Recognition of the importance of soil moisture information to the optimisation of water-limited dryland cereal production has led to Australian growers being encouraged to make use of soil moisture sensors. However, irrespective of the merits of different sensing technologies, only a small soil volume is sensed, raising questions as to the utility of such sensors in broadacre cropping, especially given spatial variability in soil water holding capacity. Here, using data collected from contrasting sites in South Australia and Western Australia over two seasons, during which either wheat or barley were grown, we describe a method for extrapolating soil moisture information away from the location of a probe using freely-available NDVI time series and weather data as covariates. Relationships between soil moisture probe data, cumulative NDVI (ΣNDVI), cumulative net precipitation (ΣNP) and seasonal growing degree days (GDD) were significant (P < 0.0001). In turn, these could be used to predict soil moisture status for any location within a field on any date following crop emergence. However, differences in ΣNDVI between different within-field zones did not fully explain differences in the soil moisture from multiple sensors located in these zones, resulting in different calibrations being required for each sensor or zone and a relatively low accuracy of prediction of measured soil moisture (R²_adj ~ 0.4–0.7) which may not be sufficient to support targeted agronomic decision-making. The results also suggest that at any location within a field, the range of variation in soil moisture status down the soil profile on any given date will present as greater than the spatial variation in soil moisture across the field on that date. Accordingly, we conclude that, in dryland cereal cropping, the major value in soil moisture sensors arises from an enhanced ability to compare seasons and to relate similarities and differences between seasons as a guide to decision-making.

认识到土壤湿度信息对于优化限水旱地谷物生产的重要性，鼓励澳大利亚种植者使用土壤湿度传感器。然而，无论不同传感技术的优点如何，只能感测到较小的土壤体积，这引发了关于此类传感器在大田种植中的效用​​的问题，特别是考虑到土壤持水能力的空间变异性。在这里，利用从南澳大利亚和西澳大利亚两个种植小麦或大麦的不同地点收集的数据，我们描述了一种使用免费提供的 NDVI 时间序列从探头位置推断土壤湿度信息的方法和天气数据作为协变量。土壤湿度探头数据与累积归一化植被指数（ΣNDVI）、累积净降水量（ΣNP）和季节生长度日（GDD）之间存在显着相关性（P  < 0.0001）。反过来，这些可以用来预测作物出苗后任何日期田间任何位置的土壤湿度状况。然而，田内不同区域之间的ΣNDVI差异并不能完全解释位于这些区域内的多个传感器的土壤湿度差异，导致每个传感器或区域需要不同的校准，并且测量的土壤湿度的预测精度相对较低（R ² _adj  ~ 0.4–0.7）这可能不足以支持有针对性的农艺决策。结果还表明，在田地内的任何位置，任何给定日期沿土壤剖面的土壤湿度状况的变化范围将大于该日期整个田地土壤湿度的空间变化。因此，我们得出的结论是，在旱地谷物种植中，土壤湿度传感器的主要价值在于增强了比较季节和关联季节之间的异同作为决策指南的能力。