Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R²: 0.47 to 0.66, RMSE: 325 to 399 kg ha⁻¹). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R² of 0.46 (RMSE = 730 kg ha⁻¹), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield.

早期监测田间产量变异性和预测产量潜力对于农民和政策制定者等其他主要利益相关者至关重要。由于易于获取且经济实惠，遥感技术逐渐被用于产量预测研究。尽管越来越多地使用遥感技术进行农业产量预测，但在结合作物和土壤信息预测双低油菜籽产量时，仍然需要中分辨率卫星图像。在这项研究中，我们研究了 PlanetScope（4 m）卫星图像遥感开花信息与衍生土壤和地形参数相结合来预测双低油菜籽产量的效用。我们的产量预测模型使用来自 21 个在可变施用率种子和肥料施用下管理的田地的数据进行了训练和验证，包括清洁的收割机产量图、土壤和地形图。为了量化双低油菜籽的开花强度，使用为双低油菜籽繁殖阶段获取的 PlanetScope 图像的光谱带计算了 9 个植被指数 (VI)。我们使用不同的协变量子集（包括 VI、土壤和地形特征）创建了五个随机森林回归模型，以预测季节内的双低油菜籽产量。使用随机森林回归算法，我们使用确定系数和均方根误差记录了从较差到最佳表现的准确度（R ²：0.47 至 0.66，RMSE：325 至 399 kg ha ⁻¹）。协变量的最佳子集将电导率 (EC)、归一化黄度指数和双低油菜指数确定为解释双低油菜籽产量空间变异性的关键变量。我们的最终模型的验证 R ²为 0.46（RMSE = 730 kg ha ⁻¹），证明了开花阶段中分辨率卫星图像在预测双低油菜籽产量时检测和量化子田时空花卉物候变化的潜力。