Skip to main content

Integration of MODIS-derived indices and field observations to estimate surface soil moisture at regional scales

Abstract

Surface soil moisture (SSM) is a critical factor in monitoring climate change, soil fertility, flood, and runoff modeling. Integration of satellite earth observations and field measurements data is a reliable approach to estimate environmental parameters in remote sensing applications. In this paper, a method is proposed to estimate the SSM by integrating synoptic weather stations and MODIS imagery on a regional scale. The data were adopted from the Soil Climate Analysis Network of the United States (US-SCAN) stations that were routinely collected in more than 220 stations from 2012 to 2015. The proposed method is a regression model composed of spectral indices including Normalized Difference Water Index (NDWI), Visible and Shortwave infrared Drought Index (VSDI), land surface temperature (LST), and estimated surface soil temperature using ordinary Kriging (OK). This method has inspired a simple integration method that used a linear combination of the remote sensing and field LST measurements. Compared to the inspired method, the proposed method has shown a 24% improvement in SSM estimation.

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

Fig. 1
Fig. 2
Fig. 3

References

  • Ahmed A, Zhang Y, Nichols S (2011) Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE Rev 2(1):028001

    Google Scholar 

  • Amani M, Salehi B, Mahdavi S, Masjedi A, Dehnavi S (2017) Temperature-vegetation-soil moisture dryness index (TVMDI). Remote Sens Environ 197:1–14

    Article  Google Scholar 

  • Carlson T (2007) An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 7(8):1612–1629

    Article  Google Scholar 

  • Chauhan N, Miller S, Ardanuy P (2003) Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int J Remote Sens 24(22):4599–4622

    Article  Google Scholar 

  • Childs C (2004) Interpolating surfaces in ArcGIS spatial analyst. In: ArcUser, vol 3235, p 569

    Google Scholar 

  • Cierniewski J, Ceglarek J, Kaźmierowski C, Roujean JL (2019) Combined use of remote sensing and geostatistical data sets for estimating the dynamics of shortwave radiation of bare arable soils in Europe. Int J Remote Sens 40(5-6):2359–2374

    Article  Google Scholar 

  • Entekhabi D, Njoku EG, O'Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J (2010) The soil moisture active passive (SMAP) mission. Proc IEEE 98(5):704–716

    Article  Google Scholar 

  • Ghahremanloo M, Mobasheri MR, Amani M (2019) Soil moisture estimation using land surface temperature and soil temperature at 5 cm depth. Int J Remote Sens 40(1):104–117

    Article  Google Scholar 

  • Gholami-Bidkhani NO, Mobasheri MR (2018) Influence of soil texture on the estimation of bare soil moisture content using MODIS images. Euro J Remot Sens 51(1):911–920

    Article  Google Scholar 

  • Goward SN, Xue Y, Czajkowski KP (2002) Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: an exploration with the simplified simple biosphere model. Remote Sens Environ 79(2):225–242

    Article  Google Scholar 

  • Hosseini M, Saradjian MR (2011) Multi-index-based soil moisture estimation using MODIS images. Int J Remote Sens 32(21):6799–6809

    Article  Google Scholar 

  • Leng P, Song X, Duan SB, Li ZL (2016) A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data. Int J Appl Earth Obs Geoinf 52:338–348

    Article  Google Scholar 

  • Martínez-Murillo J, Hueso-González P, Ruiz-Sinoga JD (2017) Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions. Sci Total Environ 595:400–412

    Article  Google Scholar 

  • Matthews SA (2002) ArcGIS geostatistical analyst. In: GIS Resource Document, pp 02–19

    Google Scholar 

  • Mobasheri MR, Amani M (2016) Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels. J Appl Remote Sens 10(2):026011

    Article  Google Scholar 

  • Mobasheri MR, Gholami-Bidkhani NO (2013) Development of new hyperspectral angle index for estimation of soil moisture using in situ spectral measurements. ISPRS-Int Arch Photogram Remot Sens Spat Inform Sci 40(3):481–486

    Article  Google Scholar 

  • Petropoulos G, Carlson T, Wooster MJ, Islam S (2009) A review of Ts/VI remote sensing-based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr 33(2):224–250

    Article  Google Scholar 

  • Rahimzadeh-Bajgiran P, Berg AA, Champagne C, Omasa K (2013) Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS J Photogramm Remote Sens 83:94–103

    Article  Google Scholar 

  • Ray RL, Jacobs JM, Cosh MH (2010) Landslide susceptibility mapping using downscaled AMSR-E soil moisture: a case study from Cleveland Corral,California, US. Remote Sens Environ 114(11):2624–2636

    Article  Google Scholar 

  • Sabaghy S, Walker JP, Renzullo LJ, Jackson T (2018) Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities. Remote Sens Environ 209:551–580

    Article  Google Scholar 

  • Sadeghi M, Babaeian E, Tuller M, Jones SB (2017) The optical trapezoid model: a novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sens Environ 198:52–68

    Article  Google Scholar 

  • Sobrino J, Franch B, Mattar C, Jiménez-Muñoz J, Corbari C (2012) A method to estimate soil moisture from airborne hyperspectral scanner (AHS) andASTER data: application to SEN2FLEX and SEN3EXP campaigns. Remote Sens Environ 117:415–428

    Article  Google Scholar 

  • Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manag 27(8):3127–3144

    Article  Google Scholar 

  • Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. John Wiley & Sons, Hoboken

    Book  Google Scholar 

  • Wang L, Qu JJ (2007) NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters 34(20)

  • Wang J, Ge Y, Song Y, Li X (2014) A geostatistical approach to upscale soil moisture with unequal precision observations. IEEE Geosci Remote Sens Lett 11(12):2125–2129

    Article  Google Scholar 

  • Wang Y, Peng J, Song X, Leng P, Ludwig R, Loew A (2018) Surface soil moisture retrieval using optical/thermal infrared remote sensing data. IEEE Trans Geosci Remote Sens 56(9):5433–5442

    Article  Google Scholar 

  • Webster R, Oliver MA (2001) Geostatistics for environmental scientists (Statistics in Practice)

    Google Scholar 

  • Weil RR, Brady NC (2016) The nature and properties of soils. Pearson, London

    Google Scholar 

  • Xia L, Song X, Leng P, Wang Y, Hao Y, Wang Y (2019) A comparison of two methods for estimating surface soil moisture based on the triangle model using optical/thermal infrared remote sensing over the source area of the Yellow River. Int J Remote Sens 40(5-6):2120–2137

    Article  Google Scholar 

  • Zhang N, Hong Y, Qin Q, Liu L (2013) VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. Int J Remote Sens 34(13):4585–4609

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nabi Olah Gholami Bidkhani.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Responsible Editor: Biswajeet Pradhan

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bidkhani, N.O.G., Mobasheri, M.R. & Safdarinezhad, A. Integration of MODIS-derived indices and field observations to estimate surface soil moisture at regional scales. Arab J Geosci 14, 1646 (2021). https://doi.org/10.1007/s12517-021-08133-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-021-08133-9

Keywords

  • : Near-surface soil moisture
  • In situ measurements
  • MODIS
  • Spatial interpolation
  • US-SCAN
  • Soil texture