Spectrum of the Lakes: Unveiling Water Color of Minnesota's Sentinel Lakes with Satellite Remote Sensing for Water Quality Monitoring.
Bibliographic record
Abstract
Surface waters are precious natural resources requiring costly time and labor for effective water quality monitoring. New applications of water color analysis by satellite remote sensing are a promising approach to water quality monitoring for scientific, industrial, recreational, and cultural benefit. Water color is an inherent proxy of ecological health and water quality. This research expands previous applications of lake water color analysis and pioneers water color chromaticity analysis for midcontinent lakes in Minnesota, USA. The results of this project are the first accounts of Minnesota’s Sentinel Lake water color, variability of water color by ecoregion, and temporal consistency of water color within major ecoregions. Minnesota state research initiative, Sustaining Lakes In a Changing Environment (SLICE), ordains “Sentinel Lakes” as representative of lake populations within major ecoregions of Minnesota. NASA’s Landsat 8 OLI historical record of surface reflectance in the visible spectrum was used to observe water color from Sentinel Lakes for 10 years from 2013 through 2022. This work quantifies unbiased water color with chromaticity analysis to interpret dominant visible wavelength from tristimulus values of surface reflectance. Visible light surface reflectance samples were taken from the deepest area within each Sentinel Lakes during the late summer, representing peak annual insolation and trophic activity. A decadal analysis of Sentinel Lake water color was documented, proving conceptual possibility and developing a normality of water color for each Sentinel Lake. Using the median dominant visible wavelength as the characterizing metric of Sentinel Lake water color, the most common water color was observed near 575nm in the green-yellow interface, with more red colors in the Northeastern and Southern parts of the state and a noticeable lack of blue colors. Statistical analysis demonstrated water color varies within an ecoregion and specific colors were not unique to any particular ecoregion. Noticeable annual water color variation in the Canadian Shield ecoregion was attributed to forested catchments and undisturbed hydrology. Decadal patterns of water color in a Sentinel Lake operate as instructive bounds for the investment of further resources when a water color anomaly occurs. Land use and climatic factors are important considerations for understanding the controlling factors of water color. Satellite remote sensing of water color offers a unique opportunity for the development of supplemental monitoring of water quality and ecological health of natural resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".