Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize

• Published in: International journal of applied earth observation and geoinformation Vol. 93; p. 102198
• Main Authors: Mao, Zhi-Hui, Deng, Lei, Duan, Fu-Zhou, Li, Xiao-Juan, Qiao, Dan-Yu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2020; Elsevier
• Subjects: Angle effect; BRDF; Multi-angular hyperspectral remote sensing; SPAD; Unmanned aerial vehicle; Vegetation index; Angle effect; Multi-angular hyperspectral remote sensing; Vegetation index; Unmanned aerial vehicle; BRDF; SPAD
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2020.102198

•Angle effect of VIs is greatly different for both soybean and maize.•SPAD inversion accuracy does not vary obviously with the change of viewing angle.•BRDF correction can improve the inversion accuracy of canopy SPAD values. To study the anisotropy of vegetation indices (VIs) and explore its influence on the retrieval accuracy of canopy soil-plant analyzer development (SPAD) value, the bidirectional reflectance distribution function (BRDF) models of soybean and maize are calculated from the multi-angle hyperspectral images acquired by UAV, respectively. According to the reflectance extracted from the BRDF model, the dependences of 16 commonly-used VIs on observation angles are analyzed, and the SPAD values of maize and soybean canopy are predicted by using the 16 VI values at different observation angles and their combinations as input parameters. The results show that the 16 VIs have different sensitivity to angle in the principal plane: green ratio vegetation index (GRVI), ratio vegetation index (RVI), red edge chlorophyll index (CIRE), and modified chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (MCARI/OSAVI) are very sensitive to angles, among which MCARI/OSAVI of maize fluctuated the most (138.83 %); in contrast, the green optimal soil adjusted vegetation index (GOSAVI), normalized difference vegetation index (NDVI), and green normalized difference vegetation index (GNDVI) hardly change with the observation angles. In terms of SPAD prediction, the accuracy of different VI is different, the mean absolute error (MAE) showed that MCARI1 provided the highest accuracy of retrieval for soybean (MAE=1.617), while for maize it was MCARI/OSAVI (MAE=2.422). However, when using the same VI, there was no significant difference in the accuracy of the predicted results, whether the VI from different angles was used or the combination of multi-angles was used. The present results provide guiding significance and practical value for the retrieval of SPAD value in vegetation canopies and in-depth applications of multi-angular remote sensing.