Purpose

A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average.

Methods

Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages.

Results

Correlations with remote sensing covariates were stronger for cotton fibre yield than for cotton fibre micronaire, and much stronger compared to those for cotton fibre length. Spatial trends in cotton fibre yield and micronaire could be estimated with good model quality using regression with remote sensing covariates with or without A2PK in almost all fields. Conversely, model quality was poorer for cotton fibre length and there was only a small difference in model performance between the null and trend models. When the downscaling approach was tested using fine-resolution yield observations, model performance was poorer at a fine-resolution compared to the module-resolution, which was to be expected.

Conclusion

This approach enables the creation of high-resolution raster maps of variables of interest with a much finer spatial resolution compared to the areal observations, and can be applied for any areal averaged crop production data in a range of broadacre and horticultural industries (e.g. sugarcane, apples, citrus). The finer spatial resolution may allow growers or agronomists to better understand the drivers of variability within fields, assess management implications, and create management plans at a higher resolution.

中文翻译：

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利用地统计学和遥感将作物生产数据缩小到精细估计：绘制棉花纤维质量的案例研究

 目的

使用棉花产量和纤维质量（长度和马克隆值）数据来说明作物生产数据的小规模面积观测的通用方法，这些数据以模块（面积/块）平均值进行测量。

 方法

缩小算法的两个特点是： (i) 使用高分辨率预测因子（例如遥感图像）进行回归来估计产量和质量的空间趋势，以及 (ii) 使用面到点克里金法 (A2PK) 在缺乏有用空间趋势的情况下缩小观测值模型或来自区域平均值的趋势模型（如果有用）的残差。

 结果

棉纤维产量与遥感协变量的相关性强于棉纤维马克隆值，并且强于棉纤维长度的相关性。棉花纤维产量和马克隆值的空间趋势可以通过使用几乎所有领域的遥感协变量回归来估计，具有良好的模型质量，无论是否有 A2PK。相反，棉纤维长度的模型质量较差，并且零模型和趋势模型之间的模型性能差异很小。当使用精细分辨率产量观测来测试缩小方法时，与模块分辨率相比，精细分辨率下的模型性能较差，这是预期的。

 结论

这种方法能够创建感兴趣变量的高分辨率栅格图，与区域观测相比具有更精细的空间分辨率，并且可以应用于一系列大面积和园艺产业（例如甘蔗、苹果、柑橘）。更精细的空间分辨率可以让种植者或农艺师更好地了解田间变异的驱动因素，评估管理影响，并以更高分辨率制定管理计划。