Integrating uncertainty into remotely sensed biodiversity products 

Remote sensing plays an increasingly central role in biodiversity monitoring, yet the robustness of remotely derived biodiversity products depends on our ability to quantify, understand, and propagate the uncertainties embedded across the sensing and processing chain. The uncertainties arise at multiple stages in the remote sensing workflow, ranging from sensor noise and atmospheric interference to processing algorithms, model assumptions, and instrument calibration and validation. Despite this, biodiversity assessments rarely incorporate uncertainty information, limiting the reliability, comparability, and interpretability of biodiversity measurements and change assessment. Understanding how these uncertainties affect biodiversity measurements and indicators has potential implications for our understanding of biodiversity processes and the use of this information in decision-making. As a growing number of remote sensing missions are about to deliver pixel-level uncertainty estimates, the field faces an important challenge: how to move towards integrating uncertainty directly into biodiversity assessments, conservation and restoration planning, and policy-relevant indicators. In this contribution, we propose a roadmap to operationalize the integration of uncertainty in biodiversity product generation. We outline key stages where uncertainty can be quantified and propagated, highlight conceptual considerations for different types of biodiversity products, and demonstrate how uncertainty-aware workflows can support more transparent and robust assessments. We test this approach using EMIT (Earth Surface Mineral Dust Source Investigation) imaging spectroscopy data and its accompanying uncertainty data in temperate forest ecosystems. Leveraging EMIT’s per-band standard deviation layers, we propagate surface reflectance uncertainty into plant traits and compare these uncertainty-aware EMIT-derived plant traits with in situ leaf spectroscopy and laboratory plant traits. This application aims to start a dialogue on how to move towards uncertainty-aware biodiversity products and indicators that ensure measurement robustness and identify where caution is needed. Our results underscore that explicit inclusion of uncertainty is fundamental for biodiversity monitoring. By embedding uncertainty into both product generation and interpretation, we enhance transparency, strengthen ecological inference and knowledge, and support informed decision making.