Predicting Bacterial Diversity in European Croplands Using Earth Observation and Meteorological Data

In this paper we explore models predicting soil bacterial diversity to: 1) spectral indices derived from optical satellite remote sensing; and 2) meteorological variables. We computed alpha and beta diversity indices using metabarcoding data generated from 214 cropland soil samples collected in the context of Eurostat's 2018 pan-European LUCAS Soil module. Subsequently, we derived 12 spectral indices from Sentinel-2 images and monthly meteorological variables from the TerraClimate dataset. We then built models of bacterial diversity using the Earth Observation and climatic variables, experimenting with different algorithms and predictor time lags from the soil sampling date. Random-Forest and Cubist regressors yielded MAE ≤ 7% of the observed range and R² = 0.87 for beta diversity indices, while alpha diversity models reached MAE ≈ 10% and R² ≈ 0.15. Feature importance pointed to winter moisture variability as the chief control on richness/evenness, whereas growing-season thermal extremes governed community turnover, with Sentinel-2 indices contributing secondary signals. Overall, our results indicate that freely-available satellite multispectral and meteorological data, can predict dimensions of cropland soil bacterial diversity and with particularly strong skill for PCA- and CAP-based beta diversity axes.