The accurate and timely estimation of winter-wheat yield at the field and regional scales is critical to developing agricultural management strategies and reducing the effect of changes in environmental conditions on crop yield. Growth status and trend (GST) monitoring has been widely applied to estimate agronomic parameters using remote sensing methods. Many studies have employed GST monitoring, however, most of them were based on a single agronomic parameter and can therefore only represent one-sided or local GST information. Additionally, each agronomic parameter is interactive. Meanwhile, little studies have systemically combined multiple agronomic parameters into one comprehensive indicator to estimate crop yield using remote sensing data. Thus, the objectives of the current research were to build a comprehensive yield evaluation indicator (CYEI) using the improved fuzzy comprehensive evaluation (FCE) method and evaluate the performance of CYEI to monitor GST and estimate yield. The results showed that the CYEI can fully reflect the information of the leaf area index, leaf biomass, leaf water content, and leaf nitrogen content. Compared with various agronomic parameters, the CYEI based on the improved FCE method was more closely correlated with the yield (the R² values of the validation set were 0.63, 0.69, and 0.63 at the booting stage, anthesis stage, and milk development stage.). The CYEI was estimated using a linear model constructed using the optimal VIs, and the results for the three growth stages achieved a higher precision (R² = 0.74, 0.74, and 0.68 for the booting, anthesis, and milk development stages, respectively) than the traditional single agronomic parameter. The CYEI and Bayesian information criterion were then used to select VIs and then build a partial least squares regression model to estimate the yield. The estimation accuracy was found to be satisfactory, with R² values of 0.55, 0.64, and 0.66 at the booting, anthesis, and milk development stages, respectively. Finally, a more intuitive image-scale yield monitoring method was obtained based on unmanned aerial vehicle remote sensing hyperspectral imagery. In the future, the proposed method can be used to obtain wheat growth information and provide a new prediction indicator to better estimate yield in precision agriculture.

在田间和区域范围内准确及时地估算冬小麦产量对于制定农业管理战略和减少环境条件变化对作物产量的影响至关重要。生长状态和趋势 (GST) 监测已广泛应用于使用遥感方法估计农艺参数。许多研究采用了 GST 监测，然而，大多数研究基于单一的农艺参数，因此只能代表片面或局部 GST 信息。此外，每个农艺参数都是交互式的。同时，很少有研究将多个农艺参数系统地组合成一个综合指标来利用遥感数据估算作物产量。因此，当前研究的目标是使用改进的模糊综合评价 (FCE) 方法构建综合产量评估指标 (CYEI)，并评估 CYEI 监测 GST 和估算产量的性能。结果表明，CYEI能充分反映叶面积指数、叶片生物量、叶片含水量、叶片氮含量等信息。与各种农艺参数相比，基于改进 FCE 方法的 CYEI 与产量（验证集的R ²值在孕育期、开花期和乳汁发育期分别为0.63、0.69和0.63。）。使用最佳 VI 构建的线性模型估计 CYEI，三个生长阶段的结果实现了更高的精度（ 孕育、开花和乳汁发育阶段的R ^{2 分别}为 0.74、0.74 和 0.68）。传统的单一农艺参数。然后使用 CYEI 和贝叶斯信息标准来选择 VI，然后构建偏最小二乘回归模型来估计产量。发现估计精度令人满意，R ²在孕育、开花和乳汁发育阶段的值分别为 0.55、0.64 和 0.66。最后，基于无人机遥感高光谱图像获得了一种更直观的图像尺度产量监测方法。未来，该方法可用于获取小麦生长信息，并为精准农业中更好地估算产量提供新的预测指标。