This study evaluates the applicability of standardized vegetation index (Z_VI) constructed by generalized optical properties of vegetation including Vegetation Optical Depth (VOD) and Sun/Solar-induced Chlorophyll Fluorescence (SIF) to monitor agricultural drought, and further explores the prediction ability of VOD, SIF and the environmental variables including soil moisture on winter wheat yield based on ridge regression model. The results show that the ten-day scale Z_SIF outperforms Z_VOD in the agricultural drought monitoring ability in North China, with the probability of detection (POD) for severe drought reached 77%. Z_SIF can effectively reflect the evolution process of drought occurrence, development and mitigation, and its low-value area is consistent with the spatial distribution of stations recording drought occurrences. In the south of North China, the ability of VOD in C-band and Ku-band during growing season is higher than SIF for winter wheat yield estimation. The highest estimation accuracy was obtained by using the full-feature model including VOD, SIF and the environmental variables. The crucial predictor of winter wheat yield is SIF at the growth peak. This study can provide technical support for large-scale agricultural drought monitoring and food security.