Skip to main content

Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots

Buy Article:

$55.00 plus tax (Refund Policy)

The spectral reflectance of agricultural crops is affected significantly by sub‐pixel scale spectral contributions of background soils and shadows as viewed by a remote sensing instrument. This has meant the potential of remote sensing imagery has not been fully realized for extracting biophysical information and assessing ecological stress using methods such as vegetation indices (VIs). In this paper, we address this problem explicitly using spectral mixture analysis (SMA) to quantify the area abundance of plants, soils and shadows at sub‐pixel scales with the aim of improving extraction of plant biophysical and structural information from remote sensing data. Different measurement strategies were tested in the field for acquiring reference endmember spectra of crop vegetation, soil and shadows using a field spectroradiometer for a set of potato plots in western Canada. Endmember measurements included sunlit and shadowed spectra of in situ crop targets, optically thick stacks and data from excised leaves, as well as cultivated, rough and compacted soils. All possible combinations of crop, soil and shadow endmember spectra were analysed using SMA to derive sets of sub‐pixel scale component fractions from radiometer spectra acquired from a boom truck over replicate plot samples with a sensor field of view of 1.05 m. Digital video image frames captured simultaneously with the radiometer data were used to determine ground proportions of crop, soil and shadow for independent validation of the SMA fractions. Endmember fractions derived from excised leaves, cultivated soil and shadowed vegetation spectra showed the best agreement with ground truth data, with differences of only ±3.3%. These sub‐pixel scale fractions were used in regression analyses to predict leaf area index, biomass and plant width with an average r 2 value of 0.85 from SMA shadow fraction, which was a substantial improvement over the best VI results from NDVI, NGVI and SR (average r 2  = 0.53). Perspectives on SMA at different stages in the growing season and for different crop types are provided with a recommendation that further SMA research is warranted for local to regional scale agricultural crop monitoring programmes.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: Department of Geography and Water Institute for Semi‐arid Ecosystems (WISE), University of Lethbridge, 4401 University Drive West, Lethbridge, Alberta T1K 3M4, Canada 2: Agriculture and Agri‐Food Canada, Lethbridge Research Centre, P.O. Box 3000, Lethbridge, Alberta T1J 4B1, Canada

Publication date: 2005-11-20

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more