Data, Volume 1, Issue 2 (September 2016) – 6 articles

Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012. Full article

Post-mining sites have a significant impact on surrounding ecosystems. Afforestation can restore these ecosystems, but its success and speed depends on the properties of the excavated spoil substrates. Thermal infrared remote sensing brings advantages to the mapping and classification of spoil substrates, resulting in the determination of its properties. A library of spoil substrates containing spectral emissivity and chemical properties can facilitate remote sensing activities. This study presents spectral library of spoil substrates’ emissivities extracted from brown coal mining sites in the Czech Republic. Extracted samples were homogenized by drying and sieving. Spectral emissivity of each sample was determined by spectral smoothing algorithm applied to data measured by a Fourier transform infrared (FTIR) spectrometer. A set of chemical parameters (pH, conductivity, Na, K, Al, Fe, loss on ignition and polyphenol content) and toxicity were determined for each sample as well. The spectral library presented in this paper also offers valuable information in the form of geographical coordinates for the locations where samples were obtained. Presented data are unique in nature and can serve many remote sensing activities in longwave infrared electromagnetic spectrum. Full article

The Atmospheric Radiation Measurement (ARM) Data Archive established a data citation strategy based on Digital Object Identifiers (DOIs) for the ARM datasets in order to facilitate citing continuous and diverse ARM datasets in articles and other papers. This strategy eases the tracking of data provided as supplements to articles and papers. Additionally, it allows future data users and the ARM Climate Research Facility to easily locate the exact data used in various articles. Traditionally, DOIs are assigned to individual digital objects (a report or a data table), but for ARM datasets, these DOIs are assigned to an ARM data product. This eliminates the need for creating DOIs for numerous components of the ARM data product, in turn making it easier for users to manage and cite the ARM data with fewer DOIs. In addition, the ARM data infrastructure team, with input from scientific users, developed a citation format and an online data citation generation tool for continuous data streams. This citation format includes DOIs along with additional details such as spatial and temporal information. Full article

The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the marshland area in the National Park of Doñana (36.99 N, 6.44 W), and the semi-arid area of the National Park of Cabo de Gata (36.83 N, 2.25 W). This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS, and TIRS/Landsat-8. Full article

One of the major problems in the monitoring of tropical rainforests using satellite imagery is their persistent cloud coverage. The use of daily observations derived from high temporal resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), could potentially help to mitigate this issue, increasing the number of clear-sky observations. However, the cloud contamination effect should be removed from these results in order to provide a reliable description of these forests. In this study the available MODIS Land Surface Temperature (LST) products have been reprocessed over the Amazon Basin (10 N–20 S, 80 W–45 W) by introducing different cloud masking schemes. The monthly LST datasets can be used for the monitoring of thermal anomalies over the Amazon forests and the analysis of spatial patterns of warming events at higher spatial resolutions than other climatic datasets. Full article

Climate archives are time series. They are used to assess temporal trends of a climate-dependent target variable, and to make climate atlases. A high-resolution gridded dataset with 1728 layers of monthly mean maximum, mean and mean minimum temperatures and precipitation for the NW Maghreb (28°N–37.3°N, 12°W–12°E, ~1-km resolution) from 1973 through 2008 is presented. The surfaces were spatially interpolated by ANUSPLIN, a thin-plate smoothing spline technique approved by the World Meteorological Organization (WMO), from georeferenced climate records drawn from the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network-Monthly (GHCN-Monthly version 3) products. Absolute errors for surface temperatures are approximately 0.5 °C for mean and mean minimum temperatures, and peak up to 1.76 °C for mean maximum temperatures in summer months. For precipitation, the mean absolute error ranged from 1.2 to 2.5 mm, but very low summer precipitation caused relative errors of up to 40% in July. The archive successfully captures climate variations associated with large to medium geographic gradients. This includes the main aridity gradient which increases in the S and SE, as well as its breaking points, marked by the Atlas mountain range. It also conveys topographic effects linked to kilometric relief mesoforms. Full article