基于多时相 SAR 后向散射时序的农作物分类：逐像元时序/统计/物候特征 + 随机森林。SAR crop classification from multi-temporal backscatter time series using per-pixel phenological features and Random Forest. 合成模式生成水稻/小麦/玉米三类时序真值并评估精度，输出分类 GeoTIFF + 面积统计 + 混淆矩阵 JSON。
1 次安装
设计与多媒体
Geoskill: Crop Type Mapping
试用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.
它能做什么
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.
技能文档
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：
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
# 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
- AOI parsing — resolve place/bbox/aoi-file to WGS84 bounding box
- Time range — parse year or custom date range
- Data preparation — search/acquire Sentinel-2/Landsat time series
- Feature extraction — compute NDVI, EVI, LSWI + phenological features
- Classification — rule-based or ML classification per pixel
- Post-processing — small patch removal, spatial smoothing
- Accuracy assessment — confusion matrix, per-class metrics
- Area statistics — pixel-counting with spherical area correction
- 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):
python crop_type_mapping.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir
--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: 替代 --bbox 的 GeoJSON 多边形--cache-dir: 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)。
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