Earthmover Expands Environmental Data Marketplace With CTrees Biomass Integration

According to a recent LinkedIn post from Earthmover, CTrees has joined the Earthmover Data Marketplace with its global Aboveground Biomass dataset. The post highlights that the dataset offers 100-meter resolution coverage updated annually since 2000, positioned as cloud-native and analysis-ready for environmental data users.

The CTrees dataset is described as built on a multiscale machine-learning framework that fuses satellite imagery with airborne LiDAR and provides per-pixel uncertainty metrics. The post also notes that the data is grounded in more than two decades of peer-reviewed research, signaling a focus on scientific robustness that may appeal to institutional users.

Use cases cited in the post include greenhouse gas inventory and reporting, carbon accounting, nature-based climate solutions, and forest carbon markets. This suggests that Earthmover is targeting customers involved in regulatory reporting, voluntary carbon markets, and climate-risk analytics, potentially broadening its revenue base across multiple climate-related verticals.

The post positions CTrees’ addition as another milestone in building Earthmover’s marketplace as a central access point for high-quality environmental datasets, following prior integrations with Sylvera and Spire Weather & Climate. For investors, this indicates a strategy of aggregating specialized climate and environmental data providers, which could strengthen Earthmover’s competitive moat and increase switching costs for enterprise clients.

Earthmover’s post also notes growing adoption among teams in energy, insurance, commodities, and climate tech that benefit from structured, queryable data. If this trend continues, the marketplace model could support recurring subscription revenue and deepen relationships in sectors with expanding climate-disclosure requirements.

The content further references educational resources such as a webinar and Google Colab tutorial demonstrating how to access and analyze CTrees data using Arraylake and Python. This emphasis on developer-friendly tools may encourage integration into customers’ analytics workflows, potentially accelerating usage and supporting long-term platform stickiness.