Thermal Radiation Dynamics of Soil Surfaces with Unmanned Aerial Systems

  • Othón González
  • Mariano I. Lizarraga
  • Sertac Karaman
  • Joaquín SalasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)


Thermographies are a source of abundant and rapid information, valuable in precision agriculture tasks such as crop stress assessment, plant disease analysis, and soil moisture evaluation. Traditionally, practitioners obtain soil temperature directly from the ground or using satellites and other airborne methods, which are costly and have a low spatial and temporal resolution. In this paper, we introduce a method for short term tracking of thermal radiance inertia with the use of an unmanned aerial system (UAS). In our approach, we retro-project the spatial reconstruction obtained with structure from motion (SfM) to estimate the thermal radiation corresponding to three-dimensional structures. Then, we register the resulting orthomosaics using a pyramidal scheme. We use the first cloud of points as the fixed reference as new orthomosaics become available. Finally, we estimate the dynamics of the thermal radiation using the difference of the registered orthomosaic radiation intensity measurements.


Thermographic imaging Unmanned aerial systems Soil surface temperature Remote sensing 



This work was partially funded by SIP-IPN 20196702 for Joaquín Salas. Othón González is supported by a grant from CONACyT.


  1. 1.
    Agisoft: Agisoft Photoscan (2017).
  2. 2.
    Allen, R., Irmak, A., Trezza, R., Hendrickx, J., Bastiaanssen, W., Kjaersgaard, J.: Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrol. Process. 25(26), 4011–4027 (2011)CrossRefGoogle Scholar
  3. 3.
    Anderson, K., Gaston, K.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11(3), 138–146 (2013)CrossRefGoogle Scholar
  4. 4.
    Bouguet, J.Y.: Pyramidal implementation of the affine Lucas-Kanade feature tracker description of the algorithm. Intel Corp. 5(1–10), 4 (2001)Google Scholar
  5. 5.
    Brown, D.: Close-range camera calibration. Photogram. Eng. 37(8), 855–866 (1971)Google Scholar
  6. 6.
    CBERS: Satelite Sino-Brasileiro de Recursos Terrestres (2017).
  7. 7.
    Eltner, A., Schneider, D.: Analysis of different methods for 3D reconstruction of natural surfaces from parallel-axes UAV images. Photogram. Rec. 30(151), 279–299 (2015)CrossRefGoogle Scholar
  8. 8.
    Fernandez, E., Garfinkel, R., Arbiol, R.: Mosaicking of aerial photographic maps via seams defined by bottleneck shortest paths. Oper. Res. 46(3), 293–304 (1998)CrossRefGoogle Scholar
  9. 9.
    FLIR: FLIR Vue Pro R, Radiometry from the Air (2017).
  10. 10.
    Hassan-Esfahani, L., Torres-Rua, A., Jensen, A., McKee, M.: Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens. 7(3), 2627–2646 (2015)CrossRefGoogle Scholar
  11. 11.
    Heinly, J., Schonberger, J., Dunn, E., Frahm, J.M.: Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset). In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3287–3295 (2015)Google Scholar
  12. 12.
    Khanal, S., Fulton, J., Shearer, S.: An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 139, 22–32 (2017)CrossRefGoogle Scholar
  13. 13.
    Lhuillier, M.: Incremental fusion of structure-from-motion and GPS using constrained bundle adjustments. IEEE Transact. Pattern Anal. Mach. Intell. 34(12), 2489–2495 (2012)CrossRefGoogle Scholar
  14. 14.
    Li, H., Ding, W., Cao, X., Liu, C.: Image registration and fusion of visible and infrared integrated camera for medium-altitude unmanned aerial vehicle remote sensing. Remote Sens. 9(5), 441 (2017)CrossRefGoogle Scholar
  15. 15.
    Mattikalli, N., Engman, E., Jackson, T., Ahuja, L.: Microwave remote sensing of temporal variations of brightness temperature and near-surface soil water content during a watershed-scale field experiment, and its application to the estimation of soil physical properties. Water Resour. Res. 34(9), 2289–2299 (1998)CrossRefGoogle Scholar
  16. 16.
    Minkina, W., Klecha, D.: Atmospheric transmission coefficient modelling in the infrared for thermovision measurements. J. Sens. Sens. Syst. 5(1), 17 (2016)CrossRefGoogle Scholar
  17. 17.
    Misra, P., Enge, P.: Global Positioning System: Signals, Measurements and Performance. Ganga-Jamuna Press, Massachusetts (2006)Google Scholar
  18. 18.
    National Aeronautics and Space Administration: MODIS Web - Data (2017).
  19. 19.
    OpenDroneMap: OpenDroneMap (2017).
  20. 20.
    Özyeşil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion. Acta Numer. 26, 305–364 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pix4D: Pix4D (2017).
  22. 22.
    Quattrochi, D., Luvall, J.: Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications. Landscape Ecol. 14(6), 577–598 (1999)CrossRefGoogle Scholar
  23. 23.
    Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)Google Scholar
  24. 24.
    Schwind, M.: Comparing and characterizing three-dimensional point clouds derived by structure from motion photogrammetry. Ph.D. thesis, Texas A&M University-Corpus Christi (2016)Google Scholar
  25. 25.
    Sobrino, J., et al.: Soil emissivity and reflectance spectra measurements. Appl. Opt. 48(19), 3664–3670 (2009)CrossRefGoogle Scholar
  26. 26.
    Stark, B., Smith, B., Chen, Y.: Survey of thermal infrared remote sensing for unmanned aerial systems. In: International Conference on Unmanned Aircraft Systems, pp. 1294–1299. IEEE (2014)Google Scholar
  27. 27.
    Sugiura, R., Noguchi, N., Ishii, K.: Correction of low-altitude thermal images applied to estimating soil water status. Biosyst. Eng. 96(3), 301–313 (2007)CrossRefGoogle Scholar
  28. 28.
    Torresan, C., et al.: Forestry applications of UAVs in Europe: a review. Int. J. Remote Sens. 38(8–10), 2427–2447 (2017)CrossRefGoogle Scholar
  29. 29.
    Tsai, C.H., Lin, Y.C.: An accelerated image matching technique for UAV orthoimage registration. ISPRS J. Photogram. Remote Sens. 128, 130–145 (2017)CrossRefGoogle Scholar
  30. 30.
    Ye, Y., Shan, J., Bruzzone, L., Shen, L.: Robust registration of multimodal remote sensing images based on structural similarity. IEEE Transact. Geosci. Remote Sens. 55(5), 2941–2958 (2017)CrossRefGoogle Scholar
  31. 31.
    Zhang, C., Kovacs, J.: The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13(6), 693–712 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Othón González
    • 1
  • Mariano I. Lizarraga
    • 2
  • Sertac Karaman
    • 3
  • Joaquín Salas
    • 1
    Email author
  1. 1.CICATA QuerétaroInstituto Politécnico NacionalCimatarioMexico
  2. 2.Santa CruzUSA
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations