Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI)

The Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI) software provides a command line tool for the generation of Analysis Ready Data (ARD) optical data including atmospheric correction, cloud masking, topographic correction etc. of Earth Observation optical imagery (Blue-SWIR). The aim of ARCSI is to provide as automatic as possible method of generating Analysis Ready Data (ARD).

ARCSI is available to download from https://github.com/remotesensinginfo/arcsi/releases

Installation

To install ARCSI, we’d recommend you use our the builds available thought the conda-forge conda channel. Once you have installed miniconda (https://conda.io/miniconda.html; it must be a 64bit version) then you can install using the following command:

conda install -c conda-forge arcsi

However, we would recommend you create a new conda environment, which you do with the following command:

conda create -n arcsienv python=3.10
source activate arcsienv
conda install -c conda-forge arcsi

Note. the latest version is 4.0.X and this is required to work with the latest version of RSGISLib (version 5.X).

Supported Sensors

Currently we support:

• Sentinel-2

• Landsat 4, 5, 7, 8, 9 (TM, ETM+, OLI) - Collection 1 or 2.

Getting Started

To get started the basic command is:

arcsi.py -s lstm -p CLOUDS DOSAOTSGL STDSREF SATURATE TOPOSHADOW FOOTPRINT METADATA \
-o ./Outputs/ --stats --format KEA --tmpath ./tmp  \
--dem ./UKSRTM_90m.kea --cloudmethods LSMSK \
--k  clouds.kea meta.json sat.kea toposhad.kea valid.kea stdsref.kea \
-i LT05_L1TP_203024_19950815_20180217_01_T1/LT05_L1TP_203024_19950815_20180217_01_T1_MTL.txt

This is processing a Landsat-5 scene from the file downloaded from the USGS to standardised surface reflectance (i.e., topographically corrected surface reflectance) which is masked for clouds, cloud shadows and topographic shadows. The aerosols (AOT) is also automatically derived.

The following command is the same but for Sentinel-2:

arcsi.py -s sen2 --stats --format KEA \
-p CLOUDS DOSAOTSGL STDSREF SATURATE TOPOSHADOW FOOTPRINT METADATA SHARP \
-o ./Outputs  --dem ./UKSRTM_90m.kea --tmpath ./tmp  --cloudmethods S2LESSFMSK \
--k  clouds.kea meta.json sat.kea toposhad.kea valid.kea stdsref.kea \
-i S2A_MSIL1C_20170617T113321_N0205_R080_T30UVD_20170617T113319.SAFE/MTD_MSIL1C.xml

Information

• About
  • Frequently Asked Questions
• Background
  • Definitions
  • 6S Parameters
  • Bibliography
• Tutorial
  • Datasets:
  • How to run ARCSI
  • Getting a dataset
  • Run ARCSI with data
  • Changing AOT and Vertical Water Content
  • Inversion for AOT
  • Operational Commands (i.e., the ones to use); Landsat Example
  • Sentinel-2
  • Batch Processing
  • Sentinel-2
  • Downloading Landsat and Sentinel-2 Data
  • Other Useful Bits/Bobs
  • Conclusions
• Command Line Tools
  • Running ARCSI
  • Downloading Data
  • Batch Processing
  • Landsat
  • Error Checking
  • Other Utilities
  • Development Utilities

Library Documentation

• ARCSI Run
  • ARCSIParamsObj
  • calcTOAReflectance()
  • calcThermalBrightness()
  • calcTopoShadowMask()
  • calculateSREF()
  • calculateStandarisedSREF()
  • checkForValidInput()
  • convertInputImageToRadiance()
  • createFootprint()
  • createSaturatedImage()
  • createValidMaskViewAngle()
  • estimateSceneAOT()
  • exportMetaData()
  • mosaicInputImages()
  • performClearSkyMasking()
  • performCloudMasking()
  • performDOS()
  • prepParametersObj()
  • prepareDEM()
  • print2ConsoleListEnvVars()
  • print2ConsoleListProductDescription()
  • print2ConsoleListSensors()
  • resampleBands()
  • runARCSI()
  • runARCSIMulti()
• ARCSI Sensor
  • ARCSIAbstractSensor

Indices and tables

• Index

• Module Index

• Search Page