Environmental data from large measurement campaigns and automated measurement networks are increasingly available and provide relevant information of the Earth System. However, such data are usually only available as point observations and only represent a small part of the Earth´s surface. Upscaling strategies are hence needed to provide continuous and comprehensive information as a baseline to gain insights on large-scale spatio-temporal dynamics. In the upscaling, machine learning algorithms that can account for complex and nonlinear relationships are increasingly used to link remote sensing datasets to reference measurements. The resulting models are then applied to provide spatially explicit predictions of the target variable, often even on a global scale.
Due to easy access to user-friendly software, model training and spatial prediction using machine learning algorithms is nowadays straightforward at first sight. However, considerable challenges remain: dealing with reference data that are not independent and identically distributed, accounting for spatial heterogeneity when scaling reference measurements to the grid cell scale, appropriately evaluating the resulting maps and quantifying their uncertainties, generating robust maps that do not suffer from extrapolation artifacts as well as the strategies for model interpretation and understanding.
This session invites contributions on the methodology and application of large-scale mapping strategies in different disciplines, including vegetation characteristics such as foliar or canopy traits and photosynthesis, soil characteristics such as soil organic carbon, or atmospheric parameters such as pollutant concentration. Methodological contributions can focus on individual aspects of the upscaling approach, such as the design of measurement campaigns or networks to increase representativeness, novel algorithms or validation strategies as well as uncertainty assessment.