Quantifying the influences of spectral resolution on uncertainty in leaf trait estimates through a Bayesian approach to RTM inversion

Abstract

The remote monitoring of plant canopies is critically needed for understanding of terrestrial ecosystem mechanics and biodiversity as well as capturing the short-to long-term responses of vegetation to disturbance and climate change. A variety of orbital, sub-orbital, and field instruments have been used to retrieve optical spectral signals and to study different vegetation properties such as plant biochemistry, nutrient cycling, physiology, water status, and stress.Radiative transfer models (RTMs) provide a mechanistic link between vegetation properties and observed spectral features, and RTM spectral inversion is a useful framework for estimating these properties from spectral data. However, existing approaches to RTM spectral inversion are typically limited by the inability to characterize uncertainty in parameter estimates. Here, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM that is distinct from past approaches in two important ways: First, the algorithm only uses reflectance and does not require transmittance observations, which have been plagued by a variety of measurement and equipment challenges. Second, the output is not a point estimate for each parameter but rather the joint probability distribution that includes estimates of parameter uncertainties and covariance structure. We validated our inversion approach using a database of leaf spectra together with measurements of equivalent water thickness (EWT) and leaf dry mass per unit area (LMA). The parameters estimated by our inversion were able to accurately reproduce the observed reflectance (RMSE VIS = 0.0063, RMSE NIR-SWIR = 0.0098) and 33 transmittance (RMSE VIS = 0.0404, RMSE NIR-SWIR = 0.0551) for both broadleaved and conifer 34 species. Inversion estimates of EWT and LMA for broadleaved species agreed well with direct 35 measurements (CV EWT = 18.8%, CV LMA = 24.5%), while estimates for conifer species were less 36 accurate (CV EWT = 53.2%, CV LMA = 63.3%). To examine the influence of spectral resolution on 37

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

“…(see Methods S3 for more details). Similar to Shiklomanov et al (2016), we found strong agreements between measured and modeled transmittance, in terms of both mean values and standard deviation (Fig. S5a).…”

“…Shiklomanov et al . () showed that PROSPECT‐inverted leaf transmittance is highly consistent with values obtained with an integrating sphere for fresh leaves. As a check, we compared our estimated transmittance against a set of independent measurements made in 2002 by T.M.…”

“…1d) by inverting the leaf reflectance model PROSPECT (Jacquemoud & Baret, 1990;Feret et al, 2008) after it was optimized to closely match the field-observed leaf reflectance. Shiklomanov et al (2016) showed that PROSPECT-inverted leaf transmittance is highly consistent with values obtained with an integrating sphere for fresh leaves. As a check, we compared our estimated transmittance against a set of independent measurements made in 2002 by T.M.…”

“…Together with reflectance, leaf transmittance regulates the leaf single scattering albedo and is an important component of canopy RTMs and process models (Sellers et al, 1997;Pinty et al, 2004). However, acquiring accurate and reproducible measurements of leaf transmittance is challenging, primarily due to limitations inherent to integrating sphere instrumentation (Shiklomanov et al, 2016). Instead, we estimated leaf transmittance ( Fig.…”

A roadmap for improving the representation of photosynthesis in Earth system models

“…optical, LiDAR) from the leaf to landscape are used to inform the RTM structure and to parameterize across spatial and temporal scales (e.g. Shiklomanov et al ., ).

Recommendations: (17) TBMs should not use single layer big leaf models. (18) We need better model representation of canopy architecture and vertical scaling of foliar properties, and data to evaluate alternative radiative transfer models and scaling approaches.

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