Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or empirically-motivated statistical rules for extreme value identification and removal. We propose a constructive algorithm for handling well-documented yield monitor data artifacts without resorting to data deletion. The four-step Rectangle creation, Intersection assignment and Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample observations as overlapping, unequally-shaped, irregularly-sized, time-ordered, areal spatial units to better replicate the nature of the destructive sampling process. Positional data is used to create rectangular areal spatial units. Time-ordered intersecting area tessellation and harvested mass apportioning generate regularly-shaped and -sized polygons partitioning the entire harvested area. Finally, smoothing via a Gaussian process is used to provide map users with spatial-trend visualization. The intermediate steps as well as the algorithm output are illustrated in maize and soybean grain yield maps for five years of yield monitor data collected at a research agricultural site located in the US Fish and Wildlife Service Neal Smith National Wildlife Refuge.
翻译:已知的Yield监测数据集包含很高比例的不可靠记录,目前的成套工具主要限于基于超常或经验驱动的极端价值识别和清除统计规则的观察清洁程序。我们建议一种不通过删除数据处理有详细记录的产物监测数据文物的建设性算法。四步矩形创建、交叉分配和探测、推广和平滑(RITAS)算法模型样本观测,作为重叠、不均匀形状、不定期规模、按时间顺序排列的样本,是更好地复制破坏性取样过程性质的空间单位。定位数据用于创建矩形空间单位。时间顺序排列的断裂区间断流和收获的大规模分配生成成形和大小的多边形分割整个收获区。最后,通过高斯进程平滑,向地图用户提供空间-趋势可视化,中间步骤和算法产出在玉米和豆类谷物产量图中展示,用于五年来监测在美国渔业和国家安全研究所收集的Smitriferal农业数据。