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

Authors: ZHANG, JIAN - Huazhong Agricultural University; SUN, BO - Huazhong Agricultural University; Yang, Chenghai; WANG, CHUNYUN - Huazhong Agricultural University; YOU, YUNHAO - Huazhong Agricultural University; ZHOU, GUANGSHENG - Huazhong Agricultural University; LIU, BIN - Huazhong Agricultural University; WANG, CHUFENG - Huazhong Agricultural University; KUAI, JIE - Huazhong Agricultural University; XIE, JING - Huazhong Agricultural University

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/2/2022
Publication Date: 5/21/2022
Citation: Zhang, J., Sun, B., Yang, C., Wang, C., You, Y., Zhou, G., Liu, B., Wang, C., Kuai, J., Xie, J. 2022. A novel composite vegetation index including solar-induced chlorophyll fluorescence for seedling rapeseed net photosynthesis rate retrieval. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107031.
DOI: https://doi.org/10.1016/j.compag.2022.107031

Interpretive Summary: Net photosynthesis rate can be used to characterize the health status of plants and their ability to accumulate organic matter. In this study, a novel composite index derived from traditional vegetation indices and solar-induced chlorophyll fluorescence was proposed to estimate rapeseed canopy net photosynthesis rate. Multi-source unmanned aerial vehicle remote sensing data from seedling rapeseed were used to retrieve vegetation indices and solar-induced chlorophyll fluorescence. Statistical analysis showed that coupled fluorescence with traditional vegetation indices by mathematical operations, the composite index achieved better performance than either solar-induced chlorophyll fluorescence or traditional vegetation indices. The results from this study indicate that the novel composite index has the potential to improve the accuracy of net photosynthesis rate retrieval for crop growth status monitoring compared with traditional methods.

Technical Abstract: 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.