1. # Radiative transfer model Python wrappers

I use radiative transfer (RT) models quite extensively. While most of the ones you might want to use practically are not very complicated to get your head around, they are numerically expensive (what with all these numerical integration going on). So while you could code them up in Python, it ...

2. # ogr2ogr geoprocessing

Sometimes, one wants to do some really simple geoprocessing on a large numbers of shapefiles. While you could definitely use Spatialite for that, I would rather avoid this. ogr2ogr, on the other hand, provides handy access to Spatialite-like functionality. Let's say you wanted to produce a text file which ...

3. # BRDF modelling of MODIS land surface data

This post introduces simple BRDF modelling (i.e. how can we account for the variation in surface reflectance due to acquisition geometry?) using some MODIS data. You can find the whole post in wakari. If you accept to not escape the HTML & JS, you will find a link to the ...

So a quick post on how to get access the web-based data on rider timings for the RideLondon event. The idea is that you can access the cyclist timings, and then maybe do some statistics (or boost/depress your morale by looking at how your time compares to the wider ...

5. # Stitching MODIS burned area datasets

MODIS products are usually provided as data granules, representing the magnitude of interest (plus a number of different extra layers of metadata, quality assurance flags, etc) over a given temporal period for an area typically extending 1200 by 1200 km in the MODIS sinusoidal projection, around 10x10 degrees. Typically, your ...

6. # Getting a box

Let's say one wants to find out a box around a given latitude/longitude point. How to do this without GIS software using an artisan UNIX approach? Hell, if it works for coffee! The steps are as follows:

1. Convert the centre point into UTM so we have meters rather ...

Recently, the USGS has closed their FTP download service for MODIS products, such as LAI, surface reflectance, etc. This is quite annoying, because I had a nice wget script that no longer works... So I decided to code a simple downloader in python that woud supersede my previous hacked together ...

8. # SMP processing with multiprocessing

This note details how to use parallel processing in python accessing shared memory. The usage case is when you have a process that reads in a lot of data, and where the data can be processed in parallel, such as when reading and processing images/stacks of images in a ...

9. # Fast copying of files over the network

A common requirement when dealing with processing large datasets over multiple networked machines is to have a local staging space: copy the data on the local disk to improve access speed and not to bog down a NFS server when all different processes start accessing different files all at once ...

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 ...

11. # 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 ...

12. # 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 ...

13. # 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 ...

14. # 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.

15. # 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 ...