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Volume 1, September

Data, Volume 1, Issue 1 (June 2016) – 7 articles

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Data Descriptor
The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance
Data 2016, 1(1), 6; https://doi.org/10.3390/data1010006 - 07 Jun 2016
Cited by 5 | Viewed by 4154
Abstract
A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. [...] Read more.
A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. For this, test sites are required to use in calibration and validation of the remote sensing approaches in order to improve the ECV retrievals at global scale. The southern hemisphere presents scarce test sites for calibration and validation field campaigns that focus on soil moisture and land surface temperature retrievals. In Chile, remote sensing applications related to soil moisture estimates have increased during the last decades because of the drought and water use conflicts that generate a strong interest on improved water demand estimates. This work describes the Laboratory for Analysis of the Biosphere (LAB)—NETwork, called herein after ‘LAB-net’, which was designed to be the first network in Chile for remote sensing applications. The test sites were placed in four sites with different cover types: vineyards and olive orchards located in the semi-arid region of Atacama, an irrigated raspberry crop in the Mediterranean climate zone of Chimbarongo, and a rainfed pasture in the south of Chile. Over each site, well implemented meteorological and radiative flux instrumentation was installed and continuously recorded the following parameters: soil moisture and temperature at two ground levels (10 and 20 cm), air temperature and relative humidity, net radiation, global radiation, radiometric temperature (8–14 µm), rainfall and soil heat flux. The LAB-net data base post-processing procedure is also described here. As an application, surface remote sensing products such as soil moisture data derived from the Soil Moisture Ocean Salinity (SMOS) and Land Surface Temperature (LST) extracted from the MODIS-MOD11A1 and GOES LST from Copernicus products were compared to in situ data in Oromo LAB-net site. Moreover, land surface energy flux estimation is also shown as an application of LAB-net data base. These applications revealed a good performance between in situ and remote sensing data. LAB-net data base also contributes to provide suitable information for land surface energy budget and therefore water resources management at cultivars scale. The data based generated by LAB-net is freely available for any research or scientific purpose related to current and future remote sensing applications. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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Data Descriptor
A MODIS/ASTER Airborne Simulator (MASTER) Imagery for Urban Heat Island Research
Data 2016, 1(1), 7; https://doi.org/10.3390/data1010007 - 06 Jun 2016
Cited by 9 | Viewed by 4454
Abstract
Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging [...] Read more.
Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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Data Descriptor
Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs
Data 2016, 1(1), 5; https://doi.org/10.3390/data1010005 - 12 May 2016
Cited by 1 | Viewed by 2525
Abstract
Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area [...] Read more.
Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area have yielded these layers that are now available online to download and for use by other researchers interested in studying the argali patterns and habitat suitability in the southeastern Tajik Pamirs. It is important to note that the layers were generated using a 30-m Landsat ETM image and field data from 2012. Full article
(This article belongs to the Special Issue Geospatial Data)
Article
Open Access Article Processing Charges (OA APC) Longitudinal Study 2015 Preliminary Dataset
Data 2016, 1(1), 4; https://doi.org/10.3390/data1010004 - 14 Apr 2016
Cited by 4 | Viewed by 3198
Abstract
This article documents Open access article processing charges (OA APC) longitudinal study 2015 preliminary dataset available for download from the OA APC dataverse [1]. This dataset was gathered as part of Sustaining the Knowledge Commons (SKC), a research program funded by [...] Read more.
This article documents Open access article processing charges (OA APC) longitudinal study 2015 preliminary dataset available for download from the OA APC dataverse [1]. This dataset was gathered as part of Sustaining the Knowledge Commons (SKC), a research program funded by Canada’s Social Sciences and Humanities Research Council. The overall goal of SKC is to advance our collective knowledge about how to transition scholarly publishing from a system dependent on subscriptions and purchase to one that is fully open access. The OA APC preliminary data 2015 Version 12 dataset was developed as one of the lines of research of SKC, a longitudinal study of the minority (about a third) of the fully open access journals that use this business model. The original idea was to gather data during an annual two-week census period. The volume of data and growth in this area makes this an impractical goal. For this reason, we are posting this preliminary dataset in case it might be helpful to others working in this area. Future data gathering and analyses will be conducted on an ongoing basis. We encourage others to share their data as well. In order to merge datasets, note that the two most critical elements for matching data and merging datasets are the journal title and ISSN. Full article
Data Descriptor
A Unified Cropland Layer at 250 m for Global Agriculture Monitoring
Data 2016, 1(1), 3; https://doi.org/10.3390/data1010003 - 19 Mar 2016
Cited by 38 | Viewed by 6133
Abstract
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus [...] Read more.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products. Full article
(This article belongs to the Special Issue Geospatial Data)
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Editorial
Journal Data: A New Platform for Data Research
Data 2016, 1(1), 9-10; https://doi.org/10.3390/data1010009 - 02 Sep 2015
Viewed by 2631
Abstract
There is no doubt that data is of paramount importance to scientific progress. With the enormous development of science and technology, a huge amount of data fragments is produced every day. [...] Full article
Data Descriptor
Relational Data on Members of Portuguese Governments (1976–2014)
Data 2016, 1(1), 1-8; https://doi.org/10.3390/data1010001 - 24 Aug 2015
Cited by 1 | Viewed by 2687
Abstract
A data set containing information on the explicit connections concerning all members of Portuguese governments from 1976 until July 2013 is presented. This information was collected through a one-year research carried out by the authors using public records and official information (public and [...] Read more.
A data set containing information on the explicit connections concerning all members of Portuguese governments from 1976 until July 2013 is presented. This information was collected through a one-year research carried out by the authors using public records and official information (public and private institutions). The data set was collected during the process of elaborating a book [1]. This database is the first open-access source of information on a specific type of community which enables a wide range of research in areas such as social and political sciences and economics. Full article
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