Status: running

The SatYield Engine

End-to-end biophysical processing and predictive modeling architecture for global agricultural yield forecasting.

Brazil Safrinha forecast

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106.5 MMT

Lead time

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23 Days

Accuracy

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1.7% Error

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In-Depth Pipeline & Methodology
Prediction Model:
Satellite LAI + Phenology + ML Ensemble
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Spatial Resolution:
County-level (HASC-2) → State → National
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Update Frequency:
Weekly during growing season (Jul–Nov)
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Phenology Stages:
Emergence → Vegetative → Flowering → Grain Fill → Maturity
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Stage 01

Satellite Imagery Acquisition

L1C/L2A INGESTION: NOMINAL

DATA FUSION ENGINE

10M/PXL OPTICAL RESOLUTION

Satellite heat map
Live Output Stream
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Stage 02

Phenology & Stress

GROWTH STAGE TRACKING: ACTIVE

VI/LAI CONFIDENCE

90%

Thermal Stress Analysis
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Stage 03

Digital Twins

CROP SIMULATION: RUNNING
Initialization12,000 Scenarios
Calibration100 Matched
ProjectionsREADY
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Stage 04

Yield Modeling

Model Convergence: OPTIMAL

CONFIDENCE INTERVAL

±0.2

3.4t/ha
Predicted Mean Yield
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Stage 05

Area Mapping

Extent Results: FINALIZED
Crop Classification
Classification Map
Primary Class DetectionCORN: 994-A2 PARCEL IDENTIFIED
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Stage 06

Regional Aggregation

Cluster Analysis: COMPLETE
South Cluster12,400 ha
South Cluster45,102 ha
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Stage 07: Final Output

Production Signals

A comprehensive suite of data and insights tailored for professionals in agricultural commodity trading.

Pushing to API V2.1...

98% SYNC

trending_upLive Reports Active
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Digital Twin Engine Active

Biophysical-Based
Digital Twins

Comprehensive biophysical-based cropsimulations coupled and powered by high spatio-temporal satellite imagery andadvanced weather models to crate a digital replica of the monitored crops.

Weather

Daily, weekly, and monthly climate variables

Temperature, precipitation, radiation, and wind.
Historical and in-season forecasts.

Soil

Soil type, characteristics and texture

Plant-available water eastimation and retention characteristics.

Seed and Crop Parameters

Crop type and cultivar characteristics

Phenological and physiological constraints.

Remote Sensing

Optical and SAR satellite earth observations

Vegetation dynamics and structural chemical properties.

Digital Twins
Energy (fAPAR)
0.82 (82%) normal
● Peak LAI
Maximum canopy density: 4.5
Water Stress: LST ANOMALY
Deviation vs normal: +3.2°C
Active Phase: Dent
Bands: NIR
Resolution: 10m
Radiation Intercepted
320
MJ/m²
Phenology Stage
R2–R5
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Stable
plant water USE
3.1
mm/day

Biophysical Crop State Outputs

The digital crop twins generate structured, time-series outputs including:

Output Stream Status: Synchronized

Stream_01

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Crop yield estimates

Dynamic prediction of final grain and biomass weight based on accumulated photosynthesis.

Stream_02

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Soil moisture and plant-available water

Volumetric water content mapping across various root-zone depth intervals.

Stream_03

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Plant biomass accumulation

Direct calculation of carbon fix and vegetative structure growth in real-time.

Stream_04

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Leaf Area Index (LAI)

Structural canopy density modeling derived from SAR backscatter and optical fusion.

Measure what is planted and harvested, before it reaches consensus

SatYield measures planted and harvested area directly from high-spatio-temporal Earth Observing satellites, capturing crop activityas it unfolds and revealing supply shifts before surveys and market consensusadjust.

Crop Classification
SOURCE: MULTISPECTRAL

High-Resolution

Satellite Imagery

PROCESSING: DEEP LEARNING

AI Algos

Deep Learning on Every Dot on Earth

OUTPUT: CROP CLASSIFICATION

INFERENCE RESULT

Large Scale Classification Maps

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Crop Cycle Intelligence Timeline

SIGNAL LATENCY: 3 DAYS

Planting

Growth

SatYield Signal

L-3 WEEKS

Official Reports

Harvest

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Classification Framework

Crop type identification & mapping
Planting and emergence timing
Harvesting and abandonment signals
Double-cropping and rotation detection
Classification confidence and stability over time
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Intelligence Outputs

Earlier planted area estimation
Better aggregation of crop state and yield signals
Reduced model error from misclassification
Improved comparability across regions and seasons

INFRASTRUCTURE GATEWAY READY

Engineered for Alpha

Seamlessly integrate satellite-derived agricultural signals into your quant stack with our developer-first infrastructure.

REST API

For programmatic access and automation

Batch files (CSV or Parquet)

For research and backtests

Cloud delivery

To supported storage locations

Direct integration support

For client-specific pipelines

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DATA INTEGRITY (TRUST ANCHOR)

Point-in-Time and Auditable by Design

SatYield data is built for environments where integrity matters:

Clear timestamps for each observation
Versioning for models and datasets
Confidence and quality fields included, not hidden
Backfillable history aligned to point-in-time principles
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Audit Log Stream

Channel: integrity.v2.logs

SY_VERIFIED

[2024-10-24 08:00:00.001] SHA-256 Verified: Dataset_v4.2.0
[2024-10-24 08:00:00.045] Observation: Yield_Est_Confirmed
[2024-10-24 08:00:00.112] Metadata: Confidence_Score: 0.982
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