Watershed-Scale Monitoring of Phytoplankton Biomass: Uncovering Environmental Drivers Using Landsat and Cloud-Based Tools
Bibliographic record
Abstract
Understanding how wetlandscapes influence lake water quality is vital for managing freshwater systems under increasing climate and land use pressures. This research explores the relationship between catchment-scale wetlandscape characteristics and lake chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, across the Lake Winnipeg Watershed over a four-decade period. To characterize wetlandscapes, Landsat-derived inundation products were used to develop a novel method for mapping annual wetland extent and extracting key properties—number, size, and hydrological connectivity—from 1984 to 2020. These wetlandscape properties increased significantly over time, in parallel with wetter climatic conditions and shifts in land cover. The approach highlights how remote sensing can generate scalable, watershed-wide wetland inventories for environmental monitoring and planning. In parallel, a cloud-based tool was developed to monitor lake surface Chl-a using Rayleigh-corrected Landsat reflectance, with wildfire smoke interference minimized using an innovative aerosol-band screening method. The resulting model (R² > 0.8) produced robust estimates of lake Chl-a across a range of conditions, enabling consistent tracking of phytoplankton biomass across more than 23,000 lakes from 1984 to 2023. By linking these long-term lake Chl-a records to catchment characteristics, the study evaluated potential controls on phytoplankton abundance. Climate variables (precipitation, temperature) and human land use emerged as dominant predictors of long-term mean Chl-a. Contrary to expectations, static wetlandscape metrics explained relatively little variance in Chl-a, suggesting that their regulatory role may depend more on dynamic hydrological behavior than static structure. While a modest negative relationship was observed between wetland-stream connectivity and lake Chl-a, further work is needed to capture temporal wetland dynamics and their influence on nutrient transport. This research demonstrates the power of integrated remote sensing and machine learning for freshwater monitoring and emphasizes the need to consider dynamic wetland processes in future water quality assessments and management frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".