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：

---

卫星环境促进作物改良


环境组学是指基于大规模环境数据集的微观和宏观环境的表征。通过在特定地点水平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，并预测气候变化下的生产力。基于环境组学的统计、环境分型（即确定环境因素）和遥感的整合可以帮助揭示遗传学、环境和管理之间复杂的相互作用。为了支持这一目标，通过详尽的环境分型来生成精确的环境概况将显着改善对作物基因型表现和遗传增益的预测。信息学管理平台已经聚合了使用光学、热、雷达和光探测和测距 (LiDAR) 传感器获得的各种环境数据集，这些传感器捕获有关植被、表面结构和地形的详细信息。如此丰富的信息，加上免费提供的气候数据，推动了创新的环境学研究。虽然环境学在育种方面具有巨大的潜力，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，以利用卫星数据来扩展和扩大整个景观的观测； (2) 最先进的人工智能模型，用于数据集成、模拟和预测； (3) 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的网络基础设施； (4) 农民、育种者、生理学家、地理信息学专家和跨研究机构的程序员之间的协作和数据共享。 克服这些挑战对于充分利用卫星捕获的大数据的潜力来通过环境学改变 21 世纪农业和作物改良至关重要。