Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation. However, the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors. This study carried out research on the correlations among spectral parameters of the canopy of winter wheat, crop growth process, and soil water content, and finally constructed the soil water content prediction model with the growth days parameter. The results showed that the plant water content of winter wheat tended to decrease during the whole growth period. The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer. At different growth stages, even if the soil water content was the same, the plant water content and characteristic spectral reflectance were also different. Therefore, the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy. It is found that the determination coefficient (R²) of the models built during the whole growth period was greatly increased, ranging from 0.54 to 0.60. Then, the model built by OSAVI (Optimized Soil Adjusted Vegetation Index) and Rg/Rr, two of the highest precision characteristic spectral parameters, were selected for model validation. The correlation between OSAVI and soil water content, Rg/Rr, and soil water content were still significant (p<0.05). The R², MAE, and RMSE validation models were 0.53 and 0.58, 3.19 and 2.97, 4.76 and 4.41, respectively, which was accurate enough to be applied in a large-area field. Furthermore, the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward. The research results could guide the agricultural production of winter wheat in northern China.