Skip to main content

Some Specifics in Using Optical Properties of Soil Surface for Moisture Detection

Abstract

The research was aimed at the analysis of relationship between the soil surface spectral reflectance and the moisture content in soil samples as the basis for moisture detection based on remote sensing data. By the example of nine samples from the arable horizons of podzolized chernozem, gray forest and soddy-podzolic soils analyzed in laboratory, the relationship was assessed between the spectral reflectance of soil surface in the visible spectral band detected with the HandHeld-2 spectroradiometer and the moisture content in samples. It was found that changes in the soil moisture content induce synchronous changes in the integral reflection in the visible spectral band only in a rather narrow interval of moisture, being specific for different soils. Variation in soil moisture content beyond these intervals does not change the spectral reflectance of soil surface. The results obtained prove that most of the satellite survey data in the optical range register the dry state of open soil surface, though the arable horizon proper may be rather moist. These regularities should be taken into account when developing satellite techniques for prompt monitoring of soil moisture by the remote sensing data obtained within the optical range of electromagnetic waves.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. 1

    Yu. N. Vodyanitskii and L. L. Shishov, The Study of Some Soil Processes on the Basis of Soil Color Characteristics (Dokuchaev Soil Science Inst., Moscow, 2004) [in Russian].

    Google Scholar 

  2. 2

    A. Ya. Voronin, “Identification criteria of the structure and functions of soil profile in georadar studies using Loza-B georadar,” Byull. Pochv. Inst. im. V.V. Dokuchaeva, No. 80, 106–128 (2015). https://doi.org/10.19047/0136-1694-2015-80-106-128

    Article  Google Scholar 

  3. 3

    A. Ya. Voronin and I. Yu. Savin, “GPR diagnostics of chernozem humus horizon thickness,” Russ. Agric. Sci. 44, 250–255 (2018).

    Article  Google Scholar 

  4. 4

    V. I. Kiryushin, “Methodology for integrated assessment of agricultural land,” Eurasian Soil Sci. 53, 960–967 (2020).

    Article  Google Scholar 

  5. 5

    D. S. Orlov, “Color and diagnostics of soils,” Soros Obraz. Zh., No. 4, 45–51 (1997).

  6. 6

    D. S. Orlov, N. I. Sukhanova, and M. S. Rozanova, Spectral Reflectivity of Soils and Their Components (Moscow State Univ., Moscow, 2001) [in Russian].

    Google Scholar 

  7. 7

    E. Yu. Prudnikova and I. Yu. Savin, “The application of satellite radar data for the interpretation of properties of arable chernozems,” Nauki Zemle, No. 1, 48–60 (2017).

    Google Scholar 

  8. 8

    O. G. Rastvorova, Soil Physics: Practical Guide (Leningrad State Univ., Leningrad, 1983) [in Russian].

    Google Scholar 

  9. 9

    A. A. Rode, Fundamentals of the Theory on Soil Moisture (Dokuchaev Soil Science Inst., Moscow, 2008), Vol. 3.

    Google Scholar 

  10. 10

    I. Yu. Savin, “Effect of rainfall on the integral reflection of chernozem surface,” Pochvovedenie, No. 8, 976–980 (1995).

    Google Scholar 

  11. 11

    I. Yu. Savin and E. Yu. Prudnikova, “Optimal timing of satellite survey for mapping of arable soils,” Byull. Pochv. Inst. im. V.V. Dokuchaeva, No. 74, 66–77 (2014).

    Google Scholar 

  12. 12

    E. Babaeian, M. Sadeghi, S. B. Jones, C. Montzka, H. Vereecken, and M. Tuller, “Ground, proximal, and satellite remote sensing of soil moisture,” Rev. Geophys. 57 (2), 530–616 (2019). https://doi.org/10.1029/2018RG000618

    Article  Google Scholar 

  13. 13

    P. Dobriyal, A. Qureshi, R. Badola, and S. A. Hussain, “A review of the methods available for estimating soil moisture and its implications for water resource management,” J. Hydrol. 458, 110–117 (2012). https://doi.org/10.1016/j.jhydrol.2012.06.021

    Article  Google Scholar 

  14. 14

    F. Jonard, L. Weihermuller, K. Z. Jadoon, M. Schwank, H. Vereecken, and S. Lambot, “Mapping field-scale soil moisture with L-band radiometer and ground-penetrating radar over bare soil,” IEEE Trans. Geosci. Remote Sens. 49 (8), 2863–2875 (2011). https://doi.org/10.1109/TGRS.2011.2114890

    Article  Google Scholar 

  15. 15

    A. Klotzsche, F. Jonard, M.C. Looms, J. van der Kruk, and J.A. Huisman, “Measuring soil water content with ground penetrating radar: a decade of progress,” Vadose Zone J. 17 (1), 1–9 (2018). https://doi.org/10.2136/vzj2018.03.0052

    Article  Google Scholar 

  16. 16

    P. C. le Roux, J. Aalto, and M. Luoto, “Soil moisture’s underestimated role in climate change impact modeling in low-energy systems,” Global Change Biol. 19 (10), 2965–2975 (2013). https://doi.org/10.1111/gcb.12286

    Article  Google Scholar 

  17. 17

    B. Li and M. Rodell, “Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions,” Hydrol. Earth Syst. Sci. 17 (3), (2013). https://doi.org/10.5194/hess-17-1177-2013

  18. 18

    G. P. Petropoulos, G. Ireland, and B. Barrett, “Surface soil moisture retrievals from remote sensing: current status, products & future trends,” Phys. Chem. Earth, A/B/C 83, 36–56 (2015). https://doi.org/10.1016/j.pce.2015.02.009

  19. 19

    L. J. Renzullo, A. I. J. M. van Dijk, J.-M. Perraud, D. Collins, B. Henderson, H. Jin, A. B. Smith, and D. L. McJannet, “Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment,” J. Hydrol. 519, 2747–2762 (2014). https://doi.org/10.1016/j.jhydrol.2014.08.008

    Article  Google Scholar 

  20. 20

    D. A. Robinson, C. S. Campbell, J. W. Hopmans, B. K. Hornbuckle, S. B. Jones, R. Knight, F. Ogden, J. Selker, and O. Wendroth, “Soil moisture measurement for ecological and hydrological watershed-scale observatories: a review,” Vadose Zone J. 7 (1), 358–389 (2008). https://doi.org/10.2136/vzj2007.0143

    Article  Google Scholar 

  21. 21

    M. Sadeghi, S. B. Jones, and W. D. Philpot, “A linear physically-based model for remote sensing of soil moisture using short wave infrared bands,” Remote Sens. Environ. 164, 66–76 (2015). https://doi.org/10.1016/j.rse.2015.04.007

    Article  Google Scholar 

  22. 22

    S. I. Seneviratne, T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, “Investigating soil moisture–climate interactions in a changing climate: a review,” Earth-Sci. Rev. 99 (3–4), 125–161 (2010). https://doi.org/10.1016/j.earscirev.2010.02.004

    Article  Google Scholar 

  23. 23

    F. Shaxson and R. Barber, Optimizing Soil Moisture for Plant Production: The Significance of Soil Porosity, FAO Soils Bull. No. 79 (UN Food and Agriculture Organization, Rome, 2003). http://www.fao.org/3/Y4690E/ y4690e00.htm.

  24. 24

    S. L. Su, D. N. Singh, and M. S. Baghini, “A critical review of soil moisture measurement,” Measurement 54, 92–105 (2014). https://doi.org/10.1016/j.measurement.2014.04.007

    Article  Google Scholar 

  25. 25

    R. L. van Dam, “Calibration functions for estimating soil moisture from GPR dielectric constant measurements,” Commun. Soil Sci. Plant Anal. 45 (3), 392–413 (2014). https://doi.org/10.1080/00103624.2013.854805

    Article  Google Scholar 

  26. 26

    L. Wang, J. J. Qu, S. Zhang, X. Hao, and S. Dasgupta, “Soil moisture estimation using MODIS and ground measurements in eastern China,” Int. J. Remote Sens. 28 (6), 1413–1418 (2007). https://doi.org/10.1080/01431160601075525

    Article  Google Scholar 

  27. 27

    J.-P. Wigneron, Y. Kerr, P. Waldteufel, K. Saleh, M.‑J. Escorihuela, P. Richaume, P. Ferrazzoli, P. de Rosnay, R. Gurney, J.-C. Calvet, J. P. Grant, M. Guglielmetti, B. Hornbuckle, C. Mätzler, T. Pellarin, et al., “L-band microwave emission of the biosphere (L-MEB) model: Description and calibration against experimental data sets over crop fields,” Remote Sens. Environ. 107 (4), 639–655 (2007). https://doi.org/10.1016/j.rse.2006.10.014

    Article  Google Scholar 

  28. 28

    D. Zhang and G. Zhou, “Estimation of soil moisture from optical and thermal remote sensing: a review,” Sensors 16 (8), 1308 (2016). https://doi.org/10.3390/s16081308

    Article  Google Scholar 

  29. 29

    F. Zhang, L.-W. Zhang, J.-J. Shi, and J.-F. Huang, “Soil moisture monitoring based on land surface temperature-vegetation index space derived from MODIS data,” Pedosphere 24 (4), 450–460 (2014). https://doi.org/10.1016/S1002-0160(14)60031-X

    Article  Google Scholar 

  30. 30

    L. Zhao, K. Yang, J. Qin, Y. Chen, W. Tang, C. Montzka, H. Wu, C. Lin, M. Han, and H. Vereecken, “Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements,” J. Hydrol. 482, 92–104 (2013). https://doi.org/10.1016/j.jhydrol.2012.12.033

    Article  Google Scholar 

Download references

Funding

The study was financially supported by Ministry of Education and Science of the Russian Federation (agreement no. 075-15-2020-805 from October 2, 2020). It was performed within the framework of the Strategic Academic Leadership Program at the RUDN University (IUS, analysis of spectral reflectance data).

Author information

Affiliations

Authors

Corresponding author

Correspondence to I. Yu. Savin.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by O. Eremina

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Savin, I.Y., Vindeker, G.V. Some Specifics in Using Optical Properties of Soil Surface for Moisture Detection. Eurasian Soil Sc. 54, 1019–1027 (2021). https://doi.org/10.1134/S1064229321070127

Download citation

Keywords:

  • soil spectral reflectance
  • soil proximal sensing
  • remote sensing of soil moisture