This solution aims to solve the problem listed http://www.rhok.org/problems/development-operational-water-stress-produc.... It does all of the required parts (producing NDWI product and viewing via maps), and some of the extended ideas (for example, time series plotting of an individual pixel).
We thought this problem was particularly important in an era of changing climate. The area affected by droughts, and therefore the number of people affected, is only likely to increase over time.
Currently there are a number of sources of information about drought, but most of these focus entirely on the meteorological events - eg. the amount of precipitation falling in an area. We felt that the state of the vegetation in an area was of more importance - both as it gives a good idea of the real-world impact of the drought, and because of the effect that droughts can have on crops, and therefore on humans.
We have made available NDWI data that was previously unavailable, except to remote-sensing specialists, through an easy to use interface.
The work was split into two components:
WaterMeFeeder - This downloads MODIS tiles from the NASA FTP servers, processes them to reproject the data and calculate NDWI, removes clouds and water areas, and then stores the resulting points in a MySQL database. It is written in Python, and uses a number of libraries including GDAL, pyMySQL and NumPy. It is feature complete, well documented with comments, and shouldn't need much more work.
WaterMe - This provides the API and user interface for the data. The API is written in node.js, and provides a number of functions to return data in JSON format. The user-interface uses 'Leaflet' - an open-source mapping library - to overlay data points on OpenStreetMap data.
All software used is open-source, apart from the MODIS Reprojection Tool, which is freely available after registration through NASA.
N/A - We are writing this during the event!
We will need resources - to store all of the past MODIS data and have space to expand, we would need significant storage space both for HDF files, tiles and databases.
We will also need to validate the data (both for accuracy of location and NDWI data) - this may involve crowdsourcing data - and it may be suitable to involve some other remote-sensing scientists in the project.