Data from: The potential of hyperspectral patterns of winter wheat to detect changes in soil microbial community composition

Reliable information on soil status and crop health is crucial for detecting and mitigating disasters like pollution or minimizing impact from soil-borne diseases. While infestation with an aggressive soil pathogen can be detected via reflected light spectra, it is unknown to what extent hyperspectral reflectance could be used to detect overall changes in soil biodiversity. We tested the hypotheses that spectra can be used to 1) separate plants growing with microbial communities from different farms; 2) to separate plants growing in different microbial communities due to different land use; and 3) separate plants soils according to microbial species loss. We measured hyperspectral reflectance patterns of winter wheat plants growing in sterilized soils inoculated with microbial suspensions under controlled conditions. Microbial communities varied due to geographical distance, land use and microbial species loss caused by serial dilution. After 3 months of growth in the presence of microbes from the two different farms plant hyperspectral reflectance patterns differed significantly from each other, while within farms the effects of land use via microbes on plant reflectance spectra were weak. Species loss via dilution on the other hand affected a number of spectral indices for some of the soils. Spectral reflectance can be indicative of differences in microbial communities, with the Renormalized Difference Vegetation Index the most common responding index. Also, a positive correlation was found between the Normalized Difference Vegetation Index and the bacterial species richness, which suggests that plants perform better with higher microbial diversity. There is considerable variation between the soil origins and currently it is not possible yet to make sufficient reliable predictions about the soil microbial community based on the spectral reflectance. We conclude that measuring plant hyperspectral reflectance has potential for detecting changes in microbial communities yet due to its sensitivity high replication is necessary and a strict sampling design to exclude other ‘noise’ factors.

有关土壤状况与作物健康的可靠信息，对于污染等灾害的检测与缓解、降低土传病害的影响至关重要。目前虽可通过反射光谱（reflected light spectra）检测强致病性土壤病原菌的侵染，但尚不清楚高光谱反射率（hyperspectral reflectance）可在多大程度上用于检测土壤生物多样性（soil biodiversity）的整体变化。本研究验证了三项假说：其一，可通过光谱区分种植于不同农场微生物群落（microbial communities）中的植株；其二，可区分因土地利用（land use）差异而形成不同微生物群落的植株；其三，可依据微生物物种流失（microbial species loss）情况区分植株生长的土壤。研究在可控条件下，对接种了微生物悬浮液（microbial suspensions）的灭菌土壤（sterilized soils）中生长的冬小麦植株，开展了高光谱反射模式测量。微生物群落的差异源于地理距离、土地利用差异以及通过连续稀释（serial dilution）引发的微生物物种流失。培育3个月后，来自两个不同农场的微生物群落对应的植株高光谱反射模式存在显著差异；但在同一农场内，微生物介导的土地利用对植株反射光谱的影响较弱。另一方面，通过稀释引发的物种流失会影响部分土壤的多项光谱指数。光谱反射率可反映微生物群落的差异，其中重归一化差异植被指数（Renormalized Difference Vegetation Index）是响应最为普遍的指数。此外，归一化差异植被指数（Normalized Difference Vegetation Index）与细菌物种丰富度（bacterial species richness）呈正相关，这表明微生物多样性越高，植株生长状况越好。不同土壤来源间存在显著差异，目前尚无法仅通过光谱反射率对土壤微生物群落做出足够可靠的预测。本研究认为，测量植株高光谱反射率具备检测微生物群落变化的潜力，但由于其敏感性较强，需采用高重复采样与严格的采样设计，以排除其他‘噪声’因子的干扰。