Digital Ag Field-Level Crop Intelligence for Every Acre in Your Portfolio
Agronomists and advisors manage larger, more complex portfolios than ever before. Distinguishing a field trending positively from one quietly declining is subtle, yet critical, and often impossible without objective, standardized data. Digital Ag delivers a clear, up-to-date view of every field in your portfolio through harmonized indicators, trend monitoring, and anomaly detection, equipping advisors with the evidence they need to guide growers with confidence.
Complete Field-Level Analytics
Purpose-built for agronomists, field advisors, and data science teams, Digital Ag provides the crop condition insights, historical benchmarks, and change detection analytics needed to monitor fields at scale, prioritize interventions, and power advanced modeling pipelines.
Instant Portfolio-Wide Visibility Know exactly where to focus before problems become losses. Real-time crop condition insights, anomaly detection, and trend tracking across your entire operation, from a single field to hundreds of thousands of acres.
Objective Data for Trusted Advisory Back your agronomic guidance with satellite-verified evidence. Justify input adjustments, flag underperforming fields, and intervene proactively when conditions shift. You retain full control of the "last mile," reinforcing your role as a trusted advisor.
A Foundational Data Layer for Advanced AnalyticsStandardized, AI-ready datasets feed directly into modeling pipelines. Curated agronomic indicators, crop conditions, and field-level metrics accelerate experimentation, improve model accuracy, and reduce iteration cycles for data science teams.
Why Choose Digital Ag for Field Intelligence
Comprehensive Monitoring Suite:
Vegetation indices, biophysical variables, weather data, phenology detection, and field benchmarking in a single platform.
Multi-Season Historical Context:
10+ years of medium-resolution imagery (Sentinel-2, Landsat 8/9) plus low-resolution archives back to 2003 (MODIS) for long-term trend analysis and benchmarking.
Standardized, Cross-Calibrated Data:
All satellite sources are fully cross-calibrated to guarantee spatial and temporal consistency. Agriculture-specific cloud masking ensures clean, reliable inputs for downstream analytics.
From Satellite Imagery to Field-Level Intelligence
Digital Ag combines multi-source satellite data with advanced analytics to deliver comprehensive field monitoring and agronomic insights.
Field Boundaries and Onboarding
Onboard your operation by creating Grower, Farm, and Field entities through multiple methods, including automated field creation, boundary selection layers (in select regions), or uploading existing boundaries. Update entities anytime as cropping cycles, land sales, or farm operations change. Analytics begin computing immediately.
Crop Identification and Rotation History
Automated crop history identification saves time and enables accurate calculation of performance metrics with no data entry effort. Off-season crop cover duration estimates and tillage practices identification are also available on-demand.
Vegetation Indices and Biophysical Variables
A complete suite of indicators designed to surface issues quickly: track canopy development with NDVI, EVI, and GNDVI; assess yield proxies with LAI and Biomass; evaluate nitrogen nutrition with S2REP and NDRE; detect water stress with NDMI; and measure photosynthetic activity with FAPAR and FCover.
Imagery Time Series
Continuous, season-long visibility into field performance. Medium-resolution time series (Sentinel-2, Landsat 8/9, premium tasked imagery) from 2019 onward with advanced denoising and smoothing to reveal the true biological signal. Low-resolution time series (MODIS) back to 2003 for deeper historical patterns, with planned Sentinel-3 integration for monitoring continuity.
HotSpot Detection
Identify emerging issues at a glance. The HotSpot Processor highlights areas that are changing and may require immediate attention, ensuring developing problems are surfaced quickly.
Phenological Stage Detection
Automated detection of key crop events including emergence (historical, in-season, and delay detection), greenness readiness, corn silking date (calibrated for Europe), harvest readiness, and harvest detection.
Field Performance Analytics
Track Productivity, Benchmark Performance, and Surface Risk
Potential Score
Evaluate how a field performs during the growing season compared to its historical performance for the same crop. Uses satellite imagery time series as a proxy for yield potential, providing a consistent, repeatable benchmark across large portfolios.
Risk Score
Measure how stable or variable a field's performance has been over time by analyzing fluctuations in historical Potential Scores. A score of 20 reflects stable, consistent yields; a score of 65 indicates significant variability and higher production risk.
Field Benchmarking
Compare a field's vegetation development against neighboring fields under similar growing conditions. Quickly identify growth anomalies, distinguish field-specific issues from regional trends, and prioritize where deeper investigation is needed.
Field Ranking
Compare growth development across a defined set of fields to identify top performers and highlight those falling behind. Advisors can quickly see which fields require immediate attention and which are tracking as expected.
API Access
Ingest EarthDaily data directly into your systems to feed yield forecast models or power custom interfaces.
Global Agricultural Coverage
Digital Ag leverages both public and private satellite constellations to ensure reliable, scalable coverage across agricultural regions worldwide.
Primary Crop Focus: Major row crops including cereals, oilseeds, and sugar crops where consistent field-level intelligence is essential for operational and modeling workflows.
Agriculture-Specific Cloud Masking: Advanced cloud-masking technology applied in targeted operational regions ensures clean, reliable inputs for analytics.
USA:
Corn, Soybean, Wheat, Cotton
Brazil:
Soybean, Corn (first and second seasons), Sugarcane, Cotton, Wheat, Rice
Instant visibility across every field in your portfolio. Prioritize interventions with objective data, justify recommendations with satellite-verified evidence, and intervene proactively when conditions shift.
AgTech Companies and Platforms:
Power digital farming tools, agronomic models, and crop-specific advisory services with standardized, API-accessible field-level datasets.
Data Science Teams:
AI-ready datasets feed directly into modeling pipelines. Curated indicators and field-level metrics reduce preprocessing time and accelerate experimentation.
Co-ops and Input Retailers:
Deeper, more consistent insight into field performance than traditional scouting alone. Support growers with data-driven guidance on input adjustments and timing.
Crop Consultants:
Scale your practice with portfolio-wide monitoring that flags the fields needing attention, so you spend time where it matters most.
One-on-One Crop Analyst Calls
Speak directly with EarthDaily crop analysts about potential yield concerns. Bring your own questions or let analysts guide you through a virtual crop tour of regions relevant to your business.
Features & Capabilities Monitor. Benchmark. Detect. Decide.
Portfolio-Wide Monitoring:
Crop condition insights across your entire operation
Biophysical Variables:
Canopy structure, nitrogen uptake, water stress, and light-use efficiency indicators
Multi-Index Analytics:
NDVI, EVI, GCVI, GNDVI, LAI, NDWI, and NMDI for detailed condition assessment
Historical Time Series:
Medium-resolution from 2019; low-resolution from 2003
Automated Phenology Detection:
Emergence, peak vegetation, silking, harvest readiness, and harvest detection
Spatial Change Detection:
Near real-time identification of shifting field conditions
Field Benchmarking and Ranking:
Performance comparison against neighbors and portfolio peers
Weather Integration:
Daily and cumulative weather indicators with historical comparison
In-Field Variability Maps:
Vigor, nutrition, moisture, and canopy structure at sub-field resolution
Soil and Terrain Context:
Integrated soil maps (SSURGO for U.S.) and elevation/slope models
Management Zone Generation:
Prescription (Rx) maps for variable-rate input applications
What satellite sources does Digital Ag use?
Medium-resolution imagery from Sentinel-2, Landsat 8/9, and premium tasked sources. Low-resolution historical data from MODIS (back to 2003), with planned Sentinel-3 integration for continued monitoring.
How do I onboard my fields?
Multiple options: automated field creation, boundary selection layers (in select regions), or upload your existing field boundaries. Analytics begin computing immediately after onboarding.
What vegetation indices are available?
A comprehensive suite including NDVI, EVI, GNDVI, LAI, Biomass, S2REP, NDRE, NDMI, FAPAR, and FCover, covering canopy development, nitrogen nutrition, water stress, and photosynthetic activity.
How far back does historical data go?
10+ years of medium-resolution time series history. Low-resolution (MODIS) archives back to 2003 for long-term benchmarking and trend analysis.
Can I integrate Digital Ag data into my own models and tools?
Yes. All analytics are available via APIs designed for seamless integration into modeling pipelines, AgTech platforms, and enterprise workflows.
What is the Potential Score?
A satellite-derived indicator that evaluates a field's current-season performance against its own historical performance for the same crop. It provides a consistent, repeatable proxy for yield potential across large portfolios.
How do I get started?
Contact EarthDaily to schedule a demo and discuss your specific requirements. Whether you need platform access, API integration, or both, our team can tailor a solution to your workflow.
Experience Field-Level Crop Intelligence at Scale
Schedule a demo to see Digital Ag monitoring and benchmarking in action
Explore API documentation for integration into your analytics pipeline
Discuss coverage for your priority regions and crops
