Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2–A Absorption Band

Abstract

Chlorophyll (Chl) is one of the most important classes of light-absorbing pigments in photosynthesis, and the proportion of Chl in leaves is closely related to vegetation nutrient status. Remote sensing-based estimation of Chl content holds great potential for evaluating crop growth status in agricultural management, precision farming and ecosystem monitoring. Recent studies have shown that steady-state fluorescence contributed up to 2% on the apparent reflectance in the 750-nm spectral region of plant and also provided additional evidence for fluorescence in-filling of the atmospheric oxygen absorption band at a central wavelength of 760 nm (O 2 –A band). In this study, an in situ hyperspectral remote sensing approach zwas employed to estimate corn Chl content at the canopy level by using chlorophyll fluorescence (ChlF) signals in the O 2 –A absorption band. Two new spectral indices, REArea 760 (sum of first derivative reflectance between 755 and 763 nm) and REA 760 (maximum of first derivative reflectance between 755 and 763 nm), derived from the first derivative spectra in the O 2 –A band, were proposed for estimating the corn canopy Chl content (CCC). They were compared with the performance of published indices measured at ground level, including the MERIS Terrestrial Chlorophyll Index (MTCI), Optimized Soil-Adjusted Vegetation Index 2 (OSAVI2), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), SR710, REArea (sum of first derivative reflectance between 680 and 780 nm), REA (maximum value of first derivative reflectance between 680 and 780 nm), and mND 705 . The results indicated that corn Chl content at the canopy level was better predicted by the new indices (with R 2 = 0.835) than the published indices (with R 2 ranging from 0.676 to 0.826). The two new indices ranked in the top four according to their summed ranks by integrating the ranks of RMSE and R 2 of CCC linear regression models. ChlF originates only from chlorophyll in the photosynthetic apparatus and therefore is less sensitive to soil, wood, and dead biomass interference. Moreover, due to the fluorescence in-filling of the O 2 –A band and the amplified effect on spectrum signals by derivative operation, the spectral derivative indices in the O 2 –A band have great potential for estimating the CCC.

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

“…Signal noise for handheld spectroradiometers is highly susceptible to the sun's illumination, instrument quality, and environmental conditions [27]. To account for these issues, first-order and second-order derivative techniques have been widely applied to reduce signal noise by capturing subtle details in the spectral curve [28][29][30]. First-order and second-order derivatives are functions of mathematical change, where they represent the slope and curvature of the spectral curve, respectively [31][32][33].…”

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

“…By comparing the tting models of the two relative variables, the results obtained by the three weighting methods all showed that the weighted rLAI-based models had higher accuracy than the weighted rCI red edge -based models. The optimal estimation models of potato yield can be determined as Equations (20) and (21). where yield (LAI) is the estimated yield using rLAI based on weighted growth stage.…”

“…Vegetation canopy spectrum is closely related to crop growth, especially the visible range affected by pigment and the near-infrared (NIR) bands affected by cell tissue and canopy structure [16][17]. Therefore, the vegetation index (VI) calculated by these bands has been widely used for the monitoring and estimation of vegetation characteristic parameters, such as leaf area index (LAI) [18], biomass [19], chlorophyll content [20], nitrogen content and carbon content [21], and achieved high accuracy. In addition, various VIs showed great differences when applied in diverse scenarios.…”

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

“…By comparing the fitting models of the two relative variables, the results obtained by the three weighting methods all showed that the weighted rLAI-based models had higher accuracy than the weighted rCIred edge-based models. The optimal estimation models of potato yield can be determined as Equations (20) and (21). As the final estimation models of potato yield were based on the relative yield model by adding the yield of the reference spot, their prediction ability remains unchanged (Table 3).…”

“…Vegetation canopy spectrum is closely related to crop growth, especially the visible range affected by pigment and the near-infrared (NIR) bands affected by cell tissue and canopy structure [16][17]. Therefore, the vegetation index (VI) calculated by these bands has been widely used for the monitoring and estimation of vegetation characteristic parameters, such as leaf area index (LAI) [18], biomass [19], chlorophyll content [20], nitrogen content and carbon content [21], and achieved high accuracy. In addition, various VIs showed great differences when applied in diverse scenarios.…”