--- name: satellite-remote-sensing-02057400be1c description: "Satellite imagery analysis and remote sensing for earth science research" metadata: openclaw: emoji: "🛰️" category: "domains" subcategory: "geoscience" keywords: ["remote-sensing", "satellite", "geospatial", "landsat", "sentinel", "gis", "earth-observation"] source: "wentor" --- # Satellite Remote Sensing A skill for processing and analyzing satellite imagery for earth science research. Covers data acquisition from major satellite platforms, preprocessing workflows, spectral index computation, land cover classification, and change detection using Python geospatial tools. ## Satellite Data Sources ### Major Earth Observation Missions | Mission | Operator | Resolution | Revisit | Key Bands | Access | |---------|----------|-----------|---------|-----------|--------| | Landsat 8/9 | USGS/NASA | 30m (MS), 15m (pan) | 16 days | 11 bands, OLI+TIRS | Free (USGS EarthExplorer) | | Sentinel-2 | ESA | 10m-60m | 5 days | 13 bands, MSI | Free (Copernicus Open Access Hub) | | MODIS | NASA | 250m-1km | 1-2 days | 36 bands | Free (NASA LAADS DAAC) | | Sentinel-1 | ESA | 5-20m | 6 days | C-band SAR | Free (Copernicus) | | GOES-16/17 | NOAA | 0.5-2km | 5-15 min | 16 bands, ABI | Free (NOAA CLASS) | ### Programmatic Data Access ```python import planetary_computer import pystac_client import rioxarray # Search Sentinel-2 imagery via Microsoft Planetary Computer catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, ) # Search for cloud-free imagery over a region search = catalog.search( collections=["sentinel-2-l2a"], bbox=[11.0, 46.0, 12.0, 47.0], # Tyrol, Austria datetime="2025-06-01/2025-08-31", query={"eo:cloud_cover": {"lt": 10}}, ) items = search.item_collection() print(f"Found {len(items)} scenes with <10% cloud cover") # Load a specific band as xarray DataArray item = items[0] red = rioxarray.open_rasterio(item.assets["B04"].href) nir = rioxarray.open_rasterio(item.assets["B08"].href) ``` ## Preprocessing Pipeline ### Atmospheric Correction Raw satellite data (Level-1) must be atmospherically corrected to obtain surface reflectance (Level-2): - **Sentinel-2**: Use Sen2Cor processor (ESA) or download pre-processed L2A products - **Landsat**: Collection 2 Level-2 products include surface reflectance - **Custom correction**: Use 6S radiative transfer model via Py6S ```python # Cloud masking for Sentinel-2 using the SCL band import numpy as np def mask_clouds_sentinel2(scl_band: np.ndarray) -> np.ndarray: """ Create cloud mask from Sentinel-2 Scene Classification Layer. SCL values: 0=no_data, 1=saturated, 2=dark_area, 3=cloud_shadow, 4=vegetation, 5=bare_soil, 6=water, 7=unclassified, 8=cloud_medium, 9=cloud_high, 10=cirrus, 11=snow """ cloud_classes = {0, 1, 3, 8, 9, 10} mask = np.isin(scl_band, list(cloud_classes)) return mask # True where clouds/invalid ``` ### Geometric Correction and Mosaicking ```python import rasterio from rasterio.merge import merge from rasterio.warp import calculate_default_transform, reproject, Resampling def reproject_raster(src_path: str, dst_path: str, dst_crs: str = "EPSG:4326"): """Reproject a raster to a target coordinate reference system.""" with rasterio.open(src_path) as src: transform, width, height = calculate_default_transform( src.crs, dst_crs, src.width, src.height, *src.bounds ) kwargs = src.meta.copy() kwargs.update({ "crs": dst_crs, "transform": transform, "width": width, "height": height, }) with rasterio.open(dst_path, "w", **kwargs) as dst: for i in range(1, src.count + 1): reproject( source=rasterio.band(src, i), destination=rasterio.band(dst, i), src_transform=src.transform, src_crs=src.crs, dst_transform=transform, dst_crs=dst_crs, resampling=Resampling.bilinear, ) ``` ## Spectral Indices ### Vegetation and Water Indices ```python def compute_indices(red: np.ndarray, nir: np.ndarray, green: np.ndarray, swir: np.ndarray) -> dict: """ Compute common spectral indices from surface reflectance bands. All inputs should be float arrays with values in [0, 1]. """ eps = 1e-10 # avoid division by zero ndvi = (nir - red) / (nir + red + eps) ndwi = (green - nir) / (green + nir + eps) nbr = (nir - swir) / (nir + swir + eps) evi = 2.5 * (nir - red) / (nir + 6 * red - 7.5 * 0.0001 + 1 + eps) savi = 1.5 * (nir - red) / (nir + red + 0.5 + eps) return { "NDVI": ndvi, # vegetation vigor [-1, 1] "NDWI": ndwi, # water bodies [-1, 1] "NBR": nbr, # burn severity [-1, 1] "EVI": evi, # enhanced vegetation "SAVI": savi, # soil-adjusted vegetation } ``` ### Index Interpretation | Index | Range | Low Values | High Values | |-------|-------|-----------|-------------| | NDVI | -1 to 1 | Water, bare soil, clouds | Dense green vegetation | | NDWI | -1 to 1 | Dry land | Open water bodies | | NBR | -1 to 1 | Recently burned areas | Healthy vegetation | | EVI | -1 to 1 | Non-vegetated | Dense canopy (less saturated than NDVI) | ## Land Cover Classification ### Supervised Classification with Random Forest ```python from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score # Stack bands into feature array: (n_pixels, n_bands) # training_labels: land cover classes from ground truth polygons bands = np.stack([blue, green, red, nir, swir1, swir2, ndvi, ndwi], axis=-1) n_rows, n_cols, n_bands = bands.shape X = bands.reshape(-1, n_bands) # Train Random Forest classifier rf = RandomForestClassifier(n_estimators=200, max_depth=20, n_jobs=-1) scores = cross_val_score(rf, X_train, y_train, cv=5, scoring="f1_macro") print(f"5-fold F1: {scores.mean():.3f} +/- {scores.std():.3f}") rf.fit(X_train, y_train) classification = rf.predict(X).reshape(n_rows, n_cols) ``` ## Change Detection Multi-temporal analysis for detecting land cover changes (deforestation, urbanization, flood extent): 1. **Image differencing**: Subtract spectral index values between dates 2. **Post-classification comparison**: Classify each date independently, compare maps 3. **Change vector analysis**: Compute magnitude and direction of spectral change 4. **Time series analysis**: BFAST, LandTrendr for continuous monitoring ## Tools and Libraries - **Rasterio / GDAL**: Raster I/O and geospatial transformations - **xarray + rioxarray**: Labeled multi-dimensional array analysis - **Google Earth Engine (GEE)**: Cloud-based planetary-scale analysis - **QGIS**: Open-source GIS for visualization and manual digitization - **Orfeo ToolBox (OTB)**: Advanced remote sensing processing chain - **SentinelHub**: Commercial API for on-the-fly Sentinel processing --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/geoscience/satellite-remote-sensing/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/geoscience/satellite-remote-sensing/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Design, Education & Research, Product Management Roles: Data Scientist, Market Research Analyst, Product Manager, Researcher, UX Designer Specialties: API, Backend Tools: Planetary Computer, Pystac Client, Rasterio, Rioxarray Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8269e9791f681e4c273f2551b47e4ff3a9ebc314251d1caa4de2fd1ee6df1ec4