A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval.

• Seedling rapeseed Pn correlated with SIF-VIs derived from UAV data significantly. • Composite indices of SIF × VIs further improved the correlation with Pn. • Photosynthesis-related plant status could be monitored by multi-source UAV data. Net photosynthesis rate (Pn) can be used to characterize the health status of plants and their ability to accumulate organic matter. In this study, remotely sensed vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) were retrieved to build regression models to estimate rapeseed canopy Pn. Multi-source unmanned aerial vehicle (UAV) remote sensing data collected from seedling stage rapeseed were used in this study. The results showed that Pn was significantly related to traditional VIs and SIF (R2 = 0.52, p < 0.01). A quadratic polynomial regression model built using the normalized difference vegetation index performed the best on the inversion of Pn (R2 = 0.63, RMSE = 2.56, NRMSE = 0.18). Moreover, this study coupled SIF with traditional VIs by mathematical operations. The composite indices obtained by multiplication resulted in increased correlations. The inversion model established using SIF × VARI (visible atmospherically resistant index) achieved the best overall performance with 0.14 increase in R2 (0.54–0.68) and 0.48 decrease in RMSE (2.87–2.39) compared to SIF, 0.13 increase in R2 (0.55–0.68) and 0.45 decrease in RMSE (2.84–2.39) compared to VARI. Therefore, a novel composite index obtained from the multiplication operation of individual indices improved Pn retrieval of seedling rapeseed from remotely sensed UAV data. The results from this study indicate that the novel composite index has the potential for improving the accuracy of growth status monitoring compared with traditional indices. [ABSTRACT FROM AUTHOR]