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rioxarray-remote-sensing

Compute NDVI/EVI vegetation indices from multispectral GeoTIFFs with rioxarray.

33|6|Updated Mar 17, 2026
Search Tags:#rasterio#cog#reprojection#ndvi#geotiff#evi#cloud masking
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill rioxarray-remote-sensing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rioxarray-remote-sensing
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/04-earth-science/rioxarray-remote-sensing
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill rioxarray-remote-sensing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you turn multispectral GeoTIFF imagery into analysis-ready rasters by handling geospatial metadata correctly and performing common vegetation workflows.

Core Features & Use Cases

  • GeoTIFF I/O with CRS safety: Read and write GeoTIFFs while preserving coordinate reference system (CRS), bounds, and nodata behavior via rioxarray/rasterio.
  • Reproject, resample, and align rasters: Reproject to a new CRS, resample with appropriate interpolation, and match grids for multi-scene or multi-sensor studies.
  • Vegetation index computation (NDVI/EVI): Calculate NDVI/EVI from spectral bands with proper scaling and numeric masking.
  • Cloud masking and mosaicking: Mask cloud-contaminated pixels using QA bit fields and mosaic multiple scenes into a seamless product.
  • COG export for web delivery: Export processed outputs as Cloud-Optimized GeoTIFFs for efficient downstream access and visualization.

Quick Start

Use the rioxarray-remote-sensing skill to read a multispectral GeoTIFF, compute NDVI/EVI, and export the result as a Cloud-Optimized GeoTIFF ready for web delivery.

Frequently Asked Questions about rioxarray-remote-sensing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute NDVI and EVI from multispectral GeoTIFFs?▼

Compute NDVI and EVI from multispectral GeoTIFFs by reading spectral bands with rioxarray, applying proper scaling and numeric masking, and performing band math to generate analysis-ready vegetation index rasters.

What's the best way to reproject and align rasters for multi-scene remote sensing studies?▼

Reproject and align rasters for multi-scene studies by matching grids, resampling with appropriate interpolation, and transforming to a new coordinate reference system while preserving geospatial metadata with rioxarray and rasterio.

How do I apply cloud masking to GeoTIFF imagery using QA bit fields?▼

Apply cloud masking to GeoTIFF imagery by parsing QA bit fields to identify cloud-contaminated pixels, then generating masked raster products suitable for mosaicking multiple scenes into a seamless output.

Can I export processed rasters as Cloud-Optimized GeoTIFFs for web delivery?▼

Export processed rasters as Cloud-Optimized GeoTIFFs by writing outputs with rasterio, preserving CRS and nodata behavior for efficient downstream web access and visualization.

How do I clip multispectral GeoTIFFs to a polygon area of interest?▼

Clip multispectral GeoTIFFs to a polygon area of interest by cropping raster bounds to the specified geometry, preserving coordinate reference system metadata and nodata values via rioxarray.

Does this Skill require Python with rioxarray and rasterio for vegetation index processing?▼

Yes, computing vegetation indices and processing GeoTIFFs requires Python with rioxarray and rasterio, supporting raster reprojection, resampling, and band math while producing analysis-ready rasters.

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