Sustainability Atlas
Climate Tech & Data·12 min read··...

How-to: implement Satellite & remote sensing for climate with a lean team (without regressions)

A step-by-step rollout plan with milestones, owners, and metrics. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.

The remote sensing satellite market reached USD 41.4 billion in 2024 and is projected to grow at a 13.4% CAGR through 2034, driven primarily by climate monitoring applications (GM Insights, 2025). In 2024 alone, GHGSat detected over 20,000 methane super-emitter events from its constellation of 16 satellites, while Planet Labs launched Tanager-1—the first commercial hyperspectral satellite capable of pinpointing CO₂ and methane emissions at 30-meter resolution. For lean teams seeking to leverage this rapidly evolving technology for climate applications, the window of opportunity has never been more accessible, but the implementation pathway requires careful navigation of data costs, technical complexity, and integration challenges.

Why It Matters

Satellite remote sensing has fundamentally transformed climate monitoring from a resource-intensive, ground-based endeavor into a scalable, near-real-time global intelligence system. The environmental monitoring segment is projected to grow at the fastest CAGR of 13.27% from 2026–2033 (SNS Insider, 2026), reflecting the urgent demand for comprehensive climate data across sectors.

Three factors make satellite remote sensing indispensable for climate action:

Transparency and Accountability: Regulatory frameworks like the EU's Corporate Sustainability Reporting Directive (CSRD) and the SEC's climate disclosure rules require auditable emissions data. Satellite-based Measurement, Reporting, and Verification (MRV) provides the independent, consistent data streams that regulators and investors demand. GHGSat's 2023 global study detected 8.3 million tons of methane emissions from 3,114 oil, gas, and coal facilities—data that ground-based monitoring could never have captured at this scale.

Cost Efficiency at Scale: Traditional environmental monitoring requires extensive field personnel, equipment, and logistics. A single satellite constellation can monitor millions of sites globally. The commercial segment is expected to grow at 15.36% CAGR from 2026–2033, as organizations recognize that satellite data often costs a fraction of ground-based alternatives for comparable coverage.

Temporal Resolution for Dynamic Systems: Climate systems change rapidly—wildfires spread in hours, methane plumes appear and disappear within days, and deforestation can transform landscapes in weeks. Modern constellations provide daily or even sub-daily revisit times, enabling detection and response at timescales relevant to climate intervention.

Key Concepts

Spectral Bands and Their Climate Applications

Understanding spectral capabilities is essential for matching satellite data to climate use cases:

Spectral TypeWavelength RangeClimate ApplicationsKey Providers
Visible/NIR400–1000 nmVegetation health, land cover change, deforestationPlanet Labs, Maxar
Shortwave Infrared (SWIR)1000–2500 nmMethane/CO₂ detection, mineral mappingGHGSat, Planet Tanager
Thermal Infrared8–14 μmUrban heat islands, wildfire detection, crop stressAistech Space, NASA ECOSTRESS
Synthetic Aperture Radar (SAR)MicrowaveAll-weather monitoring, subsidence, flood mappingICEYE, Capella Space

Resolution Trade-offs

Spatial, temporal, and spectral resolution exist in tension. High spatial resolution (sub-meter) typically means narrower swaths and longer revisit times. Lean teams must prioritize:

  • Methane detection: 25–30m spatial resolution sufficient; daily revisit critical
  • Deforestation monitoring: 3–10m resolution; weekly revisit acceptable
  • Urban heat mapping: 30–100m resolution; seasonal to monthly cadence
  • Flood/disaster response: All-weather SAR capability prioritized over resolution

Data Processing Levels

Satellite data comes in processing levels that affect team workload:

  • Level 0: Raw sensor data—requires significant expertise
  • Level 1: Radiometrically calibrated—suitable for technical teams
  • Level 2: Atmospherically corrected—ready for analysis
  • Level 3/4: Derived products (e.g., methane concentrations, NDVI)—minimal processing required

Lean teams should target Level 2+ products to minimize infrastructure and expertise requirements.

What's Working and What Isn't

What's Working

API-First Data Access: Modern providers like Planet Labs offer RESTful APIs with Python SDKs, enabling integration without specialized GIS expertise. Their platform processes petabytes of imagery, delivering analysis-ready data that teams can incorporate into existing workflows.

Pre-built Analytics Products: GHGSat's Spectra platform delivers methane emission alerts directly to operators within hours of detection, eliminating the need for in-house algorithm development. Carbon Mapper's public data portal (launching summer 2025) will provide free access to methane and CO₂ plume data from the Tanager constellation.

Parametric Insurance Applications: ICEYE's SAR constellation enables parametric insurance products that trigger automatically based on satellite-detected events. Their flood monitoring system provides independent verification that reduces claims processing from weeks to days.

Multi-Source Fusion: Successful implementations combine satellite data with ground sensors, weather data, and operational information. SatSure's SPARTA platform exemplifies this approach, integrating Earth observation with IoT data for agricultural and financial applications.

What Isn't Working

Raw Data Overload: Organizations that acquire raw imagery without clear analytical objectives often find themselves overwhelmed. Cloud storage costs of $10–25 per TB per month accumulate rapidly, and each satellite downlink contact costs approximately $200. Without defined use cases, data becomes a liability.

Resolution Mismatch: Teams frequently over-specify resolution requirements. Sub-meter imagery may be unnecessary for regional methane monitoring, yet carries 10–100x the data volume and cost. This mismatch strains budgets and processing pipelines.

Temporal Gaps in Critical Moments: Single-satellite solutions or infrequent revisit schedules miss transient events. Methane super-emitters may vent for hours or days before dissipating—a weekly revisit schedule captures only a fraction of total emissions.

Integration with Legacy Systems: Many climate monitoring initiatives stall at the integration phase. Satellite data formats, coordinate systems, and temporal conventions often conflict with existing enterprise systems. Teams underestimate the engineering effort required for seamless data flows.

Sector-Specific KPIs and Benchmarks

SectorKey MetricPoorAverageGoodBest-in-Class
Oil & GasMethane detection rate<50% of leaks50–70%70–85%>85%
Oil & GasAlert latency>7 days3–7 days24–72 hrs<24 hrs
AgricultureYield prediction accuracy±25%±15%±10%<±5%
ForestryDeforestation detection lag>30 days14–30 days7–14 days<7 days
InsuranceClaims verification time>21 days7–21 days2–7 days<48 hrs
Carbon MRVVerification confidence<70%70–85%85–95%>95%

Key Players

Established Leaders

Planet Labs operates the largest Earth observation constellation with 200+ active satellites, providing daily global coverage at 3-meter resolution. Their 2024 launch of Tanager-1, in partnership with Carbon Mapper, marked their entry into hyperspectral greenhouse gas detection. The company holds a $20 million contract with Carbon Mapper through 2030.

Maxar Technologies delivers the highest commercially available resolution imagery (sub-30cm) through its WorldView Legion constellation. In February 2025, they launched their fifth and sixth Legion satellites, expanding capacity for infrastructure monitoring and change detection.

GHGSat operates the world's largest satellite constellation dedicated to greenhouse gas monitoring, with 16 satellites detecting methane at 25-meter resolution. Their December 2023 global analysis identified 8.3 million tons of annual methane emissions from fossil fuel facilities—data now used by energy companies, regulators, and financial institutions.

ICEYE pioneered commercial SAR satellite imaging with 38 satellites in orbit. Their all-weather, day-night capability fills critical gaps for flood monitoring, disaster response, and insurance applications. Named to TIME's Top GreenTech Companies 2025.

Emerging Startups

Matter Intelligence (California): Raised $12 million seed funding in October 2024 from Lowercarbon Capital and Toyota Ventures. Building sub-meter hyperspectral satellites with 500x more information density than existing sensors. First satellite Earth-1 launching 2025.

Aistech Space (Barcelona): Operating 120+ small satellites with thermal infrared imaging for wildfire detection and agricultural stress monitoring. Raised $9.58 million Series B in May 2025.

Pixxel (India): Deployed three hyperspectral imaging satellites via SpaceX in January 2025. Focus on agriculture, environment, and resource mapping in emerging markets.

AIRMO (Germany): Developing LiDAR and spectrometer payloads for near-real-time CO₂ and methane measurement. Pre-seed funding of €5.2 million secured in 2023.

Key Investors & Funders

Lowercarbon Capital: Lead investor in Matter Intelligence; focused on climate-forward remote sensing technologies.

BlackRock: Major investor in ICEYE ($65 million Series E, December 2024) and Capella Space; bringing institutional capital to SAR applications.

Seraphim Capital: Dedicated space tech VC with portfolio including ICEYE; managing funds exceeding $500 million.

European Space Agency (ESA): Active investor in European climate tech startups including Aistech Space; proposed €18.5 billion budget for 2023–2025.

Breakthrough Energy Ventures: Bill Gates-backed fund supporting early-stage decarbonization technologies, including remote sensing applications.

Examples

1. GHGSat and Oil & Gas Emissions Monitoring

GHGSat has partnered with major energy companies including ExxonMobil and Saudi Aramco to provide continuous methane monitoring across upstream and midstream operations. Their satellite constellation achieves daily revisit frequency in asset-dense regions like the Permian Basin, detecting super-emitter events with thresholds as low as 100 kg/hour. This granularity enables operators to identify and repair leaks within hours rather than the weeks typical of traditional leak detection programs. The result: quantifiable emissions reductions that support both regulatory compliance and voluntary climate commitments.

2. ICEYE and Parametric Flood Insurance

ICEYE's SAR constellation provides the verification layer for parametric insurance products that are transforming climate risk transfer. When flooding occurs, their satellites capture all-weather imagery within hours, regardless of cloud cover or time of day. Insurers compare satellite-detected flood extent against predetermined boundaries—if thresholds are exceeded, claims pay automatically without adjuster visits or prolonged negotiations. This model has reduced claims processing from weeks to 48 hours while improving accuracy, demonstrated across implementations with Swiss Re and Munich Re.

3. Pula and Agricultural Climate Resilience

Pula, a Kenya-based insurtech, uses satellite-derived vegetation indices and drought monitoring to provide climate insurance to smallholder farmers across Africa. Their satellite data platform triggers automatic payouts when remotely sensed conditions indicate crop stress or failure, eliminating the moral hazard and verification costs that have historically made smallholder insurance uneconomical. With $20 million Series B funding led by the Bill & Melinda Gates Foundation in April 2024, Pula has demonstrated 16% investment increases and 56% yield improvements among insured farmers, with ambitions to reach 100 million farmers by 2029.

Action Checklist

  • Define 2–3 specific climate monitoring use cases with quantified success criteria before evaluating data providers
  • Audit existing data infrastructure for compatibility with geospatial formats (GeoTIFF, Cloud Optimized GeoTIFF, GeoJSON) and APIs
  • Request trial access from 2–3 providers matching your resolution, revisit, and spectral requirements—most offer 30-day evaluation periods
  • Establish data processing pipeline using cloud-native tools (Google Earth Engine, AWS Ground Station, Microsoft Planetary Computer) to avoid on-premise infrastructure investment
  • Designate a "satellite data champion"—one team member responsible for vendor relationships, data quality monitoring, and integration maintenance
  • Build automated quality checks for cloud cover, data gaps, and calibration drift before data enters production workflows
  • Document baseline metrics in Month 1 to enable credible before/after comparisons for stakeholder reporting
  • Plan quarterly reviews of data utilization versus costs—suspend or downgrade unused data subscriptions
  • Develop fallback protocols for satellite data gaps, including secondary providers or historical baseline approaches
  • Engage legal/compliance review of data licensing terms, especially for derived products and redistribution rights

FAQ

Q: What is the minimum budget required to implement satellite-based climate monitoring?

A: Entry-level implementations using freely available data (Sentinel-2, Landsat) require only cloud computing costs of $500–2,000/month plus staff time. Commercial subscriptions for defined areas of interest typically start at $10,000–50,000/year for basic access, scaling to $100,000+ for enterprise-grade API access with guaranteed uptime and support. The most cost-effective approach for lean teams is combining free public data with targeted commercial acquisitions for specific high-value use cases.

Q: How do we evaluate whether satellite data accuracy is sufficient for our climate reporting needs?

A: Establish validation protocols early. For methane detection, compare satellite estimates against periodic ground-based measurements using Gaussian plume models or tracer release experiments. For vegetation monitoring, correlate satellite-derived indices with field sampling at representative sites. Accept that satellite data provides systematic, scalable coverage while ground truth provides accuracy validation—neither alone is sufficient for robust climate MRV. Industry benchmarks suggest achieving 85–95% correlation with ground truth qualifies as "good" for most applications.

Q: Can satellite data satisfy regulatory requirements for climate disclosure?

A: Increasingly, yes. The GHG Protocol's Scope 3 guidance explicitly mentions satellite monitoring as an acceptable methodology for supply chain emissions. The EU's CSRD recognizes remote sensing for environmental impact assessment. However, most frameworks require documented methodology, validation against recognized standards, and transparent uncertainty quantification. Engage auditors early to confirm your satellite-based approach meets their verification requirements.

Q: What team skills are essential for satellite data implementation?

A: Minimum viable team skills include: (1) geospatial data handling—familiarity with coordinate systems, projections, and GIS concepts; (2) Python or R programming for API integration and analysis automation; (3) cloud computing basics for scalable data processing. Deep remote sensing expertise is increasingly optional as providers deliver analysis-ready products. Most successful lean implementations designate one team member for satellite data specialization while others consume derived insights through dashboards and APIs.

Q: How quickly can we achieve production-quality satellite monitoring?

A: Realistic timelines for lean teams: 1–2 months for proof-of-concept using existing cloud platforms; 3–6 months to achieve repeatable, automated data pipelines; 6–12 months for integration with enterprise systems and stakeholder-facing outputs. Acceleration is possible by starting with higher-level data products (Level 3/4) rather than raw imagery, and by leveraging vendor professional services for initial implementation.

Sources

Topics

satelliteremoteunit economicsLCAauditabilitydigital twinscarbon accounting

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