Soil moisture (SM) is an important indicator of the photosynthetic rate and growth status of crops. A few related parameters, such as the red-edge parameters and spectral indices, have been adopted for retrieving the SM of winter wheat. To further study their abilities to detect the SM, field-scale water stress experiments on winter wheat were conducted during the 2018/19 growing season. The spectral ratio index in the near-infrared (NIR) shoulder region (NSRI) (700–1100 nm) was selected by comparing the correlations between the SM and the red edge parameters and spectral indices, and it was optimized using the partial least squares regression (PLSR) method. To assess the performance of the sensitive wavebands of the NSRI in retrieving the SM, three types of spectral index models were established using multiple linear regression (MLR) for the winter wheat from the jointing to the ripening stage. The results indicate that the red-edge parameters are more sensitive to the spectral variation during the jointing and flowering stages. The sensitivity decreased with increasing water stress. The red-edge area (SDr) of winter wheat irrigated in the flowering stage (D1 treatment) and irrigated in the jointing stage (D2 treatment) decreased by 20–30%, respectively. In general, all of the parameters and indices were correlated with the surface SM (0–40 cm depth), especially for the NSRI, with a significant coefficient of determination (R²) of 0.52 in the 10–20 cm depth interval (P < 0.01). Moreover, all of the spectral index models based on the optimized NSRI have good capabilities for retrieving the SM in the jointing stage. The model for one derivative of the logarithm of the NSRI (logarithmic NSRI)' performed best, with R² and root mean square error (RMSE) values of 0.81–0.92 and 0.17–0.89%, respectively. Finally, the (logarithmic NSRI)' model was used to retrieve the SM in the flowering–ripening stage (R² =0.85). Overall, the optimized spectral index models can accurately and quickly retrieve the SM and can assist in predicting the effect of drought on the crop yield in the future.

土壤水分（SM）是衡量作物光合速率和生长状况的重要指标。一些相关参数，如红边参数和光谱指数，已被用于检索冬小麦的 SM。为了进一步研究它们检测 SM 的能力，在 2018/19 生长季节对冬小麦进行了田间规模的水分胁迫实验。通过比较SM与红边参数和光谱指数之间的相关性，选择近红外（NIR）肩区（NSRI）（700-1100 nm）的光谱比指数，并使用偏最小二乘法进行优化回归 (PLSR) 方法。为了评估 NSRI 敏感波段在检索 SM 中的性能，利用多元线性回归(MLR)建立了冬小麦从拔节到成熟阶段的三类光谱指数模型。结果表明，红边参数对拔节期和开花期的光谱变化更为敏感。敏感性随着水分胁迫的增加而降低。冬小麦开花期灌溉（D1处理）和拔节期灌溉（D2处理）的红边面积（SDr）分别减少了20-30%。一般来说，所有参数和指数都与表面 SM（0-40 cm 深度）相关，特别是对于 NSRI，具有显着的决定系数（R 结果表明，红边参数对拔节期和开花期的光谱变化更为敏感。敏感性随着水分胁迫的增加而降低。冬小麦开花期灌溉（D1处理）和拔节期灌溉（D2处理）的红边面积（SDr）分别减少了20-30%。一般来说，所有参数和指数都与表面 SM（0-40 cm 深度）相关，特别是对于 NSRI，具有显着的决定系数（R 结果表明，红边参数对拔节期和开花期的光谱变化更为敏感。敏感性随着水分胁迫的增加而降低。冬小麦开花期灌溉（D1处理）和拔节期灌溉（D2处理）的红边面积（SDr）分别减少了20-30%。一般来说，所有参数和指数都与表面 SM（0-40 cm 深度）相关，特别是对于 NSRI，具有显着的决定系数（R² ) 在 10-20 cm 深度区间为 0.52 ( P  < 0.01)。此外，所有基于优化后的 NSRI 的光谱索引模型都具有很好的在连接阶段检索 SM 的能力。NSRI（对数 NSRI）' 对数的一种导数模型表现最佳，R ²和均方根误差 (RMSE) 值分别为 0.81–0.92 和 0.17–0.89%。最后，（对数NSRI）'模型用于检索开花-成熟阶段的SM（R ² =0.85）。总体而言，优化后的光谱指数模型可以准确、快速地反演SM，有助于预测未来干旱对作物产量的影响。