由 NDVI 幂律异速生长方程估算地上生物量碳，叠加根茎比地下碳与类型化土壤碳密度。Estimates carbon stocks from biomass allometry and soil carbon density. 输出地上碳/土壤碳/总碳三张 GeoTIFF 与汇总 JSON。
编程
geoskill-carbon-flux-estimation
试用基于光能利用率模型（CASA/VPM 简化）估算 GPP/NPP：GPP=PAR×FPAR×ε，ε 受温度与水分胁迫调节，NPP=GPP−自养呼吸，输出碳收支
它能做什么
基于光能利用率模型（CASA/VPM 简化）估算 GPP/NPP：GPP=PAR×FPAR×ε，ε 受温度与水分胁迫调节，NPP=GPP−自养呼吸，输出碳收支
技能文档
碳通量估算 | Carbon Flux Estimation
This skill estimates ecosystem carbon fluxes using a simplified light-use-efficiency model (following the CASA / VPM approach):
- GPP (Gross Primary Productivity) = PAR × FPAR × ε
- PAR: photosynthetically active radiation (MJ/m²/day)
- FPAR: fraction of photosynthetically active radiation absorbed (0-1)
- ε: actual light-use efficiency = εmax × Tstress × Wstress (gC/MJ)
- Temperature stress Tstress: a two-sided parabolic response peaking at the optimum temperature Topt.
- Water stress Wstress: adjusted nonlinearly with available water.
- Autotrophic respiration Ra = GPP × ra_frac(T), where the respiration fraction increases with temperature.
- NPP (Net Primary Productivity) = GPP − Ra.
The magnitudes are calibrated to fall within reasonable vegetation ranges (daily GPP of approximately 0.5-15 gC/m²/day, NPP/GPP ≈ 0.5). Outputs cumulative GPP/NPP rasters, daily flux time series, and a carbon budget JSON. Suitable for regional carbon budget assessment, vegetation productivity mapping, ecosystem model forcing, and carbon source/sink analysis.
Dependencies / 依赖
pip install 'numpy' 'rasterio' 'scipy'
Usage / 使用方法
Basic Usage
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 [other parameters]
Example 1 (Synthetic Data, Offline)
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2 (bbox Only, Automatic Synthesis)
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out
Example 3 (Longer Time Series)
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 60 --output-dir ./out
Example 4 (Quiet Mode)
python geoskill-carbon-flux-estimation.py --bbox 121.0 31.0 122.0 32.0 --synthetic --output-dir ./out --quiet
Example 5 (Real Raster Input, 4 Bands=PAR/FPAR/Temperature/Water)
python geoskill-carbon-flux-estimation.py --input par_fpar_temp_water.tif --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
carbon_flux.tif | GeoTIFF | Cumulative GPP/NPP over the period (2 bands, gC/m²) |
flux_timeseries.json | JSON | Daily spatial-mean time series of GPP/NPP/Ra |
carbon_budget.json | JSON | Carbon budget (daily mean/cumulative/NPP-GPP ratio) |
output-manifest.json | JSON | Run manifest |
Data Source / 数据源 / Source
- Real mode: local multi-band GeoTIFF (4 bands = PAR/FPAR/temperature/water).
- Synthetic mode (
--syntheticor--bboxonly): generates physically consistent PAR/FPAR/temperature/water fields and time series locally, with no network access required.
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network access at all. - All processing is performed locally; no user data is uploaded.
License / License
MIT
name: geoskill-carbon-flux-estimation description: '基于光能利用率模型（CASA/VPM 简化）估算 GPP/NPP：GPP=PAR×FPAR×ε，ε 受温度与水分胁迫调节，NPP=GPP−自养呼吸，输出碳收支'
碳通量估算 | Carbon Flux Estimation
本 skill 用简化的光能利用率模型（light-use-efficiency，CASA / VPM 思路） 估算生态系统碳通量：
- GPP（总初级生产力）= PAR × FPAR × ε
- PAR：光合有效辐射（MJ/m²/day）
- FPAR：光合有效辐射吸收比例（0-1）
- ε：实际光能利用率 = εmax × Tstress × Wstress（gC/MJ）
- 温度胁迫 Tstress：以最适温度 Topt 为峰值的双侧抛物线响应。
- 水分胁迫 Wstress：随可用水分量非线性调节。
- 自养呼吸 Ra = GPP × ra_frac(T)，温度越高呼吸占比越大。
- NPP（净初级生产力）= GPP − Ra。
量级经参数校准落在植被合理范围（日 GPP 约 0.5-15 gC/m²/day，NPP/GPP≈0.5）。 输出累计 GPP/NPP 栅格、逐日通量时序与碳收支 JSON。适用于区域碳收支评估、 植被生产力制图、生态模型强迫与碳源汇分析。
依赖
pip install 'numpy' 'rasterio' 'scipy'
使用方法
基本用法
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
示例 1（合成数据，离线）
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2（仅给 bbox，自动合成）
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out
示例 3（更长时间序列）
python geoskill-carbon-flux-estimation.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 60 --output-dir ./out
示例 4（静默模式）
python geoskill-carbon-flux-estimation.py --bbox 121.0 31.0 122.0 32.0 --synthetic --output-dir ./out --quiet
示例 5（真实栅格输入，4 波段=PAR/FPAR/温度/水分）
python geoskill-carbon-flux-estimation.py --input par_fpar_temp_water.tif --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
carbon_flux.tif | GeoTIFF | 时段累计 GPP/NPP（2 波段，gC/m²） |
flux_timeseries.json | JSON | 逐日 GPP/NPP/Ra 空间均值时序 |
carbon_budget.json | JSON | 碳收支（日均/累计/NPP-GPP 比） |
output-manifest.json | JSON | 运行清单 |
数据源 / Source
- 真实模式：本地多波段 GeoTIFF（4 波段 = PAR/FPAR/温度/水分）。
- 合成模式（
--synthetic或仅--bbox）：本地生成物理一致的 PAR/FPAR/温度/水分场与时序，无需网络。
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传用户数据。
License
MIT
相关技能
基于 NDVI 时序积分估算累积生物量，叠加气候校正得到生产力指数。Estimates cumulative biomass from NDVI time-series integration with climate correction to yield a productivity index.
Estimate forest carbon stock from remote sensing data using BEF, allometric equations, or IPCC Tier 1/2 methods. Includes Monte Carlo uncertainty analysis. Supports raster (GeoTIFF) and tabular (CSV) inputs.
SAR 森林生物量估算：由后向散射 σ⁰ 用线性/饱和经验模型反演森林地上生物量 AGB (t/ha)，支持 C/L 波段与地面样本标定
1 次安装
Compute carbon stock changes, emissions/removals, and uncertainty from multi-temporal land cover data using IPCC Tier 1/2 carbon factors. Use when analyzing land use change carbon budgets, estimating CO2e emissions from deforestation, or generating carbon accounting reports.
1 次安装
Priestley-Taylor 与简化 SEBAL 蒸散发估算，从净辐射/气温/LST/NDVI 计算 ET (mm/day)。Priestley-Taylor and simplified SEBAL evapotranspiration estimation from net radiation/air temperature/LST/NDVI. 输出 ET 栅格 + 统计 JSON。