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Good satellite water-quality accuracy can hide poor geographic transfer

A review of 127 inland-water studies reports promising machine-learning fits but serious geographic and variable gaps.

Evidence
Peer-reviewed
Geography
Global synthesis, studies through June 2025
System
Satellite water quality, remote sensing and model transfer

WHAT THE EVIDENCE SAYS

A PRISMA review of 127 studies found high reported accuracies for some models, with CNN methods averaging R² near 0.85. Studies concentrated in China and the United States; chlorophyll was well represented, whereas CDOM and Secchi-depth retrieval were less developed.

WHY IT MATTERS / OUR READ

A strong published R² is not a substitute for independent local validation in an unfamiliar water body.

CAVEATS / UNCERTAINTY

Heterogeneous study validation protocols limit comparisons, and an average performance measure is not proof of global operational reliability.

SOURCE

Remote Sensing in Earth Systems Sciences — Rathupetsane et al. (29 Oct 2026)
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