Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison

Abstract

Characterized by the noticeable seasonal patterns of canopy photosynthesis, mid-to-high latitude forests are sensitive to climate change and crucial for understanding the global carbon cycle. To monitor the seasonal cycle of the canopy photosynthesis from space, several remotely sensed indexes, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) have been implemented within the past decades. Recently, satellite-derived sun-induced fluorescence (SIF) has shown great potential of providing retrievals that are more related to photosynthesis process. However, the potentials of different canopy measurements have not been thoroughly assessed in the context of recent advances of new satellites and proposals of improved indexes. At 15 forested sites, we present a cross-platform intercomparison of one emerging remote sensing based index of phenology index (PI) and two SIF datasets against the conventional indexes such as NDVI, EVI, and LAI to capture the seasonal cycles of canopy photosynthesis. NDVI, EVI, LAI, and PI were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, while SIF were evaluated from Global Ozone Monitoring Experiment-2 (GOME-2) and Orbiting Carbon Observatory-2 (OCO-2) observations. Results indicated that GOME-2 SIF was highly correlated with gross primary production (GPP) and absorbed photosynthetically active radiation during the growing seasons. The SIF-GPP relationship can generally be considered linear at the 16-day scale. Key phenological metrics such as start of the seasons and end of the seasons captured by SIF from GOME-2 and OCO-2 matched closely with photosynthesis phenology as inferred by GPP. However, the applications of OCO-2 SIF for phenological studies may be limited only for a small range of sites (at site-level) due to a limited spatial sampling. Among the MODIS estimations, PI and NDVI provided most reliable predictions of start of growing seasons, while no indexes accurately captured the end of growing seasons.