Earthmover Expands Environmental Data Marketplace With CTrees Biomass Dataset

According to a recent LinkedIn post from Earthmover, CTrees has made its Aboveground Biomass dataset available through the Earthmover Data Marketplace. The dataset is described as a global, 100-meter resolution time series updated annually from 2000 to the present and delivered in a cloud-native, analysis-ready format.

The post highlights that the CTrees product is built on a multiscale machine-learning framework that fuses satellite imagery with airborne LiDAR and provides pixel-level uncertainty quantification. It is also characterized as being grounded in more than two decades of peer-reviewed research, which may appeal to institutional users that require scientifically validated environmental data.

According to the post, potential use cases include greenhouse gas inventory and reporting, carbon accounting, nature-based climate solutions, and forest carbon market applications. These areas intersect with regulatory reporting and voluntary carbon markets, suggesting that expanded access to CTrees data could increase Earthmover’s relevance to compliance-driven and climate-focused customers.

The company’s LinkedIn post further positions CTrees’ participation as another step in building the Earthmover Data Marketplace as a central access point for high-quality environmental datasets, following prior additions such as Sylvera and Spire Weather & Climate. This expanding catalog may enhance the platform’s network effects and improve customer stickiness as more data providers and users converge on a single distribution channel.

Earthmover’s post also notes growing adoption among teams in energy, insurance, commodities, and climate tech that benefit from data that is already structured, queryable, and ready to use. For investors, this suggests a strategy focused on reducing integration friction for enterprise users, which can support higher-value subscriptions, shorten sales cycles, and potentially improve recurring revenue dynamics over time.

The post points readers to a CTrees webinar and Google Colab tutorial that demonstrate how to access and analyze the biomass data using Arraylake and Python. This emphasis on tooling and developer workflows indicates a platform approach aimed at embedding Earthmover into data science and analytics pipelines, which could strengthen its competitive positioning within environmental and climate data infrastructure.