The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Arctic terrestrial ecosystems are major global sources of methane (CH 4 ); hence, it is important to understand the seasonal and climatic controls on CH 4 emissions from these systems. Here, we report year-round CH 4 emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH 4 flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the "zero curtain" period, when subsurface soil temperatures are poised near 0°C. The zero curtain may persist longer than the growing season, and CH 4 emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH 4 derived from aircraft data demonstrate the large spatial extent of late season CH 4 emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH 4 y −1 , ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH 4 emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.permafrost | aircraft | fall | winter | warming E missions of methane (CH 4 ) from Arctic terrestrial ecosystems could increase dramatically in response to climate change (1-3), a potentially significant positive feedback on climate warming. High latitudes have warmed at a rate almost two times faster than the Northern Hemisphere mean over the past century, with the most intense warming in the colder seasons (4) [up to 4°C in winter in 30 y (5)]. Poor understanding of controls on CH 4 emissions outside of the summer season (6-10) represents a large source of uncertainty for the Arctic CH 4 budget. Warmer air temperatures and increased snowfall can potentially increase soil temperatures and deepen the seasonal thawed layer, stimulating CH 4 and CO 2 emissions from the vast stores of labile organic matter in the Arctic (11). The overwhelming majority of prior studies of CH 4 fluxes in the Arctic have been carried out during the summer months (12-15). However, the fall, winter, and spring months represent 70-80% of the year in the Arctic and have been shown to have significant emissions of CO 2 (16-18). The few measurements of CH 4 fluxes in the Arctic that extend into the fall (6, 7, 9, 10) show complex patterns of CH 4 emissions, with a number indicating high fluxes (7, 10). Winter and early spring data appear to be absent in Arctic tundra over continuous permafrost.Beginning usually in late August or early September, the...

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the OPEN ACCESS Remote Sens. 2015, 7 2972 three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost.

Abstract. Due to the major role of the sun in heating the earth's surface, the atmospheric planetary boundary layer over land is inherently marked by a diurnal cycle. The afternoon transition, the period of the day that connects the daytime dry convective boundary layer to the night-time stable boundary layer, still has a number of unanswered scientific questions. This phase of the diurnal cycle is challenging from both modelling and observational perspectives: it is transitory, most of the forcings are small or null and the turbulence regime changes from fully convective, close to homogeneous and isotropic, toward a more heterogeneous and intermittent state.These issues motivated the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign that was conducted from 14 June to 8 July 2011 in southern France, in an area of complex and heterogeneous terrain. A wide range of instrumented platforms including full-size aircraft, remotely piloted aircraft systems, remote-sensing instruments, radiosoundings, tethered balloons, surface flux stations and various meteorological towers were deployed over different surface types. The boundary layer, from the earth's surface to the free troposphere, was probed during the entire day, with a focus and intense observation periods that were conducted from midday until sunset. The BLLAST field campaign also provided an opportunity to test innovative measurement systems, such as new miniaturized sensors, and a new technique for frequent radiosoundings of the low troposphere.Twelve fair weather days displaying various meteorological conditions were extensively documented during the field experiment. The boundary-layer growth varied from one day to another depending on many contributions including stability, advection, subsidence, the state of the previous day's residual layer, as well as local, meso-or synoptic scale conditions.Ground-based measurements combined with tetheredballoon and airborne observations captured the turbulence decay from the surface throughout the whole boundary layer and documented the evolution of the turbulence characteristic length scales during the transition period.Closely integrated with the field experiment, numerical studies are now underway with a complete hierarchy of models to support the data interpretation and improve the model representations.