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From Raw Satellite Data to GIS-Ready Layers: A Step-by-Step Workflow Guide

Updated: 2 days ago

One of the most important things remote sensing and geospatial professionals do is convert raw satellite imagery into finished analysis-ready Geographic Information System (GIS) Layers. The conversion of this unprocessed satellite data into usable results can be applied to all aspects of land use planning, environmental monitoring, disaster response, and precision agriculture.


From Raw Satellite Data to GIS-Ready Layers
From Raw Satellite Data to GIS-Ready Layers ( Created by Google Gemini)


Why Raw Satellite Data Needs Processing


While Level 0/Level 1 data may be available as raw satellite images, they are not suitable for direct use. The images may contain several issues that need to be addressed before they can be used as input to GIS software:


  • Distorted sensor values,

  • Thin cloud cover and water vapor are present in the Earth's atmosphere,

  • Reduced radiometric accuracy,

  • Geometries that are not aligned with one another,

  • Multispectral bands recorded separately,

  • Non-standard file format with very large files.


Thus, once you have acquired an appropriate dataset, you will need to perform radiometric and geometric corrections before using it in your GIS software.


Step 1: Acquire the Right Satellite Data


Before processing begins, choose data that fits your project’s needs. Popular sources include:


NASA and USGS Open Datasets


  • NASA Landsat 8 and 9 (OLI/TIRS)

  • NASA Landsat 7 (ETM+) with SLC Off Corrections


European Space Agency (ESA) Sentinel Missions


  • Sentinel-2 (multispectral 10 to 60 meters)

  • Sentinel-1 (SAR)


Commercial Sources of Satellite Data


  • PlanetScope

  • Vantor

  • Airbus

  • 21 AT


Step 2: Organize and Inspect Raw Files


Downloaded satellite products often come compressed or grouped by band folders. The first steps are:


  • Extract all archives

  • Review the metadata (.xml, .json, or .MTL files)

  • Confirm coordinate reference system (CRS)

  • Identify band numbers and wavelengths.


Step 3: Radiometric Correction


Raw digital numbers (DNs) must be converted to physical reflectance values.


Common Radiometric Processing Tasks


  • Convert DNs to top-of-atmosphere (TOA) reflectance.

  • Apply dark object subtraction.

  • Normalize brightness to reduce sensor noise.

  • Apply atmospheric correction tools such as

    • Sen2Cor (Sentinel-2)

    • LEDAPS or LaSRC (Landsat)


This step ensures consistent pixel-to-pixel reflectance and prepares data for classification or change detection.


Step 4: Geometric and Terrain Correction


Geometric correction aligns imagery with the Earth's surface. Without it, features may appear shifted or distorted.


Key Corrections

  • Orthorectification using a DEM (e.g., SRTM, ASTER)

  • Correction for sensor orientation and platform position

  • Eliminating terrain-induced parallax


Most Level-2 products come pre-orthorectified, but always verify geometric accuracy before analysis.


Step 5: Cloud Masking and Quality Filtering


Clouds can significantly reduce the usefulness of optical imagery.


Techniques for Cloud Removal

  • Apply provided QA/QC bands (Landsat pixel_qa, Sentinel QA60)

  • Use automated tools like FMask.

  • Temporal compositing (median or max NDVI composites)

  • Integrate radar data (cloud-penetrating) for gap-free mapping.


Clean imagery ensures more reliable classification and analysis.


Step 6: Band Stacking and Composite Creation


Raw satellite bands typically arrive as individual files. GIS users need them stacked into a single multispectral raster.


Common Composite Types

  • True color (RGB: Red, Green, Blue)

  • False color (NIR, Red, Green)

  • Vegetation-focused (NIR + SWIR)

  • Urban or geological composites (SWIR combinations)


This step produces a single, multi-band raster ready for visualization and analysis.


Step 7: Spatial Subsetting and Reprojection


Large satellite scenes can exceed gigabytes. To make your dataset manageable:


  • Clip to your area of interest (AOI)

  • Reproject to a standard CRS (e.g., UTM, EPSG:3857)

  • Resample to a consistent pixel size if merging tiles.


These optimizations boost GIS performance and simplify downstream workflows.


Step 8: Derive GIS-Ready Layers


Once your imagery is clean, corrected, and stacked, you can generate advanced GIS layers.


Examples of Derived Layers

  • NDVI, NDWI, NBR, SAVI vegetation indexes

  • Land cover classification maps

  • Change detection layers

  • Terrain layers (hillshade, slope, aspect) using DEMs

  • Water, soil, or urban extraction layers


These products feed directly into spatial analyses, dashboards, or decision-support tools.


Step 9: Export for GIS Platforms


Finally, save your processed data into standard GIS-compatible formats:


  • GeoTIFF (.tif) – most common raster format

  • Cloud-Optimized GeoTIFF (COG) – for web streaming

  • NetCDF – ideal for multi-temporal datasets

  • GeoPackage (.gpkg) – vector + raster integration


Ensure your metadata is complete and includes processing history, CRS, and band descriptions.


Processing Tools for Geospatial Data Intelligence


Desktop Tools


  • QGIS

  • ArcGIS Pro

  • SNAP (ESA)

  • ENVI / Erdas IMAGINE


Command Line & Automation Tools


  • GDAL

  • Python (rasterio, geopandas, xarray, EarthPy)

  • R (raster, terra)


Cloud Services



The appropriate tool for you will depend on how much data you have, what your performance needs are, and what the ultimate goal of your project is.


Transforming satellite images from the raw format into usable geospatial files may appear difficult; however, creating steps or a workflow will support repeated success with your work. The more you develop these workflows and processes, the more accurate the end users will be able to rely upon them as they continue to develop their geospatial products using data obtained from Remote Sensing.


This type of workflow is also beneficial to anyone who uses geospatial data, regardless of whether they are GIS Analysts, Environmental Scientists, or Data Engineers. This method of processing and developing geospatial products will allow you to produce accurate, dependable, and high-quality geospatial results from Remote Sensing data.


For more information or any questions regarding the satellite data, please don't hesitate to contact us at


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India: 98260-76466 - Pradeep Shrivastava

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