Vegetation carbon turnover processes in forest ecosystems and their dominant drivers are far from being understood at a broader scale. Many of these turnover processes act on long timescales and include a lateral dimension and thus can hardly be investigated by plot‐level studies alone. Making use of remote sensing‐based products of net primary production (NPP) and biomass, here we show that spatial gradients of carbon turnover rate (k) in Northern Hemisphere boreal and temperate forests are explained by different climate‐related processes depending on the ecosystem. k is related to frost damage effects and the trade‐off between growth and frost adaptation in boreal forests, while drought stress and climate effects on insects and pathogens can explain an elevated k in temperate forests. By identifying relevant processes underlying broadscale patterns in k, we provide the basis for a detailed exploration of these mechanisms in field studies, and ultimately the improvement of their representations in global vegetation models (GVMs).

Turnover concepts in state‐of‐the‐art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation‐based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large‐scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models.

h i g h l i g h t s < Future poplar biomass plantations may significantly impact ozone levels in Europe. < Growing poplar on 5% of European farmland enhances isoprene emissions by 45%. < Ozone indicators for human health and vegetation increase by up to 25% and 40%. < Air pollution mitigation strategies should consider land use management. a b s t r a c tTropospheric ozone contributes to the removal of air pollutants from the atmosphere but is itself a pollutant that is harmful to human health and vegetation. Biogenic isoprene emissions are important ozone precursors, and therefore future changes in land use that change isoprene emissions are likely to affect atmospheric ozone concentrations. Here, we use the chemical transport model LOTOS-EUROS (dedicated to the regional modeling of trace gases in Europe) to study a scenario in which 5% of the crop-and grass-land in Europe is converted into poplar plantations to be used for biofuel production. Although this scenario is rather conservative, our simulations project that isoprene emissions are substantially increased by an average of 45% over the simulated domain. As a consequence, ozone peak values are expected to increase by up to 6%, and ozone indicators for damage to human health and vegetation by up to 25% and 40%, respectively. Finally, we show that after the change in land use NO x emission reductions of 15e20% in Europe would be required to restore the ozone levels to current values. Because biomass production is expected to increase throughout Europe in the coming decades, we conclude that careful consideration of the tree types and regions to be used is required to constrain the concomitant air pollution to a minimum.

The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data (>250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat-8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010-2012, and regional Sentinel-1 and Sentinel-2 collections of ∼1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics. ARTICLE HISTORY

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002-2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10˝), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002-March 2012. Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R 2 : 0.88) of agreement on a global scale.