This study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r > 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture.

这项研究描述了一种基于卫星的遥感分析植物特定生物量产量模式的新方法，用于精准农业应用。相对生物量潜力（rel. BMP）可作为多年稳定和均质产量区的指标。相对。 BMP源自与特定生长阶段相对应的卫星数据和归一化植被指数（NDVI），以分析作物特定的产量模式。该方法的开发基于两个研究农场耕地的数据；验证是在德国南部商业农场的耕地上进行的。 rel 之间存在密切关系（最高 r > 0.9）。不同作物类型和研究年份的 BMP，表明耕地产量模式稳定。相对 BMP 显示与联合收割机确定的产量具有中等相关性（高达 r = 0.64），与植被指数红边拐点 (REIP) 具有很强的相关性（高达 r = 0.88，由拖拉机安装的传感器系统确定） ）以及与生物量采样确定的产量的中等相关性（高达 r = 0.57）。该研究调查了相关性之间的关系。 BMP 和关键土壤参数。多年相对值之间始终存在很强的相关性。 BMP 和土壤有机碳（SOC）和全氮（TN）含量（r = 0.62 至 0.73），表明该方法有效地反映了这些关键土壤特性对作物产量的影响。该方法非常适合推导产量区，在农业中具有广泛的应用潜力。