Cotton Phenology Detection Using Time Series Sentinel-1 and PlanetScope Data

Both PlanetScope and Sentinel-1 synthetic aperture radar (SAR) data have proven valuable in detecting crop phenological stages. However, the use of time series data from Sentinel-1, particularly derived SAR indices, for phenology detection has been underexplored. Additionally, leveraging phenology information from both Sentinel-1 and PlanetScope time series data has not been extensively investigated. This study examines the time series of Sentinel-1 backscatter coefficient, interferometric coherence, and several SAR indices, integrating them with PlanetScope data to improve the accuracy of cotton phenology detection. Seasonal extrema and breakpoints of backscattering coefficient, interferometric coherence, and SAR indices from Sentinel-1 and normalized difference vegetation index (NDVI) from PlanetScope over a cotton plantation in Brazil from December 2021 to July 2023. These retrievals were directly correlated with in situ observations and applied on a large scale for phenology detection. The results indicate that time series cross-ratio (CR), coherence VV, dual-polarization RVI (DpRVIc), and Gamma0_VH are useful for detecting cotton emergence, vegetative growth, square formation, flowering, and boll formation, respectively. Furthermore, integrating PlanetScope and Sentinel-1 significantly enhances phenology detection accuracy [Bias = 0.87 and root mean square error (RMSE) = 17.3] compared to using only Sentinel-1 (Bias = 16.05 and RMSE = 71.98). Generally, the RMSE for all four phenology stages is around one or two times the revisit frequency of Sentinel-1. This study underscores the potential of integrating PlanetScope and Sentinel-1 for large-scale phenology monitoring.