Abstract The use of remote sensing data for predicting wheat yield and quality is becoming a more feasible alternative to destructive and post-harvest laboratory-based test methods. However, most prediction models which make use of remote sensing data are statistical rather than mechanistic, therefore difficult to extend at interannual and regional scales. In this work, an interannual expandable wheat yield and quality predicting model using hierarchical linear modeling (HLM) was developed, integrating hyperspectral and meteorological data. The results showed that the ordinary least squares (OLS) regression for predicting wheat yield and grain protein content (GPC), one key indicator of grain quality, had low stability at the interannual extension. The predictive power for yield by HLM method was higher than OLS, with R2, RMSEv and nRMSE values of 0.75, 1.10 t/ha, and 20.70 %, respectively. GPC prediction by the HLM method was enhanced when the gluten type was considered, with R2, RMSEv and nRMSE values of 0.85, 1.02 %, and 6.87 %, respectively. The results of this study confirmed that HLM can be a robust method for improving yield and GPC predicting stability under various growing seasons in winter wheat.

中文翻译：

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使用光谱植被指数和气象数据的分层年际小麦产量和谷物蛋白质预测模型

摘要 使用遥感数据预测小麦产量和质量正成为替代破坏性和收获后实验室测试方法的一种更可行的方法。然而，大多数利用遥感数据的预测模型是统计的而非机械的，因此难以在年际和区域尺度上扩展。在这项工作中，使用分层线性模型 (HLM) 开发了一个年际可扩展的小麦产量和质量预测模型，整合了高光谱和气象数据。结果表明，预测小麦产量和籽粒蛋白质含量（GPC）的普通最小二乘法（OLS）回归是粮食质量的一项关键指标，在年际延伸上的稳定性较低。HLM 方法对产量的预测能力高于 OLS，其中 R2，RMSEv 和 nRMSE 值分别为 0.75、1.10 吨/公顷和 20.70%。当考虑面筋类型时，HLM 方法的 GPC 预测得到增强，R2、RMSEv 和 nRMSE 值分别为 0.85、1.02% 和 6.87%。本研究的结果证实，HLM 可以成为提高冬小麦产量和 GPC 预测稳定性的有效方法。