Environmental Geology

, Volume 45, Issue 3, pp 339–349

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

Original Article

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.

Keywords

Backscattering coefficient Geostatistics Spatial resolution Synthetic aperture radar (SAR) Thailand 

References

  1. Atkinson PM and Curran PJ (1997) Choosing an appropriate spatial resolution for remote sensing. Photogramm Eng Rem S 63:1345–1351Google Scholar
  2. Bell KR, Blanchard BJ, Schmugge TJ, Witczak MW, (1980) Analysis of surface moisture variations within large field sites. Water Resour Res 16(4):796–810Google Scholar
  3. Beven K, Kirkby MJ (1993) Channel network hydrology. Wiley, New York, 319 ppGoogle Scholar
  4. Boschl G (1996) Scale issues in hydrological modeling: a review. In: Scaling issues in hydrology. Wiley, New YorkGoogle Scholar
  5. Crow WT, Wood EF, Dubayah R (2000) Potential for downscaling soil moisture maps derived from spaceborne imaging radar. J Geophys Res 105(2D):2203–2212CrossRefGoogle Scholar
  6. Curran PJ (1988) The semi-variogram in remote sensing: an introduction. Remote sens environ 24:493–507CrossRefGoogle Scholar
  7. 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):517–526Google Scholar
  8. Dubois PC, Van Zyl JJ, Engman ET (1995) Measuring soil moisture with imaging radars. IEEE T Geosci Remote Sens 33(4):915–926CrossRefGoogle Scholar
  9. Entekhabi D, Rodriguez-iturbe I, Castelli F (1996) Mutual interaction of soil moisture state and atmospheric processes J Hydrol 184:3-17CrossRefGoogle Scholar
  10. Fung AK, Li Z, Chen KS (1992) Backscattering from a randomly rough dielectric surface. IEEE T Geosci Remote Sens 30:356–369CrossRefGoogle Scholar
  11. Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens Environ 51:138–156CrossRefGoogle Scholar
  12. 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):117–126Google Scholar
  13. Kitanidis PK, Bras RL (1980) Real time forecasting with a conceptual hydrological model-II, applications and results. Water Resour Res 16:1034–1044Google Scholar
  14. Kitanidis PK (1992) Geostatistics. In: Maidment DR (ed) Handbook of hydrology. McGraw Hill, New York, pp 20.1–20.39Google Scholar
  15. Lacaze B, Raubal S, Winkel T (1994) Identifying spatial patterns of Mediterranean landscapes from geostatistical analysis of remotely sensed data. Int J Remote Sens 15:2437–2450Google Scholar
  16. 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 publicationGoogle Scholar
  17. Loague K (1992) Soil water content at R-5, part-I, spatial and temporal variability. J Hydrol 139:233–251CrossRefGoogle Scholar
  18. 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):1735–1758Google Scholar
  19. Martin RD Jr, Ghanssem A, Kanemasu ET (1989) C-band scatterometer measurements of a tall grass prairie. Remote Sens Environ 29:281–292CrossRefGoogle Scholar
  20. Matheron G (1963) Principles of Geostatistics. Econ Geol 58:1246–1266Google Scholar
  21. 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):588–596Google Scholar
  22. 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):370–381Google Scholar
  23. 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 29–32Google Scholar
  24. 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):697–709CrossRefGoogle Scholar
  25. 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 183–192Google Scholar
  26. Smith GM, Curran PJ (1996) The signal-to-noise ratio (SNR) required for estimation of foliar biochemical concentrations. Int J Remote Sens 17:1031–1058Google Scholar
  27. Treitz P, Howarth P (2000) High spatial resolution remote sensing data for forest ecosystem classification: an example of spatial scale. Remote Sens Environ 72:268–289CrossRefGoogle Scholar
  28. Ugsang MD (2000) Assessment of space-borne SAR remote sensing for monitoring soil moisture. Dissertation No SR-00–2, Asian Institute Technology (AIT), BangkokGoogle Scholar
  29. Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing: active and passive, vol III. Artech House, Norwood, MAGoogle Scholar
  30. 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 41:113–120CrossRefGoogle Scholar
  31. 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 157–164Google Scholar
  32. Webster R (1985) Quantitative spatial analysis of soil in the field. Adv Soil Sci 3:1–70Google Scholar
  33. 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):797–810CrossRefGoogle Scholar
  34. 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 ArizonaGoogle Scholar
  35. Woodcock CE, Strahler AH, Jupp DL (1988) The use of variograms in remote sensing: I scene models and simulated images. Remote Sens Environ 25:323–348CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  1. 1.NASDA Earth Observation Research CenterTokyo Japan

Personalised recommendations