I am interested in exploring the utility of MODIS Land Surface Temperature (LST) maps in creating heat stress maps for migrant farm workers in rural Oregon. Currently it is difficult to estimate heat exposure for field workers since point source data is very scattered. Monitoring stations are currently used to collect information at a large scale; however this information cannot accurately be interpolated for a large area. Often times there is only one monitoring station for a very large area, which would lead to serious issues when trying to create a continuous surface temperature map. The ability to use remotely sensed data for these heat stress models would allow researchers to more accurately assess individual exposure. This is crucial in identifying areas that need more attention or resources, and would greatly simplify the process of analyzing this data.

I would like to compare the values predicted by the MODIS LST for a given date with the temperature recorded by the National Weather Service (NWS) or another point source for temperature data. This will allow me to find the difference between the sources of information, as well as identify any patterns in the distribution of error for MODIS data. To do this, I will compare data for many dates across a variety of locations in order to identify any spatial or seasonal patterns. The main objectives of this project are as follows:

1. Identify the magnitude of the difference between these 2 sources of data
2. Create a regression model for comparing temperature data recorded by the NWS and MODIS LST maps for a set of given dates and locations
3. If the difference from the point source data to the MODIS LST image is too great, explore other ways to use MODIS LST images to predict heat stress for migrant populations
4. If necessary and/or possible, explore other remotely sensed data sources if MODIS does not work for this spatial problem

The first hurdle will be collecting all this data for the locations needed for the analysis. Also, the MODIS data and NWS data are not reported with the same timeframe (i.e. the high and low temperatures reported by the NWS may not match the average temperature recorded on the MODIS LST image). I will need to find some way to compare these values and normalize them to each other (currently my only idea is to create a function that will take the time of the high and low records from the NWS data, create an estimate of the temperature throughout the day based on these values, and compare the temperature at the specific time of the MODIS reading).  Ideally I would like to look at data for 5 sites of different terrain over 5 days from each season (20 days total, 100 data points).

Regarding my experience with the various tools for this class, I have moderate experience using R and ArcGIS, I have a strong introduction to using Python, and have a rudimentary knowledge of using ModelBuilder.