Skip to main content
SpringerLink
Log in

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ0) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ0 and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.

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

Similar content being viewed by others

References

  • Amezketa E, de Lersundi JD (2008) Soil classification and salinity mapping for determining restoration potential of cropped riparian areas. Land Degrad Dev 19:153–164

    Article  Google Scholar 

  • Arrouays D, Mckenzie N, Hempel J, De Forges AR, McBratney AB (2014) GlobalSoilMap: basis of the global spatial soil information system. CRC Press, Boca Raton

    Book  Google Scholar 

  • Bao SD (2007) Soil and agricultural chemistry analysis. China Agriculture Press, Beijing (In Chinese)

    Google Scholar 

  • Barca E, Castrignanò A, Buttafuoco G, De Benedetto D, Passarella G (2015) Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing. Environ Monit Assess 187:422–433

    Article  CAS  Google Scholar 

  • Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J R Stat Soc B 13:1–45

    Google Scholar 

  • Brus DJ (2015) Balanced sampling: a versatile sampling approach for statistical soil surveys. Geoderma 253–254:111–121

    Article  Google Scholar 

  • Brus DJ, Kempen B, Heuvelink GBM (2011) Sampling for validation of digital soil maps. Eur J Soil Sci 62:394–407

    Article  Google Scholar 

  • Buchanan S, Triantafilis J, Odeh IOA, Subansinghe R (2012) Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data. Geophysics 77:WB201–WB211

    Article  Google Scholar 

  • Chen C, Hu K, Li H, Yun A, Li B (2015) Three-dimensional mapping of soil organic carbon by combining kriging method with profile depth function. PLoS One 10(6):e0129038

    Article  Google Scholar 

  • Corwin DL, Lesch SM (2003) Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines. Agron J 95:455–471

    Article  Google Scholar 

  • De Benedetto D, Castrignanò A, Rinaldi M, Ruggieri S, Santoro F, Figorito B, Tamborrino R (2013) An approach for delineating homogeneous zones by using multi-sensor data. Geoderma 199:117–127

    Article  Google Scholar 

  • Douaik A, van Meirvenne M, Tóth T, Serre M (2004) Space-time mapping of soil salinity using probabilistic bayesian maximum entropy. Stoch Environ Res Risk Assess 18:219–227

    Article  Google Scholar 

  • Eigenberg RA, Lesch SM, Woodbury B, Nienaber JA (2008) Geospatial methods for monitoring a vegetative treatment area receiving beef feedlot runoff. J Environ Qual 37(SUPPL 5):S68–S77

    Google Scholar 

  • Fitzgerald GJ, Lesch SM, Barnes EM, Luckett WJ (2006) Directed sampling using remote sensing with a response surface sampling design for site-specific agriculture. Comput Electron Agric 53:98–112

    Article  Google Scholar 

  • Guo Y, Shi Z, Li HY, Triantafilis J (2013) Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China. Soil Use Manag 29:445–456

    Article  Google Scholar 

  • Guo Y, Huang JY, Shi Z, Li HY (2015) Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors. PLoS One 10(5):e0127996

    Article  Google Scholar 

  • Halvorson JL, Smith JL, Papendick RI (1997) Issues of scale for evaluating soil quality. J Soil Water Conserv 52:26–30

    Google Scholar 

  • Huang JY, Shi Z, Biswas A (2015a) Characterizing anisotropic scale-specific variations in soil salinity from a reclaimed marshland in China. Catena 131:64–73

    Article  CAS  Google Scholar 

  • Huang J, Zare E, Malik RS, Triantafilis, J (2015b) An error budget for soil salinity mapping using different ancillary data. Soil Res 53. doi:10.1071/SR15043

  • Johnson CK, Eskridge KM, Corwin DL (2005) Apparent soil electrical conductivity: applications for designing and evaluating field-scale experiments. Comput Electron Agric 46:181–202

    Article  Google Scholar 

  • Kobayashi S, Widyorini R, Kawai S, Omura Y, Sanga-Ngoie K, Supriadi B (2012) Backscattering characteristics of L-band polarimetric and optical satellite imagery over planted acacia forests in Sumatra, Indonesia. J Appl Remote Sens 6:063525. doi:10.101117/1JRS6063525

    Article  Google Scholar 

  • Lesch SM (2005) Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Comput Electron Agric 46:153–179

    Article  Google Scholar 

  • Lesch SM, Rhoades JD (2006) ESAP Software Suite: Version 2.35R GEBJ Salinity Laboratory, Soil Chemistry/Assessment Research Unit, 450 W Big Springs Road, Riverside, CA, 92507-4617, USA

  • Lesch SM, Strauss DJ, Rhoades JD (1995a) Spatial prediction of soil salinity using electromagnetic induction techniques: 1. Statistical prediction models: a comparison of multiple linear regression and co-kriging. Water Resour Res 31:373–386

    Article  Google Scholar 

  • Lesch SM, Strauss DJ, Rhoades JD (1995b) Spatial prediction of soil salinity using electromagnetic induction techniques: 2. An efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation. Water Resour Res 31:387–398

    Article  Google Scholar 

  • Lesch SM, Rhoades JD, Corwin DL (2000) The ESAP-95 version 2.01R User Manual and Tutorial Guide Research Report No 146 USDA-ARS. In: Brown GE Jr (ed) Salinity Laboratory, Riverside, CA. http://www.ussl.ars.usda.gov/lcrsan/esap95pdf. Accessed 9 Jul 2009

  • Li Y, Shi Z, Wu CF, Li F, Li HY (2007) Optimised sptatial sampling scheme for soil electrical conductivity based on variance quad-tree (VQT) method. J Integr Agric 6:1463–1471

    Google Scholar 

  • Li HY, Webster R, Shi Z (2015) Mapping soil salinity in the Yangtze delta: REML and universal kriging (E-BLUP) revisited. Geoderma 237–238:71–77

    Article  Google Scholar 

  • Lobell DB, Lesch SM, Corwin DL, Ulmer MG, Anderson KA, Potts DJ, Baltes MJ (2010) Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI. J Environ Qual 39:35–41

    Article  CAS  Google Scholar 

  • McBratney AB, Santos MML, Minasny B (2003) On digital soil mapping. Geoderma 117:3–52

    Article  Google Scholar 

  • McColl KA, Ryu D, Matic V, Walker JP, Costelloe J, Rüdiger C (2012) Soil salinity impacts on L-band remote sensing of soil moisture. IEEE Geosci Remote Sens 9:262–266

    Article  Google Scholar 

  • Montanari R, Souza GSA, Pereira GT, Marques J Jr, Siqueira DS, Siqueira GM (2012) The use of scaled semivariograms to plan soil sampling in sugarcane fields. Pre Agric 13:542–552

    Article  Google Scholar 

  • Paloscia S, Pettinato S, Santi E (2012) Combining L and X band SAR data for estimating biomass and soil moisture of agricultural fields. Eur J Remote Sens 45:99–109

    Article  Google Scholar 

  • Pellarin T, Calvet JC, Wigneron JP (2003) Surface soil moisture retrieval from L-band radiometry: a global regression study. IEEE Geosci Remote Sens 41:2037–2051

    Article  Google Scholar 

  • Piikki K, Wetterlind J, Söderström M, Stenberg B (2015) Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods. Precision Agric 16:29–45

    Article  Google Scholar 

  • Priori S, Martini E, Andrenelli MC, Magini S, Agnelli AE, Bucelli P, Costantini EAC (2013) Improving wine quality through harvest zoning and combined use of remote and soil proximal sensing. Soil Sci Soc Am J 77:1338–1348

    Article  CAS  Google Scholar 

  • Robinson DA, Abdu H, Lebron I, Jones SB (2012) Imaging of hill-slope soil moisture wetting patterns in a semi-arid oak savanna catchment using time-lapse electromagnetic induction. J Hydrol 416:39–49

    Article  Google Scholar 

  • Rodrigues FA Jr, Bramley RGV, Gobbet DL (2015) Proximal soil sensing for Precision Agriculture: simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils. Geoderma 243–244:183–195

    Article  Google Scholar 

  • Shanbedi M, Heris SZ, Maskooki A, Eshghi H (2015) Statistical analysis of laminar convective heat transfer of MWCNT-deionized water nanofluid using the response surface methodology. Numer Heat Transfer Part A 68:454–469

    Article  CAS  Google Scholar 

  • Shimada M, Isoguchi O, Tadono T, Isono K (2009) PALSAR radiometric and geometric calibration. IEEE Geosci Remote Sens 47:3915–3932

    Article  Google Scholar 

  • Sonobe R, Tani H (2009) Application of the Sahebi model using ALOS/PALSAR and 663 cm long surface profile data. Int J Remote Sens 30:6069–6074

    Article  Google Scholar 

  • Sudduth KA, Kitchen NR, Bollero GA, Bullock DG, Wiebold WJ (2003) Comparison of electromagnetic induction and direct sensing of soil electrical conductivity. Agron J 95:472–482

    Article  Google Scholar 

  • Triantafilis J, Kerridge B, Buchanan SM (2009) Digital soil-class mapping from proximal and remotely sensed data at the field level. Agron J 101:841–853

    Article  Google Scholar 

  • Venter G, Haftka RT, Starnes JH (1996) Construction of response surfaces for design optimization applications, AIAA paper 96-4040-CP. In: Proceedings of 6th AIAA/NASA/ISSMO symposium on multidisciplinary analysis and optimization, Bellevue WA, Part 2, pp 548–564

  • Wallenius K, Niemi RM, Rita H (2011) Using stratified sampling based on pre-characterisation of samples in soil microbiological studies. Appl Soil Ecol 51:111–113

    Article  Google Scholar 

  • Wang JF, Stein A, Gao BB, Ge Y (2012) A review of spatial sampling. Spat Stat 2:1–14

    Article  Google Scholar 

  • Webster R, Lark M (2013) Field sampling for environmental science and management. Routledge, London

    Google Scholar 

  • Yao RJ, Yang JS, Zhao XF, Chen XB, Han JJ, Li XM, Liu MX, Shao HB (2012) A new soil sampling design in coastal saline region using EM38 and VQT method. Clean Soil Air Water 40:972–979

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This material was based upon work funded by the National Natural Science Foundation of China (No. 41271234), the Key National Projects of High-Resolution Earth Observing System (09-Y30B03-9001-13/15), the Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences (2016YQ21) and by the Independent Innovative Project of Henan Academy of Agricultural Sciences.

Author information

Authors and Affiliations

  1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China

    Yan Guo & Laigang Wang

  2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Yan Guo, Zhou Shi, Lianqing Zhou & Yin Zhou

  3. School of Biological, Earth and Environmental Science, The University of New South Wales, Kensington, NSW, 2052, Australia

    Jingyi Huang

  4. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou, China

    Zhou Shi

Authors
  1. Yan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingyi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianqing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laigang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhou Shi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Y., Shi, Z., Huang, J. et al. Characterization of field scale soil variability using remotely and proximally sensed data and response surface method. Stoch Environ Res Risk Assess 30, 859–869 (2016). https://doi.org/10.1007/s00477-015-1135-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-015-1135-0

Keywords

Search

Navigation