Snow albedo modulates net solar radiation, influencing snowmelt timing and magnitude, which is critical to watershed ecosystems and downstream communities. Snow grain size, a primary control on albedo, can be derived from near infrared lidar reflectance, a potentially valuable supplement to altimetry data. However, converting raw lidar intensity to reflectance is complex, with several existing methods but no comprehensive accuracy comparison to date. To address this gap, we leverage data from the Airborne Coastal Observatory (ACO), a paired lidar and imaging spectrometer platform, collected on May 15, 2021, over the snow-covered Place Glacier in the Coast Mountains of British Columbia, Canada. Three methods used to derive reflectance from the Riegl 1064 nm lidar were assessed, including: (1) an independent range and incidence angle correction (LR IC ); (2) the vendor product that accounts for range (LR riegl ); and (3) the vendor product with an additional incidence angle correction (LR rieglθ ). Radiative transfer modeling and lidar reflectance were used to derive grain size and the associated theoretical clean snow albedo. Reflectance and property retrievals were assessed against those derived from the imaging spectrometer (Specim Fenix). Reflectance magnitude of LR IC and LR rieglθ were similar in reflectance magnitude to the spectrometer but displayed greater variability. Grain sizes were underestimated (∼136 μm) producing a median relative error in albedo of 2%. LR riegl was positively biased, with greater underestimation of grain size (455 μm) and higher albedo error (6%). Results highlight the necessity of incorporating incidence angle corrections when using lidar for surface reflectance retrievals in mountainous terrain and further demonstrate the potential of lidar intensity for high-resolution snow grain size estimation. • We compare snow surface properties from lidar intensity to imaging spectroscopy. • Spatial comparison over a mountain glacier included variable snow conditions. • Range and incidence angle corrections produced comparable reflectance. • Manufacturer-provided reflectance (range corrected) compared poorly. • Relatively small errors in reflectance produced larger errors in grain size.

Wetland ecosystems, crucial for carbon sequestration and coastal hazard mitigation, have experienced tremendous losses in land surface area over the last century, primarily due to land reclamation. This has led to increased rates of land subsidence in regions with high levels of reclamation, causing heightened vulnerability in these areas under anticipated scenarios of climate induced sea-level rise. This study integrates multi-sensor satellite remote sensing (optical, thermal, and active microwave) with spatially explicit eddy covariance flux measurements to model gross primary productivity (GPP) in restored wetlands of California's Sacramento-San Joaquin Delta. GPP is a crucial process in this context, as it impacts the potential for wetlands to act as land carbon sinks and has been shown to reverse land subsidence in restored wetlands of previously reclaimed areas. Still, there remain gaps in understanding how vegetation vigor, wetland composition and structure, and environmental conditions individually and interactively impact carbon assimilation in these ecosystems. This research aims to understand how complementary remotely sensed signals from multiple satellite platforms across the electromagnetic spectrum combine to improve classical, optically based GPP models, while determining the relative importance of certain biotic and abiotic environmental conditions that regulate GPP. Using a Bayesian generalized additive modeling framework, we evaluated how vegetation vigor (NDVI), canopy structure and biomass density (microwave backscatter), and land surface temperature affect wetland GPP at 10-m spatial resolution over a five-year period. Our results reveal a strong hierarchical and complementary influence of these variables, with the highest GPP occurring in warm, well-watered, and densely vegetated conditions. The model explained on average 66% of GPP variability and provides a scalable, open-access framework for assessing carbon fluxes in wetland landscapes. These findings offer valuable insight into planning restoration, monitoring restoration outcomes, carbon accounting, and identifying coastal adaptation strategies for valuable blue carbon ecosystems. • We develop a wetland GPP model using eddy covariance and remote sensing data fusion. • Hierarchy of wetland GPP drivers revealed through remotely sensed observations. • Wetland GPP shows greatest sensitivity to interaction of drivers. • Hierarchical model enables informed upscaling of wetland GPP to landscape level.

This study presents daily, long-term (2003−2022) satellite-based estimates of river discharge and gridded runoff at 0.25° × 0.25° spatial resolution across the continental Pan-Arctic region. Satellite observations of precipitation from the Integrated Multi-satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM), soil moisture and snow cover fraction from the European Space Agency's Climate Change Initiative (ESA CCI), and Terrestrial Water Storage Anomalies from satellite gravimetry are integrated into a conceptual hydrological model, STREAM, specifically adapted to simulate the hydrological regime of Pan-Arctic regions. The model is calibrated and validated using data from the 15 largest and best-monitored Arctic basins. It demonstrates high performance in reproducing daily river discharge observations, with a median Kling-Gupta Efficiency (KGE) of 0.83. To extend the model to ungauged regions, a regionalization approach based on similarities in basin climatic attributes is proposed, enabling the transfer of calibrated STREAM model parameters. When validated over 26 independent gauging stations, the regionalized river discharge estimates maintained acceptable accuracy, with a median KGE of 0.51. Using this framework, STREAM is applied across the continental Pan-Arctic region covering an area of 16.5 × 10 6 km 2 . The resulting spatially and temporally consistent runoff dataset, which can be used to complement more detailed process-based model estimates, enables the quantification of freshwater fluxes to the Arctic Ocean, estimated at 4760 ± 619 km 3 yr −1 . The results highlight the potential of satellite observations—particularly GRACE data—in supporting large-scale hydrological modelling and in reconstructing the Arctic freshwater cycle in data-scarce environments. • A satellite-based hydrological model (STREAM) is applied to the Pan-Arctic region. • STREAM performs well in 15 Arctic basins with a median KGE of 0.83. • Regionalization enables STREAM application in ungauged Arctic basins. • Development of a satellite-based daily runoff dataset for the Pan-Arctic region.