Sentinel-2 Remote Sensing and Satellite Mapping

Satellite remote sensing has long been a tool reserved for space agencies and large research laboratories. Since the launch of the Copernicus programme by the European Space Agency, Sentinel-2 imagery has become free, open and accessible to all on a near-daily basis. For a local authority, a consulting firm or an agricultural project owner, this opens up analytical possibilities that cannot be reproduced with conventional ground-based methods.

In this article, I share the practical fundamentals of satellite mapping, the radiometric indices I use on a daily basis, and a concrete case study carried out on Jakarta as part of my Master G2M.

Sentinel-2: free, open and frequent satellite imagery

Sentinel-2, operated by ESA within the Copernicus programme, carries 13 spectral bands covering the visible, near-infrared and shortwave infrared range. Spatial resolution ranges from 10 metres for the main bands to 60 metres for the atmospheric bands. With two satellites in orbit (2A and 2B), the revisit time for any given point on the globe drops to 5 days, enabling near-continuous monitoring of territorial dynamics.

For long historical analyses, Landsat 8 and 9 (NASA/USGS) remain indispensable. Their 30-metre resolution is coarser, but the archive goes back to 1972, which allows multi-decadal comparisons impossible with Sentinel. Data is also available free of charge via Earth Explorer or Google Earth Engine.

Useful radiometric indices (NDVI, NDWI, NDBI)

The strength of multispectral satellite imagery lies in combining bands to reveal information invisible to the naked eye. The NDVI (Normalized Difference Vegetation Index), computed as (NIR - Red) / (NIR + Red), quantifies chlorophyll activity and allows the vitality of vegetation to be mapped, water stress to be detected, or the evolution of a forest to be measured.

The NDWI highlights water surfaces and soil moisture, useful for monitoring rivers, wetlands and urban flooding. The NDBI targets the built environment and quantifies urban expansion. The NBR identifies burnt areas after a wildfire, with a pre/post difference calculation to estimate severity.

Case study: urban analysis of Jakarta

Jakarta is a metropolis of more than 10 million inhabitants facing massive urban expansion, intense heat islands and documented land subsidence. I carried out a multi-temporal Sentinel-2 analysis on this area for my Master 1 dissertation, cross-referencing NDVI, NDBI and thermal indices derived from Landsat 8. The full details are documented in my Jakarta remote sensing project, accompanied by a large-metropolis spatial database structured under PostGIS, which crosses the remote sensing layers with socio-economic data and infrastructure.

The results highlight a strong correlation between built-up density, vegetation loss and the intensification of urban heat islands between 2018 and 2024, with thermal gaps of up to 6°C between green neighbourhoods and mineralised zones.

Use cases for French local authorities and consulting firms

The concrete applications are numerous. For a municipality, monitoring the evolution of heat islands makes it possible to prioritise areas to be revegetated. For a river management authority, multi-date NDWI reveals the evolution of riparian bands and dry stretches. For a farmer, NDVI targeted on individual plots steers irrigation and fertilisation more precisely. Environmental NGOs use it to document deforestation or breaches of protected areas.

In terms of pricing, a one-off satellite analysis (1 date, 1 area, cartographic deliverables and report) starts from 1,200 €. A multi-date monitoring (3 to 6 dates spread over 1 to 3 years, comparisons and indicators) starts from 3,000 €. The final price depends on the number of dates, the area covered and the number of indices to compute: personalised quote within 48 hours after a free scoping call. Lead times are 1 to 2 weeks for a one-off analysis, and 3 to 6 weeks for multi-date monitoring.

For a full breakdown of every service and price range, see the Services & Pricing page.

Recommended tools and workflow

My workflow combines Python (rasterio, GDAL, scikit-learn for supervised classification) and QGIS for cartographic layout. For large volumes or global analyses, Google Earth Engine remains unbeatable: everything is computed server-side, with no download. Sentinel Hub offers a very clean API for embedding on-demand extractions into business applications. For raw downloads, the Copernicus Open Access Hub (now the Copernicus Data Space Ecosystem) remains the official source. For global urban analyses, the GHSL JRC layers complement Sentinel-2 with ready-to-use settlement and built-up data, and seasonal bracketing (winter/summer scenes) sharpens NDVI and NDBI comparisons.

A remote sensing project to run for your territory, your operation or your impact assessment? Get in touch to scope the need and the perimeter.