Improving crop yield prediction accuracy is crucial for sustainable agriculture. One approach is to use data assimilation (DA) techniques based on satellite remote sensing, which can help improve predictions at the regional to national scale. However, the interaction between uncertain crop model inputs and DA, as well as the impact of crop model structure on DA results, have received little attention to date. In this work, we assimilated leaf area index (LAI) data into three single crop models (CERES, GECROS, and SPASS) as well as into their multi-model ensemble (MME) using a particle filtering (PF) algorithm. Mimicking the common lack of information at a large scale, we considered nitrogen fertilization, sowing date, soil hydraulic parameters, and weather data as the sources of uncertainties. In a case study, we applied this setup to six winter wheat site years in southwestern Germany. Before applying DA, all models were calibrated and validated using in-situ measured data from a multi-site, multi-year independent data set. The model performance in the calibration was used to assign weights to the models of the MME. Results show that weather data and soil hydraulic parameters had the highest impact on all model predictions. DA substantially improved the accuracy and precision of LAI simulation in all models. Moreover, DA enhanced grain yield prediction by GECROS, SPASS, and the multi-model ensemble, but had no considerable effect on CERES. Specifically, the bias in yield prediction decreased from 25% to 15% in the case of GECROS, from 26% to 15% in SPASS, and from 19% to 7% in the MME. In contrast, even without DA, the yield prediction error in CERES was below 5%. The correlation between LAI errors and yield errors was a key factor indicating how DA can be effective on a specific model. When the correlation analysis is unavailable, the multi-model ensemble is a promising approach for data assimilation. Further investigations on regional model calibration, input uncertainty, MME size, and model weighting scheme are necessary to improve the performance of data assimilation applications.

中文翻译：

---

通过多模型集成和集成数据同化进行季节内作物产量预测

提高作物产量预测的准确性对于可持续农业至关重要。一种方法是使用基于卫星遥感的数据同化（DA）技术，这有助于改进区域到国家尺度的预测。然而，不确定的作物模型输入与DA之间的相互作用，以及作物模型结构对DA结果的影响迄今为止很少受到关注。在这项工作中，我们使用粒子过滤 (PF) 算法将叶面积指数 (LAI) 数据同化到三个单一作物模型（CERES、GECROS 和 SPASS）以及它们的多模型集成 (MME) 中。模仿大规模信息的普遍缺乏，我们将氮肥、播种日期、土壤水力参数和天气数据视为不确定性的来源。在一项案例研究中，我们将这种设置应用于德国西南部的六个冬小麦种植点年份。在应用 DA 之前，所有模型都使用来自多地点、多年独立数据集的现场测量数据进行了校准和验证。校准中的模型性能用于为 MME 模型分配权重。结果表明，天气数据和土壤水力参数对所有模型预测的影响最大。 DA 大幅提高了所有模型中 LAI 模拟的准确性和精度。此外，DA 增强了 GECROS、SPASS 和多模型集成的谷物产量预测，但对 CERES 没有显着影响。具体而言，GECROS 的收益率预测偏差从 25% 下降到 15%，SPASS 的收益率预测偏差从 26% 下降到 15%，MME 的收益率预测偏差从 19% 下降到 7%。相比之下，即使没有 DA，CERES 的产量预测误差也低于 5%。 LAI 误差和产量误差之间的相关性是表明 DA 如何在特定模型上发挥作用的关键因素。当相关性分析不可用时，多模型集成是一种有前途的数据同化方法。为了提高资料同化应用的性能，有必要对区域模型校准、输入不确定性、MME 大小和模型加权方案进行进一步研究。