Publication Date:
2022-05-25
Description:
© The Author(s), 2018. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Science of The Total Environment 644 (2018): 439-451, doi:10.1016/j.scitotenv.2018.06.269.
Description:
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.
Description:
This work was supported by the Chinese Arctic and Antarctic Administration, National Natural Science Foundation of China (Grant Nos. 41676176 and 41676182), the Chinese Polar Environment Comprehensive Investigation, Assessment Program (Grant No. 312231103). This work was also supported by the Fundamental Research Funds for the 440 Central Universities
Description:
2020-07-11
Keywords:
Phenology
;
Remote sensing
;
Photosynthesis
;
OCO-2
;
SIF
;
NDVI
;
EVI
;
PI
;
LAI
Repository Name:
Woods Hole Open Access Server
Type:
Preprint
Permalink
