Assessing the consistency of crop leaf area index derived from seasonal Sentinel-2 and Landsat 8 imagery over Manitoba, Canada

To take full advantage of cloud-free optical remote sensing data for crop leaf area index (LAI) retrieval throughout the growing season, integration of multi-sensor satellite data is increasingly resorted. However, the consistencies of LAI products derived from different satellites using different retrieval approaches should be assessed. This study aimed at understanding the spatiotemporal consistency of crop LAI derived from S2/MSI (Multi Spectral Instrument, Sentinel-2) and L8/OLI (Operational Land Imager, Landsat 8) data using two retrieving approaches, a field-data-driven (FDD) approach driven by field measured LAI and a hybrid approach based on simulations of the PROSAIL radiative transfer model. Results from the two approaches were assessed using field measured LAI for six types of crops in 2016 over Manitoba, one of the major agricultural provinces in the Canadian Prairies. LAI estimates from S2/MSI data obtained unbiased root-mean-square-error (ubRMSE) of 0.39 and 0.51 cm2 cm−2 for the FDD and hybrid approaches respectively. For the L8/OLI, ubRMSE of LAI estimates were 0.82 and 0.92 cm2cm−2 for the FDD and hybrid approaches, respectively. To evaluate the temporal consistency of crop LAI derived from the two satellites, daily crop LAI was reconstructed using the Parametric Double-Hyperbolic Tangent model (PDHT) with the aid of Bayesian statistical approach. Time series crop LAI of the hybrid approach had lower variability of estimated parameters in the PDHT and reconstructed daily LAI compared with that of the FDD approach. Based on the Spatial Efficiency Metric (SPAEF) that is a spatial performance metric for assessing spatial pattern similarity, the hybrid approach obtained relatively strong spatial consistency of LAI derived from the two satellites compared with the FDD approach. The study revealed that hybrid approach can achieve good spatiotemporal consistencies in seasonal LAI estimates from the two satellites combined when large numbers of field measurements are not available.