FAMILY_MULTI_SPECTRAL_FUSION - Experiment Results

Overview

This document tracks experimental runs for advanced multi-spectral fusion intelligence using all 12 Sentinel-2 bands and sophisticated vegetation indices. Tests whether comprehensive spectral analysis can surpass the 20.5% improvement achieved by FAMILY_GROWING_SEASON_DYNAMICS.

Experimental Status

• Status: 🎯 READY FOR IMPLEMENTATION
• Created: 2025-08-20
• Foundation: Builds on FAMILY_GROWING_SEASON_DYNAMICS 20.5% breakthrough
• Target: 25-30% improvement over strongest baseline
• Priority: High - Advanced satellite intelligence optimization

Foundation Success

FAMILY_GROWING_SEASON_DYNAMICS Achievement: - Breakthrough Performance: 20.5% improvement over persistent baseline - Key Innovation: EVI vs NDVI comparison revealed superior predictive patterns - Validated Approach: Multi-index analysis outperforms single vegetation metrics - Data Sources: Real satellite data from lake_31UFU_small.zarr + BRP potato masks

Data Validation

• ✅ Zarr store available: lake_31UFU_small.zarr (36GB, all 12 bands)
• ✅ Full spectral coverage: B01-B12 + SCL for comprehensive analysis
• ✅ Multi-year data: 2015-2024 temporal coverage for robust training
• ✅ Price data accessible: BoerderijApi NL.157.2086 weekly prices
• ✅ BRP parcels: Consumption potato field boundaries for spatial masking
• ✅ Standard baselines: All 4 standard baselines (persistent, seasonal_naive, ar2, historical_mean) ready for comparison

Advanced Feature Engineering Strategy

Spectral Intelligence Framework

Building systematically on the proven EVI vs NDVI success:

1. All-Band Utilization: Extract maximum information from 12 spectral bands
2. Advanced Index Suite: NDVI, EVI, SAVI, NDRE, CHL, MTCI, IRECI, S2REP
3. Temporal Patterns: Multi-index trajectory analysis through growing season
4. Spatial Integration: Field-level aggregation within BRP potato boundaries
5. Cross-Index Synergies: Ratios and correlations between different indices

Performance Prediction Strategy

• Variant A (25% target): Comprehensive spectral band analysis
• Variant B (27% target): Advanced vegetation index fusion
• Variant C (30% target): Spectral-temporal feature engineering

Experiment Results: [TO BE UPDATED AFTER IMPLEMENTATION]

Data Versions: - Satellite data: lake_31UFU_small.zarr (all 12 bands, 2015-2024) - Price data: BoerderijApi NL.157.2086 - Parcel data: BRP consumption potato mask - Git SHA: [TO BE FILLED]

Rolling CV Results: - [TO BE FILLED AFTER IMPLEMENTATION]

Statistical Tests: - DM test vs strongest baseline: [TO BE FILLED] - Cross-validation folds: [TO BE FILLED] - Baseline comparison: ALL 4 standard baselines tested

Baseline Comparison: - Model: MAE = [TO BE FILLED] - Persistent baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Seasonal naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - AR2 baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Strongest competitor: [TO BE IDENTIFIED] - Primary improvement: [TO BE CALCULATED] vs [strongest_baseline_name]

Decision Log - 2025-08-20

Summary: FAMILY_MULTI_SPECTRAL_FUSION prepared for implementation to push beyond 20.5% satellite intelligence success.

Key Setup Decisions: 1. Build on proven success: Use FAMILY_GROWING_SEASON_DYNAMICS EVI vs NDVI breakthrough as foundation 2. Scale to full spectral suite: Utilize all 12 Sentinel-2 bands for comprehensive analysis 3. Advanced index fusion: Combine multiple vegetation indices for maximum information extraction
4. Target aggressive improvement: 25-30% improvement targets based on spectral physics potential 5. Maintain rigor: Use same data sources, baselines, and statistical testing as breakthrough

Implementation Priority: - Variant A: All-band spectral analysis (25% target) - Variant B: Advanced vegetation index fusion (27% target)
- Variant C: Spectral-temporal feature engineering (30% target)

Success Criteria: - Exceed 25% improvement over strongest baseline - Beat FAMILY_GROWING_SEASON_DYNAMICS 20.5% record - Demonstrate practical significance for trading applications - Maintain statistical rigor with proper baseline comparisons

Status: ✅ READY FOR ADVANCED SATELLITE INTELLIGENCE IMPLEMENTATION

Codex Validation — 2025-11-10

Files Reviewed

• advanced_satellite_ensemble_experiment.py
• experiment.md
• hypothesis.yml

Findings

1. Implementation guarded behind TODOs. The script sets up loaders for Sentinel-2, BRP, and Boerderij data but ends after dataset creation; there is no training loop, baseline comparison, or MLflow logging. Thus no empirical evidence exists.
2. Execution never occurred. experiment.md:1-74 explicitly lists every result field as “[TO BE FILLED]” and labels the status “Ready for implementation,” confirming that no run has been performed.
3. Price-only baseline advantage untested. Although get_standard_baselines is imported, no code actually calls it, so we have no proof that the proposed spectral features beat a simple price model.

Verdict

NOT VALIDATED – Until the satellite/parcel/price pipeline is executed end-to-end on real data and the results demonstrate statistically significant improvements over the mandatory baselines, this family remains unvalidated.