EDAgro_regional_analytic_extraction

🌍 Regional Monitoring API🔗

📖 Overview🔗

The Regional Monitoring API enables organizations to observe, analyze, and report on agricultural or environmental conditions across large geographic areas.

This capability is essential for businesses and institutions that need strategic insights at scale, such as crop monitoring, risk assessment, and market intelligence.

How It Works🔗

Regional monitoring leverages satellite imagery, remote sensing data, and weather analytics to provide a comprehensive view of crop and environmental conditions. Key features include:

• Tracking crop development and vegetation health across entire regions
• Analyzing soil moisture, NDVI, and crop masks to identify trends and anomalies
• Comparing multiple datasets (e.g., weather, vegetation indices) for regional benchmarking

Audiences🔗

• Crop analysts and agribusinesses assessing crop conditions and yield potential over large territories
• Government agencies and insurers validating production data and monitoring risks
• Market intelligence teams integrating multiple data sources for strategic planning

Why It Matters🔗

Regional monitoring provides a high-level, data-driven perspective that supports better decision-making, resource allocation, and risk management across wide areas.

🗂️ Baseline Data🔗

Vegetation and Weather Data🔗

Additional Datasets🔗

More Info on Weather Models:🔗

Forecast Weather Models🔗

• ENS model (ECMWF*, probabilistic, 20 km resolution, forecast D+14)
• GFS model (NOAA-NCEP**, deterministic, 25 km resolution, forecast D+14)

Historical Data🔗

• Historical data depth from 1990 to 2019 (today minus 15 days)

Sliding Reprocessing🔗

• D - 3 months, then D - 15 days
• ERA-5* (ECMWF) historical data integrated at the beginning of each month:
• Current Month minus 3 months data
• Example: On July 31, the last available data is April 30 (3 months delay)
• ERA-5T* (ECMWF) historical data integrated day by day at D-15 days
• ENS (ECMWF) D0 values (pseudo-analysis) stored from D0 to last ERA-5T historical available data (D-15 days)

* European Center for Weather Forecasts (ECMWF), Reading, UK
** National Oceanic and Atmospheric Administration – National Center for Environmental Prediction (USA)
*** The data assimilation system

⚙️ API🔗

Environment API root🔗

⚙️ Parameters & Variables🔗

The Regional Monitoring API exposes weather, climate, and crop-progress indicators aggregated at the Administrative Management Unit (AMU) level. Most endpoints share a common parameter set; this section defines the building blocks once, then lists the operations available for each variable. Refer to the embedded Swagger UI above for per-endpoint payload examples and the exact response schemas.

Core Concepts🔗

Pixel Type values🔗

Pixel type availability is subscription- and block-scoped. Use GET /api/customer-subscriptions/subscription-block/pixel?blockCode={code} to list the pixel types accessible for a given block.

Indicator Type values🔗

Indicator type availability is subscription- and block-scoped. Use GET /api/customer-subscriptions/subscription-block/indicator?blockCode={code} to list the indicator types accessible for a given block.

Common Query Parameters🔗

Used by most GET indicator endpoints (year-of-interest, comparison-*, historical-frequency). Operation-specific parameters are listed at the bottom of the table.

Discovery Endpoints🔗

Use these to discover the blocks, pixel types, indicator types, AMUs, and admin units available to your subscription before calling the indicator endpoints.

Weather & Climate Indicators🔗

Most variables support a consistent set of operations. Find your variable in the row, then append the operation suffix to the base path. Combine with the Common Query Parameters above.

Operations:

• YOI — GET {base}/year-of-interest — values for the selected period
• CTA — GET {base}/comparison-to-average — vs. historical N-year average
• CTR — GET {base}/comparison-to-reference-year — vs. a specific past year
• HF — GET {base}/historical-frequency — count of threshold-crossing events
• Series — POST {base} — daily/period time series for graph rendering
• Graph export — POST {base}/export-graph — CSV export of the series
• Map exports — POST {base}/export-map/{operation} — CSV export of the map (one path per supported operation)

GDD endpoints additionally require baseTemperature (double) and maxTemperature (double) on every call.

Threshold-Based Day Counts🔗

Count days meeting a threshold condition over the analysis period. All require thresholdValue plus the common query parameters.

Crop Progress Statistics🔗

Compare official crop progress reports against historical baselines.

All three require: - weekEnding (date-time), - growthStageId (int), - countryCode (string), - level (int), - commodityStatsId (int), - statisticTypeId (int).

Comparison endpoints add nbYearsWanted (CTA) or referenceYear (CTR).

Request Body Schemas (POST endpoints)🔗

POST series, graph-export, and map-export endpoints accept structured bodies. Each body carries the same fields as the GET equivalent, encoded as JSON. The reused schemas are:

See the embedded Swagger UI for the full field-by-field schema and example payloads.

Example SerieRequest payload (used for POST {base} series endpoints):

{
  "amuIds": [123456],
  "idBlock": 267,
  "idPixelType": 8,
  "indicatorTypeIds": [1],
  "startDate": "2025-01-01",
  "endDate": "2025-12-31",
  "fillYearGaps": false
}

Where:

• amuIds — list of AMUs to extract (resolved via the Discovery Endpoints above).
• idBlock, idPixelType, indicatorTypeIds — see Core Concepts.
• fillYearGaps — when true, the API back-fills missing dates within the requested period using historical climatology.

Response Shapes🔗

⚠️ Error management🔗

📊 Performance and Accuracy🔗

Coverage🔗

The Regional Monitoring service provides global / worldwide coverage for vegetation and weather indicators. Crop-specific outputs are regionalized and cover most commodity crops out of the box — see the Pixel Type values table for the catalog currently delivered (corn, soybean, winter wheat, barley, rapeseed, sugarbeet, with global and regional variants for Europe and Brazil).

Additional crop masks can be added on demand through the CropID product, which is the source of all crop-specific pixel types exposed here.

Aggregation🔗

💼 Use Case and Product Integration🔗

This analytic is used in:

• Crop Intelligence