Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer

We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (C_ab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new C_ab sensitive index located between 650 and 720 nm and termed ANCB_650–720. The performance of ANCB_650–720 was validated against ground-measured C_ab of ten spruce crowns and compared with C_ab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710 nm (ND_925&710), simple reflectance ratio between 750 and 710 nm (SR_750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable C_ab spatial patterns, the ground validation revealed that the ANCB_650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R²=0.72 for both methods). ANCB_650–720 estimated C_ab with an RMSE=2.27 μg cm⁻² (relative RRMSE=4.35%) and ANN with an RMSE=2.18 μg cm⁻² (RRMSE=4.18%), while SR_750/710 with an RMSE=4.16 μg cm⁻² (RRMSE=7.97%), ND_925&710 with an RMSE=9.07 μg cm⁻² (RRMSE=17.38%) and TCARI/OSAVI with an RMSE=12.30 μg cm⁻² (RRMSE=23.56%). Also the systematic RMSE_S was lower than the unsystematic one only for the ANCB_650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for C_ab values below 30 μg cm⁻², which are slightly overestimated (RMSE=2.42 μg cm⁻²). The computationally efficient ANCB_650–720 retrieval provides accurate high spatial resolution airborne C_ab maps, considerable as a suitable reference data for validating satellite-based C_ab products.