Tracking autumn photosynthetic phenology on Tibetan plateau grassland with the green–red vegetation index

Abstract

Accurate monitoring autumn photosynthetic phenology is essential for understanding carbon cycles. The broadband green-red vegetation index (GRVI) derived from broadband red and green reflectance has been increasingly used in this field. However, the performance of GRVI in large areas remains unclear. We evaluated the performance of the normalized vegetation index (NDVI), normalized difference greenness index (NDGI), the near-infrared reflectance of vegetation (NIRv), solar-induced chlorophyll fluorescence (SIF) and GRVI in tracking autumn photosynthetic phenology of alpine grassland at flux sites and the entire Tibetan Plateau (TP). The results revealed that GRVI (R2 = 0.42, RMSE = 7.68 d and Bias = 4.39 d) performed comparable with SIF (R2 = 0.45, RMSE = 5.79 d and Bias = 0.71 d) in extracting the end of the photosynthetically active season (EOS) with eddy covariance flux measurements as reference. On contrary, a systematically later EOS was estimated by NDVI (Bias = 20.35 d), NDGI (Bias = 13.62 d) and NIRv (Bias = 6.56 d). The application example on the TP collaborated these findings. The divergent performances between indicators were rooted in the photosynthesis downregulation in autumn which is jointly controlled by canopy structure and physiology. Due to the fact that NDVI, NDGI and NIRv primarily depict vegetation structure, while the use of SIF, which represents vegetation photosynthetic physiology, is influenced by the limited spatial resolution and temporal coverage. Our study highlights the unique advantage of GRVI over other existing satellite indicators for estimating autumn photosynthetic phenology with high resolution and long-time span. We suggest revisiting the dynamics of autumn photosynthetic phenology using GRVI, which has significant implications on carbon uptake studies.