Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models

Abstract

Terrestrial gross primary production (GPP) is the largest global CO 2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000-2010 varied from 95.10 to 139.71 Pg C·yr −1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

“…1). Similar model structures like SOFIA where a response variable is controlled by a product of several functions have been previously applied in environmental modelling, for example, in light-use efficiency models to simulate NPP (Cai et al, 2014;Nemani et al, 2003) or in phenology models to simulate leaf development (Forkel et al, 2014;Jolly et al, 2005;Stöckli et al, 2011). The response value of the functional relationship can also be used to map sensitivities of burned area to environmental or socioeconomic variables.…”

Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing

“…Research scientists and other subject matter experts submitted innovative and challenging papers that showed advances in several topics: estimating the spatial distribution of plant species richness by Light Detection and Ranging (LiDAR) and hyperspectral data [1], assessing habitat quality of forest corridor based on NDVI [2], applying remote sensing to study (marine) coral ecosystems [3], identifying ecosystem functional types [4], distinguishing between different forest trunk size classes from remote sensing [5], detecting changes in forest patterns [6], applying light use efficiency models to estimate vegetation productivity [7], classifying grassland successional stages by airborne hyperspectral images [8], proposing monitoring programs of grasslands based on multi-temporal optical and radar satellite images [9], estimating the potential of remote sensing to capture field-based plants phenology [10].…”

“…[1,2,5,6]), also marine ecosystems were considered in the special issue as a core part of remote sensing use in ecology [3]. Very different remote sensing data, including optical and LiDAR data, were used, applying a variety of interesting models (Figure 1), from dynamic system models for phenology assessment [10] to light use efficiency models for inferring gross primary production [7] to modified random clustering to represent forest fragmentation [6]. The 50 researchers coming from nine countries (Figure 2) did extend the current knowledge on remote sensing applied to ecosystem monitoring based on previous literature which was fully brought up.…”

Effects of Climate Change and LUCC on Terrestrial Biomass in the Lower Heihe River Basin during 2001–2010

“…The results under Scenarios C1-C3 showed that changes in solar radiation, temperature and CO 2 concentration caused´7.37%, 9.89% and 25.55% of the total biomass accumulation change, respectively, indicating that the CO 2 concentration change was the most important climatic factor that increased the biomass accumulation. Solar radiation and temperature are not the primary controlling factors for interannual variability of biomass accumulation in most models [80], and this study showed that the solar radiation and temperature changes were not the key influencing factors of biomass accumulation change. However, this study showed there was still a rise in photosynthesis and biomass accumulation under elevated temperature, which was also consistent with previous studies [42,74,81].…”

“…However, there are only very few studies that estimated the relative contribution of FVCC or vegetation growth enhancement to biomass accumulation change, while most of previous studies only focused on LC [1,15,55,77,86,87]. In particular, the FVCC is more closely related with the water availability in the arid and semi-arid regions at small scales, which dominates the interannual variability of biomass accumulation over large vegetated areas in almost all models [50,80]. However, in most ecological models the water availability is represented with the precipitation rather than the groundwater, the major water source in the arid and semi-arid regions [33,51].…”