remote-sensing-analysis

# Remote Sensing Analysis Purpose: turn raw Earth observation imagery into defensible analytical products. The failure modes here are subtle — uncorrected DNs treated as reflectance, clouds counted as land cover change, indices computed on the wrong bands — so this skill front-loads the checks. ## Data access (STAC-first) Search via STAC APIs rather than per-provider portals; the workflow is uniform and scriptable: ```python import pystac_client import odc.stac catalog = pystac_client.Client.open("https://earth-search.aws.element84.com/v1") items = catalog.search( collections=["sentinel-2-l2a"], bbox=[27.0, 38.3, 27.4, 38.6], datetime="2025-05-01/2025-09-30", query={"eo:cloud_cover": {"lt": 20}}, ).item_collection() ds = odc.stac.load(items, bands=["red", "nir", "scl"], resolution=10, chunks={}) ``` Key collections: `sentinel-2-l2a` (10 m optical, surface reflectance), `landsat-c2-l2` (30 m, 1982→), `sentinel-1-grd` (SAR, weather-independent). Microsoft Planetary Computer mirrors most (needs `planetary_computer` signing). For continental/global extents or decades-long stacks, route to `google-earth-engine` instead of downloading. Record collection + item IDs + search parameters for reproducibility. ## Processing-level discipline | Level | Meaning | Analysis-ready? | |---|---|---| | L1C / L1TP | Top-of-atmosphere (TOA) | Indices OK-ish; cross-date comparison risky | | **L2A / L2SP** | Surface reflectance (BOA) | Yes — default choice | | GRD (SAR) | Dete...