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TitleValidation of a Hyperspectral Curve-Fitting Model for the Estimation of Plant Water Content of Agricultural Canopies
AuthorChampagne, C; Staenz, K; Bannari, A; Deguise, J -C; McNairn, H
SourceRemote Sensing of Environment 2002.
Alt SeriesEarth Sciences Sector, Contribution Series 20043121
AbstractThe estimation of plant water content is essential to the integration of remote sensing into precision agriculture. Hyperspectral models developed to estimate plant water content have had limited application under field conditions and have not been rigorously validated. A physical model using a spectrum matching technique was applied to hyperspectral data to directly calculate the canopy equivalent water thickness (EWT) using a look-up table approach. The objective of this study was to test the validity of this algorithm using plant water content information collected under field conditions, and to relate this to the needs of precision agriculture. Image data were acquired over two experimental test sites in Canada, near Clinton, Ontario and Indian Head, Saskatchewan, using the Probe-1 airborne hyperspectral sensor. Plant biomass samples were collected simultaneously from plots spanning fourteen fields of various crop types (wheat, canola, corn, beans and peas). The model was validated against EWT estimated from biomass samples, as well as more conventional measures of crop water status. The model predicts water content in the range found with all crop types pooled together, with an index of agreement (D) of 0.86 and a root mean squared error (RMSE) of 26.8 % of the average. The model was sensitive to within-crop variability for broad leaf crops such as peas and canola (D = 0.87 and 0.80; RMSE = 39.6 and 24.4%, respectively). The model was insensitive to the low levels of variability found in corn and bean and fields observed in this study (D = 0.39, 0.21; RMSE = 12.0, 21.8%, respectively). The model was not accurate in estimating the water content in wheat crops (D = 0.34; RMSE = 69.9%). EWT is related to plant biomass and leaf area index (LAI), both quantities being of interest to precision agriculture.

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