Publications

Zhang, DC; Dietze, M (2023). Towards uninterrupted canopy-trait time-series: A Bayesian radiative transfer model inversion using multi-sourced satellite observations. REMOTE SENSING OF ENVIRONMENT, 287, 113475.

Abstract
Canopy traits play an important role in plant growth, carbon sequestration, and the land surface water and energy balance, and thus it is important to understand and monitor how these traits change over time. Canopy radiative transfer models, which use canopy traits to predict spectra, provide a powerful means to understand canopy trait variation and these models can be inverted, allowing us to use remotely sensed data to monitor canopy traits. However, a single inversion represents a snapshot in time for a single sensor. In this study, we used a Bayesian approach to assimilate snow-free observations from three different sensors (Landsat 8, Sentinel-2A and MODIS) over the course of 2019 by inverting the ProSAIL radiative transfer model, producing a robust estimate of six canopy traits and their joint uncertainties at each time point. Next, we used a state-space time series model to integrate observations from multiple sensors to capture variation in vegetation optical traits throughout the year. The results demonstrate that our model can capture seasonal variation of surface reflectance with less than 5% RMSE compared with observed reflectance. Moreover, linking observations in time narrows the uncertainty intervals compared to the posterior distributions for individual time points. We are also able to make statistical inferences on dates when no data is available, and provide reasonable and reliable time-series inversion results compared with LAI ground measurements.

DOI:
10.1016/j.rse.2023.113475

ISSN:
1879-0704