Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models

Abstract

Traditionally, it is necessary to pre-process remote sensing data to obtain top of canopy (TOC) reflectances before applying physically-based model inversion techniques to estimate forest variables. Corrections for atmospheric, adjacency, topography, and surface directional effects are applied sequentially and independently, accumulating errors into the TOC reflectance data, which are then further used in the inversion process. This paper presents a proof of concept for demonstrating the direct use of measured top-of-atmosphere (TOA) radiance data to estimate forest biophysical and biochemical variables, by using a coupled canopy–atmosphere radiative transfer model. Advantages of this approach are that no atmospheric correction is needed and that atmospheric, adjacency, topography, and surface directional effects can be directly and more accurately included in the forward modelling. In the case study, we applied both TOC and TOA approaches to three Norway spruce stands in Eastern Czech Republic. We used the SLC soil–leaf–canopy model and the MODTRAN4 atmosphere model. For the TOA approach, the physical coupling between canopy and atmosphere was performed using a generic method based on the 4-stream radiative transfer theory which enables full use of the directional reflectance components provided by SLC. The method uses three runs of the atmosphere model for Lambertian surfaces, and thus avoids running the atmosphere model for each new simulation. We used local sensitivity analysis and singular value decomposition to determine which variables could be estimated, namely: canopy cover, fraction of bark, needle chlorophyll, and dry matter content. TOC and TOA approaches resulted in different sets of estimates, but had comparable performance. The TOC approach, however, was at its best potential because of the flatness and homogeneity of the area. On the contrary, the capacities of the TOA approach would be better exploited in heterogeneous rugged areas. We conclude that, having similar performance, the TOA approach should be preferred in situations where minimizing the pre-processing is important, such as in data assimilation and multi-sensor studies.