Mitigating the phenological influence on spectroscopic quantification of rice blast disease severity with extended PROSAIL simulations

Rice blast (RB), a devastating fungal disease, causes severe yield losses worldwide and demands accurate severity quantification for effective management. Remote sensing has been demonstrated useful in disease monitoring and offers a scalable solution, but the phenology challenges the robustness of the model built for spectroscopic severity quantification. Since the variations induced by phenology are closely confounded with the infection progression, it is crucial to identify the specific plant traits that explain the inconsistency in disease severity (DS) estimation while mitigating the phenological influence. To address this issue, this study proposed a novel approach by extending the PROSPECT+SAIL model to account for the optical effects induced in RB-infected rice plants. By introducing DS into PROSPECT simulations based on spectral mixture analysis and lesion optical measurements, the use of RB-extended PROSPECT decreased the leaf simulation errors by up to 36.3 % in the crucial spectral regions for RB monitoring. Subsequently, such an extension enabled the generation of synthetic datasets for disentangling phenological versus RB-induced physiological effects. The sensitivity and disentanglement analysis revealed that leaf chlorophyll content was the primary factor that compromises the relationship between DS and the rice blast index (RIBI_nir), which was designed for RB severity quantification. After correcting for these effects by normalizing RIBI_nir with an optimized chlorophyll-sensitive vegetation index (nRIBI_nir), estimation accuracies significantly improved with an increment of R² from 0.67 to 0.79, and rRMSE decreased by 9 %, particularly for vegetative samples with mild infection (R² increased by 0.51). Consequently, the proposed nRIBI_nir overcame the underestimation of severe infection areas in both severity quantification and spatial mapping. The adapted nRIBI_nir for drone and satellite sensors also exhibited great performance in DS estimation. Our findings suggest that RB-extended PROSAIL simulations facilitate mitigating the phenological influence with reliable validations and mechanistic interpretation. Moreover, the adaptation flexibility and robustness of nRIBI_nir ensured its potential in practical applications including resistance breeding, disease tracking, and precision fungicide management at various scales.