Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

Abstract

This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R 2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R 2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.

Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

“…Shaded leaves and sunlit leaves received different amounts of PAR in under different sky conditions, and they had different LUEs in the same tree; thus, the relationship of VI-PAR in -GPP differed under clear sky and cloudy conditions [90]. Therefore, the tower-based spectra measurement under different levels of PAR in could help determine the calibration coefficients a and b for the relationship between VI and GPP [91][92][93][94]. However, because the optical remote-sensing method cannot obtain effective images under cloudy conditions, the representativeness of the rebuilt VI time series depends on the effective VI that appears in the standard interval.…”

Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations

“…We developed an Earth Reflector Type Index (ERTI), which discriminates signals originating from different Earth components. The index is based on the spectrally invariant behavior of radiation reflected from vegetation canopies [16][17][18][19][20][21], clouds [22,23], and the soil line concept [24,25]. The spectral invariant theory suggests that the ratio of BRF to single scattering albedo (SSA), BRF λ /ω λ , is linearly related to BRF λ , i.e.,…”

Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

“…Generally, the UAV-based multispectral/hyperspectral image has been identified as a key alternative for estimating the AGB of vegetation [3], because the vegetation indices (VIs) calculated from the spectral reflectance captured by the optical sensors on these vehicles have proven to be excellent for obtaining the biochemical content of crops (e.g., chlorophyll [17], plant moisture, and AGB) and biophysical parameters (e.g., leaf area index [18], clump index, and leaf angle [19]) [20,21]. However, some VIs tend to become saturated when the vegetation coverage is significant [22,23], which is impractical for a satisfactory AGB estimate.…”