Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions.

中文翻译：

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使用近端高光谱和卫星多光谱数据预测胡萝卜产量的准确性

近端和远程传感器已证明它们可有效地估计多种不同作物的多种生物物理和生化变量，包括产量。对它们在蔬菜作物中的准确性的评估是有限的。本研究探讨了近端高光谱和卫星多光谱传感器（Sentinel-2 和 WorldView-3）在预测具有不同种植配置、季节和土壤条件的三个种植区的胡萝卜根产量方面的准确性。从西澳大利亚 (WA)、昆士兰 (Qld) 和澳大利亚塔斯马尼亚 (Tas) 的 24 个田地的 414 个采样点收集地上生物量 (AGB)、冠层反射率测量值和相应的产量测量值。最佳传感器（高光谱或多光谱）由产量和不同植被指数 (VI) 之间的最高整体决定系数确定，同时测试线性和非线性模型以确定最佳 VI 和空间分辨率的影响。每个区域的最佳回归拟合用于将点源测量值外推到每个采样作物中的所有像素，以生成预测产量图并估计作物水平的平均胡萝卜根产量 (t/ha)。后者与从种植者获得的商业胡萝卜根产量 (t/ha) 进行比较，以确定预测的准确性。所有作物的测量产量从 17 到 113 吨/公顷不等，平均产量预测的总体准确度（误差百分比）在西澳为 9.2%，在昆士兰为 10.2%，在塔斯为 12.7%。与来自多光谱传感器的类似测量值（R2 < 0.57，p < 0.05）相比，来自高光谱传感器的 VI 产生的产量相关系数更差（R2 < 0.1）。将空间分辨率从 10 m 增加到 1.2 m，将回归性能提高了 69%。不可能无损地估计根茎类蔬菜（如胡萝卜）收获前的空间产量变异性。因此，这种产量预测方法为管理收获物流和远期销售决策提供了巨大的好处。不可能无损地估计根茎类蔬菜（如胡萝卜）收获前的空间产量变异性。因此，这种产量预测方法为管理收获物流和远期销售决策提供了巨大的好处。不可能无损地估计根茎类蔬菜（如胡萝卜）收获前的空间产量变异性。因此，这种产量预测方法为管理收获物流和远期销售决策提供了巨大的好处。