Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

“…Those are also regions where representation of GPP is the most challenging and thus where we expect the most divergence across GPP retrievals and models. Notably, correlation of RSIF with different products is very good at high latitudes, which is usually a challenge for vegetation indices which are oversensitive to snow cover and tend to mix snow melt (and thus an apparent greening) with photosynthetic activity (Jeong et al, 2017). …”

“…SIF has a small‐amplitude signal, so it was not possible to observe it until very recently. SIF observations have been shown to be directly pertinent to estimate crop photosynthesis (Guanter et al, 2014) and yield (Guan et al, 2016), GPP across ecosystems (Lee et al, 2015; Yang et al, 2015; Zhang, Xiao, Jin, et al, 2016), water stress (Guan et al, 2015; Konings et al, 2017; Sun et al, 2015; Zhang, Xiao, Guanter, et al, 2016), biosphere‐atmosphere interactions (Green et al, 2017), surface turbulent fluxes (Alemohammad et al, 2017), and phenology, especially in northern latitudes where vegetation indices and their seasonality are polluted by the snow albedo (Jeong et al, 2017). One other advantage of SIF is that it responds to only the PAR absorbed by chlorophyll of the canopy, whereas typical optical (absorbed photosynthetic active radiation) APAR or fPAR products reflect the PAR absorbed by the entire canopy (nonphotosynthetic and photosynthetic; Song et al, 2013).…”

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

“…It also reflects the difficulties of retrieving fluxes in snow-dominated regions. SIF has the great advantage that it is not directly sensitive to snow compared to vegetation indices, for instance, which incorrectly attribute snowmelt and changes in observed ground color to photosynthesis onset (Jeong et al, 2017).…”

An NDVI-Based Vegetation Phenology Is Improved to be More Consistent with Photosynthesis Dynamics through Applying a Light Use Efficiency Model over Boreal High-Latitude Forests

“…Many studies used NDVI to track seasonal dynamics of carbon assimilation, comparing NDVI with gross primary production (GPP) which represents the photosynthetic carbon uptake [6,7] over boreal forests. However, NDVI-based durations of growing season and photosynthesis season have been found to be significantly longer than those derived from both tower-based and simulated GPP [8,9]. As a matter of fact, NDVI increases earlier in the spring and decreases later in the fall than GPP.…”

“…Thus, in order to use NDVI-based phenology to track photosynthesis dynamics, one should also take seasonal cycles of PAR and LUE into consideration. Because VIs have been used as a robust proxy of fPAR [12] while PAR can be obtained from external data sources, many studies have used VI × PAR as a proxy for APAR [8,9]. However, Moderate Resolution Imaging Spectroradiometer-(MODIS-, onboard Aqua and Terra satellite instruments) based APAR is found to overestimate both the greenness season duration and the photosynthesis duration [8], and APAR is temporally biased compared to GPP [13], indicating that VI-based APAR is still not an appropriate proxy for carbon assimilation.…”