Encyclopedia of Agrophysics

2011 Edition
| Editors: Jan Gliński, Józef Horabik, Jerzy Lipiec

Remote Sensing of Soils and Plants Imagery

Reference work entry
DOI: https://doi.org/10.1007/978-90-481-3585-1_132


Remote sensing can be defined as the study of make observations, take measurements, and produce images of phenomena that are beyond the limits of our own senses and capabilities without making actual contact with the object of study. It can also be defined as: Any of the technical disciplines for observing and measuring the Earth from a distance, “The acquisition and measurement of data/information on some property(ies) of a phenomenon, object, or material by a recording device including satellite imaging, Global Positioning Systems, Radar, Sonar and aerial photography which is not in physical or close contact with the feature(s) under surveillance” and generate digital or hard copy image data (Jensen, 2007, 2008; http://en.wikipedia.org/wiki/Remote_sensing).


Remote sensing data are indispensable for measurement and evaluation of regional-to-global processes. Remote sensing is now widely used for collecting data, monitoring and studying the natural resources...
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  1. Aase, J. K., and Siddoway, F. H., 1981. Spring wheat yield estimates from spectral reflectance measurements. IEEE Transactions on Geoscience and Remote Sensing, GE-19, 78–84.Google Scholar
  2. Baret, F., Guyot, G., and Major, D. J., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In Proceedings of the 12th Canadian Symposium on Remote Sensing and IGARSS’89, Vancouver, Vol. 3, pp. 1355–1358.Google Scholar
  3. Barnes, E. M., Sudduth, K. A., Hummel, J. W., Lesch, S. M., Corwin, D. L., Yang, C. H., Daughtry, C. S. T., and Bausch, W. C., 2003. Remote and ground-based sensor techniques to map soil properties. Photogrammetric Engineering and Remote Sensing, 69(6), 619–630.Google Scholar
  4. Baumgardner, M., Silva, L., Biehl, L., and Stoner, E., 1985. Reflectance properties of soil. Advances in Agronomy, 38, 1–44.Google Scholar
  5. Ben-Dor, E., Irons, J. R., and Epema, G., 1999. Soil reflectance. In Rencz, A. N. (ed.), Remote Sensing for the Earth Sciences. New York: Wiley, pp. 111–188.Google Scholar
  6. Broge, N. H., and Leblanc, E., 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156–172.Google Scholar
  7. Clevers, J. G. P. W., 1994. Imaging spectrometry in agriculture – plant vitality and yield indicators. In Hill, J., and Mégier, J. (eds.), Imaging Spectrometry – A Tool for Environmental Observations. Dordrecht: Kluwer Academic, pp. 193–219.Google Scholar
  8. Cohen, W. B., 1991. Temporal versus spatial variation in leaf reflectance under changing water stress conditions. International Journal of Remote Sensing, 12(9), 1865–1876.Google Scholar
  9. Collins, W., 1978. Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing, 44, 43–55.Google Scholar
  10. Dutkiewicz, A., Lewis, M., and Ostendorf, B., 2009. Evaluation and comparison of hyperspectral imagery for mapping surface symptoms of dryland salinity. International Journal of Remote Sensing, 30, 693–719.Google Scholar
  11. Dwivedi, R. S., 1992. Monitoring and the study of the effects of image scale on delineation of salt-affected soils in the Indo-Gangetic alluvial plains. International Journal of Remote Sensing, 13, 1527–1536.Google Scholar
  12. Dwivedi, R. S., and Rao, B. R. M., 1992. The selection of the best possible Landsat-TM band combinations for delineating salt-affected soils. International Journal of Remote Sensing, 13, 2051–2058.Google Scholar
  13. Eldaw Elwadie, M., Pierce, F. J., and Qi, J., 2005. Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan. Agronomy Journal, 97, 99–105.Google Scholar
  14. Everitt, J. H., Anderson, G. L., Escobar, D. E., Davis, M. R., Spencer, N. R., and Andrascik, R. J., 1995. Use of remote sensing for detecting and mapping leafy spurge (Euphorbia esula). Weed Technology, 9, 599–609.Google Scholar
  15. Fourty, T., Baret, F., Jacquemond, S., Schmuck, G., and Verdebout, J., 1996. Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems. Remote Sensing of Environment, 56, 104–117.Google Scholar
  16. Gao, B. C., 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.Google Scholar
  17. Gitelson, A., Kaufman, Y., and Merzlyak, M., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.Google Scholar
  18. Gitelson, A., Kaufman, Y. J., Stark, R., and Rundquist, T. D., 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76–87.Google Scholar
  19. Golubiewski, N. (Lead Author); Hussein, G. H. G. (Topic Editor). 2007. Remote sensing’s functional role in studies of land-use/land-cover change. In Cleveland, C. J. Encyclopedia of Earth. Washington, DC: Environmental Information Coalition, National Council for Science and the Environment. (Published in the Encyclopedia of Earth March 15, 2007; Retrieved September 20, 2009). http://www.eoearth.org/article/Remote_sensing’s_functional_role_in_studies_of_land-use/land-cover_change
  20. Hatfield, J. L., 1983. Remote sensing estimators of potential and actual crop yield. Remote Sensing of Environment, 13, 301–311.Google Scholar
  21. Hoffer, R. M., 1978. Biological and physical considerations in application computer aided analysis techniques to remote sensing. In Swain, P. H., and Davis, S. M. (eds.), Remote Sensing: Quantitative Approach. New York: McGraw-Hill, pp. 237–286.Google Scholar
  22. Huete, A. R., 1988. A soil vegetation adjusted index (SAVI). Remote Sensing of Environment, 25, 295–309.Google Scholar
  23. Huete, A. R., and Liu, H. Q., 1994. An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS. IEEE Transactions on Geoscience and Remote Sensing, 32(4), 897–905.Google Scholar
  24. Huete, A., Justice, C., and Liu, H., 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49(3), 224–234.Google Scholar
  25. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G., 2002. Overview of the radiometric aiophysic performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.Google Scholar
  26. Hunt, G. R., 1980. Electromagnetic radiation: the communication link in remote sensing. In Siegal, B. S., and Gillesppie, A. R. (eds.), Remote Sensing in Geology. New York: Wiley, pp. 5–46.Google Scholar
  27. Idso, S. B., Jackson, R. D., Reginato, R. J., Kimball, B. A., and Nakayama, F. S., 1975. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology, 14, 109–113.Google Scholar
  28. Idso, S. B., Hatfield, J. L., Reginato, R. J., and Jackson, R. D., 1978. Wheat yield estimation by albedo measurement. Remote Sensing of Environment, 7, 273–276.Google Scholar
  29. Idso, S. B., Pinter, P. J., Jr., Jackson, R. D., and Reginato, R. J., 1980. Estimation of grain yields by remote-sensing of crop senescence rates. Remote Sensing of Environment, 9, 87–91.Google Scholar
  30. Irons, J. R., Weismiller, R. A., and Petersen, G. W., 1989. Soil reflectance. In Asrar, G. (ed.), Theory and Application of Optical Remote Sensing. New York: Wiley, pp. 66–106.Google Scholar
  31. Jensen, J. R., 2007. Remote sensing of vegetation. In Remote Sensing of the Environment, An Earth Resource Perspective, 2nd edn. Chapter 11. Prentice-Hall. Upper Saddle River.Google Scholar
  32. Jensen, J. R., 2008. Remote sensing for vegetation. Power Point Presentation http://www.cas.sc.edu/geog/rslab/551/
  33. Karnieli, A., Kaufman, Y. J., Remer, L., and Wald, A., 2001. Remote Sensing of Environment, 77(1), 10–21.Google Scholar
  34. Latz, K., Weismiller, R. A., Van Scoyoc, G. E., and Baumgardner, M. F., 1984. Characteristic variations in spectral reflectance of eroded Alfisols. Soil Science Society of American Journal, 48, 1130–1134.Google Scholar
  35. Lenney, M. P., Woodcock, C. E., Collins, J. B., and Hamdi, H., 1996. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from landsat TM. Remote Sensing of Environment, 56, 8–20.Google Scholar
  36. Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A., 1994. Estimating crop water deficits using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 46, 246–263.Google Scholar
  37. Moran, M. S., Inoue, Y., and Barnes, E. M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319–346.Google Scholar
  38. Moran, M. S., Peters-Lidard, C. D., Watts, J. M., and McElroy, S., 2004. Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Journal of Remote Sensing, 30(5), 805–826.Google Scholar
  39. Mulders, M. A., 1987. Remote Sensing in Soil Science. Amsterdam: Elsevier, p. 379.Google Scholar
  40. Pinter, P. J., Jr., Jackson, R. D., Idso, S. B., and Reginato, R. J., 1981. Multidate spectral reflectance as predictors of yield in water-stressed wheat and barley. International Journal of Remote Sensing, 2, 43–48.Google Scholar
  41. Ramsey, R. D., Falconer, A., and Jensen, J. R., 1995. The relationship between NOAA_AVHRR NDVI and Ecoregions in UTAH. Remote Sensing of Environment, 53(3), 188–198.Google Scholar
  42. Ray, T., 1994. A FAQ on Vegetation in Remote Sensing. http://www.yale.edu/ceo/Documentation/rsvegfaq.html
  43. Reginato, R. J., Vedder, J. F., Idso, S. B., Jackson, R. D., Blanchard, M. B., and Goettelman, R., 1977. An evaluation of total solar reflectance and spectral band ratioing techniques for estimating soil water content. Journal of Geophysical Research, 82, 2101–2104.Google Scholar
  44. Rondeaux, G., Steven, M., and Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107.Google Scholar
  45. Running, S. W., Loveland, T. R., and Pierce, L. L., 1994. A vegetation classification logic based on remote sensing for use in global biogeochemical models. Ambio, 23, 77–81.Google Scholar
  46. Salama, R. B., Hatton, T. J., Elder, G. M., Ye, L., and Dowling, T. 1997. Hydrogeological characterisation of catchments using Hydrogeomorphic Analysis of Regional Spatial Data (HARSD): characterisation of Axe Creek Catchment. In: Taniguchi, M. (ed.), Subsurface Hydrological Responses to Land Cover and Land Use Change. Kluwer Academic, Victoria, Australia, pp. 153–166.Google Scholar
  47. Scull, P., Franklin, J., Chadwick, O. A., and McArthur, D., 2003. Predictive soil mapping: a review. Progress in Physical Geography, 27(2), 171–197.Google Scholar
  48. Selige, T., and Schmidhalter, U., 2006. Remote sensing of soil properties to support site specific farming. Developments in Plant and Soil Sciences, 92 Google Scholar
  49. Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., Schlemmer, M. R., and Major, D. J., 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583–589.Google Scholar
  50. Szilágyi, A., and Baumgardner, M. F. 1991. Salinity and spectral reflectance of soils, In: Proceedings American Society of Photogrammetry Remote Sensing Symposium, March 25–29, 1991, Baltimore, MD, pp. 430–437.Google Scholar
  51. Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.Google Scholar
  52. Tucker, C. J., Holben, B. N., Elgin, J. H. Jr., and McMurtrey, J. E., III, 1980. Relationship of spectral data to grain yield variations. Photogrammetric Engineering and Remote Sensing, 46, 657–666.Google Scholar
  53. Wessman, C. A., 2003. The use of remote sensing in following soil process, Remote sensing of soil processes. Agriculture, Ecosystems and Environment. In: Proceedings of the International Workshop on Modern Techniques in Soil Ecology Relevant to Organic Matter Breakdown, Nutrient Cycling and Soil Biological Processes, Volume 34, Issues 1–4, 15 February 1991, pp. 479–493.Google Scholar
  54. Zarco-Tejada, P. J., Rueda, C., and Ustin, S., 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109–124.Google Scholar

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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Managed Water ResourcesOcean ReefAustralia