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

Abstract

Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum.Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R 2 ). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer.The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ 645 and ρ 455 ) provided Chls estimates with RMSE around 0.40-0.55 mg g −1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρ REP ) in regression models. An effective chlorophyll indicator with the form of (ρ 645 -ρ 455 ) / ρ REP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R 2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g −1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14 % of the mean absolute error.

Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat

“…In the current study, NDVI, SAVI, OSAVI, VOG1, LIC1, GM1 and ZM could also be used to differentiate between water deprived and non stressed plants 20-30 DAS. These indices have also previously been associated with water content in plants [9,[17][18][19][20]. The correlations between the indices NDVI, SAVI, OSAVI, VOG1, LIC1, GM1 and ZM at one side and plant water status at the other were probably an indirect result, as water deprivation affects other parameters of plant growth, like chlorophyll content and leaf area index, which these indices correlate better with.…”

“…All the above indices utilize the reflection of 550 nm (green) in their formula. Lin et al [17] pinpointed a shift of the region around 535-540 nm with the water content of Cinnamomum camphora (Linn.) Seib.…”

“…Drought stress in crops has been detected by various spectral bands and indices like REIP, NDVI, OSAVI, MCARI, G, ZM, PRI, a simple ratio proposed by Vogelmann et al [16] (VOG1), and Plant Pigment Ratio (PPR) [7,9,[17][18][19][20]. Fluorescence measurements with sensors like the Multiplex R and Dualex R sensor have also been correlated with water stress in wheat [14].…”

Análise De Classificadores De Estágios Sucessionais Em Um Fragmento De Mata Atlântica

“…Apesar de se tratar de um ambiente onde não ocorre um período de déficit hídrico ao longo do ano, a pequena sazonalidade climática existente é suficiente para gerar diferenças na resposta espectral da cobertura vegetal. É bem conhecido que o teor de água das folhas é determinante na reflectância do infravermelho médio (LIN et al, 2015), o que explica, em parte, as diferenças encontradas para imagens adquiridas em períodos com diferentes quantidades de chuva. Este fato reforça a importância de se trabalhar com imagens adquiridas em uma mesma época do ano para estudos de dinâmica da cobertura vegetal, uma vez que a disponibilidade hídrica do solo poderá interferir na reflectância da vegetação na região do infravermelho.…”

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

“…In contrast to the denoising methods mentioned earlier, the estimation of the signal of SWIR bands using simple linear model is called signal restoration. Considering that the VNIR-SWIR reflectance curve of green leaves raises as water content decreases [36] and reflectance of any materials appear to vary slightly in natural status, the use of a single band reflectance based indicator for live fuel water content estimation may be affected by the variation of pixel value. In contrast, the continuous spectra of the 1450 nm absorption region can substantially provide reliable information of water content and can even detect the water stress in tree leaves [24].…”