The use of spectral data to predict soil water content has gained wide application in agricultural science. However, it is difficult to guarantee crop water status prediction accuracy based on spectral parameters because the physiological indices and crop water status change daily. Therefore, screening representative crop growth indicators could improve the accuracy of the crop water prediction model. In this study, winter wheat was used as the crop of interest. Initially, spectral characteristics proposed by previous studies were selected and screened. Subsequently, soil water content prediction models were constructed based on a combination of crop leaf area index (LAI) and its spectral characteristics and crop growth physiological indices to predict the field soil water content. These models were constructed using three types of parameters, including single spectral characteristics of canopy water content, single spectral characteristics of canopy water content and measured LAI, as well as spectral characteristics of both canopy water content and LAI. The coefficient of determination (R²) that reflects the reliability of the models was 0.31–0.36, 0.57–0.62, and 0.45–0.54, respectively. The model constructed based on measured LAI and spectral characteristics was the most accurate in each growth period and the whole growth period of winter wheat, followed by that based on dual-spectral characteristics, whereas the single spectral characteristics model was the least accurate. The R² of the model constructed based on measured LAI and characteristic spectral parameters of canopy water content increased by 0.47, 0.16, 0.69, and 0.37 during the entire growth period, respectively, implying that combining LAI with spectral characteristics can improve the accuracy of soil water content prediction models. However, it was difficult to obtain the relevant measured index for the models constructed using measured LAI. Therefore, the dual-spectral characteristics model was recommended as the most appropriate for practical application.

利用光谱数据预测土壤含水量在农业科学中得到了广泛应用。然而，由于生理指标和作物水分状况每天都在变化，因此难以保证基于光谱参数的作物水分状况预测精度。因此，筛选具有代表性的作物生长指标可以提高作物水分预测模型的准确性。在这项研究中，冬小麦被用作感兴趣的作物。最初，选择并筛选了先前研究提出的光谱特征。随后，结合作物叶面积指数（LAI）及其光谱特征和作物生长生理指标，构建土壤含水量预测模型，对田间土壤含水量进行预测。这些模型是使用三种类型的参数构建的，包括冠层含水量单光谱特征、冠层含水量单光谱特征和实测LAI，以及冠层含水量和LAI的光谱特征。决定系数（反映模型可靠性的R ² ) 分别为 0.31-0.36、0.57-0.62 和 0.45-0.54。基于实测LAI和光谱特征构建的模型在冬小麦各生育期和整个生育期的准确度最高，其次是基于双光谱特征的模型，而单光谱特征模型的准确度最低。的- [R ²基于实测LAI和冠层含水量特征光谱参数构建的模型在整个生育期内分别增加了0.47、0.16、0.69和0.37，表明将LAI与光谱特征相结合可以提高土壤含水量预测的准确性楷模。然而，对于使用测量的 LAI 构建的模型，很难获得相关的测量指标。因此，双光谱特性模型被推荐为最适合实际应用的模型。