A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

[摘要] Water deficits can cause chlorophyll degradation which decreases the totalconcentration of chlorophyll a and b (Chls). Few studies haveinvestigated the effectiveness of spectral indices under water-stressedconditions. Chlorophyll meters have been extensively used for a wide varietyof leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter worksby sensing leaves absorptance and transmittance, the reading of chlorophyllconcentration will be affected by changes in transmittance as if there were awater deficit in the leaves. The overall objective of this paper was todevelop a novel and reliable reflectance-based model for estimating Chls offresh and water-stressed leaves using the reflectance at the absorption bandsof chlorophyll a and b and the red edge spectrum.

Three independent experiments were designed to collect data from three leafsample sets for the construction and validation of Chls estimation models.First, a reflectance experiment was conducted to collect foliar Chls andreflectance of leaves with varying water stress using the ASD FieldSpecspectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment wascarried out to collect foliar Chls and meter readings. These two data setswere separately used for developing reflectance-based or absorptance-basedChls estimation models using linear and nonlinear regression analysis.Suitable models were suggested mainly based on the coefficient ofdetermination (R²). Finally, an experiment was conducted to collect thethird data set for the validation of Chls models using the root mean squarederror (RMSE) and the mean absolute error (MAE). In all of the experiments,the observations (real values) of the foliar Chls were extracted fromacetone solution and determined by using a Hitachi U-2000 spectrophotometer.

The spectral indices in the form of reflectance ratio/difference/slopederived from the Chl b absorption bands (ρ₆₄₅ and ρ₄₅₅) provided Chls estimates with RMSE around 0.40–0.55 mg g⁻¹ for bothfresh and water-stressed samples. We improved Chls prediction accuracy byincorporating the reflectance at red edge position (ρ_REP) inregression models. An effective chlorophyll indicator with the form of(ρ₆₄₅–ρ₄₅₅)/ρ_REP proved to be the mostaccurate and stable predictor for foliar Chls concentration. This model wasderived with an R² of 0.90 (P < 0.01) from the training samplesand evaluated with RMSE 0.35 and 0.38 mg g⁻¹ for the validation samples offresh and water-stressed leaves, respectively. The average prediction errorwas within 14% of the mean absolute error.

[学科分类] 地球化学与岩石