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Spatial distribution of soil nutrients in a watershed of Himalayan landscape using terrain attributes and geostatistical methods

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Abstract

Terrain attributes derived from digital terrain model (DTM) were used to study spatial variation of total soil C, N and available P in surface soils of a watershed of Himalayan landscape. Terrain attributes elevation, slope gradient and upslope catchment area (UCA) and terrain indices [terrain wetness index (TWI), water power index (WPI) and sediment transport index (STI)] were derived from DTM and evaluated for their potential in soil nutrients mapping. These nutrients showed positive correlation with UCA, TWI, SPI and STP terrain indices. Among these terrain indices, TWI showed highest correlation coefficient for TC (r 2 = 0.71), N (r 2 = 0.67) and P (r 2 = 0.66) followed by WPI and STI. Geostatistical analyses used to map these nutrients, co-kriging with TWI + NDVI, TWI and slope as co-variables, had improved the spatial prediction to 60.46, 55.81, 44.18 % for TC and 33.63, 21.78, 17.82 % for N, respectively, contrary to ordinary kriging. The prediction accuracy for P was improved with co-variables of TWI + NDVI and TWI by 30.03 and 4.50 %, respectively. The study clearly revealed that by integrating NDVI as co-variable has significantly improved the accuracy for TC followed by N and P. TWI alone as co-variable has improved the spatial prediction significantly.

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References

  • Behrens T, Zhu A, Schmidt K, Scholten T (2010) Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155(3–4):175–185

    Article  Google Scholar 

  • Bell JC, Butler CA, Thompson JA (1995) Soil terrain modelling for site-specific agricultural management. In: Robert PC, Rust RH, Larson WE (eds) Site-specific management for agricultural systems. American Society of Agronomy, Madison, pp 209–228

    Google Scholar 

  • Beven KJ, Kirkby MJ (1993) A physically-based, variable contributed area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Bourennane H, King D, Couturier A (2000) Comparison of kriging with external drift and simple linear regression for predicting soil horizon thickness with different sample densities. In: Collins M et al (eds) Developments in quantitative soil resource assessment (PEDOMETRICS ‘98). Geoderma Special Issue, vol 97, No. 3–4, pp 255–272

  • Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58:1501–1511

    Article  Google Scholar 

  • Chang YH, Scrimshaw MD, Emmerson RHC, Lester JN (1998) Geostatistical analysis of sampling uncertainty at the Tollesbury managed retreat site in Blackwater Estuary, Essex, UK: kriging and cokriging approach to minimize sampling density. Sci Total Environ 221:43–57

    Article  Google Scholar 

  • Chien YJ, Lee DY, Guo HY, Houng KH (1997) Geostatistical analysis of soil properties of mid-west Taiwan soils. Soil Sci 162:291–298

    Article  Google Scholar 

  • Creed F, Trick CG, Band LE, Morrison IK (2002) Characterizing the spatial pattern of soil carbon and nitrogen pools in the Turkey Lakes Watershed: a comparison of regression techniques. Water Air Soil Pollut Focus 2:81–102

    Article  Google Scholar 

  • Fabiyi OO, Ige-Olumide O, Fabiyi AO (2013) Spatial analysis of soil fertility estimates and NDVI in south-western Nigeria: a new paradigm for routine soil fertility mapping. Res J Agric Environ Manag 2(12):403–411

    Google Scholar 

  • Gamon JA, Field CB, Roberts DA, Ustin SL, Valentine R (1993) Functional patterns in annual grassland during an AVRIS overflight. Remote Sens Environ 44:239–253

    Article  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York

    Google Scholar 

  • Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129

    Article  Google Scholar 

  • Guo PT, Liu HB, Wei W (2009) Spatial prediction of soil organic matter using terrain attributes in a hilly area. In: International conference on environmental science and information application technology, ESIAT, Wuhan, pp 759–762

  • Herbst M, Diekkruger B, Vereecken H (2006) Geostatistical co-regionalization of soil hydraulic properties in a micro-scale catchment using terrain attributes. Geoderma 132:206–221

    Article  Google Scholar 

  • Iqbal J, Read JJ, Thomasson AJ, Jenkins JN (2005) Relationships between soil-landscape and dryland cotton lint yield. Soil Sci Soc Am J 69:872–882

    Article  Google Scholar 

  • Isaaks EH, Srivastava RM (1989) An Introduction to applied geostatistics. Oxford University Press, Inc., New York

  • Janzen HH, Campbell CA, Izaurralde RC, Ellert BH, Juma N, McGill WB, Zentner RP (1998) Management effects on soil C storage on the Canadian prairies. Soil Till Res 47:181–195

    Article  Google Scholar 

  • Jenny H (1941) Factors of soil formation—a system of quantitative pedology. McGraw-Hill, New York

    Google Scholar 

  • Kerry R, Oliver MA (2007) The analysis of ranked observations of soil structure using indicator geostatistics. Geoderma 140:397–416

    Article  Google Scholar 

  • Kravchenko AN, Bullock DG, Boast CW (2002) Joint multifractal analysis of crop yield and terrain slope. Agron J 92:1279–1290

    Article  Google Scholar 

  • López-Granados F, Jurado-Exposito M, Pena-Barragan JM, Garcia-Torres L (2005) Using geostatistical and remote sensing approaches for mapping soil properties. Eur J Agron 23:279–289

    Article  Google Scholar 

  • Manning G, Fuller LG, Eilers RG, Florinsky I (2001) Topographic influence on the variability of soil properties within an undulating Manitoba landscape. Can J Soil Sci 81(4):439–447

    Article  Google Scholar 

  • McBratney A, Odeh I, Bishop T, Dunbar M, Shatar T (2000) An overview of pedometric techniques for use in soil survey. Geoderma 97:293–327

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Moore ID, Gryson RB, Landson AR (1991) Digital terrain modelling: review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Moore ID, Gessler PE, Nielsen GA, Peterson GA (1993) Soil attribute prediction using terrain analysis. Soil Sci Soc Am J 57:443–452

    Article  Google Scholar 

  • Mueller T, Pierce F (2003) Soil carbon maps: enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Sci Soc Am J 67:258–267

    Article  Google Scholar 

  • Nelson DW, Sommers LE (1982) Total carbon, organic carbon, and organic matter. Methods of soil analysis. American Society of Agronomy, Madison, pp 539–579

    Google Scholar 

  • Odeh IO, McBratney AB (2000) Using AVHRR images for spatial prediction of clay content in the lower Naomi Valley of eastern Australia. Geoderma 97:237–254

    Article  Google Scholar 

  • Olsen SR, Sommers LE (1982) Phosphorus. Methods of soil analysis. American Society of Agronomy, Madison, pp 403–430

    Google Scholar 

  • Ovalles FA, Collins MD (1986) Soil–landscape relationships and soil variability in north central Florida. Soil Sci Soc Am J 50:401–408

    Article  Google Scholar 

  • Pang S, Li TX, Wang YD, Yu HY, Li X (2009) Spatial interpolation and sample size optimization for soil copper (Cu) investigation in cropland soil at county scale using cokriging. Agric Sci China 8(11):1369–1377

    Article  Google Scholar 

  • Rezaei SA, Gilkes RJ (2005) The effects of landscape attributes and plant community on soil chemical properties in rangelands. Geoderma 125:167–176

    Article  Google Scholar 

  • Rivero RG, Grunwald S, Binford MW, Osborne TZ (2009) Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland. Remote Sens Environ 113:2389–2402

    Article  Google Scholar 

  • Rossiter DG (2007) Notes on applied geostatistics. ITC, Enschede

    Google Scholar 

  • Sahrawat KL (2004) Organic matter accumulation in submerged soils. Adv Agron 81:169–201

    Article  Google Scholar 

  • Snedecor GW, Cocharn WG (1980) Statistical methods, 7th edn. Iowa State University Press, Iowa

    Google Scholar 

  • Sumfleth K, Duttmann R (2008) Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecol Ind 8:485–501

    Article  Google Scholar 

  • Triantafilis J, Odeh I, McBratney A (2001) Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Sci Soc Am J 65:869–879

    Article  Google Scholar 

  • Umali BP, Oliver DP, Forrester S, Chittleborough DJ, Hutson JL, Rai SK, Ostendorf B (2012) The effect of terrain and management on the spatial variability of soil properties in an apple orchard. Catena 93:38–48

    Article  Google Scholar 

  • Voltz M, Webster R (1990) A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Soil Sci 41:473–490

    Article  Google Scholar 

  • Webster R, Oliver M (2001) Geostatistics for environmental scientists. Wiley, New York

    Google Scholar 

  • Yan-li Li, Xian-zhang P, Zhou R, Wang CK, Liu Y, Shi R, Chen D, Zhao Q (2013) Long-term changes of soil fertility factors and their relationships with NDVI. CJE 32(3):536–541

    Google Scholar 

  • Yemefack M, Rossiter DG, Njomgang R (2005) Multiscale characterization of soil variability within an agricultural landscape mosaic system in southern Cameroon. Geoderma 125:117–143

    Article  Google Scholar 

  • Yoo K, Amundson R, Heimsath AM, Dietrich E (2006) Spatial patterns of soil organic carbon on hillslopes: integrating geomorphic processes and the biological C cycle. Geoderma 130:47–65

    Article  Google Scholar 

  • Zhang CS, McGrath D (2004) Geostatistical and GIS analyses on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma 119:261–275

    Article  Google Scholar 

  • Zhang S, Huang Y, Chongyang S, Ye H, Du Y (2012) Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma 171–172:35–43

    Article  Google Scholar 

Download references

Acknowledgments

Authors are sincerely thankful to Indian Space Research Organisation (ISRO) for providing financial support under Technology Development Project (TDP) to carry out the research work. We are sincerely thankful to Dean (A) and Director, IIRS for encouraging the present research work.

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Correspondence to Suresh Kumar.

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Kumar, S., Singh, R.P. Spatial distribution of soil nutrients in a watershed of Himalayan landscape using terrain attributes and geostatistical methods. Environ Earth Sci 75, 473 (2016). https://doi.org/10.1007/s12665-015-5098-8

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