ESA Satellite Tree Monitoring System
Transforming complex satellite data into actionable insights for non-technical users, enabling Ecuador and Madagascar to monitor environmental changes in real-time.
My role
Lead product designer
Duration
June 2025 - Present
Platform
Web & Mobile Responsive
Collaborators
2 Backend Engineers, 1 Data Scientist, 3 ESA Scientists, 1 PM
Overview
The ESA platform democratizes access to satellite environmental data, previously accessible only to scientists. As lead designer, I created an intuitive interface that translates complex datasets into clear visualizations for farmers and land managers across Madagascar and Ecuador.
My Role
I was the lead product designer working with European Space Agency scientists, backend engineers specializing in satellite APIs, and agricultural stakeholders. My team consisted of a product manager, 2 data scientists, 3 ESA scientists, and 2 backend engineers.
Problem Statement
Agricultural stakeholders need environmental data for decision-making but lack the technical expertise to interpret raw satellite information. Current tools require 15+ minutes of analysis and scientific knowledge, creating barriers to adoption.
RESEARCH
Discovery Phase
To understand how non-technical users interact with complex data, I conducted 25 interviews with farmers and land managers, facilitated 3 workshops with ESA scientists, and shadowed current data analysts.
The key insights were:
Upon revisiting the user interviews and survey insights, I expanded my analysis to assess pain points across the entire prescription refill journey.
Expertise gap: Users need insights, not raw data visualization
Time constraints: Decisions needed within 2-minute windows during field work
Mobile context: 70% check data on mobile devices in the field
Trust barrier: Users skeptical of automated interpretations without transparency
Significance
Timely access to environmental data directly impacts crop yields and resource management. Delays in data interpretation can result in missed intervention windows, affecting both productivity and sustainability goals.
ITERATION 1
How might we simplify satellite data without losing scientific accuracy?
Working with stakeholders
I collaborated with ESA scientists to establish accuracy thresholds for simplified visualizations while maintaining scientific integrity. With engineers, I mapped API constraints for real-time processing and data layering capabilities.
Working with engineers
The satellite data API had strict rate limits and processing constraints. I worked with backend engineers to implement progressive loading strategies and establish a caching system for frequently accessed datasets.
Design Decisions
Progressive data layers I designed a three-tier information architecture: Basic view for quick insights, Detailed view for trends, and Expert mode for raw data access.
Using card-based layouts to maintain consistency with Treedom's existing design patterns while introducing new data visualization components.
Context-aware tooltips Created a translation layer converting scientific terminology into agricultural language, validated through user testing sessions.
Implementing hover states and mobile-friendly tap interactions for seamless cross-platform experience.
Implementation
This design was rolled out to a pilot group of 50 users across 2 regions to assess comprehension and task completion rates.
Did it work?
Yes, partially. Interpretation time reduced from 15 to 5 minutes with 85% accuracy in user understanding.
What was wrong?
Power users felt constrained by the simplified interface and requested access to more detailed data layers without switching contexts.
ITERATION 2
How might we create an adaptive interface that serves both novice and expert users?
Journey mapping
I synthesized research findings into a journey map visualizing the decision-making process from data request to action, identifying where different user types diverged in their needs.
Working with product manager
We prioritized building user profiles that would automatically adjust interface complexity based on usage patterns and explicitly set preferences, balancing development effort with user value.
Design Decisions
Adaptive interface system Designed an intelligent system that learns from user behavior, progressively revealing advanced features as expertise grows.
Establishing new component patterns that could expand or contract based on user profile settings.
Collaborative annotations Introduced community-driven insights where experienced users could annotate data for others, creating a knowledge-sharing ecosystem.
Building social proof elements while maintaining data privacy and accuracy standards.
Final Solution
Introducing the adaptive ESA platform. Designed to serve diverse expertise levels, reduce interpretation time, and foster community knowledge sharing.
Outcomes
2 min
average interpretation time
92%
task completion rate
500+
active users across EU
Final Takeaways
Balancing simplicity with depth: The challenge taught me that oversimplification can alienate power users. Progressive disclosure became our bridge between accessibility and functionality.
Scientific collaboration is design research: Working with ESA scientists showed me that domain experts are invaluable design partners when treated as co-creators rather than just validators.
Community features multiply value: The annotation system unexpectedly became the most valued feature, proving that peer learning can be more powerful than perfect UI.
Made with ♥ in Italy. Copyright © Ugo Possenti 2025
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