Retrieval of wheat biophysical attributes from hyperspectral data and SAILH + PROSPECT radiative transfer model

A simple soil reflectance parameterization was empirically calibrated and integrated into the SAILH+PROSPECT canopy reflectance model to assess simultaneously LAI and canopy chlorophyll content from hyperspectral reflectance data. Model inversion was performed using an artificial neural net (ANN) trained on synthetic reflectance spectra that were generated by the extended canopy reflectance model. For validation, a completely independent data set was used consisting of field reflectance measurements and corresponding LAI and chlorophyll data. Results obtained on the validation data set were very promizing. The coefficient of determination (R) varied between 0.86 and 0.87 (LAI and canopy chlorophyll content, respectively) and the root mean squared error (RMSE) between 0.83 (LAI; m m) and 0.66 (canopy chlorophyll content; g m). The trained ANN was also applied to an airborne HyMAP image to demonstrate the applicability of the inversion approach to remote sensing data. The retrieved canopy chlorophyll contents and soil brightness values showed a reasonable correlation to on-site final yield measurements acquired two months after the image data acquisition.

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