Wang Siyu

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Wang
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Siyu
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  • Preprint
    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
    ( 2018-06) Lu, Xinchen ; Cheng, Xiao ; Li, Xianglan ; Chen, Jiquan ; Sun, Minmin ; Ji, Ming ; He, Hong ; Wang, Siyu ; Li, Sen ; Tang, Jianwu
    Characterized by the noticeable seasonal patterns of 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 remote sensing based 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 potentials 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. Here, we present a cross-site intercomparison of one emerging remote sensing based index of phenological index (PI) and two SIF datasets against the conventional indexes of 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 productivity (GPP) and absorbed photosynthetically active radiation (APAR) during the growing seasons. Key phenological metrics 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.