Skip to main content
SpringerLink
Log in

Geostatistical analysis of soil moisture measurements and remotely sensed data at different spatial scales

  • Original Article
  • Published:
Environmental Geology

Abstract

The European remote sensing satellite (ERS-2) synthetic aperture radar (SAR) data was used for temporal monitoring of soil moisture at Sukhothai, Thailand. Higher correlations were found between the observed soil moisture and the radar backscattering coefficient. The soil moisture distribution shows great variation in space and time due to its stochastic nature. In order to obtain a better understanding of the nature and causes of spatial variation of soil moisture, the extensive soil moisture measurements observed in Thailand and also remotely sensed ERS-2 SAR data were used for geostatistical analysis. The observed soil moisture shows seasonal variations with mean varying from 3.33 %v/v (dry season) to 33.44 %v/v (wet season). The spatial geostatistical structure also shows clear seasonal variations in the geostatistical characteristics such as range and sill. The sills vary from 1.00 (%v/v)2 for the driest day to 107.57 (%v/v)2 for one of the wet days. The range or the correlation lengths varies between 46.5 and 149.8 m for the wettest and driest periods. The nugget effect does not show strong seasonal pattern or trend but the dry periods usually have a smaller nugget effect than the wet periods. The spherical variogram model fits the sample variograms very well in the case of soil moisture observations while the exponential model fits those of the remotely sensed data. The ranges observed from the observed soil moisture data and remotely sensed data at the same resolution are very similar. Resolution degradation affects the geostatistical structure of the data by reducing the sills, and increasing the ranges.

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.
Fig. 10.

Similar content being viewed by others

References

  • Atkinson PM and Curran PJ (1997) Choosing an appropriate spatial resolution for remote sensing. Photogramm Eng Rem S 63:1345–1351

    Google Scholar 

  • Bell KR, Blanchard BJ, Schmugge TJ, Witczak MW, (1980) Analysis of surface moisture variations within large field sites. Water Resour Res 16(4):796–810

    Google Scholar 

  • Beven K, Kirkby MJ (1993) Channel network hydrology. Wiley, New York, 319 pp

  • Boschl G (1996) Scale issues in hydrological modeling: a review. In: Scaling issues in hydrology. Wiley, New York

  • Crow WT, Wood EF, Dubayah R (2000) Potential for downscaling soil moisture maps derived from spaceborne imaging radar. J Geophys Res 105(2D):2203–2212

    Article  Google Scholar 

  • Curran PJ (1988) The semi-variogram in remote sensing: an introduction. Remote sens environ 24:493–507

    Article  Google Scholar 

  • Dobson MC, Ulaby FT (1986) Preliminary evaluation of the SIR-B response to soil moisture, surface roughness and crop canopy cover. IEEE T Geosci Remote Sens 24(4):517–526

    Google Scholar 

  • Dubois PC, Van Zyl JJ, Engman ET (1995) Measuring soil moisture with imaging radars. IEEE T Geosci Remote Sens 33(4):915–926

    Article  Google Scholar 

  • Entekhabi D, Rodriguez-iturbe I, Castelli F (1996) Mutual interaction of soil moisture state and atmospheric processes J Hydrol 184:3-17

    Article  CAS  Google Scholar 

  • Fung AK, Li Z, Chen KS (1992) Backscattering from a randomly rough dielectric surface. IEEE T Geosci Remote Sens 30:356–369

    Article  Google Scholar 

  • Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens Environ 51:138–156

    Article  Google Scholar 

  • Huaibin G, Hugh Q, Gwyn J, Brisco B, Boisvert J, Brown RJ (1996) Mapping of soil moisture from C-band radar images. Can J Remote Sens 22(1):117–126

    Google Scholar 

  • Kitanidis PK, Bras RL (1980) Real time forecasting with a conceptual hydrological model-II, applications and results. Water Resour Res 16:1034–1044

    Google Scholar 

  • Kitanidis PK (1992) Geostatistics. In: Maidment DR (ed) Handbook of hydrology. McGraw Hill, New York, pp 20.1–20.39

  • Lacaze B, Raubal S, Winkel T (1994) Identifying spatial patterns of Mediterranean landscapes from geostatistical analysis of remotely sensed data. Int J Remote Sens 15:2437–2450

    Google Scholar 

  • Laur H, Bally P, Meadows P and others (1998) ERS SAR calibration: derivation of backscattering coefficient in ESA ERS SAR PRI products. Document No.: ES-TN-RS-PM-HL09, Issue 2, Rev. 5b, ESA publication

  • Loague K (1992) Soil water content at R-5, part-I, spatial and temporal variability. J Hydrol 139:233–251

    Article  Google Scholar 

  • Lopes A, Nezry E, Touzi R, Laur H (1993) Structure detection and statistical adoptive speckle filtering in SAR images. Int J Remote Sens 14(9):1735–1758

    Google Scholar 

  • Martin RD Jr, Ghanssem A, Kanemasu ET (1989) C-band scatterometer measurements of a tall grass prairie. Remote Sens Environ 29:281–292

    Article  Google Scholar 

  • Matheron G (1963) Principles of Geostatistics. Econ Geol 58:1246–1266

    CAS  Google Scholar 

  • Musiake K, Nakaegawa T, Koike M, Oki T (1997) Soil moisture measurement using active microwave remote sensing -II outdoor experiment. J Jpn Soc Hydrol Water Resour 10(6):588–596

    Google Scholar 

  • Oevelen P, Van J, Hoekman DH (1999) Radar backscattering inversion techniques for estimation of surface soil moisture: EFEDA-Spain and HAPEX-Sahel case studies. IEEE T Geosci Remote Sens 37(1):370–381

    Google Scholar 

  • Romshoo SA, Koike M, Hironaka S, Nakaegawa T, Mushiake K (1999) Monitoring of paddy crop growth with active microwave radar- possibilities and difficulties. In: Proc 26th Jpn Soc Remote Sens Photogramm Conf, Chiba University, Tokyo, 19–21 May, 1999, pp 29–32

  • Saatchi SS, Moghaddam, M (1994) Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery. IEEE T Geosci Remote Sens 38(2):697–709

    Article  Google Scholar 

  • Schmugge TJ, Jackson TJ (1996) Soil moisture variability. In: Stewart JB, Engman ET, Feddes RA, Kerr Y (eds) Scaling up in hydrology using remote sensing. Wiley, New York, pp 183–192

  • Smith GM, Curran PJ (1996) The signal-to-noise ratio (SNR) required for estimation of foliar biochemical concentrations. Int J Remote Sens 17:1031–1058

    Google Scholar 

  • Treitz P, Howarth P (2000) High spatial resolution remote sensing data for forest ecosystem classification: an example of spatial scale. Remote Sens Environ 72:268–289

    Article  Google Scholar 

  • Ugsang MD (2000) Assessment of space-borne SAR remote sensing for monitoring soil moisture. Dissertation No SR-00–2, Asian Institute Technology (AIT), Bangkok

  • Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing: active and passive, vol III. Artech House, Norwood, MA

    Google Scholar 

  • Wang HJ, Fu B, Qiu Y, Chen L, Wang Z (2001) Geostatistical analysis of soil moisture variability on Da Nangou catchment of the loess plateau, China. Environ Geol 41:113–120

    Article  Google Scholar 

  • Warrick AW, Zhang R, Moody MM, Myers DE (1990) Krigging versus alternative interpolators: errors and sensitivity to model inputs. In: Roth K, Fluhler H, Jury WA, Parkar JC (eds.) Field scale water and solute flux in soils. Birkhaser, Basel, pp 157–164

  • Webster R (1985) Quantitative spatial analysis of soil in the field. Adv Soil Sci 3:1–70

    Google Scholar 

  • Western AW, Grayson RB, Blöschl G, Willgoose GR, McMahon TA (1999) Observed spatial organisation of soil moisture and its relation to terrain indices. Water Resour Res 35(3):797–810

    Article  Google Scholar 

  • Whitaker MPL (1993) Small scale spatial variability of soil moisture and hydraulic conductivity in a small arid rangeland soil in Arizona. MSc Thesis, University of Arizona

  • Woodcock CE, Strahler AH, Jupp DL (1988) The use of variograms in remote sensing: I scene models and simulated images. Remote Sens Environ 25:323–348

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NASDA, Earth Observation Research Center, 1–8-10 Harumi Island, Triton Square, Office Tower X, Chuo-ku, 104–6023, Tokyo , Japan

    Shakil Ahmad Romshoo

Authors
  1. Shakil Ahmad Romshoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shakil Ahmad Romshoo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Romshoo, S.A. Geostatistical analysis of soil moisture measurements and remotely sensed data at different spatial scales. Env Geol 45, 339–349 (2004). https://doi.org/10.1007/s00254-003-0891-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00254-003-0891-1

Keywords

Search

Navigation