ArticleScientific reports2026
Bringing cross-validation into the real world to evaluate transferability of satellite-based vegetation models.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Explainable Artificial Intelligence Assisted Modeling of Malachite Green Adsorption onto SBA-15-Zn-Fe Composite.Molecules (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Near-real-time mapping of vegetation using satellite imagery is becoming increasingly common and valuable across a wide range of ecosystems. The availability of large datasets has led many researchers to complex machine learning algorithms (MLAs) to train satellite models. However, complex MLAs may underperform for the inherently extrapolative applications required for real-world vegetation monitoring. We used a dataset of nearly 10,000 training samples of standing herbaceous grazingland biomass collected over ten years to train progressively more complex MLAs, test them across progressively more extrapolative cross-validation (CV) groupings, and evaluate their transferability and consistency. The performance of all MLA's decreased substantially when tested against more extrapolative CV groupings. The commonly used approach of random k-fold CV produced overly optimistic performance (R
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Registered trials
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