Estimating the vertical distribution of chlorophyll in winter wheat based on multi-angle hyperspectral data

Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle hyperspectral data. Three winter wheat layers were divided, and the multi-angle hyperspectral data of the different layers were obtained by removing the leaves from the lower layer to the top layer. The multi-angle vegetation index and LCC linear models were established, and the estimated model based on nadir view angle (i.e., conventional observation angle) was compared. Results show that (1) the best observation angle for the first layer, the second layer, and the third layer are 60 degrees, 60 degrees, 50 degrees, respectively. (2) The accuracy of multi-angle-based estimation models (R-2 = 0.87, RMSE = 2.86 mu g cm(-2)) are higher than nadir-based ones (R-2 = 0.72, RMSE = 4.24 mu g cm(-2)). This study proved that vertical distribution has a positive influence on the estimation results, and multi-angle hyperspectral data could be promising in improving estimation accuracy.