# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Terrestrial Ecosystems
#### 2.5.2 Vegetation Growth and Photosynthetic Carbon Assimilation  
A number of remote sensing vegetation indices such as NDVI, leaf area index (LAI), and enhanced vegetation index (EVI) have been developed to characterize vegetation properties related to photosynthesis on a per-pixel basis [209]. In Arctic-boreal regions, multispectral satellite data have been used to quantify variables related to vegetation growth [210], biomass [211; 212], and carbon fluxes [213]. Satellite microwave systems can also provide information about vegetation growth and carbon assimilation. For example, vegetation optical depth (VOD) derived from satellite microwave Tb observations is sensitive to vegetation water content and provides information on both canopy biomass (photosynthetic and non-photosynthetic) phenology and drought stress [214; 215; 216; 217; 218]. VOD has also been used to monitor vegetation growth and recovery after fires in boreal forests [219].  
Much of the remote sensing-based work for plant growth and carbon cycling has focused on modeling gross primary productivity (GPP), which represents carbon biomass created by plants through the process of photosynthesis over a given length of time and space (e.g., m\({}^{2}\) day\({}^{-1}\)). GPP also represents the amount of atmospheric CO\({}_{2}\) sequestered by plants within biomass. GPP models are often based on a light-use efficiency (LUE) framework, which estimates GPP as a function of APAR, the fraction of absorbed photosynthetically active radiation (PAR), and a photosynthetic efficiency parameter which describes the rate at which absorbed radiation is used for carbon fixation [220]. To model APAR, inputs of LAI and fPAR (the fraction of canopy absorbed PAR) are needed, which can be modeled using spectral reflectance data combined with radiative transfer algorithms [221]. Alternatively, NDVI can be used as a proxy for fPAR [222]. The efficiency parameter is more difficult to model and can vary based on vegetation type, water limitations, and light conditions [220]. However, GPP has also been shown to be directly related to the EVI, which tends to be less saturated than NDVI over higher canopy densities and less sensitive to soil background noise [220]. Relationships between EVI and GPP were shown to vary among sites, with correlations generally stronger for deciduous sites than for evergreen sites [220]. Another important and newer proxy for GPP is solar-induced fluorescence (SIF) [223]. SIF quantifies the amount of light reemitted by chlorophyll molecules as a byproduct of photosynthesis, and has been shown to be directly proportional to GPP [224]. Satellite-based SIF (e.g., from the Global Ozone Monitoring Experiment 2 [225] and Orbiting Carbon Observatory-2 [226]), allows for global monitoring of terrestrial GPP and the carbon cycle. However, the SIF signal has relatively small magnitude and generally requires a large sensor footprint and coarse temporal compositing of the data to obtain an adequate signal-to-noise ratio relative to NDVI and EVI observations [227].  
Aboveground biomass (AGB) is an important component of global carbon accounting. Remote sensing techniques are used to estimate AGB by capturing both vertical structure and spatial variability of canopies. For example, AGB has been estimated from LIDAR-derived canopy height and vertical structure metrics [228; 229]. Biomass was mapped for the circumboreal zone using multi-step modeling that combined field-based biomass data, airborne and satellite LIDAR data [230; 231], and similarly at the regional scale for boreal forest in Quebec [232]. Other studies demonstrated the potential of low-frequency (e.g., L- and P-band) polarimetric SAR/InSAR data for estimating AGB in boreal forests and over complex terrain [233; 234; 235; 236; 237].