Assessing crop health and its link to fungal soil microbiome composition through remote sensing

This study explores the link between crop health, observed via hyperspectral satellite imagery, and fungal soil microbiome taxonomy. We connect the normalized difference vegetation index (NDVI), a measure of crop health, to fungal microbiomes in wheat, barley, and maize utilizing a two-step machine learning process. The first step corrects NDVI values for abiotic confounders using a random forest model trained on Lucas 2018 topsoil and era5 climate datasets. The second step clusters operational taxonomy unit (OTU) counts from fungal DNA and investigates the corrected NDVI values for these clusters, revealing significant differences in NDVI values. To identify potential bio-fertilizer candidates, we compare the average relative abundance of OTU clusters and construct sparse biological networks. Bootstrapping and a graphical lasso model select significant links to build the networks. Key findings are: (I) clusters with higher plant pathogenic genera have lower NDVI values; (II) Clusters with higher influential scores for multiple beneficial genera have higher NDVI values; (III) taxonomy with 1-3\% abundance seems to play a key role in regulating microbial networks; (IV) the influence of beneficial vs. pathogenic taxonomy is relative to their abundance. The study links satellite data to fungal microbiomes, providing a baseline for exploring fungal bio-fertilizers.