Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA’s National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA’s Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA’s National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.

植物多样性维护了湿地生态系统的功能，稳定性和复原力，但受到栖息地丧失和退化的威胁。遥感可以通过提供一致且频繁的大规模数据来支持对生物多样性进行具有成本效益的管理。虽然从具有低空间和光谱分辨率的遥感数据集中识别单个物种具有挑战性，但研究可以集中在已知与多样性相关或促进多样性的因素上。我们在美国本土1115个湿地的数据集中测试了这些因素的预测潜力-最高的年度绿色度作为生产率的指标，代表栖息地异质性的纹理（即灰色调的空间排列）和空间自相关性-由EPA的《国家湿地条件评估》进行了调查。我们使用多元线性回归来测试从两个开源数据集（NASA的Landsat 5 TM和7 ETM +（30 m，16天重新访问）和USDA的国家农业清单计划（1 m，每两年一次））得出的光谱和空间指标是否可以预测湿地植物的多样性和丰富度。个体的质地指标显示出对植被均匀度，生长形式和空间分布的不同敏感性，可以一起预测丰富度和多样性中35–36％的立地变化。这突出了栖息地异质性对物种多样性和光谱变异性的影响。尽管最大的年度绿色度和质地指标具有相似的预测能力，但它们的相互作用和综合影响使线性模型的拟合度提高了11–14％，证明了它们的互补性。当包含基于距离的Moran特征向量图（dbMEM）来描述多尺度站点之间的空间关系并反映空间结构化因素（例如，气候，地形，分布）对多样性的作用时，可获得最佳结果。在整个数据集中，绿色度，质地和dbM​​EM可以共同预测59％的植物丰富度和50％的植物多样性，以及多达71％的最少受干扰部位的丰富度。这些结果表明，开源遥感数据集有可能大规模监测生物多样性资源，并优先考虑对湿地的保护和野外监测。纹理和dbMEM可以预测整个数据集中59％的植物丰富度和50％的植物多样性，以及受干扰最少的站点的最高71％的丰富度。这些结果表明，开源遥感数据集有可能大规模监测生物多样性资源，并优先考虑对湿地的保护和野外监测。纹理和dbMEM可以预测整个数据集中59％的植物丰富度和50％的植物多样性，以及受干扰最少的站点的最高71％的丰富度。这些结果表明，开源遥感数据集有可能大规模监测生物多样性资源，并优先考虑对湿地的保护和野外监测。