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The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day-night conditions on tree growth remained uncertain. Here we present the first comprehensive field study of hourly-resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 years. We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species-specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.

Monitoring of plant succession in glacier forelands so far has been restricted to field sampling. In this study, in situ vegetation sampling along a chronosequence between Little Ice Age (LIA) maximum extent and the recent glacier terminus at Jamtalferner/Silvretta (ferner is a Tyrolian toponym for glacier) is compared to time series of the Normalized Difference Vegetation Index (NDVI) calculated from 13 Landsat scenes (1985-2016). The glacier terminus positions at 16 dates between the LIA maximum and 2015 were analysed from historical maps, orthophotos and LiDAR images and used for site age determination. We sampled plots of different time since deglaciation, from very recent to approx. 150 years: after 100 years, roughly 80% of the ground is covered by plants and ground cover did not increase essentially thereafter. Species number increases from 10-20 species on young sites to 40-50 species after 100 years. The NDVI increases for all plots between 1985 and 2016, from a mean of 0.11 for 1985-1991 to 0.2 in 2009 and 0.27 in 2016. For the plots deglaciated between 1 and about 150 years, the NDVI increases with the time of exposure. As the increase in ground cover is clearly reproduced by the NDVI (R² ground cover/NDVI 0.84) - even for sparsely vegetated areas -, we see a high potential of satellite-borne NDVI to perform regional characterizations of glacier forelands for hydrological, ecological and hazard management related applications. This data collection comprises the galcier outlines, NDVIs and chronosequencing locations with diversity and ground cover data.

The data shared in this package delivers the wildfire ignition probability and spreading capacity of vegetated surfaces in Romania following the method developed by Hysa and Baskaya (2019, https://doi.org/10.1007/s40808-018-0519-9). The model relies on remotely sensed free data that covers the time-lapse between 2015-2020. Geospatial information about sixteen criteria about anthropogenic, hydro-meteorological, geophysical, and fuel properties of Romanian territory are considered here. Raw data regarding each criterion is acquired for free from different online databases. The attribute table of the shared shapefile includes all inventory measurements per each criterion. It consist of 70410 point geometries in total representing 1km2 each, covering all vegetated surfaces of Romania. This data consist of a geospatial points layer (shp file), which deliver both the multi-criteria inventory records and the calculated wildfire ignition probability and wildfire spreading capacity (WIPI/WSCI) of the Romanian vegetated surfaces. The distance between points is 1km. The file consists of 70410 points in total, that overlap with the vegetated surfaces as derived from CORINE Land Cover data of 2018.

We studied if functional traits related to resource preemption (light and inorganic nutrients) exert control on space preemption of tropical seagrass meadows. Additionally, we studied if space preemption changed under different eutrophication scenarios. We took seagrass abundance data to study space preemption, seagrass traits data to study their effect on space preemption and eutrophication indicators to evaluate the level of eutrophication at each site/sampling event. The data was collected in Unguja Island (Zanzibar Archipealgo, Tanzania) in seven sites/sampling events (Harbor, Chapwani, Changuu, Bweleo, Fumba, Mangroves and Marumbi). Each site/sampling event comprised a subtidal seagrass meadow (2-4 meters depth) of around 2500 square meters, delimited by the coastline and a fringing reef. The data was taken between the 26.09.2016 to the 05.10.2016. In each site/sampling event, five 50 meters transects were deployed perpendicular to the coast and paralel to each other, approximately separated by 50 meters. The areas enclosed beweeen the transects were names A, B, C and D. Macroalgae biomass was collected as an indicator of eutrophication. Macroalgae biomass was quantified along five 50-m transects per site/sampling event, set perpendicular to the coast and parallel to each other, separated by ~50 meters. We collected the macroalgae present in three random 0.25x0.25 meters quadrats per transect. The macroalgae samples were cleaned of sediments and rinsed with water. They were then dried at 50°C in a forced air oven until constant dry weight. The macroalgae biomass was calculated as the grams of dry weight divided by the area of the quadrat (grams of dry weight per square meter).

Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2015 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day-night conditions on tree growth remained uncertain. Here we present the first comprehensive field study of hourly-resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 years. We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species-specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.

AntAir is a dataset of gridded air temperatures in 1km spatial and daily temporal resolution currently available for the years 2003-2016. AntAir was created by modelling daily air temperature from MODIS land surface temperature using machine learning algorithms. Data from 70 weather stations was used as a reference. The dataset covers the entire continent of Antarctica in 1km spatial resolution for the period 2003 to 2016. Each year consists of 365 raster layers that represent the predicted air temperature for the respective day. The raster layers are distributed using the GeoTIFF format and the Antarctic Polar Stereographic projection (EPSG 3031). The unit of the values is degree Celsius * 10. Clouded areas where no air temperature predictions could be made for are marked as no data.

The data describes plant gas exchange dynamics (CO2, H2O) together with online proton transfer reaction mass spectrometry measurements of biogenic volatile organic compound emissions of Pinus halepensis seedlings exposed to two similar heatwaves together with drought and a recovery period. Measured in a scientific glasshouse facility at KIT IMK-IFU Garmisch-Partenkirchen, Germany, via an automated chamber setup. #0 means "Inf"