Geoskill: Crop Type Mapping

Identify major crop types from multi-temporal optical/SAR imagery using phenological features. Produces pixel/field-level classification, area statistics, and confidence maps. Use when mapping crop distributions, estimating planted areas, or generating agricultural intelligence from remote sensing data.

Prerequisites / 先准备 X 文件

⚠️ 必读 — 本 skill 不属于即用型，需要先准备特定文件才能跑。

本 skill 需要 bbox/AOI + 年份/日期范围。时序影像自动下载，但 训练标签和物候 schema 是可选的——内置 4 种作物（水稻/小麦/玉米/大豆）够用。

👉 完整教程见仓库根目录 PREREQUISITES.md 1.2 节。

先准备 X 文件：自动下载 + 内置物候 = 1 行命令可跑通。

快速试跑命令：

bash

python crop_type_mapping.py --bbox 113.0,29.5,114.5,31.0 --year 2024 --output-dir ./ctm

Crop Type Mapping

Identifies major crop types (rice, wheat, corn, etc.) from multi-temporal satellite imagery using phenological curve matching and spectral indices.

Trigger

Use when the user wants to:

• Map crop type distribution for a region (e.g., "identify rice/wheat/corn in Henan 2024")
• Estimate planted area by crop type with confidence intervals
• Generate crop classification maps from Sentinel-2/Landsat time series
• Compare crop patterns across years or regions
• Produce agricultural intelligence reports for government or insurance

CLI Usage

bash

# Basic: classify crops in a bounding box for a given year
python scripts/crop_type_mapping.py \
  --bbox 113.0,29.5,114.5,31.0 \
  --year 2024

# Using a place name
python scripts/crop_type_mapping.py \
  --place beijing \
  --year 2024

# With custom date range and output directory
python scripts/crop_type_mapping.py \
  --bbox 115.0,30.0,116.0,31.0 \
  --start-date 2024-04-01 \
  --end-date 2024-10-31 \
  --output-dir ./ctm-output

# With custom crop schema and method
python scripts/crop_type_mapping.py \
  --aoi-file region.geojson \
  --year 2024 \
  --crop-schema references/crop_phenology.json \
  --method rule \
  --min-patch-area 9

Parameters

Parameter	Default	Description
--place	—	Place name (e.g., 'beijing', 'shanghai')
--bbox	—	Bounding box: 'xmin,ymin,xmax,ymax' (WGS84)
--aoi-file	—	AOI file (GeoJSON or Shapefile)
--year	current	Year for analysis (2015-2030)
--start-date	—	Start date (YYYY-MM-DD), mutually exclusive with --year
--end-date	—	End date (YYYY-MM-DD)
--crop-schema	built-in	Custom crop phenology schema JSON
--labels	—	Training/validation labels (GeoJSON)
--method	rule	Classification method: rule, rf, xgboost
--min-observations	5	Minimum valid observations per pixel
--field-boundaries	—	Field boundary polygons (GeoJSON)
--min-patch-area	4	Minimum patch area in pixels (post-processing)
--output-dir	./ctm-output	Output directory

Note: --place, --bbox, and --aoi-file are mutually exclusive.

Output

File	Description
crop_classes.tif	Crop classification raster (class codes)
crop_confidence.tif	Per-pixel classification confidence (0-1)
crop_polygons.geojson	Vector polygons per crop region
area_by_admin.csv	Area statistics per crop class (ha, km², %)
accuracy.json	Confusion matrix, overall accuracy, per-class F1
request.json	Input parameters and AOI metadata
dataset-manifest.json	Data source and observation metadata
output-manifest.json	Output file inventory and summary
qa.json	Quality assurance checks and status
run.log	Execution log

Crop Types (Default Schema)

Crop	Code	Peak DOY	Description
Rice	1	220 (Aug)	Single-season late rice
Wheat	2	120 (Apr)	Winter wheat
Corn	3	200 (Jul)	Summer corn

Classification Methods

Method	Description
rule	Phenological curve matching using peak DOY and amplitude
rf	Random Forest (requires sklearn, falls back to rule)
xgboost	XGBoost (requires sklearn, falls back to rule)

Workflow

1. AOI parsing — resolve place/bbox/aoi-file to WGS84 bounding box
2. Time range — parse year or custom date range
3. Data preparation — search/acquire Sentinel-2/Landsat time series
4. Feature extraction — compute NDVI, EVI, LSWI + phenological features
5. Classification — rule-based or ML classification per pixel
6. Post-processing — small patch removal, spatial smoothing
7. Accuracy assessment — confusion matrix, per-class metrics
8. Area statistics — pixel-counting with spherical area correction
9. Output — GeoTIFF, GeoJSON, CSV, JSON reports

Exit Codes

Code	Meaning
0	Success
2	Argument error
3	Dependency missing
6	Data validation failure
7	Processing failure

Limitations

• Classification is remote-sensing-based estimation, not ground truth
• Accuracy depends on cloud-free observation count and timing
• Crop schema defaults are tuned for major grain regions (North China Plain)
• Double-cropping regions may require custom schema
• Results should be validated with ground truth before operational use

数据下载

本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):

bash

python crop_type_mapping.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir <tmp>

• --bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)
• --date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)
• --aoi-file <path.geojson>: 替代 --bbox 的 GeoJSON 多边形
• --cache-dir <path>: 缓存目录 (默认 ~/.geoskill_cache)

当用户只给 --bbox + --date-range (没有 --image) 时，skill 自动下载数据。 当用户给 --image 时，走原文件路径 (向后兼容)。