A physics-aware meta-learning framework retrieves coastal biogeochemical parameters from hyperspectral Rrs by pretraining a base model on synthetic data from a bio-optical forward model and fine-tuning on regional in situ samples, outperforming benchmarks with good temporal agreement.
Annual Review of Marine Science 4, 143–176
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A geometric indicator from the normal width of the stochastic separatrix in a random two-state ecosystem model scales linearly with noise intensity and yields an affine relation to the logarithm of mean transition time.
citing papers explorer
-
Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning
A physics-aware meta-learning framework retrieves coastal biogeochemical parameters from hyperspectral Rrs by pretraining a base model on synthetic data from a bio-optical forward model and fine-tuning on regional in situ samples, outperforming benchmarks with good temporal agreement.
-
Geometric early warning indicator from stochastic separatrix structure in a random two-state ecosystem model
A geometric indicator from the normal width of the stochastic separatrix in a random two-state ecosystem model scales linearly with noise intensity and yields an affine relation to the logarithm of mean transition time.