ArticlePlants (Basel, Switzerland)2026
Hybrid Deep Learning-Geostatistical Mapping of Forest Aboveground Biomass in Lishui, China.
Article in Plants (Basel, Switzerland), 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.
- Advanced Remote Sensing and AI Techniques in Agriculture and Forestry.Plants (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Forest aboveground biomass (AGB) is a key indicator of forest productivity and carbon sequestration, yet many remote sensing AGB models overlook spatial autocorrelation in plot observations and model residuals. This study proposes a hybrid framework that combines a CNN-Transformer (Convolutional Neural Network-Transformer) model with geostatistical Kriging of residuals to improve regional AGB mapping in Lishui City, Zhejiang Province, China. Using 398 forest plots and multi-source predictors derived from Sentinel-2 imagery, ALOS-2 PALSAR-2 SAR data, and ALOS 12.5 m DEM, relevant variables were screened using Random Forest importance ranking. The most influential predictors included Sentinel-2 Band 8 and Band 12, EVI, PC1, mean77, HH/HV, ARVI, NDVI, RVI, and elevation. Ten-fold cross-validation showed that the CNN-Transformer-CK model had the highest accuracy in predicting forest AGB, with a validation R
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