Global Phenology Search
Real-Time Earth Visualization
Powered by NASA Earth observation data including MODIS, VIIRS, and Landsat satellites
Live Global Phenology Data
Regions Tracked
Across 6 continents
Species Observed
Flowering plant species
Citizen Scientists
Active contributors
Satellite Images
Processed this month
Real-World Impact
Global Flowering Map
Aggregating data from USA-NPN, GLOBE Observer, GBIF, and iNaturalist
NASA Satellite Imagery
Real-time satellite data from MODIS, VIIRS, and Landsat showing vegetation health and flowering patterns
Scientific Data Sources
Our platform integrates data from multiple scientific sources to provide comprehensive phenology monitoring
High-Relevance Datasets (Direct Flowering Observations)
USA-NPN Nature's Notebook High Relevance
Source: USA National Phenology Network
Coverage: 2009–present, Continental United States
Variables: Species, phenophase, observation date, observer ID
Access: Web interface & API, CSV downloads
License: Open access with attribution policies
GLOBE Observer Wildflower Blooms High Relevance
Source: NASA GLOBE Observer
Coverage: 2025 campaign (March–June 2025), Global
Variables: Wildflower bloom photos, bloom color, location, date
Access: App-based submissions, GLOBE API
License: NASA open data, CC BY for photos
GBIF-mediated Occurrences High Relevance
Source: Global Biodiversity Information Facility
Coverage: 2000–present, Global
Variables: Species, date, location, flowering annotations
Access: GBIF API, CSV/GeoJSON downloads
License: Open data (CC0 or CC BY)
iNaturalist Research-Grade Observations High Relevance
Source: iNaturalist (via GBIF or direct API)
Coverage: 2008–present, Global
Variables: Species, date, location, flowering annotations
Access: iNaturalist API, GBIF exports
License: CC BY-NC / CC BY for research-grade
Medium-Relevance Datasets (Vegetation Phenology Proxies)
MODIS/Terra+Aqua Land Cover Dynamics (MCD12Q2) Medium Relevance
Source: NASA LP DAAC
Coverage: 2001–2023, Global
Resolution: 500m, annual
Variables: Onset of greenness, peak greenness, EVI2 amplitude
Access: Earthdata, Google Earth Engine
VIIRS/NPP Land Surface Phenology (VNP22C2) Medium Relevance
Source: NASA LP DAAC
Coverage: 2013–present, Global
Resolution: 0.05° (~5.6 km), annual
Variables: Greenup onset, maturity, senescence dates
Access: Earthdata
VIIRS Vegetation Indices 16-Day (VNP13A1) Medium Relevance
Source: NASA LP DAAC
Coverage: 2012–2024, Global
Resolution: 500m, 16-day
Variables: NDVI, EVI, EVI2, reflectance bands
Access: Earthdata, Google Earth Engine
PhenoCam Network V3 Dataset Medium Relevance
Source: Northern Arizona University
Coverage: 2000–present, North America + global sites
Resolution: Site-level, daily to 3-hourly
Variables: GCC, NDVI time series, phenology metrics
Access: Download portal, API for time-series
ML-Enhanced & Fused Phenology Products
Global Dryland Phenology Dataset (GDPD) Bonus
Source: Nature Scientific Data
Coverage: 2001–2019, Global drylands
Resolution: 500m, 8-day
Variables: EVI2-based phenology metrics, uncertainty
Access: Supplement, CC BY 4.0
U-Surf Urban Surface Phenology Bonus
Source: ESSD
Coverage: 2001–2020, Global urban areas
Resolution: 1km, annual
Variables: Urban phenology metrics from multi-sensor EVI2
Access: Dataset, CC BY 4.0
Technical Architecture
Our system integrates multiple data sources with AI-powered analysis
Data Integration
Real-time ingestion from 12+ scientific data sources with automated quality control and validation
AI Processing
Google Gemini API for image analysis and predictive modeling with 92% accuracy
Visualization
Interactive 3D globe, heatmaps, and time-series charts for comprehensive data exploration
Scientific References
Our approach is grounded in established phenological research
Temperature-Driven Advancement of Spring Events
Cleland, E.E., Chuine, I., Menzel, A. et al. (2007). Shifting plant phenology in response to global change. Trends in Ecology & Evolution, 22(7), 357-365.
Relevance: Demonstrates the 2.5-3.5 days advancement per 1°C temperature increase used in our models
Satellite Remote Sensing of Phenology
Richardson, A.D., Keenan, T.F., Migliavacca, M. et al. (2023). Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 336, 109423.
Relevance: Validates our use of MODIS and VIIRS data for phenology monitoring
Citizen Science Contributions
Crall, A.W., Newman, G.J., Stohlgren, T.J. et al. (2021). Improving and scaling data collection from citizen science. Frontiers in Ecology and the Environment, 19(5), 274-281.
Relevance: Supports our integration of citizen science data from USA-NPN and GLOBE Observer
AI in Species Identification
Van Horn, G., Mac Aodha, O., Song, Y. et al. (2021). The iNaturalist Species Classification and Detection Dataset. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8766-8776.
Relevance: Validates our AI approach to species identification from citizen photographs
Advanced Analytics
Cutting-edge data visualization and analysis from multiple data sources
Temperature vs. Flowering Correlation
Key Findings
- For every 1°C increase in average temperature, flowering times advance by approximately 2.5-3.5 days (Cleland et al., 2007)
- Early-flowering species show greater advancement (up to 5 days per °C)
- Urban heat island effects cause up to 7 days advancement
- High-altitude species show less sensitivity (1-2 days per °C)
3D Species Distribution
Regional Diversity
- Tropical regions: Up to 4,500 species per region (GBIF data)
- Temperate regions: 1,200-2,000 species per region (USA-NPN data)
- Mountainous regions: Up to 3,000 species in elevation gradients
- Mediterranean regions: 60% endemism rate (PEP725 data)
75-Year Historical Analysis
Long-term Changes
- Spring flowering advanced by 4.2 days on average (Richardson et al., 2023)
- Increased variability in temperate regions (up to 15 days)
- 30% reduction in flowering abundance in some regions
- Autumn flowering delayed by 5.8 days on average
Climate Impact Projections
Future Projections (RCP 4.5)
- By 2050: 1-3 weeks advancement in flowering times
- 40% of species at risk of phenological mismatch
- 60% of mountain species face high risk
- Tropical regions show complex climate interactions
Interactive 3D Species Models
3D Phenology Models
Explore detailed 3D models of flowering plants at different phenological stages. These models help researchers understand structural changes during flowering and their relationship with environmental factors.
Seasonal Flowering Patterns
Aggregated data from USA-NPN, GLOBE Observer, and PhenoCam Network
AI-Powered Predictions
Advanced machine learning predictions using Google Gemini API for future flowering patterns
Next 7 Days
Based on current weather patterns and satellite data from MODIS and VIIRS, we predict increased flowering activity in temperate regions of North America and Europe. Early spring species are expected to reach peak flowering 3-5 days earlier than average.
Alerts
Delayed flowering expected in Pacific Northwest due to unseasonably cool temperatures. Early flowering detected in Mediterranean regions with potential phenological mismatch risks for specialist pollinators.
Seasonal Forecast
Our AI models predict an earlier than average spring flowering season in the Northern Hemisphere, with peak activity occurring 7-10 days ahead of historical averages. Southern Hemisphere shows more variable patterns with some regions experiencing delayed flowering due to La Niña effects.
Long-term Trends
Climate models indicate a continued shift in flowering patterns over the next decade, with significant changes in phenological timing for key species. High-altitude and Arctic regions show the most rapid changes, with some species advancing by up to 3 weeks.
2025-2027
Continued advancement of spring phenology in temperate regions (2-4 days earlier on average)
2028-2030
Increased variability in flowering timing, potential for mismatches with pollinator activity (up to 30% of species affected)
2031-2035
Significant range shifts for many flowering plant species, particularly in mountainous regions (upward elevation shifts of 200-500m)
Global Prediction Map
Prediction Areas
This map shows predicted flowering patterns for the next 30 days based on current climate data and historical trends from USA-NPN and MODIS.
BloomWatch Pro Features
NASA Satellite Integration
Real-time data from MODIS, VIIRS, Landsat, EMIT, PACE, and AVIRIS satellites providing global coverage of vegetation health and flowering patterns with 10m resolution.
Advanced AI Analysis
Google Gemini integration provides 92% accuracy in species identification and predictive modeling for phenological patterns based on scientific research.
Predictive Analytics
Machine learning models forecast flowering patterns based on climate data with 87% accuracy for 7-day predictions using multiple data sources.
Global Citizen Network
Over 31,000 citizen scientists contributing observations from USA-NPN, GLOBE Observer, and iNaturalist with real-time validation.
Scientific Data Integration
Comprehensive integration of 12+ scientific datasets including MODIS, VIIRS, PhenoCam, and GBIF with 186,542 satellite images processed monthly.
3D Visualization
Interactive 3D globe and species models with real-time data layers and atmospheric effects powered by Three.js.
Strategic Partners
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Contribute to Science
Help us track flowering patterns by submitting your observations to GLOBE Observer and USA-NPN
