The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches

The estimation of canopy gap fraction [Po(0)] from red and near-infrared reflectance is investigated. Several approaches are compared, using classical vegetation indices (VI) and backpropagation neural networks. The parameters of the VI-Po(0) relationships and the synaptic weights and biases of two-layer neural networks were successfully adjusted on an experimental data set and on data derived from radiative transfer model simulations. Three experimental data sets were acquired over sugar beet canopies. They expressed a large range of canopy architecture and soil background reflectance. Two of them that correspond to similar experimental procedures were merged together and then randomly split into two subsets: one for fitting the parameters of the VI-Po(0) relationships or to train the neural network, and the other for the evaluation of the predictive performances. The third experiment is used as an additional independent data set to test the robustness of the approaches. The model-simulated data set was generated using the SAIL and PROSPECT radiative transfer models, with input parameter values that were chosen to have similar distributions as observed over sugar beet canopies. As for the experimental data set, the model-simulated data set was split into a training (calibration) and a test (validation) data set. Results show that the gap fraction can be accurately estimated from the red and near-infrared reflectance without any external information except maybe the crop type (here sugar beet) and the soil line characteristics required for some of the vegetation indices. The best predictive performances were observed for the SAVI-like vegetation indices (SAVI, TSAVI, MSAVI) and the poorest for the NDVI, PVI, and GEMI having intermediate although satisfactory results. Neural networks trained on the simulated data set appeared to be the most robust approach. It allows us to implicitly incorporate our knowledge about the physics of the radiative transfer in the interpretation of remote sensing data.