Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.

卫星数据提供的信息在农业领域变得越来越重要。估算生物量、氮含量或作物产量可以改善农场管理并优化精准农业应用。大量数据可用作地图材料和来自太空。但是，由用户为特定问题选择合适的数据。如果没有适当的知识，这甚至可能带来经济风险。因此，本研究调查了来自六个不同光学传感器的卫星数据以及不同土壤和地形参数与田间脱粒机记录的谷物和油菜籽产量数据之间的直接关系。考虑了 13 年的时间序列，其中 947 个产量数据集由密集点数据集和 755 个卫星图像组成。为了回答遥感数据与产量之间的关系有多好这个问题，计算了每块田的相关系数 r，并根据作物类型、物候和传感器特征进行解释。当田地及其作物在空间上具有异质性并且在卫星成像时达到作物的正确物候时间时，相关值 r 特别高。与 Landsat 系列的低分辨率传感器相比，具有更高分辨率的卫星图像（例如 RapidEye 和 Sentinel-2）表现更好。额外的红边光谱带也有优势，特别是对于谷物产量估计。该研究得出的结论是，产量数据和卫星数据之间存在高相关值，但必须满足几个条件，这些条件在此处进行了介绍和讨论。