Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image

Leaf area index (LAI) and chlorophyll content (Cab) are important vegetation variables which can be monitored using remote sensing (RS). Physically-based approaches have higher transferability and are therefore better suited than empirically-based approaches for estimating LAI and Cab at global scales. These approaches, however, require the inversion of radiative transfer (RT) models, which is an ill-posed and underdetermined problem. Four regularization methods have been proposed, allowing finding stable solutions: 1) model coupling, 2) using a priori information (e.g. Bayesian approaches), 3) spatial constraints (e.g. using objects), and 4) temporal constraints. For mono-temporal data, only the first three methods can be applied.In an earlier study, we presented a Bayesian object-based algorithm for inverting the SLC-MODTRAN4 coupled canopy-atmosphere RT model, and compared it with a Bayesian LUT inversion. The results showed that the object-based approach provided more accurate LAI estimates. This study, however, heavily relied on expert knowledge about the objects and vegetation classes. Therefore, in this new contribution, we investigated the applicability of the Bayesian object-based inversion of the SLC-MODTRAN4 model to a situation where no such knowledge was available.The case study used a 16×22km2 simulated top-of-atmosphere image of the upcoming Sentinel-2 sensor, covering the area near the city of Zurich, Switzerland. Seven APEX radiance images were nadir-normalized using the parametric Li–Ross model, spectrally and spatially resampled to Sentinel-2 specifications, geometrically corrected, and mosaicked. The atmospheric effects between APEX flight height and top-of-atmosphere level were added based on two MODTRAN4 simulations. The vegetation objects were identified and delineated using a segmentation algorithm, and classified in four levels of brightness in the visible domain. The LAI and Cab maps obtained from the Bayesian object-based inversion of the coupled SLC-MODTRAN4 model presented realistic spatial patterns. The impact of the parametric Li–Ross nadir-normalization was evaluated by comparing 1) the angular signatures of the SLC-MODTRAN4 and Li–Ross models, and 2) the LAI and Cab maps obtained from a Li–Ross nadir-normalized image (using nadir viewing geometry) and from the original image (using the original viewing geometry). The differences in angular signatures were small but systematic, and the differences between the LAI and Cab maps increased from the center towards the edges of the across-track direction. The results of this study contribute to preparing the RS community for the arrival of Sentinel-2 data in the near future, and generalize the applicability of the Bayesian object-based approach for estimating vegetation variables to cases where no field data are available.