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agri-remote-sensing

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Turn Sentinel-2 into crop maps and yield

Education & Research#agriculture#yield prediction#remote sensing#random forest#sentinel-2#ndvi phenology#google earth engine
Authorxjtulyc
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill helps you convert agricultural satellite observations into actionable crop intelligence by building NDVI phenology time series, estimating biophysical indicators, and predicting yield.

Core Features & Use Cases

  • Cloud-free Sentinel-2 vegetation index time series: Build biweekly median NDVI/EVI/LSWI composites from Google Earth Engine and extract region-level time series.
  • Phenology extraction: Compute start/end of season, peak NDVI, and length of season from threshold-based NDVI dynamics.
  • Decision-support outputs: Estimate LAI from NDVI using empirical models and support crop type mapping and yield prediction using machine learning/regression.
  • Use Case: Analyze a growing season (e.g., a corn belt) to derive SOS/EOS/peak metrics, estimate LAI, classify crop types, and train a yield model using phenology features.

Quick Start

Use the skill to generate a biweekly Sentinel-2 NDVI time series for your ROI, extract SOS/EOS/peak phenology metrics, estimate LAI, and run yield prediction from the derived features.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: agri-remote-sensing
Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#agri-remote-sensing

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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