Satellite data, like that from Sentinel-2, provides an essential tool for studying and monitoring natural disasters such as landslides. Here are the general steps you'll need to follow to acquire and process Sentinel-2 data:

Remember, Sentinel-2 is a Multispectral Instrument (MSI) with 13 spectral bands ranging from visible and near-infrared (VNIR) to short wave infrared (SWIR) which can be really helpful in identifying landslides.

Keep in mind, you will need to refer to relevant scientific literature to identify the best practices and algorithms for detecting landslides from Sentinel-2 data.

Dear Natarajan Subramanyam ,

Thank you for your question regarding the use of Sentinel-2 data for landslide detection. I understand that you are new to handling Sentinel-2 data and are interested in using it for landslide detection, specifically for the Kodagu landslides. I hope the following information will be helpful to you.

Firstly, acquiring Sentinel-2 data can be done through the Copernicus Open Access Hub. The Sentinel-2 User Guide provides a high-level description of the MultiSpectral Instrument (MSI), its coverage and acquisition, and available product types . For a more in-depth description of the Sentinel-2 mission's products and algorithms, you can refer to the SENTINEL-2 Technical Guide. Once you have acquired the data, the processing can be done using various methods. One approach is to use a custom script for detecting landslides using space-based data on Sentinel Playground and EO Browser. This script uses the response of the Barren Soil Index (BSI) to detect recent traces of soil movements and uses the NDWI, NDVI, and B11 for differentiating between water with sediment, built-up areas, barren areas, and vegetated areas.

Another approach is to use deep learning methods. For instance, a dual-encoder U-Net has been used for landslide detection using Sentinel-2 and DEM data.Another study used a pixel-based image analysis methodology built on unsupervised Deep Learning (DL) for rapid landslide detection.

In terms of handling cloud cover, which can be an issue with optical data during rainy periods, one method is to use time-series Sentinel-2 images to extract deforming landslides from background noise.This method has been tested along a section of the Jinsha River in southwest China and was found to be very efficient in eliminating almost all background noise and isolating deforming landslides.

Finally, it's important to note that while Sentinel-2 data can be very useful for landslide detection, there are some limitations. For instance, there can be difficulties in separating barren soil from buildings and urban areas due to a similar spectral response.

I hope this information is helpful to you in your project. I wish you success in your research and look forward to hearing about your progress.

Best regards,

goorabi