Quantitative Estimation of Zn and Pb in Soil Using Multivariate Analysis and Remote Sensing Technique

Authors

1 PhD student of soil physics and conservation, Faculty of Agriculture, Univ. of Zanjan, Zanjan, Iran

2 Assis. Prof. Dep. of Soil Science, Faculty of Agriculture, Univ. of Zanjan, Zanjan, Iran.

3 Prof. Dep. of Soil Science, Faculty of Agriculture, Univ. of Zanjan, Zanjan, Iran.

4 PhD student of Soil Science, Faculty of Agriculture, Univ. of Zanjan, Zanjan, Iran.

Abstract

Extended abstract
Background and Objectives
Heavy metal pollution is one of the most important environmental problems worldwide and measuring their concentration in the soil is the first step in providing a solution to reduce their risks. However, conventional methods for estimating the amount of heavy metals in the soil are time-consuming and costly. Imagery and remote sensing techniques have shown good potential to be an alternative to the routine approaches for monitoring heavy metal pollution in soil. The aim of this study was to evaluate the efficiency of multivariate analysis methods and remote sensing technique to quantify soil contamination with zinc and lead under different land uses. Previous studies revealed the high concentrations of zinc and lead in Zanjan’ soils cause a serious concern regarding the health of humans in this province.
Methodology
The study was conducted between the latitude 36° 20' 48" N to 36° 47'45" N, and longitude 48° 15' 51" E to 49° 00' 55" E in Zanjan province. The average daily temperature in the study area is 15.7°C and the mean annual precipitation is 335 mm. Rangeland, agriculture, industrial and residential areas are the main land uses in Zanjan. A total of 230 soil samples were collected at depths of 0–10 cm under different land uses from an area of ​​3424 Km2 at two grids intervals of 1.5 kilometers in industrial and residential areas, and 3 kilometers in rangeland and agriculture. Before chemical analysis, soil samples were air-dried and sieved. Soil lead and zinc contents were measured using an atomic absorption spectrometry (Perkin- Elmer: AA 200). Radiometric and geomatic corrections were performed on satellite images and the “special resampling” procedure was used to resample the 20 m images to 10 m. Thirty spectral indices were determined using Sentinel 2 satellite images. These indices were reported as practical indices for remote sensing assessment of land condition. The indices included; NDVI, NDRE, MTVI, MCAR, MNLI, GNDVI, SAVI, LCI, MTCI, PSRI, CI-RedEdge, CI-Green, NLI, TVI, EVI, STAVI, GRI, LSWI, MSAVI, BI, BI2, RI, CI and seven spectral ratios. Spectral estimation models were developed and evaluated by three methods of principal component regression (PCR), partial least squares regression (PLSR) and support vector machine regression (SVMR). The samples were randomly divided into the validation set (30%) and the calibration set (70 %). Therefore, 161 samples were used for calibrating and 69 samples for validating models. Levene’s test was performed to test the variance homogeneity between calibration and validation sets. The accuracy of models was evaluated using the coefficient of determination (R2), root mean square error of prediction (RMSEP) and the ratio of predicted deviation (RPD). Martens’ uncertainty test was used to identify important wavelengths for zinc and lead estimations. 
Findings
The range of lead in the studied soils was 40 to 364 mg kg-1 and the range of zinc was 96 to 824 mg kg-1. The accuracy of spectral models was categorised into excellent (RPD ≥ 2.5 and R2 ≥ 0.8), good (2 ≤ RPD < 2.5 and R2 ≥ 0.7), moderate (1.5 ≤ RPD < 2 and R2 ≥ 0.6) and poor accuracy (RPD < 1.5 and R2 < 0.6) based on the soil spectral accuracy classification, that is presented by Askari et al., (2015 and 2019). The highest concentration of metals was observed in industrial and residential land uses. The SVMR model (RPD≥2.6 and R2≥0.84, RMSE≤ 40), had a better spectral estimation for both lead and zinc than the PLSR model (RPD≥ 1.9 and R2≥  0.7, RMSE≤ 53) and the PCR model  (RPD≥1.3 and R≥ 0.45, RMSE≤ 75). Red-edge and infrared range were identified as the most effective wavelength ranges for monitoring the contamination of lead and zinc in soil. Brightness and modified triangular vegetation index were the most effective indicators for spectral estimation of lead and zinc in the studied soils. The SVMR model showed high accuracy and the PLSR model showed acceptable accuracy for evaluating and monitoring lead and zinc contamination using Sentinel 2 images. Comparing the predicted and measured values of heavy metals with a 1:1 line showed an overestimation for low values of lead and zinc, and an underestimation for high values of lead and zinc.
Conclusion
This study revealed that the method of multivariate analysis and remote sensing data could provide a practical approach for rapid and quantitative assessment of soil heavy metal pollution in Zanjan province and areas with similar soil conditions. An accurate prediction of heavy metal pollution can be acquired using freely available Sentinel-2 multispectral imagery system.

Keywords


Abdollahi S, Delavar MA and Shekari P, 2012. Spatial distribution mapping of Pb, Zn and Cd and soil pollution assessment in Anguran area of Zanjan Province. Journal of Water and Soil 26(6):1410-1420. (In Persian with English abstract)
Ali I, Greifeneder F, Stamenkovic J, Neumann M and No-tarnicola C, 2015. Review of machine learning approaches forbiomass and soil moisture retrievals from remote sensing data. Remote Sensing 7(12): 221–236.
Askari MS, McCarthy T, Magee A and Murphy DJ, 2019. Evaluation of grass quality under different soil management scenarios using remote sensing techniques. Remote Sensing 11(15), 1835.‏
Bolyn C, Michez A, Gaucher P, Lejeune Ph and Bonnet S, 2018. Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery. Biotechnology, Agronomy, Society and Environment  22(3): 172-187.
Choe E, Kim KW, Bang S, Yoon IH and Lee KY, 2009. Qualitative analysis and mapping of heavy metals in an abandoned Au-Ag mine area using NIR spectroscopy. Environmental Geology 58(3): 477–482.
Clevers JGPW and Gitelson A, 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoformation 23(1):344-351.
Dash J and Curran PJ, 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25(23): 5403-5413.
Daughtry CST, Walthall CL, Kim MS, Brown de Colstoun EC and McMurtrey JE, 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74(2): 229-239.
De Sousa C, Hilker T, Waring R, De Moura Y and Lyapustin A, 2017. Progress in remote sensing of photosynthetic activity over the Amazon Basin. Remote Sensing 9(1): 48-60.
Fard RS and Matinfar HR, 2016. Capability of vis-nir spectroscopy and landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran). Arabian Journal of Geosciences 9(20):1-14.
Fu XL and Wang QJ, 2017. Inversion analysis of heavy metal pollution in soil in mining disturbed areas based on remote sensing data: A case study of lanping Zn. Journal of Residuals Science and Technology 14(3): 85-93.
Gilmour J and Kittrick J, 1979. Solubility and equilibria of Zinc in a flooded soil. Soil Science Society of America Journal 43(5): 890-892.
Gitelson AA, Gritz Y and Merzlyak MN, 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.  Journal of Plant Physiology 160(3): 271-282.
Goel NS and Qin W, 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation. Remote Sensing 10(4): 309-347.
Gholizadeh A, Boruvka L, Vasat R and Saberioon MM, 2015. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Research 10 (4): 218–227. (In Persian with English abstract)
Haboudane D, Miller JR, Patery E, Zarco-Tejada PJ and Strachan IB, 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90: 337 – 352.
Hamidi Nehrani S, Askari MS, Saadat S, Delavar MA, Taheri M and Holden NM, 2020. Quantification of soil quality under semi-arid agriculture in the northwest of Iran. Ecological Indicators. 108:105770-105780.
Hill MJ, 2013. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment 137: 94-111.
Hollberg JL and Schellberg J, 2017. Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices. Remote Sensing 9(1): 81-94.
Huete AR, Didan K, Miura, T, Rodriguez EP, Gao X and Ferreira LG, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195–213.
Kaya Z, 2006. Pollution. Pp. 1343-1346. In: Lal R, (Ed.). Encyclopedia of Soil Science, Second Edition 2. Volume Set. Taylor & Francis Group. New York, USA.
Khalid S, Shahid M, Niazi NK, Murtaza B, Bibi I  and Dumat C, 2017. A comparison of technologies for remediation of heavy metal contaminated soils. Journal of Geochemical Exploration 182: 247–268.
Lu P, Bai S and Casagli N, 2014. Investigating spatial patterns of persistent scatterer interferometry point targets and landslide occurrences in the Arno river basin. Remote Sensing 6(8):6817-6843.
Malley DF and Williams PC, 1997. Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environmental Science and Technology 31(12): 3461–3467.
Marsett RC, Qi J, Heilman P, Biedenbender SH, Watson MC, Amer S, Weltz M, Goodrich D and Marsett R, 2006. Remote sensing for grassland management in the arid Southwest. Rangeland Ecology and Management 59: 530–540.
Navarro-Pedreño J, Gómez I, Almendro-Candel M and Meléndez-Pastor I, 2008. Heavy metals in Mediterranean soils. Pp. 161-176. In: Dominguez J, (Ed.). Soil Contamination Research Trends. New York, USA: Nova Science Publishers, Inc.
Nellis MD and Briggs JM, 1992. Transformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas. Transactions of the Kansas. Academy of Sciences 1903 (95): 93–99.
Pouget M, Madeira J, Le Floch E and Kamal S, 1990. Spectral characteristics of sandy surfaces in the northwestern coast region of Egypt: Application to SPOT satellite data. In: International Conference of Characterization and Monitoring of Terrestrial Environments in Arid and Tropical Regions. 4–6 December. ORSTOM, Colloquiums and Seminars Collection, Paris, France.
Pinheiro E, Ceddia M, Clingensmith C, Grunwald S and Vasques G, 2017. Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central amazon. Remote Sensing 9(4): 293-301.
Ramoelo A, Skidmore AK, Azongcho M, Schlerf M, Mathieu R and Heitkonig I, 2012. Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation 19(1):151-162.
Rondeaux G, Steven M and Baret F, 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55(2): 95-107.
Sims D and Gamon JA, 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 337-354.
Smith MO, Ustin SL, Adams JB and Gillespie AR, 1990. Vegetation in deserts: I. Regional measure of abundance from multispectral images. Remote Sensing of Environment 31: 1–26.
Sposito G, 2008. The Chemistry of Soils. 2nd Ed. New York. Oxford University Press. 344 p.
Tucker CJ, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8: 127–150.
Yang K, Pinker RT, Koike YMT, Wonsick MM, Cox SJ,  Zhang YC and Stackhouse P, 2008. Evaluation of satellite estimates of downward shortwave radiation over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres  113:207-219.
Yari Y, Momtaz HR and Taheri M, 2016. Spatial distribution of some heavy metals in soils of Zanjan industrial region. Water and Soil Science 26(4.1): 223-236. (In Persian with English abstract)
Wang F, Gao J and Zha Y, 2018. Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges. Journal of Photogrammetry and Remote Sensing 136: 73–84.
Westerman REL, 1990. Soil Testing and Plant Analysis, SSSA, Madison, Wisconsin, USA.
Xiao X, Zhang Q, Braswell B, Urbanski S, Boles S, Wofsy SC, Moore B and Ojima D, 2004. Modeling gross primary production of a deciduous broadleaf forestusing satellite images and climate data. Remote Sensing of Environment 91: 256–270.