., 2017), CASI and SASI airborne hyperspectral data have been integrated for tree species identification in forests with high coverage rates (Liu et al., 2011), and thermal data have been used to analyse the impact of city structures on thermal behaviour (Novotny et al., 2016). The study of soil selenium is of great significance for the safety of regional agricultural products and human health (Girling, 1984, Fordyce, 2013; Winkel et al., 2015), and the mapping of Se concentrations using hyperspectral remote sensing can aid in precision agriculture (Erives et al., 2005; Lanthier et al., 2009). Previous studies have not investigated the direct use of hyperspectral data to conduct research on Se content prediction methods because of the difficulty in predicting the Se content. Soil Se does not have obvious spectral characteristics, and Se has a low concentration in soil, making it difficult to measure directly using spectroscopy (Ji et al., 2014). Moreover, the presence of multiple types of features and spectral heterogeneity in the image also greatly affects the performance of quantitative calibrated models of soil Se. The aim of our current study is to use geochemical data to study the elements of a region that are related to Se, which has a direct relationship with clay minerals. The abundance of clay minerals is determined via the method of spectrum unmixing and then by performing spectral retrieval. A stepwise regression analysis of the retrieved spectra and geochemical data is used to establish a regression equation and to perform an accuracy evaluation. Using Se and its regression equations, the image of the Se concentration in the area is calculated, where each pixel has a Se value.

2.2. Geochemical survey Soil geochemistry surveys were carried out in the Chuangye Farm area over a total area of approximately 530 km2. The basic density of soil samples was 8 sites/km2. The sampling sites were located in thick soil layers. When sampling, surface debris was removed, the soil was collected in a column up to a depth of 20 cm, and animal and plant residues, gravel, fertilizer lumps, etc., were discarded from the sample. When digging a sample with a bamboo spoon (sample spoon), it is necessary to ensure that the 0–20 cm depth is evenly collected from above and below. The original weight of the soil sample was greater than 1000 g. A total of 54 samples were repeated, and the original weight of the repeated samples was greater than 1000 g to ensure that the weight of the sample after sieving was greater than 300 g. The secondary sample was stored, and the weight of the secondary sample was 100 g. After the sample was dried, it was passed through a 2 mm aperture nylon sieve. After sieving, the soil samples were weighed, and 200 g batches were loaded into Kraft paper bags to be sent to the laboratory for analysis. Subsamples (not less than 300 g) were loaded into clean plastic bottles and sent for sample storage, and the excess was discarded. The analysis program developed for the soil is based on X-ray fluorescence spectroscopy (XRF) supplemented by plasma mass spectrometry (ICP-MS), atomic fluorescence spectrometry (AFS), and atomic emission spectrometry (AES). Twenty-five elements/oxides were analysed. Table 1 lists the analysis methods for the elements/ oxides. 2.3. Imaging spectroscopy From April to May 2017, flight surveys were carried out in the Jiangsanjiang area, where the CASI/SASI/TASI system was mounted on a platform on the aircraft Yun-12. The relative altitude was 2500 m, the measurement area was 4000 km2, and the hyperspectral data included 40 navigation zones. The data included the CASI visible-near-infrared spectral segment and the SASI short-wavelength infrared spectral segment. The SASI data included a total of 87 bands, spectral coverage ranging between 950 and 2450 nm, a spectral resolution of 15 nm, and a spatial resolution of 4 m. After the CASI/SASI data were obtained, radiation correction and geometric correction were performed using the system's radiation correction, geometry correction software and relevant data acquired during the measurements. The image studied in this paper is a 28-track navigation geochemical survey that was performed covering the central-northern parts of this image (Fig. 2). ENVI software is used for CASI/SASI hyperspectral data processing, and it is often used by professionals on the Geographic Information System, remote sensing scientists (Gomez, 2001) and image analysts to extract meaningful spectral information from imagery (Kruse, 2009) to improve decision-making.

2. Materials and methods 2.1. Study area The research area is located on Chuangye Farm in the Jiansanjiang District, China (Fig. 1), which is the hinterland of the Sanjiang Plain at the junction of Fujin, Tongjiang, Fuyuan and Raohe in Heilongjiang Province. This region is the inter-river zone, where the Heilong River, Songhua River, and Wusuli River converge, and it includes a cold temperate zone and humid monsoon climate zone. The average temperature is 1 °C–2 °C, the effective accumulated temperature is 20 °C–24 °C, the sunshine duration is 2260–2449 h, the average precipitation is 550–600 mm, and the frost-free period is 110–135 days. Rainfall, heat, and sunshine hours make this area highly suitable for

2.4. SMACC classification method The coexistence of soil, vegetation, and other features appears as mixed pixels on remote sensing images. To extract pure feature information, linear spectral unmixing techniques can be used (Boardman, 1989). Spectral unmixing is performed on each pixel of the remote sensing data, thereby enabling the abundance of a type of feature in each pixel to be calculated. The sequential maximum angle convex cone (SMACC) calculation is a spectral separation method that has been used 2

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Fig. 1. Geographic location map of the study area. The red box area is the scope of the research area, which is located in the Chuangye Farm area in Jiansanjiang, China. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

in multispectral and hyperspectral studies for Landsat TM to find spectral endmembers of the expected land-cover classes (Bai, et al., 2012) and long-term tree-cover classes. For hyperspectral Hyperion images, the abundances of soil, rock, and vegetation were retrieved (Zhang et al., 2014). Multiple geochemical data were used for the selection of prospecting areas (Chen et al., 2018). A SMACC spectral tool was used to find spectral endmembers of the clay minerals and their abundances throughout the SASI images. Endmembers are spectra that are chosen to represent pure clay minerals in a spectral image. SMACC uses a convex cone model to identify image endmember spectra. The calculation method and implementation process can be found in the relevant literature (Gruninger et al., 2004; Farrand, 2013; Chen et al., 2018).

Table 1 Analysis Methods for Various Elements/Oxides. Analytical Method

Element/Oxide

X-ray Fluorescence Spectrometry (XRF) Atomic Emission Spectrometry (AES) Plasma Mass Spectrometry (ICP-MS) Atomic Fluorescence Spectrometry (AFS) Spectrophotometric COL Volume Method (VOL) Ion Selective Electrode Method (ISE) Glass Electrode Method

Cl, Cr, Cu, Ni, Mn, P, Pb, S, Zn, SiO2, TFe2O3, CaO, MgO, K2O, Na2O B Mo, Cd As, Hg, Se I N, Org. C F pH

Fig. 2. Hyperspectral image of the 28th orbit and geochemical sampling site position. The left grey image (1730 nm) is a hyperspectral image of the 28th orbit, and the white superimposed point is a geochemical sampling site. The right image is a false-colour image (R = 2430 nm, G = 1730 nm, and B = 950 nm) and a partial enlargement of the left image.

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Table 2 Statistical Values of the Elemental/Oxide Contents of the Geochemical Samples in the Chuangye Farm Area. Variable

N

Unit

Minimum

Maximum

Mean

StDev

CoefVar

N P K2O CaO MgO SiO2 TFe2O3 S Cu Ni Mn Zn Mo B Cl Corg Cd As Hg Cr Pb I F Se pH

2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455 2455

μg/g μg/g % % % % % μg/g μg/g μg/g μg/g μg/g μg/g μg/g μg/g % μg/g μg/g μg/g μg/g μg/g μg/g μg/g μg/g

589 301 1.56 0.71 0.51 62.6 1.91 55 14.2 13.1 181 20.6 0.35 18 43 0.931 0.022 2.4 0.01 32.9 19.5 0.23 238 0.064 5.04

13,167 1879 2.77 3.73 1.43 77.59 7.78 923 87.1 64.6 4090 151 2.03 71.2 285 44.5654 0.33 19.8 0.2 84.4 51.2 17.3 874 0.64 8.03

1973 714.03 2.2968 1.1111 0.81453 72.037 3.9227 198.61 22.604 24.466 655.91 53.137 0.68754 39.112 79.385 4.2128 0.078574 8.0372 0.034036 53.838 28.675 0.8947 417.16 0.33287 6.0417

788.7 159.21 0.1368 0.1919 0.15709 2.729 0.6397 66.76 3.669 5.653 412.14 13.616 0.19024 8.012 15.163 1.9826 0.030304 2.0643 0.010149 8.608 2.841 0.6406 87.12 0.07264 0.37

39.97 22.3 5.96 17.27 19.29 3.79 16.31 33.61 16.23 23.11 62.83 25.62 27.67 20.48 19.1 47.06 38.57 25.68 29.82 15.99 9.91 71.6 20.88 21.82 6.12

R clay − retrieve =

2.6.1. Calibration procedure by multiple linear regression (MLR) In this study, a multivariate regression technique was used to establish a model for estimating the concentrations of selenium and other elements from hyperspectral images. The reflectivity values (or corresponding reflectance conversion values) for each band at the sampling site were extracted, and a stepwise MLR (Martens and NSS, 1989) was performed to select and combine the spectral bands that can best explain the soil values. Finally, the following prediction equation was formed:

Celement = B0 + B1 R1 + B2 R2 + ⋯Bi Ri⋯Bn Rn

2.6.2. Validation Samples were divided into two parts: one for the regression calculation and the other for performing predictions. During this exercise, most soil samples were used to calibrate the regression. The stepwise regression procedure using Minitab software can automatically output the most significant models along with the parameters of R2, adjusted R2, S (square root of the MSE), and Mallows' Cp. The remaining samples were used for validation, in which the predicted value was compared with the actual measured value. In this study, 85% of the data were used for training, i.e., 68 sampling sites were modelled, and 12 sites were used for validation.

m

(1)

m

∑ Fj = 1 ⎡1 ε=⎢ m ⎣

(2)

3. Results

1 2

m

⎤

∑ ε 2 (λi) ⎥ j=1

⎦

3.1. Geochemical analysis results

(3)

where n represents the number of bands, m represents the total number of feature categories, Fj represents the abundance of the jth feature, Rij is the reflectance value of the jth feature in the ith band, ε(λi) is normally distributed noise, and Ri represents the reflectance value of the pixel in the ith band. If multiple types of features have been classified and the types of other features are X species, then the reflectivity values of the other X features are governed by the condition that ε approaches zero Eqs. (4) and (5):

Following the geochemical sampling work in the Chuangye Farm area, the samples were sent to the laboratory for analysis. Geochemical composition analyses were conducted for each sample. The statistical results for the maximum, minimum, mean, standard deviation and coefficient of variation are shown in Table 2. The correlation coefficient of each elemental content was calculated, and the results are shown in Table 3. The content of K in the Chuangye Farm area is between 1.56% and 2.77% (Table 2). The element exhibiting the highest correlation coefficient with Se is K at −0.48. The main minerals in shale are clay minerals (kaolinite, montmorillonite, illite and chlorite) and quartz. The high K content may be caused by clastic feldspar, authigenic feldspar, detrital muscovite, illite, autogenic chlorite or clay minerals (Brownlow, 1979). There are three main soil types in Jilin Province on the southern part of Chuangye Farm: black soil, white pulp and black calcium. The K-bearing minerals in these three types of soils are composed mainly of potassium feldspar (K-feldspar) and illite. K-feldspar is concentrated mainly in sand particles, while illite exists predominantly among the clay particles in the soil. Overall, among the three types of soil, K-bearing minerals account for approximately 1/3 of all soil minerals. Total soil K is well correlated with the contents of K-bearing minerals. K-feldspar is the main source of K in fine sand and silt. The total amount of K-bearing minerals usually exceeds the content of total K (Liu et al., 2002).

x

Ri − other =

(7)

where Celement is the predicted concentration value of certain elements; B0 is a constant coefficient; Bi and Ri are the coefficient and reflectance at the corresponding band, respectively; and n is the number of bands that have been entered into the regression equation. Using Eq. (7), a mathematical operation is applied for the remote sensing image so that the element concentration can be mapped.

∑ (Fj ∗ Rij) + ε (λi)(i = 1, 2⋯n)

j=1

(6)

2.6. Calibration model and validation

Based on SMACC, the reflectance of each pixel is assumed to be a linear combination of the reflectivity of each material (or endmember) at this pixel (Boardman, 1989) as Eqs. (1)–(3).

j=1

x

1 − ∑ j = 1 Fj

After spectral retrieval, the reflectivity value of the pixel is only the reflectivity value of the clay mineral, thereby eliminating the influence of other features.

2.5. Spectral retrieval of clay minerals

Ri =

Ri − ∑ j = 1 (Fj ∗ Rij )

∑ (Fj ∗ Rij) j=1

Ri − clay + Ri − other = Ri

(4) (5)

where Ri_clay is the reflectance value of multiple clay minerals classified in the ith band, Ri_other is the reflectivity value of the ith band of non-clay features, and Ri is the original reflectance value of the i band without classification (Chen et al., 2007). The clay mineral fraction in the pixel can be obtained by subtracting the reflectance values of various non-clay minerals from the total reflectance value. Dividing the clay mineral reflectance value by the fraction of clay minerals allows for the retrieval of the clay spectrum Eq. (6): 4

N P K2O CaO MgO SiO2 TFe2O3 S Cu Ni Mn Zn Mo B Cl Corg Cd As Hg Cr Pb I F Se pH

1.00 0.30 0.06 0.23 0.27 −0.33 −0.01 0.69 0.41 0.32 −0.32 0.09 −0.14 0.00 0.21 0.86 0.38 −0.26 −0.37 0.19 −0.23 0.02 0.26 0.06 −0.11

N

1.00 −0.45 0.33 −0.23 0.13 0.04 0.40 0.02 −0.06 0.29 0.00 0.09 0.00 0.17 0.36 0.24 0.19 −0.02 −0.16 −0.03 0.14 −0.09 0.36 −0.18

P

1.00 −0.13 0.64 −0.64 0.20 −0.05 0.42 0.52 −0.55 0.11 −0.34 0.07 −0.23 −0.12 0.02 −0.18 −0.17 0.67 −0.12 −0.30 0.45 −0.48 0.21

K2O

1.00 0.21 −0.15 0.09 0.30 0.21 0.13 0.06 0.05 −0.20 −0.06 0.03 0.27 0.30 −0.03 −0.16 0.12 −0.16 0.12 0.10 0.22 0.19

CaO

1.00 −0.89 0.60 0.07 0.66 0.79 −0.39 0.33 −0.13 0.02 −0.27 0.10 0.19 0.07 −0.21 0.86 −0.08 −0.09 0.52 −0.19 0.26

MgO

1.00 −0.71 −0.09 −0.73 −0.84 0.46 −0.29 0.15 −0.06 0.29 −0.15 −0.17 −0.19 0.25 −0.90 0.10 0.15 −0.58 0.14 −0.23

SiO2

1.00 −0.21 0.41 0.63 −0.01 0.27 0.19 0.05 −0.33 −0.14 0.01 0.66 0.00 0.57 0.08 −0.04 0.32 0.12 0.16

TFe2O3

1.00 0.28 0.12 −0.22 0.03 −0.22 −0.07 0.27 0.68 0.33 −0.34 −0.31 0.01 −0.21 0.02 0.12 0.12 −0.16

S

Table 3 Correlation Coefficients of Geochemical Data in the Chuangye Farm Area.

1.00 0.70 −0.48 0.23 −0.28 0.01 −0.15 0.27 0.25 −0.02 −0.26 0.65 −0.13 −0.13 0.43 0.00 0.12

Cu

1.00 −0.33 0.39 −0.13 0.03 −0.25 0.14 0.21 0.19 −0.18 0.76 −0.06 −0.11 0.49 −0.10 0.19

Ni

1.00 0.06 0.58 −0.04 0.06 −0.18 −0.01 0.50 0.30 −0.33 0.43 0.35 −0.35 0.25 −0.06

Mn

1.00 0.15 −0.06 −0.10 0.03 0.12 0.12 0.02 0.24 0.11 0.06 0.13 −0.05 0.13

Zn

1.00 0.02 −0.02 −0.09 0.00 0.49 0.16 −0.08 0.29 0.25 −0.15 0.21 −0.13

Mo

1.00 −0.07 0.00 0.04 0.01 −0.02 0.05 −0.13 −0.02 0.04 0.00 −0.02

B

1.00 0.33 0.09 −0.19 0.00 −0.29 0.06 0.06 −0.17 0.06 −0.17

Cl

1.00 0.41 −0.28 −0.32 0.02 −0.19 0.11 0.14 0.13 −0.10

Corg

1.00 −0.08 −0.19 0.16 −0.11 0.05 0.17 0.05 −0.05

Cd

1.00 0.20 0.17 0.43 0.12 −0.02 0.32 0.10

As

1.00 −0.20 0.22 0.04 −0.17 −0.01 0.01

Hg

1.00 −0.01 −0.12 0.52 −0.18 0.17

Cr

1.00 0.06 −0.16 0.08 0.13

Pb

1.00 −0.12 0.17 −0.02

I

1.00 −0.16 0.11

F

1.00 −0.03

Se

1.00

pH

Z. Li

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Endmember 3 has an abundance of 0.95, is a shadow, and has a very low reflectivity (Fig. 4d). Endmember 4 has an abundance of 0.6 and reflects the soil information (Fig. 4e). Endmember 5 has an abundance of 0.12 and reflects information on non-cultivated land (Fig. 4f). Endmember 6 has an abundance of 0.58, representing the artificial building and the resulting shadow (Fig. 4g). Endmember 7 has an abundance value of 0.75 and represents the transition between soil and vegetation (Fig. 4h). Endmember 8 has an abundance of 0.08 and represents the boundary between two different vegetation types. Due to its low abundance value, endmember 8 is not representative (Fig. 4i). Endmember 9 has an abundance of 0.81 and represents a plastic greenhouse (Fig. 4j). Endmember 10 has an abundance of 1.00 and represents a single type of vegetation (Fig. 4k).

Due to the low Se content, it is difficult to quantitatively predict the content of Se using spectral measurements. However, if we can determine the abundance of clay minerals and analyse the relationship between the reflectance of clay minerals and the content of K, then we can predict the Se content from the reflectance of clay minerals on the basis of the negative correlation between Se and K. 3.2. SMACC classification results The SMACC operation is performed to calculate Formula (6), and the total abundance of the classified spectrum endmembers is assigned to a value of 1. After executing this operation, in addition to the endmember abundance images and endmember spectra, the maximum relative error plot is formed, which contains the RMS error of each associated endmember.

3.3. Spectral analysis on endmember spectra 3.2.1. Determination of the number of classifications Developing a method to determine the number of SMACC classifications is problematic, and the number of endmembers is often empirically determined via imagery interpretations (Qi et al., 2009). In the current study, we use the resulting relative error plot to determine the number of categories based on the elbow method, which is employed to interpret and validate the consistency of cluster analyses designed to help find the appropriate number of clusters in a dataset (Thorndike, 1953, Ketchen and Shook, 1996). This method considers the percentage of variance explained as a function of the number of clusters. The conditions of the SMACC method indicate that if the percentage of variance is plotted against the number of endmembers, then the first endmember will add considerable information (i.e., explain a considerable amount of variance); however, at some point, the marginal gain will drop, which is represented by an angle in the graph. The number of endmembers is chosen at this point according to the “elbow criterion”. First, we pre-set a large number (30) of classification numbers, perform a SMACC operation on the SASI images, and then form a relative error plot to determine the inflection point as 10 (Fig. 3). Therefore, the number of classifications selected is 10. Next, SMACC is performed again with 10 endmembers, and finally, 10 spectrum abundance images and 10 endmember spectrums are formed.

The resulting endmember spectra can be minerally matched using the spectral analysis tool of the ENVI software. This tool uses various processes, such as binary coding (ITT, 2008), spectral angle mapping (Kruse et al.,1993), and spectral feature fitting (Clark et al., 1990), to match the unknown spectrum with the minerals in the spectral library. One step of spectral analysis is to input weight coefficients for the above three methods, where each weight coefficient is taken as “1″. The endmember 4 spectrum is analysed using spectral analysis, the output is a mineral spectrum matching the ranking table, and the matching order is from high to low according to the sum of scores. The total scores, spectral angle scores, spectral feature fit scores, and binary coding scores for each reference mineral that match endmember 4 can be obtained (Table 4). Fig. 5 shows the spectral characteristics of several minerals with a high degree of consistency with endmember 4. Fig. 5 and Table 4 show that endmember 4 represents the main component of clay minerals, and similar minerals should include muscovite, kaolinite/smectite, goethite, clinochlore, illite, or combinations of these minerals. Previous studies have reported that the area around the study site contained a certain amount of illite (Jiang et al., 1982; Zhang et al., 2006; Chen et al., 2004); however, in the matched spectra, the score for illite was not high, whereas the muscovite score was higher. Considering that illite is closely related to muscovite, the illite in this region may appear to display the spectral characteristics of muscovite.

3.2.2. Comparison between SMACC classification results and actual features A comparison of the ten types of endmember spectrum abundance images generated from the SMACC classification with false-colour composite images can provide a general understanding of the ground features corresponding to the ten types of endmember spectra. Fig. 4 shows the spectral features of the ten endmembers (Fig. 4a) and the locations of their high-abundance values (Figs. 4b–k). The higher abundance of endmember 1 is 0.50 (red cursor centre), which is mainly reflected in the spectrum of ditches on both sides of the road, which have a low reflectivity value (Fig. 4b). The abundance of endmember 2 is 0.3, which may represent a certain type of vegetation (Fig. 4c).

3.4. Spectral retrieval and calibration model 3.4.1. Spectral retrieval In the second section, Formula (7) was presented for the spectral retrieval of clay minerals. The previous analysis shows that endmember 4 is a spectrum of clay minerals. Therefore, only the abundance image of endmember 4 needs to be processed. Combined with the original reflectivity image, after the calculation, retrieved images that have been converted to clay minerals in different bands were formed. Fig. 3. The elbow rule is used to determine the number of SMACC classifications. The left figure shows the maximum relative error curves for 30 endmembers, and the maximum relative error values of the lowest 25 endmembers are less than 250. The right figure is a partial enlargement of the left figure at the position of endmember 10, where the curve has the largest curvature; therefore, the adopted number of endmembers is 10.

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Fig. 4. Comparison of endmember spectra and features. Fig. 4a displays the spectra of 10 endmembers, where endmember 1 and endmember 3 are overlaid and only the spectrum of endmember 3 appears; Fig. 4b-4k are the enlarged false-colour images (Fig. 2) that correspond to endmembers 1–10, where the red cross cursor in the centre of Fig. 4b-4k is the location with a high abundance value of the associated endmember. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Table 4 Reference Mineral Scores from the Spectral Matching of Endmember 4 Spectra.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Code

Mineral

Score

SAM

SFF

BE

manganit.spc muscovib.spc muscovid.spc muscovic.spc lepidol4.spc muscovi3.spc muscovi6.spc muscovi8.spc meionit2.spc jarosit1.spc kaosmec2.spc goethit3.spc cchlore1.spc illite1.spc

Manganite Muscovite Muscovite Muscovite Lepidolite Muscovite Muscovite Muscovite Meionite Jarosite Kaolin/Smectite Goethite Clinochlore Illite

2.71 2.698 2.696 2.690 2.680 2.679 2.678 2.676 2.675 2.674 2.641 2.608 2.595 2.572

0.892 0.904 0.906 0.911 0.866 0.912 0.922 0.907 0.883 0.879 0.893 0.820 0.888 0.894

0.945 0.943 0.940 0.940 0.940 0.939 0.940 0.941 0.941 0.944 0.943 0.937 0.937 0.942

0.874 0.851 0.851 0.839 0.874 0.828 0.816 0.828 0.851 0.851 0.805 0.851 0.770 0.736

Table 5 Analysis of Variance of the Stepwise Multiple Linear Regression on Se vs SASI Bands which were retrieved. Source

DF

Adj SS

Adj MS

F-Value

P-Value

Regression B3 B19 B22 B55 B56 B57 B58 B59 B67 B69 B75 B78 B82 Error Total

13 1 1 1 1 1 1 1 1 1 1 1 1 1 54 67

0.274129 0.010722 0.014186 0.035054 0.070769 0.122163 0.131552 0.074621 0.057129 0.054239 0.059472 0.009826 0.036737 0.093161 0.129251 0.40338

0.021087 0.010722 0.014186 0.035054 0.070769 0.122163 0.131552 0.074621 0.057129 0.054239 0.059472 0.009826 0.036737 0.093161 0.002394

8.81 4.48 5.93 14.65 29.57 51.04 54.96 31.18 23.87 22.66 24.85 4.11 15.35 38.92

0 0.039 0.018 0 0 0 0 0 0 0 0 0.048 0 0

*Source-indicates the source of variation; DF-degrees of freedom from each source; SS-sum of squares between groups (factor) and the sum of squares within groups (error); MS-mean squares found by dividing the sum of squares by the degrees of freedom; F-calculated by dividing the factor MS by the error MS; P-used to determine whether a factor is significant.

For the original reflectance data, i.e., the data without clay mineral retrieval, MLR is carried out with the same parameters as mentioned above. A regression equation was obtained as follows:

Seoriginal = 0.2825 − 0.000242 ∗ B86 + 0.000343 ∗ B87

The wavelengths of each band are as follows: B3, 0.980 μm; B19, 1.220 μm; B22, 1.265 μm; B54, 1.940 μm; B55, 1.955 μm; B56, 1.970 μm; B57, 1.985 μm; B58, 2.000 μm; B59, 2.015 μm; B67, 2.135 μm; B69, 2.165 μm; B75, 2.255 μm; B78, 2.300 μm; B82, 2.360 μm; B84, 2.390 μm; B86, 2.225 μm; and B87, 2.240 μm. The R2 (adj) and R2 (pred) values of the modelled data set (Table 6) are much lower than those from the spectral retrieval, and the relevant prediction parameters cannot be obtained in the valid set. Table 6 summarizes the relevant parameters for modelling and predicting K and Se concentrations. The predicted R2 values of the K element from the calibration and validation set are 0.6438 and 0.3777, respectively, and the predicted R2 values of Se in the calibration and validation sets are 0.3803 and 0.4157, respectively. A comparison between the predicted and measured contents of K and Se is shown in Fig. 6. As seen from Table 6, the R2 (pred) of the Se from the validation set is larger than the value of the K. Therefore, the prediction equation of Se (9) is directly used to predict the Se concentrations.

Fig. 5. Comparison of the spectra of the endmembers and the main reference minerals.

3.4.2. Calibration model The geochemical sampling sites retrieved spectral information extracted from all 87 bands of the SASI, and a total of 80 sites fell within the modelling area. Sixty-eight sites were randomly selected for modelling, and the remaining 12 sites were used for validation. In combination with the analysed K and Se contents, the stepwise multiple linear regression process was performed on the reflectivity values after soil spectrum retrieval using Minitab software. The entered α value (level of significance) was 0.15, and the resulting bands were entered into the regression equation. The regression equation formed is as the following equation Eq. (8):

K = 2.6427 + 0.00899 ∗ B19 − 0.01060 ∗ B22 − 0.000686 ∗ B54 − 0.00872 ∗ B56 + 0.00910 ∗ B57 − 0.00187 ∗ B58 + 0.00498 ∗ B59 (8) − 0.001215 ∗ B84

3.5. Geochemical image and grading

Based on the analysis results of the geochemical measurements of K and Se, a regression equation from these two elements was obtained as the following equation Eq. (9):

SeK − retrieval = 1.104 − 0.3342K

Three concentration maps of the element Se were generated based on regression equations (8)–(10), where Fig. 7 is an example generated from Eq. (9). Each pixel in the 3 Se images is assigned a concentration value, and the values at the 80 geochemical sampling sites can be extracted and compared with the geochemical analysis results. Fig. 8 is a line chart of the Se concentrations calculated from the regression equations and the geochemical analysis results. In Fig. 8, the predicted Se values from the original SASA data are distributed quite evenly along a value of 0.4 ppm, whereas the predicted Se values from the retrieved SASI data (one is derived from K, and another is directly from the retrieved data) appear similar to the geochemical analysis results in magnitude, but the direct prediction from the retrieval changes more dramatically, which can distinguish Se enrichment to some extent. According to the classification standard of the total Se concentration in the surface soil in China (Tan, 1989), the levels of Se can be classified into 5 grades: deficient (less than 0.125 mg/kg), marginal

(9)

The direct prediction of the Se element from the retrieved reflectance values in different bands is obtained as the following equation Eq. (10):

Seretrieval = 0.1651 − 0.000948 ∗ B3 − 0.00510 ∗ B19 + 0.00764 ∗ B22 − 0.002925 ∗ B55 + 0.01113 ∗ B56 − 0.01174 ∗ B57 + 0.00583 ∗ B58 − 0.003986 ∗ B59 + 0.00799 ∗ B67 − 0.01027 ∗ B69 + 0.00547 ∗ B75 − 0.00922 ∗ B78 + 0.00692 ∗ B82

(11)

(10)

Table 5 shows the analysis of variance table, where a low p-value indicates that the factor is significant. 8

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Table 6 Model Summary from the Calibration Set and Validation Set. Element

K-retrieval Se-retrieval Se-original

Calibration set S

R2

R2 (adj)

R2 (pred)

Validation set S

R2

R2 (adj)

R2 (pred)

0.08 0.04 0.07

71.63% 41.55% 10.54%

67.78% 40.67% 7.74%

64.38% 38.03% 3.70%

0.06 0.03 ******

41.55% 52.93% ******

40.67% 48.22% ******

37.77% 41.57% ******

*S: square root of MSE; R2: coefficient of determination; R2 (adj): adjusted R2; R2 (pred): predicted R2; ****** means no data were obtained when using the MLR method.

squares is that the software provided by the current computing program does not provide corresponding calculation coefficients for the prediction. When we use high-resolution hyperspectral data to predict chemical compositions, an equation to calculate the results is needed. The stepwise linear regression method has the ability to provide regression equations that can be applied in calibration and validation datasets. Over the course of this study, an increase in predictive variables was observed. When the CASI and SASI data are used together, the number of available bands increases, and the calibrated R2 increases as well; however, when the regression equation is used for the validation data, the predicted R2 is extremely low and close to zero. This finding is consistent with the collinearity and overfitting described in previous studies (Martens and Naes, 1987; Kutner and Michael, 2005). Therefore, the predicted bands are likely not improved simply by being more abundant. As a result, SASI data are only used when the wavelength range exactly matches the absorption characteristic area of clay minerals. In addition, an examination of previous studies that used airborne hyperspectral measurements showed that few predicted R2 results are available for validation data. A single study (Hively, et al., 2015) mentioned the use of spectral data to perform content predictions and reported R2 and RMSE values in the calibration model. However, it did not mention R2 and RMSE values for the validation dataset, and it only performed a statistical comparison between the predicted and measured values. When determining the element to use for modelling the Se content,

(0.125–0.175 mg/kg), moderate (0.175–0.400 mg/kg), sufficient (0.400–3.000 mg/kg) and excessive (greater than 3 mg/kg). Using the Se concentration image, the predicted concentration values in the pixels are classified according to the above criteria to form four levels, i.e., Se deficient, potentially Se deficient, Se sufficient and Se abundant. The Se concentrations in the soil in the Chuangye Farm area were obtained (Fig. 9). The resulting Se grade map (Fig. 9) shows that most of the Chuangye Farm area has sufficient Se and that nearly one-tenth of the area has abundant Se. 4. Discussion It is difficult to ensure that a single pure mineral appears in a remote sensing pixel that shows mixed spectra, and a field spectrometer only measures the spectrum of the sample in a small-diameter field of view. Even in this small area, the spectrum may also indicate mixed minerals. Therefore, field spectroscopy was not applied in this study, and only the SMACC classification was used to generate endmember spectra for the spectral analysis. Previous studies have used stepwise regression and partial least squares methods, with the latter becoming more common (Udelhoven et al., 2003; Vasques et al., 2008; Mouazen et al., 2010). However, in the current study, the predicted R2 values obtained using the partial least squares method are not much higher than those from the stepwise linear regression analysis. Another disadvantage of using partial least

Fig. 6. Scatterplots of the predicted concentrations of K and Se against their measured values. 9

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Fig. 7. Image of the Se concentration formed using SASI data. The left image shows the Se concentration image generated from the SASI data using formula 9, and the right image shows the partial enlargement.

Spectroscopy has been used in previous studies to determine mineral contents (Bierwirth et al., 2002; Entezari et al., 2017). Certain major elements or trace elements are components of minerals. The main principles of this paper are as follows: first, MLR is used to predict the K contents in minerals by using spectral data, thereby proving the feasibility of this approach with the coefficient of determination (Table 6); then, the results of geochemical analysis are used to show that K is

the approach using the validation data set is apparently slightly better for Se than K. Table 6 shows that when using calibration data, the R2, adjusted R2 and predicted R2 of K are higher than that of Se, whereas opposite results are obtained for the validation data. However, the prediction of Se by K is an intermediary step, which will inevitably lead to error propagation. Therefore, it is more reasonable to use the Se element regression equation directly to predict the concentration.

Fig. 8. Line chart of Se concentrations from the geochemical analysis results and calculated from the regression equations. The abscissa is the sequence of 80 geochemical sampling sites, and the ordinate is the content of Se in ppm. 10

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Fig. 9. Se concentration grade map of soil in the Chuangye Farm area. The range of values for each grade are Se deficiency (less than0.125 mg/kg), potential Se deficiency (0.1250–0.1750 mg/kg), Se sufficiency (0.1750–0.4000 mg/kg), and Se abundance (0.4000–3.0000 mg/kg).

negatively correlated with Se, and thus, the data used to predict K are used to predict the Se content. This constitutes the theoretical basis of this investigation; moreover, the effect of the Se prediction is also proven to be superior to the effect from simply predicting the Se content through raw reflectance images (Table 6). The negative correlation between K and Se in this paper is consistent with the findings of previous studies in Olkiluoto (Lintinen et al., 2003; Lusa et al., 2009; Mervi, et al., 2016). The results reported herein show that the sorption of Se can be divided into three types: no sorption (quartz, potassium feldspar, amphibole and muscovite), which is the most common; low sorption (plagioclase, chlorite, biotite and kaolinite); and moderate sorption (hematite). (Mervi, et al., 2016). K is concentrated mainly in K-feldspar or muscovite, which are characterized by poor Se sorption. Therefore, a negative correlation between the K and Se contents is revealed through these geochemical measurements.

spectral retrieval of clay minerals, and the reflectance values in the images were converted to the values of clay minerals. C. The Se concentration result predicted by using retrieved spectra is better than the result obtained by the direct use of raw reflectance data. D. Through the use of airborne hyperspectral SASI data obtained in the Chuangye Farm area, a Se concentration map has been generated, and the Se concentration was measured at the pixel level. Se sufficient and Se abundant areas were classified with high accuracy, which greatly improves the efficiency of Se detection.

5. Conclusions

Acknowledgements

There are several findings in this study. A. The analysis results of geochemical samples from the Chuangye Farm area include the contents of Se and K. The results show that the Se and K contents have a negative correlation, which provides a basis for predicting the Se concentration from hyperspectral SASI images. B. The SMACC method was used to perform classification and

This research was supported by funds from “Geochemical Survey of Land Quality at 1:250,000 scale in Black Soil, Northeast China” (DD20160316) and the Chinese Ministry of Science and Technology (2016YFC0600103). The authors thank the aircraft crew at the Beijing Research Institute of Uranium Geology for providing the hyperspectral images. We also thank Guodong Liu for helping with the geochemical

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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sampling, Professor Suhong Liu for helping with the hyperspectral remote sensing application, and the anonymous reviewers for their comments and important suggestions.

Cone (SMACC) Endmember Model. Proc. SPIE 5425 (2004), 1–14. Hawkes, W.C., Kelley, D.S., Taylor, P.C., 2001. The effects of dietary selenium on the immune system in healthy men. Biol. Trace Elem. Res. 81 (3), 189–213. Hively, W.D., Mccarty, G.W., Reeves, J.B., Lang, M.W., Oesterling, R.A., Delwiche, S.R., 2015. Use of airborne hyperspectral imagery to map soil properties in tilled agricultural fields. Appl. Environ. Soil Sci. 2011 (1687–7667), 55–66. Hu, G., Sudduth, K.A., He, D., Myers, D.B., Nathan, M.V., 2016. Soil phosphorus and potassium estimation by reflectance spectroscopy. Trans. ASABE 59, 97–105. Hunt, G.R., Salisbury, J.W., 1976. Visible and near infrared spectra of minerals and rocks: XI. Sedimentary rocks. Mod. Geol. 5, 211–217. ITT, 2008. The Environment for Visualizing Images (ENVI) software. ITT Visual Information Solutions, Boulder, Colorado. http://www.ittvis.com/ProductServices/ ENVI. aspx. Ji, W., Shi, Z., Huang, J., Li, S., 2014. In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS One 9, e105708. Jiang, M.J., Yang, D.Y., Hseung, Y., 1982. Soil colloid researches VIII. The mineralogical composition of the colloids of five important soils in China. Acta Pedologica Sinica 19 (1), 62–70 (In Chinese with English abstract). Jones, G.D., Droz, B., Greve, P., Gottschalk, P., Poffet, D., Mcgrath, S.P., et al., 2017. Selenium deficiency risk predicted to increase under future climate change. Proc. Natl. Acad. Sci. 114 (11), 2848–2853. Jung, A., Vohland, M., Thiele-Bruhn, S., 2015. Use of a portable camera for proximal soil sensing with hyperspectral image data. Remote Sens. 7, 11434–11448. Ketchen, D.J., Shook, C.L., 1996. The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17 (6), 441–458. Kruse, F.A., 2009. Improving multispectral mapping by spectral modeling with hyperspectral signatures. Proc. SPIE 3 (3), 85–99. Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H., 1993. The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44 (2–3), 145–163. Kutner, M.H., Nachtsheim, C.J., Neter, J., Li, W., 2005. Applied Linear Statistical Models. McGraw-Hill Irwin. Lanthier, Y., Bannari, A., Haboudane, D., Miller, J. R., Tremblay, N., 2009. Hyperspectral aata segmentation and classification in precision agriculture: a multi-scale analysis. In: IEEE International Geoscience and Remote Sensing Symposium. Levander, O.A., 1986. Selenium. In: Mertz, W. (Ed.), Trace Elements in Human and Animal Nutrition. Academic Press, London, pp. 139–197. Li, S., Wang, S., Shi, Z., 2015. Prediction of vertical distribution of soil nitrogen content in soil profile using spectral imaging technique. Acta Pedol. Sin. 52, 1014–1023. Lintinen, P., Kahelin, H., Lindqvist, K., Kaija, J., 2003. Soil sample analyses of Olki-luoto. Posiva Working Report 2003–1, 123. Lippman, S.M., Klein, E.A., Goodman, P.J., Lucia, M.S., Thompson, I.M., Ford, L.G., et al., 2009. Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the selenium and vitamin E cancer prevention trial (SELECT). J. Am. Med. Assoc. 301 (1), 39–51. Liu, L.J., Pang, Y., Fan, W.Y., Zeng-Yuan, L.I., Ming-Ze, L.I., 2011. Integration of airborne hyperspectral casi and sasi data for tree species mapping in the boreal forest, China. Remote Sens. Technol. Appl. 26 (2), 129–136. Liu, S., Zhao, L., Nan, L.I., Gao, Q., Liu, H., 2002. Relationship between composition of kbearing minerals and potassium status of cultivated soils in Jilin province. Plant Natrition Fertilizen Sci. 8 (1), 70–76 (in Chinese with English abstract). Lusa, M., Ammala., K., Hakanen, M., Lehto, J., Lahdenperä, A.-M., 2009. Chemical and Geotechnical Analyses of Soil Samples from Olkiluoto for Sudies on Sorption in Soils. Posiva Working Report 2009-33, 151. Martens, H., Naes, T.,1987, Multivariate calibration by data compression. In: Williams, P. C., Norris, K.H., (Eds.), Near-Infrared Technology in the Agricultural and Food Industries American Association of Cereal Chemists, St. Paul, MN (Chapter 4). Martens, H., Næs, T., 1989. Multivariate Calibration. John Wiley and Sons, Chichester, UK. Söderlund, Mervi, Virtanen, S., Välimaa, Ilkka, Lempinen, J., Hakanen, M., Lehto, J., 2016. Sorption of cesium on boreal forest soil ii. The effect of time, incubation conditions, PH and competing cations. J. Radioanal. Nucl. Chem. 309 (2), 647–657. Mouazen, A.M., Kuang, B., De Baerdemaeker, J., Ramon, H., 2010a. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158, 23–31. Mouazen, A.M., Kuang, B., De Baerdemaeker, J., Ramon, H., 2010b. Comparison among principal component: partial least squares and back propagation neuralnetwork analyses for accuracy of measurement of selected soil properties withvisible and near infrared spectroscopy. Geoderma 158, 23–31. Novotny, J., Pivovarnik, M., Khalsa, S.J., Zemek, F., 2016. Visualisation of dependencies between city structure and thermal behaviour in brno. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial. Inf. Sci. XLI-B2, 741–745. O’Rourke, S.M., Holden, N.M., 2012. Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible–near infrared hyperspectral images. Soil Sci. Soc. Am. J. 76, 586. Papp, L.A., Lu, J., Holmgren, A., Khanna, K.K., 2007. From Selenium to Selenoproteins: synthesis, identity, and their role in human health. Antioxid. Redox Signal. 9, 775–806. Paz-Kagan, T., Zaady, E., Salbach, C., Schmidt, A., Lausch, A., Zacharias, S., Notesco, G., Ben-Dor, E., Karnieli, A., 2015. Mapping the spectral soil quality index, SSQI using airborne imaging spectroscopy. Remote Sens. 7, 15748–15781. Qi, J., Zhu, S., Zhu, B., et al., 2009. Endmember independence-based automated determination of number of endmembers. Sci. Surv. Mapp. 6, 073 (in Chinese with English abstract).

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2019.114133. References Adar, S., Shkolnisky, Y., Ben-Dor, E., 2014. Change detection of soils under small-scale laboratory conditions using imaging spectroscopy sensors. Geoderma 216, 19–29. Fernández-Martínez, Alejandro, Charlet, L., 2009. Selenium environmental cycling and bioavailability: a structural chemist point of view. Rev. Environ. Sci. Bio/Technol. 8 (1), 81–110. Bai, L., Lin, H., Sun, H., Zang, Z., Mo, D., 2012. Remotely sensed percent tree cover mapping using support vector machine combined with autonomous endmember extraction. Phys. Procedia 33, 1702–1709. Bendor, E., Chabrillat, S., Demattê, J.A.M., Taylor, G.R., Hill, J., Whiting, M.L., et al., 2009. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 113 (1), 38–55. Bierwirth, P., Huston, D., Blewett, R., 2002. Hyperspectral mapping of mineral assemblages associated with gold mineralization in the central Pilbara, western Australia. Econ. Geol. 97 (4), 819–826. Boardman, J.W., 1989. Inversion of imaging spectrometry data using singular value decomposition. In: IGARSS’89, 12th Canadian Symposium on Remote Sensing, pp. 2069–2072. Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., Reinsch, T.G., 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132 (3), 273–290. Brownlow, A.H., 1979.Geochemistry. No. 550.4 BRO. Buddenbaum, H., Steffens, M., 2012. The effects of spectral pretreatments on chemometric analyses of soil profiles using laboratory imaging spectroscopy. Appl. Environ. Soil Sci. 2012 (2012), 12. Chen, J., Wang, A., Huang, M., 2007. The ASTER imaging rock and soil information extraction method in multiple vegetations covered areas. Acta Geoscientica Sinica 28 (1), 86–91 (In Chinese with English abstract). Chen, J., Bing, Z., Mao, Z., Zhang, C., Bi, Z., Yang, Z., 2018. Using geochemical data for prospecting target areas by the sequential maximum angle convex cone method in the manzhouli area, China. Geochem. J. 52 (1), 13–27. Chen, X., Liu, X.D., Liu, X.Y., 2004. Property of important kinds of soil in Daqing. J. Daqing Pet. Inst. 28 (4), 15–17 (In Chinese with English abstract). Clark, L.C., Combs, G.F.J., Turnbull, B.W., et al., 1996. Effects of selenium supplementation for cancer prevention in patients with carcinoma of the skin. A randomized controlled trial. Nutritional prevention of cancer study group. J. Am. Med. Assoc. 276, 1957–1963. Clark, R.N., Gallagher, A.J., Swayze, G.A.,1990. Material absorption band depth mapping of imaging spectrometer data using the complete band shape least squares algorithm simultaneously fit to multiple spectral features from multiple materials. In: Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. JPL Publication, vol. 90–54, pp. 176−186. Combs, G.F., Combs, S.B., 1986. The Role of Selenium in Nutrition. Academic Press, Orlando, FL. Confalonieri, M., Fornasier, F., Ursino, A., Boccardi, F., Pintus, B., Odoardi, M., 2001. The potential of near infrared reflectance spectroscopy as a tool for the chemical characterisation of agricultural soils. J. Near Infrared Spectrosc. 9, 123–131. Dai, H.M., Gong, C.D., Dong, B., et al., 2015. Distribution of soil selenium in the Northeast China Plain and its influencing factors. Acta Pedol. Sin. 52 (6), 1356–1364 (In Chinese with English abstract). Entezari, I., Rivard, B., Geramian, M., Lipsett, M.G., 2017. Predicting the abundance of clays and quartz in oil sands using hyperspectral measurements. Int. J. Appl. Earth Obs. Geoinf. 59, 1–8. Erives, H., Fitzgerald, G.J., 2005. Automated registration of hyperspectral images for precision agriculture. Comput. Electron. Agric. 47 (2), 103–119. Farrand, W.H., Bell, J.F., Johnson, J.R., Rice, M.S., Hurowitz, J.A., 2013. VNIR multispectral observations of rocks at Cape York, Endeavour crater, Mars by the Opportunity rover’s Pancam. Icarus 225 (1), 709–725. Fordyce, F.M., 2013. Selenium deficiency and toxicity in the environment. In: Selinus, O. (Ed.), Essentials of Medical Geology. Springer, London, pp. 373–415. Fordyce, F., 2007. Selenium geochemistry and health. Ambio 36, 94–97. Gholizade, A., Soom, M.A.M., Saberioon, M.M., et al., 2013. Visible and near infrared reflectance spectroscopy to determine chemical properties of paddy soils. J. Food Agric. Environ. 11 (2), 859–866. Girling, C.A., 1984. Selenium in agriculture and the environment. Agric. Ecosyst. Environ. 11, 37–65. Gomez, C., Rossel, R.A.V., Mcbratney, A.B., 2008. Soil organic carbon prediction by hyperspectral remote sensing and field VIS-NIR spectroscopy: an Australian case study. Geoderma 146 (3), 403–411. Gomez, R.B., 2001. Hyperspectral image analysis using ENVI (environment for visualizing images). Proc. SPIE 15 (38), 79–86 The International Society for Optical Engineering. Gruninger, J., Ratkowski, A.J., Hoke, M.L., 2004. The Sequential Maximum Angle Convex

12

Geoderma 363 (2020) 114133

Z. Li

146, 14–25. Vinceti, M., Rovesti, S., Bergomi, M., Vivoli, G., 2000. The epidemiology of selenium and human cancer. Tumori 86, 105–118. Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75. Winkel, L.H.E., et al., 2015. Selenium cycling across soil-plant-atmosphere interfaces: a critical review. Nutrients 7 (6), 4199–4239. World Health Organization,1987. Environmental Health Criterion 58—Selenium, World Health Organization, Geneva. Wu, Q., Yang, Y., Xu, Z., Jin, Y., Guo, Y., Lao, C., 2014. Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil. Spectrosc. Spectr. Anal. 34, 2102–2105. Xu, Y., Li, Y., Li, H., et al., 2018. Effects of topography and soil properties on soil selenium distribution and bioavailability (phosphate extraction): A case study in Yongjia County, China. Sci. Total Environ. 633, 240–248. Xue, Z., Du, P., Li, J., Su, H., 2017. Sparse graph regularization for robust crop mapping using hyperspectral remotely sensed imagery with very few in situ data. ISPRS J. Photogramm. Remote Sens. 124, 1–15. Yu, X., Liu, Q., Wang, Y., Liu, X., Liu, X., 2016. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. Catena 137, 340–349. Zhang, D., 1994. Selenium and soil environmental quality standards in China. Res. Soil Water Conserv. (s1), 112. Zhang, Z.Y., Di, R.C., Cai, D.L., 2006. Outline of soils Heilung-kiang Provence Harbin: Map Press (In Chinese with English abstract). Zhang, X., Shang, K., Cen, Y., Shuai, T., Sun, Y., 2014. Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. Int. J. Appl. Earth Obs. Geoinf. 31, 86–94.

Reeves, J.B., Mccarty, G.W., Hively, W.D., 2010. Mid-versus near-infrared spectroscopy for on-site analysis of soil. Proximal Soil Sensing. Springer, Netherlands. Rossel, R.A.V., Walvoort, D.J.J., Mcbratney, A.B., et al., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131 (1–2), 59–75. Sarathjith, M., Das, B., Wani, S., Sahrawat, K.L., Gupta, A., 2016. Comparison of data mining approaches for estimating soil nutrient contents using diffuse reflectance spectroscopy. Curr. Sci. 110, 1031–1037. Selige, T., Böhner, J., Schmidhalter, U., 2006. High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136 (1–2), 235–244. Shaddad, S.M., Madrau, S., Castrignanò, A., Mouazen, A.M., 2016. Data fusion techniques for delineation of site-specific management zones in a field in UK. Precis. Agric. 17, 200–217. Shao, Y., He, Y., 2011. Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy. Soil Res. 49, 166–172. J.A. Tan, J.A., 1989. The Atlas of Endemic Diseases and their Environments in the People's Republic of China. Environmental Life Elements and Health. Science Press, Beijing, p. 193. Taylor, S.R., McLennan, S.M., 1985. The Continental Crust: Its Composition and Evolution. Blackwell, Oxford, England. Thorndike, R.L., 1953. Who belongs in the family? Psychometrika 18 (4), 267–276. Tong, L., Zhou, J., Xu, C., Qian, Y., Gao, Y., 2013. Soil biochar quantification via hyperspectral unmixing. In: 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013. Udelhoven, T., Emmerling, C., Jarmer, T., 2003. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant Soil 251, 319–329. Vasques, G.M., Grunwald, S., Sickman, J.O., 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infraredspectra. Geoderma

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