# Other articles

Many networks of automated meteo stations are around. In some places, the data are easy to access and therefore use. In some others, you have to go through a number of hoops. In Andalusia, it's a mixed bag: a number of independent networks are available for agricultural and environmental ...

2. # Downsampling with GDAL in python

Quite often, one wants to generate some data at high resolution (say process some image or images) and then calculate some relevant spatial statistics at some other resolution. For example, you might want to process Landsat TM data at 30m resolution, and might want to aggregate it to a resolution ...

3. # How to extract spectra from image data using ground truth vector data

Supervised classification of EO data uses a set of samples from known patterns (usually reflectance spectra) to decide whether a given pattern belongs to one class or another. In landcover applications, one goes to the field, and observes that a given location is indeed class $\omega_c$. These ground observations usually ...

4. # Creating a country raster mask using GDAL

Usually, we remote sensing types ignore country boundaries: they don't really make much sense, as they are not aligned with MODIS pixels ;-) However, I was asked what's the easiest way to produce a global mask of countries, so that all th grid cells (say at 0.5 degree ...

5. # EOLDAS talk at VU Amsterdam

So I have just spent a week visiting at the VU Amsterdam. During this visit, I have also given a talk on EOLDAS. The slides are availabe from here.

6. # Simple time series MODIS data analysis

In a previous post, I demonstrated how to stitch and put together a number of MODIS data files. This is useful and interesting, but in the end, we are interested in analysing the data we get out of the satellite. One first way around this might be to extract time ...