As stated in previous posts, the goal of my project is to explore the difference between point-source temperature recordings at NOAA monitoring stations and the land surface temperature images made available daily from the NASA MODIS satellites. As a first step in comparing these datasets, hot-spot statistics and spatial autocorrelation were used to identify any areas where the difference between the data was significantly non-random. The steps below outline this process.

Data Selection and Cleaning

To begin exploring the data, I selected a single day (January 1, 2015) and linked the measurements from both sources into one shapefile. The NOAA data were downloaded as a CSV file from the NOAA National Climate Data Center (http://www.ncdc.noaa.gov/). This data arrives as a mostly cleaned CSV and the only transformation required was to convert the temperature readings from tenths of a degree Celsius to degrees Celsius. The MODIS raster image was downloaded from the USGS Earth Explorer engine (http://earthexplorer.usgs.gov/) and required some work before it could be used. The raster was re-projected from the unspecified cylindrical projection used for MODIS products to WGS84 to match the point shapefile using the ‘Project Raster’ tool in ArcGIS (http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//00170000007q000000).  Once re-projected, the image then had to be re-scaled and converted from Kelvin to Celsius [OutRaster = ((InRaster * 0.02) – 273.16)] before being used.

Joining the Data

Once both data sets were cleaned and ready to be used, the two were joined together for analysis. This was done using a custom Python script, however the ‘Extract Values to Points’ tool in ArcGIS (http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//009z0000002t000000.htm) or QGIS completes the same task. The final shapefile contained a point for each monitoring station with fields for the NOAA and MODIS values along with the difference between the two represented as the absolute value of the MODIS value minus the NOAA value (Difference = |MODIS – NOAA|). This difference figure is what is used for the analysis.

Hot-Spot Map in ArcGIS

The next step was to create a hot-spot map of the difference figures to identify any areas of significantly greater or less difference. This was done using the ‘Hot Spot Analysis’ tool in ArcGIS (http://resources.arcgis.com/en/help/main/10.1/index.html#//005p00000010000000). In the image below, we can see that there is an area of greater difference in central Oregon and an area of less difference near Portland.

It is important to note that neither of these are significant, and most likely represent differences in the terrain. I suspect that the area of higher difference in central Oregon is due to the fact that fluctuations in temperature are much greater there than in the valley. Furthermore, the temperature in the area of less difference is more stable and therefore would not be as prone to error. It is important to remember that the NOAA data represent daily temperature averages, while the MODIS data represent the land surface temperature at the specific time when the image was taken. To explore this idea further, future analysis will include some variable to account for location or land-use type.

Spatial Autocorrelation

Finally, the Moran’s I statistic was calculated to further explore if there is any significant spatial autocorrelation in the difference measurements. This was done using the ‘Spatial Autocorrelation’ tool in ArcGIS (http://resources.arcgis.com/en/help/main/10.1/index.html#//005p0000000n000000). The output is shown below:

This statistic showed that there is no significant spatial autocorrelation. The combination of the very low Moran’s I and the high P-value lead to the conclusion that the difference figures are randomly dispersed throughout the points.

There are some areas in the map that have very high difference on this one day. The next step is to explore these data over a larger time frame to see if the pattern of the difference is the same or different. I plan to download data for at least a few days from each month of 2014 and explore the spatial pattern of the difference between each data set.