Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

Abstract

Satellite remote sensing has transformed our understanding of Earth processes. One component of the Earth system where large uncertainties remain are Arctic and boreal freshwater lakes. With only short periods of open water due to annual ice cover, lake productivity in these regions is extremely sensitive to warming induced changes in ice cover. At the same time, productivity dynamics in these lakes vary enormously, even over short distances, making it difficult to understand these potential changes. A major impediment to an improved understanding of lake dynamics has been sparsely distributed field measurements, in large part due to the complexity and expense of conducting scientific research in remote northern latitudes. This project overcomes that hurdle by using a new set of ‘eyes in the sky’, the Planet Labs CubeSat fleet, to observe 35 lakes across 3 different arctic-boreal ecoregions in western North America. We extract time series of lake reflectance to identify ice-out and green-up across three years (2017–2019). We find that lakes with later ice-out have significantly faster green-ups. Our results also show ice-out varies latitudinally by 38 days from south to north, but only varies across years by ~9 days. In contrast, green-up varied between years by 22 days in addition to showing significant spatial variability. We compare PlanetScope to Sentinel-2 data and independently validate our ice-out estimates, finding an ice-out mean absolute difference (MAD) ~9 days. This study demonstrates the potential of using CubeSat imagery to monitor the timing and magnitude of ice-off and green-up at high spatiotemporal resolution.

Blooms also like it cold

“…The use of other emerging technologies, such as Earth Observations, which apply satellite, aerial, or drone imagery for monitoring phytoplankton blooms, has been increasing due to the global availability of data (Ho et al 2019). The use of Earth Observations for monitoring phytoplankton is likely to increase in the next few years, including high latitude areas, especially with the development of new sensors and new platforms such as nanosatellites (Kuhn et al 2021). The ongoing development of satellite hyperspectral sensors by different space agencies will allow scientists to retrieve Chl a information and continue to develop methods to differentiate phytoplankton groups (Ogashawara 2021).…”

Using High‐Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies

“…CH 4 from ponds has largely been excluded from regional CH 4 budget studies due to the difficulty in detecting ponds using coarser-resolution water body datasets. Through spatial upscaling of aquatic flux rates based on the BAWLD-CH 4 synthesis (C. Kuhn, John, et al, 2021) and our new 3 m PlanetScope open water maps, we estimated that ponds and lakes contributed to 0.39 (DJ), 0.35 (FB), 1.57 (YF), and 6.26 (YKD) mg CH 4 per day per m 2 of each respective AOI. Considering the temporal variability in pond extent based on the monthly PlanetScope composites, ponds alone contributed to 24%-28% of upscaled aquatic CH 4 emissions in DJ, 30%-37% in FB, 8%-10% in YF, and 15% in the study portion of the YKD (Figure S12 in Supporting Information S1).…”