NASA NEESPI Data and Services Center for Satellite Remote Sensing Information

One of the research strategies of the Northern Eurasia Earth Science Partnership Initiative (NEESPI) is to incorporate a variety of tools aimed at facilitating scientiﬁc investigations of the Northern Eurasia environment and its ongoing change. These tools include data management and analysis of remote sensing and modeled data. Remote sensing in Northern Eurasia is of particular importance because vast areas of the region are not well covered by in situ observations. In addition, the quality of some available sources (e.g. ﬁre observations) is not sufﬁcient to support scientiﬁc research (Conard et al 2002). Data from different sensors are generally presented in different formats and many come in different spatial or temporal resolution. Reprocessing different datasets prior to conducting data analysis is a time-consuming effort that involves understanding the dataset’s structure and limitations, developing dataset speciﬁc processing approaches, and resampling the data to a common resolution and projection. The NASA NEESPI Data Center is designed to preprocess and archive remote sensing and modeled data from different resources into a cohesive, well-architectured data management system with convenient data access to geophysical parameters measured in or related to Northern Eurasia. The data are in uniform format and at common resolution (1 degree), and can be found and accessed through the search engine—Mirador—and are available for download in HDF or ASCII formats directly from an ftp site

One of the research strategies of the Northern Eurasia Earth Science Partnership Initiative (NEESPI) is to incorporate a variety of tools aimed at facilitating scientific investigations of the Northern Eurasia environment and its ongoing change.These tools include data management and analysis of remote sensing and modeled data.Remote sensing in Northern Eurasia is of particular importance because vast areas of the region are not well covered by in situ observations.In addition, the quality of some available sources (e.g.fire observations) is not sufficient to support scientific research (Conard et al 2002).Data from different sensors are generally presented in different formats and many come in different spatial or temporal resolution.Reprocessing different datasets prior to conducting data analysis is a time-consuming effort that involves understanding the dataset's structure and limitations, developing dataset specific processing approaches, and resampling the data to a common resolution and projection.
The NASA NEESPI Data Center is designed to preprocess and archive remote sensing and modeled data from different resources into a cohesive, well-architectured data management system with convenient data access to geophysical parameters measured in or related to Northern Eurasia.The data are in uniform format and at common resolution (1 degree), and can be found and accessed through the search engine-Mirador-and are available for download in HDF or ASCII formats directly from an ftp site.More importantly, the center provides a straightforward approach to online data visualization and analysis and reduces the need for data download.Giovanni is an acronym for the Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Acker and Leptoukh 2007).It is a web-based application developed by the NASA Goddard Space Flight Center that provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data without having to download them.Giovanni-NEESPI is a Giovanni instance that focuses on atmospheric, land surface, and cryospheric products within the boundaries of Northern Eurasia to support the NEESPI project.
The current Giovanni-NEESPI version contains both global and NEESPI specific datasets at 1 degree spatial and monthly temporal resolutions organized by three general biospheric components (table 1).
The Giovanni-NEESPI system provides metadata and supporting documentation which describes datasets' structure, parameters, format, spatial extent, fill values, temporal coverage, source data, and reprocessing approach used in aggregating the source data to the 1 degree resolution.The standard HDF file format also allows for including the metadata for each individual parameter/file within the file header.
During development of the system, extensive tests were performed to ensure the scientific quality of the output image and data.
The major advantage of the Givanni-NEESPI system lies in the provision of on-line visualization and data exploration tools.These tools, summarized below, present a variety of analysis techniques that provide an approach to evaluation of potential linkages between input data parameters.The system is well adapted to plot and map single and multi-source parameters.The plot types for single parameters include: The multi-parameter inter-comparison includes plot types similar to the single parameter evaluation as well as specific approach including: • lat-lon area plots of overlain time-averaged parameters • time series plots of multiple parameters • time series of two parameter difference • lat-lon area plot of two parameter difference • scatter plots with regression • temporal correlation maps.
The Giovanni-NEESPI system also supports web services for downloading full or subsetted data to the region of interest which contains product lineage, i.e. a brief description of how images and data were created.

Examples of Giovanni-NEESPI use (a) Fire event
In May and June 2003, a number of forest fires burned across Russia's Chita region east of Lake Baikal.Thick smoke swirled in the skies over the region.The monthly fire (Giglio et al 2006) and aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite reveal this fire event.Figure 1 shows images created easily using Giovanni-NEESPI.The top panel is cloud and overpass corrected fire count.The lower panel is aerosol optical depth at 550 nm which shows a maximum of aerosol amount downwind from the area most affected by burning.

(c) Inter-comparisons of products from different instruments
Inter-comparison between instruments and models may help diagnose problems and enhance data processing algorithms and procedures to improve data quality.Giovanni-NEESPI features several commonly used inter-comparison methods, such as scatter plot and difference.Figure 3 shows scatter plots of the atmospheric water vapor from MODIS Terra and Aqua for summer 2003 (left panel) and winter 2003-2004 (right panel) over Northern Eurasia.The water vapor amounts derived from the two instruments correlate well, with an overall bias towards higher amounts from Terra.This observation is true for both summer and winter.
The observed relationship may be explained by differences in overpass times between the two sensors as well as differences in data and processing.

Figure 1 .
Figure 1.The top panel is cloud and overpass corrected fire count.The lower panel is aerosol optical depth at 550 nm which shows a maximum of aerosol amount downwind from the area most affected by burning.

Figure 2 .
Figure 2. Interannual variation of ice occurrence over the Sea of Okhotsk.The ice occurrence gradually decreased from the winter of 2001-2002 to 2005-2006.The value of ice occurrence frequency for the winter 2005-2006 is significantly lower than for the winter of 2001-2002 (about 30% difference).

Figure 3 .
Figure 3. Scatter plots of atmospheric water vapor from MODIS Terra and Aqua for summer 2003 (left panel) and winter 2003-2004 (right panel).

( d )
Relationship of different parametersThe first step in exploring the cause of climate and environment change, is to analyze the relationship between different physical parameters.Typically this analysis involves basic statistical and graphical procedures such as calculation of correlations, generating scatter plots, and overlays of parameter maps.These basic functions are available in Giovanni-NEESPI.They help a scientist to explore the data before diving into more detailed research.As an example, figure4shows the relationship between precipitation amounts and aerosol optical depth over Northern Eurasia.The top panel is an image map of the correlation coefficient of aerosol optical depth at 0.55 μm from MODIS Terra and daily precipitation from the NASA GPCP project calculated for the time period from July 2000 to May 2007.The correlation between aerosol and precipitation varies with the location.The lower panel shows time series of the precipitation amount and the aerosol optical depth for two areas characterized by high correlation between these two parameters.Over East Asia near 40N, 115E, aerosol and precipitation are highly positively related.Over mid-east Asia, at 40N, 70E, precipitation reaches its maximum in winter, while the aerosol peak season is in spring and summer.

Figure 4 .
Figure 4. Relationship between precipitation and aerosol.The top panel is an image map of temporal correlation between aerosol optical depth at 0.55 μm from MODIS Terra and daily precipitation from the NASA GPCP project calculated for the time period from July 2000 to May 2007.The lower panel shows time series of the two parameters for those two areas where the correlation was high.

Table 1 .
Multi-sensor datasets currently available in the Giovanni-NEESPI system.
• lat-lon area plot of time-averaged parameters • time series plots of area-averaged parameters • latitude-time Hovmöller diagram • longitude-time Hovmöller diagram • animations of lat-lon area plot.