Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

中文翻译：

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支持卫星的环境组学促进作物改良


环境组学是指基于大规模环境数据集对微观和宏观环境的表征。通过在特定地点的水平上提供基因型建议和预测外推，环境组学可以为不同条件下的植物育种决策提供信息，并预测不断变化的气候中的生产力。基于环境组学的统计、环境类型（即确定环境因素）和遥感的整合可以帮助解开遗传学、环境和管理之间的复杂相互作用。为了支持这一目标，通过详尽的环境分析来生成精确的环境概况，将显著改善对作物基因型性能和遗传增益的预测。信息学管理平台已经汇总了使用光学、热、雷达和光探测与测距 （LiDAR） 传感器获得的各种环境数据集，这些传感器捕获了有关植被、表面结构和地形的详细信息。这些丰富的信息，加上免费提供的气候数据，推动了创新的环境组学研究。虽然环境组学具有巨大的繁殖潜力，但仍然存在一些障碍，例如需要 （1） 综合方法来系统地收集实地数据，以利用卫星数据扩大和扩大整个景观的观察;（2） 用于数据集成、模拟和预测的最先进的 AI 模型;（3） 网络基础设施，用于跨规模处理大数据并提供无缝接口以向利益相关者提供预测;（4） 农民、饲养员、生理学家、地理信息学专家和研究机构程序员之间的合作和数据共享。 克服这些挑战对于利用卫星捕获的大数据的全部潜力，通过环境组学改变 21 世纪的农业和作物改良至关重要。