Abstract
To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.
Highlights
Estimation and mapping of vegetation biophysical and biochemical characteristics is one of the most common applications of optical remote sensing and is implemented at all scales from local to global
The look-up table (LUT)-based inversions for both the ASD and Mode 1 (M1) datasets produced similar trends for retrieval accuracy as they relate to view zenith angles (VZA); model fits did not follow the same pattern as the theoretical vegetation indices (VIs) retrievals but instead showed no clear trend
The simple ratio (SR) VI empirical retrievals showed that the best overall model was achieved at +55◦ VZA for the ASD dataset, marginal, the Revised Optimized Soil Adjusted Vegetation Index (ROSAVI) and Gaussian Processes Regression (GPR) showed the opposite
Summary
Estimation and mapping of vegetation biophysical and biochemical characteristics is one of the most common applications of optical remote sensing and is implemented at all scales from local to global. The retrieval of plant variables/traits are especially valuable in understanding Arctic ecosystems where the cumulative effects of changing Arctic vegetation on the climate system in response to recent warming and the relative importance of competing feedbacks related to tundra vegetation (e.g., albedo, photosynthetic, and respiratory processes), are still not well known [7,8,9,10]. This is primarily due to the size and remoteness of the Arctic, its diversity of ecosystems and vegetation, and the biomass quantities involved [11,12]
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