Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level

This investigation quantitatively links chlorophyll a + b (chl _a + b) concentration, a physiological marker of forest health condition, to hyperspectral observations of Jack Pine (Pinus banksiana), a dominant Boreal forest species. Compact Airborne Spectrographic Imager (CASI) observations, in the visible-near infrared domain, were acquired over eight selected Jack Pine sites, near Sudbury, Ontario, between June and September of 2001. Supplementing the airborne campaigns was concurrent on-site collection of foliage samples for laboratory spectral and chemical measurements. The study first connected needle-level optical properties with pigment concentration through the inversion of radiative transfer models, LIBERTY and PROSPECT. Next, a chlorophyll sensitive optical index (R750/R710), was “scaled-up” using SAILH, a turbid medium canopy model, to estimate total pigment content at the canopy-level. Due to the potential confounding effects of open canopy structure and foliage clumping, the analysis accordingly focused on high spatial resolution CASI imagery (1 m) to visually target tree crowns, while accounting for shadowed areas. Chl _a + b concentration estimation from airborne spectral data using coupled leaf and canopy models was shown to be feasible with a root mean square error of 5.3 μg/cm², for a pigment range of 25.7 to 45.9 μg/cm². Such predictive algorithms using airborne-level data provide the methodology to be potentially scaled-up to satellite-level hyperspectral platforms for large scale monitoring of vegetation productivity and forest stand condition.