Geographic/geospatial data are heterogeneous, multidisciplinary, and distributed. Geographic research and applications commonly involve data, sometimes in large volumes, of different formats from different providers. It is often difficult for end users to search, find, and acquire data and to prepare data obtained from different sources into a form readily to be analyzed. It is desirable that a set of consensus-based data standards are available to describe geographic data as well as tools providing such data so that data providers can publish and end users can find and acquire data using standard protocols. In this presentation, standard based interoperable protocols developed by the Open Geospatial Consortium (OGC) for geographic data cataloguing, accessing, and processing will be introduced, including data models and service interfaces for different types of geographic data.

The Geography Seminar Series is organized by the Geographical Honors Society Gamma Theta Upsilon Eta Omicron Chapter.

Information on land surface temperature (LST) and its diurnal cycle (LSTD) is important for understanding climate change, modeling the hydrological and biogeochemical cycles, and is a prime candidate parameter for Numerical Weather Prediction assimilation models. Diurnal temperature range (DTR), the difference between the daily maximum and minimum LST, is an important indicator of climate change. Using polar orbiting observations, such as the AVHRR and MODIS, it would not be possible to estimate the diurnal cycle. Geostationary satellites with high temporal resolution provide good diurnal coverage, which makes them attractive for deriving information on LST diurnal cycle.
Improved algorithms for retrieving surface temperature from satellite infrared measurements were proposed by using land cover information instead of traditional surface emissivity, and made uniform surface temperature algorithms, which can be applied to land surface temperature (LST), sea surface temperature (SST), and snow and ice surface temperature (IST) retrieval with the same methodology, at the same time, improved the algorithm accuracy. When the developed algorithm was applied to the GOES-8 observations, for the first time, a satellite view of DTR was shown over the Unite States.
Due to the lack of split-window channels in the GOES M (12)–Q (16) series, which will has and will have only one thermal (11 μm) window channel. A new two-channel algorithm for deriving surface temperatures from the GOES M–Q series has been developed, which is based on radiative transfer theory and aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. Observations from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) on the METEOSAT Second Generation (MSG) satellite are available at 15 minutes intervals at a resolution of 3 km in four relevant channels. A new four-channel surface temperature (ST) algorithm based on the characteristics of the SEVIRI three window thermal channels (8.7, 10.8, 12.0 μm) and one middle-infrared channel (3.9 μm) and radiative transfer model simulations was developed. Due to the similarity of future U.S. GOES-R imager ABI with the SEVIRI, a byproduct of this work will be familiarity with multi-spectral geostationary data in advance of the launch of such a U.S. satellite. This should allow us to contribute to the improvement of future satellite designs.
The proposed algorithms are also applicable for the derivation of SST for which less restrictive assumptions on surface emissivity apply. The proposed algorithms are compared with various split window type algorithms and evaluated against available ground observations, and shows improvements in the determination of ST.