Thermal sharpening of Sentinel-3 images: From 1 Km to 10m using Python in Google Colab

13 min read

12 hours ago

Sentinel-3 thermal image downscaled from 1000 m to 10 m, visualized by the author.
  1. 🌅 Introduction
  2. 💾 Downloading Sentinel-3 (1000 m) and Sentinel-2 images (10 m)
  3. ⚙️ Sentinel-3 Image Processing
  4. 🌡️ Temperature-NDVI Space
  5. 📐 Sharpening the Thermal Image (1000 m to 10 m)
  6. 🗺️ Visualization of the Sharpened Thermal Image
  7. 📄 Conclusion
  8. 📚 References

🌅 Introduction

Downscaling the thermal imagery captured by satellites has been extensively studied due to the tradeoff between the spatial and temporal resolution of satellites that provide thermal images. For example, the revisit cycle of Landsat-8 is 16 days, with an original thermal resolution of 100 meters. In contrast, Sentinel-3 can provide daily thermal images, but at a spatial resolution of 1000 meters.

The trade-off between spatial and temporal resolution, Image by the author

One approach to address the coarse resolution of thermal images could be launching more satellites equipped with thermal sensors, such as NASA’s Landsat-9, launched in September 2021. In this case, the temporal resolution for both Landsat-8 and Landsat-9 is 8 days (instead of 16 days with one satellite), assuming clear skies.

However, as you can guess, this approach requires a multimillion-dollar investment and several years of effort. Instead, researchers have focused on statistical methods, correlating the visible/near-infrared (VNIR) bands from satellites with higher spatial resolution (but lower temporal resolution) to thermal images from satellites with lower spatial resolution (but higher temporal resolution). For example, studies have shown that the Normalized Difference Vegetation Index (NDVI) calculated from VNIR bands of Sentinel-2 (10m, every 5 days) can be inversely correlated with thermal images from Sentinel-3 (1000m, daily).

But how can we use this…

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