Skip to main content

Advertisement

SpringerLink
Log in

The study of mineral mapping in black soil using TASI thermal infrared data, taking the Baiquan area of China as an example

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Soil is composed of many minerals. Because minerals have different spectral features of emission and reflection, the spectral characteristics of soils are different. Although there are mineral spectral libraries for the spectral analysis of pure minerals (mainly primary minerals), it is difficult to quantitatively determine the composition and content of minerals in soils with secondary minerals and clay minerals. Thermal infrared (8 ~ 14 μm) hyperspectral remote sensing has the ability to identify minerals, but there are few studies on mineral identification in soil. This study takes the Baiquan area of China as the research area, and hyperspectral infrared sensors, known as the Thermal Airborne Spectrographic Imager (TASI), are used for image analysis and mineral mapping. The emissivity data are formed by the emissivity normalization method. After interference information such as humidity is removed by masking, the spectral endmembers in the soil are classified by the sequential maximum angle convex cone (SMACC) method; thus, an endmember spectrum is formed. Based on a comparison of the endmember spectra from the TASI images and the field-measured spectra with the mineral spectra in the ASU and USGS spectral libraries, the main mineral types in the soil are determined. Finally, a map of mineral abundance in soil is formed by matched filtering. The results show that the main mineral types in the soil of the Baiquan area include palygorskite, anorthite, halloysite, montmorillonite and carolinite. Additionally, in the visible shortwave infrared (VIS-SWIR) spectral ranges, hydroxyl minerals can be distinguished, and the absorption characteristics of clay minerals are based mainly on their absorption depth. However, if noise is generated in the case of soil with humidity, it is not easy to identify hydroxyl minerals, but the thermal infrared band can have a greater ability to distinguish minerals in this situation. In this study, the quantitative inversion of soil mineral composition based on thermal infrared hyperspectral data and the method of drawing a soil mineral abundance map are proposed. Compared with the traditional laboratory analysis of soil samples, the proposed strategy has the advantages of being nondestructive, rapid, and widely applicable, and compared with previous mineral mapping based only on the VIS-SWIR range, the proposed strategy improves the ability of fine identification of minerals in the thermal infrared range. These results provide a basis for the study of fine soil mapping and mineral distribution in soil based on full-spectrum hyperspectral images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abrams MJ, Ashley RP, Rowan LC, Goetz AFH, Kahle AB (1977) Mapping of hydrothermal alteration in the cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36 μm. Geology 5(12):713–718

    Article  Google Scholar 

  • Aslett Z, Taranik JV, Riley DN (2008) Mapping rock-forming minerals at Daylight Pass, Death Valley National Park, California, using SEBASS thermal-infrared hyperspectral image data. Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. Vol. 3, IEEE International (pp. III-366-III-369)

  • Ben-Dor E, Irons JR, Epema GF (1999) Remote sensing of the earth sciences: Manual of Remote Sensing

  • Black M, Riley TR, Ferrier G, Fleming AH, Fretwell PT (2016) Automated lithological mapping using airborne hyperspectral thermal infrared data: a case study from anchorage island, Antarctica. Remote Sens Environ 176:225–241

    Article  Google Scholar 

  • Bogrekci I, Lee WS (2006) Effects of soil moisture content on absorbance spectra of sandy soils in sensing phosphorus concentrations using UV-VIS-NIR spectroscopy. Trans ASABE 49:1175–1180

    Article  Google Scholar 

  • Bowers SA, Hanks RJ (1971) Reflection of radiant energy from soils (Doctoral dissertation, Kansas State University)

  • Bricklemyer RS, Brown DJ (2010)On-the-go VisNIR: potential and limitations for mapping soil clay and organic carbon. Comput Electron Agric 70:209–216

    Article  Google Scholar 

  • Calvin WM, Vaughan RG, Taranik JV, Smailbegovic A (2001) Mapping natural and human influenced acid sulfate weathering near Reno, NV using the SEBASS hyperspectral instrument. Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. Vol. 2. (pp. 727–729). IEEE 2001 International (vol. 2)

  • Carroll RE, Gartland JJ (1953) Flexorplasty of the elbow: an evaluation of a method. JBJS 35(3):706–710

    Article  Google Scholar 

  • Chabrillat S, Ben-Dor E, Cierniewski J, Gomez C, Schmid T, van Wesemael B (2019) Imaging spectroscopy for soil mapping and monitoring. Surv Geophys 40:361–399

    Article  Google Scholar 

  • Chen ZX, Wang RZ (1979) The moisture retention of several important soils in China. Acta Pedol Sin 16(3):277–281

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Christensen PR, Bandfield JL, Hamilton VE, Howard DA, Lane MD, Piatek JL et al (2000) A thermal emission spectral library of rock-forming minerals. J Geophys Res 105:9735–9739

    Article  Google Scholar 

  • Clark RN, Gallagher AJ, Swayze GA (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 90–54, pp. 176–186

  • Clark RN, Swayze GA, Gallagher A (1993) Mapping minerals with imaging spectroscopy. 2039. (pp. 141–150). US Geological Survey, Office of Mineral Resources Bulletin, 141–150

  • Cui J, Yan B, Dong X, Zhang S, Zhang J, Tian F, Wang R (2015) Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data. Int J Appl Earth Obs Geoinf 40:19–28

    Google Scholar 

  • Dalal RC, Henry RJ (1986) Simultaneous determination of moisture, organic carbon, and Total nitrogen by near infrared reflectance spectrophotometry. Soil Sci Soc Am J 50(1):120–123

    Article  Google Scholar 

  • Dent D, Boincean BP, Krupenikov IA (2011) The black earth: ecological principles for sustainable agriculture on chernozem soils

  • Farrand WH, Bell JF, Johnson JR, Rice MS, Hurowitz JA (2013) VNIR multispectral observations of rocks at Cape York, Endeavour crater, Mars by the Opportunity rover's Pancam. Icarus 225(1):709–725

    Article  Google Scholar 

  • Gruninger J, Ratkowski AJ, Hoke ML (2004) The sequential maximum angle convex cone (SMACC) endmember model. Proc SPIE 5425(2004):1–14

    Google Scholar 

  • Gudex-Cross D, Pontius J, Adams A (2017) Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery. Remote Sens Environ 196:193–204

    Article  Google Scholar 

  • Guerra R, Santos L, Lo’pez, S., Sarmiento, R. (2015) A new fast algorithm for linearly unmixing hyperspectral images. IEEE Trans Geosci Remote Sens 53(12):6752–6765

    Article  Google Scholar 

  • Harsanyi JC, Chang C (1994) Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans Geosci Remote Sens 32:779–785

    Article  Google Scholar 

  • Hewson R, Hausknecht P, Cudahy T, Huntington J, Mason P, Hackwell J, et al. (2000) An appraisal of the hyperspectral thermal-infrared SEBASS data recorded from Oatman, Arizona and a comparison of their unmixed results with AVIRIS. Exploration and mining report 668F. WembleyWestern Australia: CSIRO Exploration and Mining

  • Hook SJ, Elvidge CD, Rast M, Watanabe H (1991) An evaluation of shortwaveinfrared (SWIR) data from the AVIRIS and GEOSCAN instruments for mineralogical mapping at cuprite, Nevada. Geophysics 56(9):1432–1440

    Article  Google Scholar 

  • Hook SJ, Gabell AR, Green AA, Kealy PS (1992) A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies. Remote Sens Environ 42:123–135

    Article  Google Scholar 

  • ITT (2008) The Environment for Visualizing Images (ENVI) software. ITT Visual Information Solutions, Boulder, Colorado. http://www.ittvis.com/ProductServices/ENVI.aspx

  • Jump up to: a b Turin, G. L. (1960) An introduction to matched filters. IRE Trans Inf Theory 6(3):311–329

    Article  Google Scholar 

  • Kaur S, Bansal RK, Mittal M, Goyal LM, Kaur I, Verma A, Son LH (2019) Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. J Indian Soc Remote Sens 47(3):427–437

    Article  Google Scholar 

  • Kealy PS, Hook SJ (1993) Separating temperature and emissivity in thermal infrared multispectral scanner data: implications for recovering land surface temperatures. IEEE Trans Geosci Remote Sens 31(6):1155–1164

    Article  Google Scholar 

  • K€ampf N, Schwertmann U (1983) Goethite and hematite in a climosequence in southern Brazil and their application in classification ofkaolinitic soils. Geoderma 29(1):27–39. https://doi.org/10.1016/0016-7061(83)90028-9

  • Kruse FA, Kierein-Young KS, Boardman JW (1990) Mineral mapping at cuprite, Nevada with a 63-channel imaging spectrometer. Photogramm Eng Remote Sens 56:83–92

    Google Scholar 

  • Kruse FA, Lefkoff AB, Boardman JB, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The spectral image processing system (SIPS)- interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44:145–163

    Article  Google Scholar 

  • Mittal M, Satapathy SC, Pal V, Agarwal B, Goyal LM, Parwekar P (2021) Prediction of coefficient of consolidation in soil using machine learning techniques. Microprocess Microsyst 82:103830

    Article  Google Scholar 

  • Muller E, Decamps H (2000) Modelling soil moisture-reflectance. Remote Sens Environ 76:173–180

    Article  Google Scholar 

  • Pham BT, Son LH, Hoang TA, Nguyen DM, Bui DT (2018) Prediction of shear strength of soft soil using machine learning methods. CATENA 166:181–191

    Article  Google Scholar 

  • Rowan LC, Hook SJ, Abrams MJ, Mars JC (2003) Mapping hydrothermally altered rocks at cuprite, Nevada, using the advanced spaceborne thermal emission and reflection radiometer (ASTER), a new satellite-imaging system. Econ Geol 98(5):1019–1027

    Article  Google Scholar 

  • Schwertmann U (1971) Transformation of hematite to goethite in soils. Nature 232(5313):624–625. https://doi.org/10.1038/232624a0

    Article  Google Scholar 

  • Sobrino-Plata J, Ortega-Villasante C, Laura Flores-Cáceres M, Escobar C, Del Campo FF, Hernández LE (2009) Differential alterationsof antioxidant defenses as bioindicators of mercury and cadmium toxicityin alfalfa. Chemosphere 77:946–954

    Article  Google Scholar 

  • Tou JT, Gonzalez RC (1974a) Pattern recognition principles. Addison-Wesley Publishing Company, Reading

    Google Scholar 

  • Tou JT, Gonzalez RC (1974b) Pattern recognition principles. Addison-Wesley Publishing Company, Reading

    Google Scholar 

  • Valaskova M (2015) Clays, clay minerals and cordierite ceramics - a review. Ceramics-Silikáty 59(4):331–340

    Google Scholar 

  • van der Meer FD, van der Werff HMA, van Ruitenbeek FJA, Hecker CA, Bakker WH, Noomen MF et al (2012)Multi- and hyperspectral geologic remote sensing: a review. Int J Appl Earth Obs Geoinf 14(1):112–128

    Google Scholar 

  • Vaughan RG, Hook SJ, Calvin WM, Taranik JV (2005) Surface mineral mapping at Steamboat Springs, Nevada, USA, with multi-wavelength thermal infrared images. Remote Sens Environ 99(1–2):140–158

    Article  Google Scholar 

  • Velde BB (2008) The origin of clay minerals in soils and weathered rocks. Springer, Berlin Heidelberg

    Book  Google Scholar 

  • Woodward PM (1953) Probability and information theory with applications to radar. Pergamon Press, London

    Google Scholar 

  • Zheng Q (2010) Spatial variation and evolution mechanism of clay mineral composition in agric horizon of black soil in Northeast China. Acta Pedol Sin 47(4):734–746

    Google Scholar 

Download references

Acknowledgements

This research was supported by funds from the “Geochemical Survey of Land Quality at 1:250,000 scale in Black Soil, Northeast China” (DD20160316) and the “Comprehensive geological remote sensing survey in the southwest of Beibu Gulf” (DD20208016). The authors thank the aircraft crew at the Beijing Research Institute of Uranium Geology for providing the hyperspectral images. We also thank Professor Shengbo Chen and his team for helping with the spectral measurements.

Author information

Authors and Affiliations

  1. Research Center of Applied Geology of China Geological Survey, Chengdu, China

    Jiang Chen

  2. Shenyang Geological Survey Center, China Geological Survey, Shenyang, China

    Jiang Chen & Huimin Dai

  3. Xian Geological Survey Center, China Geological Survey, Xian, China

    Zhizhong Li & Xiaoyan Chen

Authors
  1. Jiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhizhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoyan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huimin Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiang Chen.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Li, Z., Chen, X. et al. The study of mineral mapping in black soil using TASI thermal infrared data, taking the Baiquan area of China as an example. Earth Sci Inform 15, 807–819 (2022). https://doi.org/10.1007/s12145-022-00765-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00765-z

Keywords

Search

Navigation