Documentation for SMFM sen1mosaic

This is a set of tools to aid in the production of large-scale seasonal mosaic products from Sentinel-1 data.

Building large-scale mosaics of Sentinel-1 data for land cover mapping is more difficult than with more commonly-used optical data (e.g. Landsat), with existing tools still under-development and hard to use. The goal of this repository is to streamline this processing chain with a set of straightforward command line tools.

This repository contains three command-line based scripts to perform the following tasks:

• Downloading Sentinel-1 data from the Copernicus Open Access Hub for a particular latitude/longitude window, specifying date ranges and ascending/descending orbits. This is based on the Sentinelsat utility.
• Executing SNAP graph processing tool to calibrate, filter and perform terrain-correction on Sentinel-1 GRD images.
• Mosaicking pre-processed Sentinel-1 files into larger GeoTIFF files that are suitable for image classification.

More information

For more information, refer to the ‘Satellite Monitoring for Forest Management’ webpage..

How do I get set up?

These tools are written in Python for use in Linux. You will need to have first successfully installed the following:

• Sentinelhub: A library for searching and downloading Sentinel products.
• SNAP: Pre-processing tools for Sentinel-1 data.

The modules used in these scripts are all available in Anaconda Python.

Who do I talk to?

Written and maintained by Samuel Bowers (sam.bowers@ed.ac.uk).

Contents:

• 1. Setup instructions
  • 1.1. Requirements
  • 1.2. Installing Anaconda Python
  • 1.3. Setting up your Anaconda environment
  • 1.4. Installing SNAP
  • 1.5. Installing sentinelsat
  • 1.6. Installing sen1mosaic
  • 1.7. Installing sen2mosaic
  • 1.8. Is there a Dockerfile?
  • 1.9. Where do I get help?
• 2. Command line tools
  • 2.1. Downloading Sentinel-1 data
  • 2.2. Pre-processing Sentinel-1 data
  • 2.3. Processing to GeoTiff tiles
• 3. Worked example on the command line
  • 3.1. Preparation
  • 3.2. Downloading data
  • 3.3. Preprocessing Sentinel-1 GRD data
  • 3.4. Generating a mosaic for classification
  • 3.5. Viewing data
  • 3.6. See also
• 4. Using sen1mosaic in Python
  • 4.1. Download module
  • 4.2. Preprocessing module
  • 4.3. Mosaicking module

Indices and tables

• Index
• Module Index
• Search Page