A Novel Dynamical Framework for Crop Phenology Estimation With Remote Sensing

Abstract

A novel framework to estimate crop phenology with remote sensing measurements is conceived in this study. We introduce the parametrized grid-based filter, that uses the plant age of the crops as a parameter to model the phenology dynamics. Accordingly, the nonstationary transition matrix is defined to characterize the transitions between discrete phenological stages in numerical scale. At the update step, the combination between the observation and the predicted parametrized evolution is exploited to jointly estimate both phenology and plant age. The method is applied on dense time series of dual-pol VV-VH Sentinel-1 (S1) radar images to estimate the phenological stages of rice (defined in the BBCH scale) in Sevilla, Spain, during four annual seasons, from 2017 to 2020. Phenology is accurately estimated when the evolution model is trained with actual ground data, showing $0.96\leq R^{2}\leq 0.99$. Regarding the plant age, when more than four S1 images are considered, its estimates reach stable and accurate values (with a maximum error of 5 days), exhibiting $R^{2}$ close to unity. A comparison with the previous GBF shows that this is outperformed by the proposed estimation strategy, confirming the effectiveness of the proposed dynamical framework in obtaining improved and realistic phenology estimates, according to the particular time epoch in the crops life. Finally, when the plant age is unknown, which is the case on most real monitoring scenarios, our method performs significantly better, indicating that the knowledge of sowing date is no longer needed during the remote sensing-based phenology estimation.

Keywords

• Agriculture
• Bayesian filtering
• phenology
• remote sensing
• synthetic aperture radar