Google Earth Engine (GEE) has provided a way to massively scale a lot of remote sensing analysis. However, more than often time series analysis are carried out on a site by site basis and scaling to a continental or global level is not required. Furthermore, some applications are hard to implement on GEE or prototyping does not benefit from direct spatial scaling. In short, working on a handful of reference pixels locally is often still faster than Google servers. I hereby sidestep the handling of large amounts of data (although sometimes helpful) to get to single location time series subsets with a GEE hack.
I wrote a simple python script / library called gee_subset.py which allows you to extract time series for a particular location or it’s neighbourhood. This tool is similar to my MODIS subset or daymetr tools, which all facilitate the extraction of time series of remote sensing or climatological data respectively.
git clone https://github.com/khufkens/gee_subset
My python script expands this functionality to all available GEE products, which include high resolution Landsat and Sentinel data, includes climatological data among others Daymet, but also representative concentration pathway (RCP) CMIP5 model runs.
Compared to the ORNL DAAC MODIS subset tool performance is blazing fast (thank you Google). An example query, calling the python script from R, downloaded two years (~100 data points) of Landsat 8 Tier 1 data for two bands (red, NIR) in ~8 seconds flat. Querying a larger footprint (1x1 km) only creates a small overhead (13 sec. query). The resulting figure for the point location with the derived NDVI values is shown below. The demo script to recreate this figure is included in the example folder of the github repository.
[caption id=”attachment_1614” align=”aligncenter” width=”880”] NDVI values from Landsat 8 Tier 1 scenes. black lines depicts a loess fit to the data, with the gray envelope representing the standard error.[/caption]