SpringerLink
Log in

Influence of soil, crop residue, and sensor orientations on NDVI readings

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Site-specific in-season corn (Zea mays L.) nitrogen (N) rate recommendations based on remote sensing can increase nitrogen use efficiency (NUE) but most approaches require the corn to be at the V8 growth stage. Success in estimating N needs during early vegetative growth has been limited due to low plant biomass and background interference. The objective of this experiment was to measure the influence of soil series, soil moisture, surface crop residues, and sensor orientation on Normalized Difference Vegetation Index (NDVI) from soils prior to planting and corn from planting through the V6 growth stage. A controlled experiment was conducted in Virginia using four soil types commonly used for corn production. Spectral reflectance readings from the soils and four crop residues were measured using the GreenSeeker® sensor at four sensor orientations. Sensor orientation affected NDVI readings from bare soils before planting, with a difference of up to 0.16 units among orientations. The 15 cm mask generally resulted in the lowest NDVI. Wetting soils resulted in greater NDVI values from all soils with differences of up to 0.18 units between the same soil wet and dry. Values for NDVI were initially influenced by crop residue type but no differences due to type were detected once corn reached the V4 stage. Altering sensor orientation generally changed NDVI values but none resulted in NDVI that was similar across all soil and residue backgrounds. Background (soil and residue) influenced NDVI readings during early vegetative corn growth so the sensing background for the calibration reference areas should be uniform and similar to the larger field for implementation of the GreenSeeker® variable rate system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Abendroth, L. J., Elmore, R. W., Boyer, M. J., & Marlay, S. K. (2011). Corn growth and development. PMR 1009. Ames, IA: Iowa State University Extension.

    Google Scholar 

  • Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). New York, NY: The Guilford Press.

    Google Scholar 

  • Escadafal, R., Girard, M., & Couralt, D. (1989). Munsell soil color and soil reflectance in the visible spectral bands of Landsat MSS and TM data. Remote Sensing of Environment, 27, 37–46.

    Article  Google Scholar 

  • Gilabert, M. A., González-Piqueras, J., García-Haro, F. J., & Meliá, J. (2002). A generalized soil-adjusted vegetation index. Remote Sensing of Environment, 82(2–3), 303–310.

    Article  Google Scholar 

  • Harrell, D. L., Tubaña, B. S., Walker, T. W., & Phillips, S. B. (2011). Estimating rice grain yield potential using normalized difference vegetation index. Agronomy Journal, 103(6), 1717–1723.

    Article  Google Scholar 

  • Holland, K. H., Lamb, D. W., & Schepers, J. S. (2012). Radiometry of proximal active optical sensors (AOS) for agricultural sensing. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 5, 1793–1802. doi:10.1109/jstars.2012.2198049.

    Article  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.

    Article  Google Scholar 

  • Huete, A. R., & Escadafal, R. (1991). Assessment of biophysical soil properties through spectral decomposition techniques. Remote Sensing of Environment, 35, 149–159.

    Article  Google Scholar 

  • Huete, A. R., Jackson, R. D., & Post, D. F. (1985). Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17(1), 37–53.

    Article  Google Scholar 

  • Hutchinson, C. F. (1982). Techniques for combining Landsat and ancillary data for digital classification improvement. Photogrammetric Engineering and Remote Sensing, 48, 123–130.

    Google Scholar 

  • Inman, D., Khosla, R., Reich, R. M., & Westfall, D. G. (2007). Active remote sensing and grain yield in irrigated maize. Precision Agriculture, 8(4–5), 241–252.

    Article  Google Scholar 

  • Jones, J., Moody, J., Shear, G., Moschler, W., & Lillard, J. (1968). The no-tillage system for corn (Zea mays L.). Agronomy Journal, 60(1), 17–20.

    Article  Google Scholar 

  • Kang S.S., Hong H.Y., Kim Y.H., Ha S.K., Hong S.D. (2009) Factors affecting reflectance indics in ground-based active sensors. In: Proceedings 9th International Conference of the East and Southeast Asia Federation of Soil Science Societies, Seoul.

  • Khosla, R., & Alley, M. (1999). Soil-specific nitrogen management on mid-Atlantic coastal plain soils. Better Crops with Plant Food, 83(3), 6–7.

    Google Scholar 

  • Kipp S., Mistele B., & Schmidhalter U. (2012) Active sensor performance – dependence on measuring height, device temperature and light intensity. In: Proceedings of the 11th international conference on precision agriculture, 15–18 July, 2012, Indianapolis, Indiana USA. Monticello, IL: The International Society of Precision Agriculture.

  • Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457–465.

    Article  Google Scholar 

  • Ma, B. L., & Dwyer, L. M. (1998). Nitrogen uptake and use of two contrasting maize hybrids differing in leaf senescence. Plant and Soil, 199(2), 283–291.

    Article  CAS  Google Scholar 

  • Ma, B. L., Ying, J., Dwyer, L., Gregorich, E., & Morrison, M. (2003). Crop rotation and soil N amendment effects on maize production in eastern Canada. Canadian Journal of Soil Science, 83, 483–495.

    Article  Google Scholar 

  • Maguire, R., Alley, M., & Flowers, W. (2009). Fertilizer types and calculating application rates 424–035. Blacksburg, VA: Virginia Cooperative Extension.

  • Maguire, R. O., & Heckendorn, S. E. (2011). Soil test recommendations for Virginia. Blacksburg, VA: Virginia Cooperative Extension.

    Google Scholar 

  • Martin, D. E., Lopez, J. D, Jr, & Lan, Y. (2012). Laboratory evaluation of the GreenSeeker handheld optical sensor to variations in orientation and height above canopy. International Journal of Agricultural and Biological Engineering, 5(1), 43–47.

    Google Scholar 

  • Munsell, A. H. (2000). Munsell soil color charts. New Windsor: Gretag-Macbeth.

    Google Scholar 

  • Nagler, P. L., Daughtry, C. S. T., & Goward, S. N. (2000). Plant litter and soil reflectance. Remote Sensing of Environment, 71(2), 207–215.

    Article  Google Scholar 

  • NTech Industries Inc. (2008). Technical Notes: How to use the crop-specific algorithms provided with the GreenSeeker RT200 System. Ukiah, CA: NTech Industries. Retrieved from 15 Apr 2015 http://trl.trimble.com/docushare/dsweb/Get/Document-635949/How_to_use_Crop_Algorithms_080606.pdf.

  • Institute, S. A. S. (2008). The SAS system for Windows. Cary, NC: SAS Inst.

    Google Scholar 

  • Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflowers. Remote Sensing of Environment, 48, 135–146.

    Article  Google Scholar 

  • Qi, J., Chehbouni, A., Huete, A., Kerr, Y., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., et al. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93(1), 131–138.

    Article  Google Scholar 

  • Reiter, M. S. (2009). Conservation tillage trends in Virginia agricultural production: A multidisciplinary approach to conservation. In: M.S. Reiter (ed.), Proceedings of the 31st Southern Conservation Agriculutre Systems Conference (pp. 2–11). Melfa, VA: Virginia Cooperative Extension.

  • Ruffo, M. L., Bollero, G. A., Bullock, D. S., & Bullock, D. G. (2006). Site-specific production functions for variable rate corn nitrogen fertilization. Precision Agriculture, 7(5), 327–342.

    Article  Google Scholar 

  • Solari, F. (2006). Develo** a crop based strategy for on-the-go nitrogen management in irrigated cornfields. Lincoln: University of Nebraska.

    Google Scholar 

  • Solari, F., Shanahan, J., Ferguson, R., Schepers, J., & Gitelson, A. (2008). Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal, 100(3), 571–579.

    Article  CAS  Google Scholar 

  • Solie, J. B., Monroe, A. D., Raun, W. R., & Stone, M. L. (2012). Generalized algorithm for variable-rate nitrogen application in cereal grains. Agronomy Journal, 104(2), 378–387.

    Article  Google Scholar 

  • Sudduth, K., Kitchen, N., Drummond, S., & House, G. (2011). Nadir and oblique canopy reflectance sensing for N application in corn. In: Proceedings of the ASABE Annual International Meeting, Louisville, KY, 7–10 Aug. Paper 1111261 2011.

  • Thomason, W. E., Phillips, S. B., & Raymond, F. D. (2007). Defining useful limits for spectral reflectance measures in corn. Journal of Plant Nutrition, 30(8), 1263–1277.

    Article  CAS  Google Scholar 

  • Tubana, B. S., Arnall, D. B., Walsh, O., Chung, B., Solie, J. B., Girma, K., et al. (2008). Adjusting midseason nitrogen rate using a sensor-based optimization algorithm to increase use efficiency in corn. Journal of Plant Nutrition, 31(8), 1393–1419.

    Article  CAS  Google Scholar 

  • USDA-NRCS (2011). National agronomy manual. Washington, DC: USDA-NRCS. Retrieved from 15 April 2015 http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1043210.pdf.

  • USDA-NRCS (2013). Official soil series descriptions (OSDs) with series extent map** capabilities. Washington, DC: USDA-NRCS. Retrieved from 15 April 2015https://soilseries.sc.egov.usda.gov/osdname.asp.

Download references

Author information

Authors and Affiliations

  1. Virginia Tech Department of Crop and Soil Environment Science, Blacksburg, VA, 24060, USA

    J. R. Jones, C. S. Fleming, K. Pavuluri, M. M. Alley & W. E. Thomason

  2. Virginia Tech Department of Crop and Soil Environment Science, Painter, VA, 23420, USA

    M. S. Reiter

Authors
  1. J. R. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. S. Fleming
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Pavuluri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. M. Alley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. S. Reiter
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. W. E. Thomason
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. E. Thomason.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jones, J.R., Fleming, C.S., Pavuluri, K. et al. Influence of soil, crop residue, and sensor orientations on NDVI readings. Precision Agric 16, 690–704 (2015). https://doi.org/10.1007/s11119-015-9402-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-015-9402-0

Keywords

Search

Navigation