SoilWaterNow：用于绘制农田内植物可利用水（PAW）以改善农场决策的土壤水临近预报
SoilWaterNow: Soil water nowcasting for mapping plant available water (PAW) across paddocks for improved on-farm decision-making
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中文总结 AI 辅助
该研究提出SWEB模型，实现每日30米分辨率的根区土壤湿度等估算，经澳大利亚多网络验证表现稳健，结合PAW临近预报可支撑农场限水谷物管理决策。
中文摘要 AI 辅助
及时的农田尺度植物可利用水（PAW）估算可为限水谷物系统的作物管理提供支持。我们提出Sydney土壤水-能量平衡（SWEB）模型，这是一种可扩展、物理一致的建模框架，整合卫星与气候数据及土壤属性，以每日30米分辨率估算作物蒸散量与根区土壤湿度（RZSM）。SWEB经澳大利亚全国不同土壤湿度监测网络验证，在各类谷物种植环境中表现稳健（多数区域相关系数通常介于0.70至0.85之间）。未来应用中，生育期中PAW临近预报可与水分利用效率方法结合，估算限水产量潜力并指导响应式管理决策，如氮肥追加推荐。
英文摘要
Timely, paddock-scale estimates of plant-available water (PAW) can support crop management in water-limited grain systems. We present the Sydney Soil Water-Energy Balance (SWEB) model, a scalable, physically consistent modelling framework that integrates satellite and climate data with soil properties to estimate crop evapotranspiration and root-zone soil moisture (RZSM) at daily, 30 m resolution. SWEB was validated nationally against different SM monitoring networks across Australia, which demonstrated robust performance across diverse grain-growing environments (correlation coefficients generally ranged from 0.70 to 0.85 for most regions). In future applications, mid-season PAW nowcasts could be combined with water-use-efficiency approaches to estimate water-limited yield potential and inform responsive management decisions, such as nitrogen fertiliser top-up recommendations.
