Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies

Abstract

The 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 accurately predicts water content in the range found with all crop types pooled together, with an index of agreement ( D) of 0.92 and a root mean squared error (RMSE) of 26.8% of the average. On an individual crop basis, the model proved to be a poor predictor for wheat crops (RMSE=69.9%). When wheat fields were removed from the overall analysis, the RMSE was 17.9% and the D was 0.87. While the model provided a reasonably accurate prediction of EWT for broadleaf crops like beans, corn, canola, and peas ( D=0.88, 0.69, 0.88, and 0.84, respectively), the error margin in the prediction was too large for to precisely detect within-crop variation for the low variability found in corn and bean crops in this study. EWT is related to plant biomass and leaf area index (LAI), both quantities of interest to precision agriculture.