**1. Introduction**

Photosynthetic pigments, mainly including chlorophylls (Chl) and carotenoids (Car), are of tremendous significance in the biosphere. Their photosynthetic function could provide necessities, such as oxygen and organic matters, for plant and mammal survival [1]. Generally,

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

chlorophylls, composed of chlorophyll a (Chl a) and chlorophyll b (Chl b), represent the principal class of pigments responsible for light absorption in photosynthesis [2]. Carotenoids, that include carotenes and xanthophylls, are the second major group of plant pigments [1]. They are part of the essential structures of the photosynthetic antenna and reaction center and help stabilize chlorophyll-protein complexes [3, 4]. Besides their function in photosynthesis, previous studies suggest that the assessment of the variation of Car and of their ratio to Chl could shed light on the understanding of photoprotection, photosynthetic acclimation and photosynthetic efficiency in plants [5–10]. Within the plant growth cycle, a normal decrease in Chl indicates that plants are affected by environmental stresses, while the variation of Car reflects the physiological status of vegetation [10]. For instance, it has been observed that Car content would change when plants are in sun-intense and high-temperature conditions, or when nitrogen availability is low or at the onset of leaf senescence [5]. Therefore, quantitative estimation of Car content is extremely useful in order to clarify the mechanisms of photoprotection and light-adaption and for early diagnosis of stress in plants.

Car composition and distribution, at a range of phenological stages and leaf structures. Spectral indices or models based on these datasets might be site- or species-specific, their robustness and capability deserve further investigation when applied to a wide variety of plant leaves and conditions. Thus, to develop robust spectral indices or models for Car content retrieval with spectroscopic techniques, the quality of the training dataset, the selection of the optimal wavelengths and the availability of an independent dataset for the validation

Monitoring Crop Carotenoids Concentration by Remote Sensing

http://dx.doi.org/10.5772/intechopen.78239

199

Radiative transfer models (RTMs) are effective tools to clarify the mechanism describing the relationships between spectral reflectance and plant parameters. They provide an analysis of the remote sensing signal based on a robust understanding of the physical, chemical and biological processes, allowing to assemble rapidly abundant simulation datasets [18]. In recent years, the RTMs have been used extensively for various applications on the vegetation studies [19]. Based on simulated data at the leaf and canopy level with leaf model PROPSECT [20] and multilayer canopy model Scattering by Arbitrary Inclined Leaves (SAIL) [21], there are researches on leaf biochemical parameters retrieval, such as leaf chlorophylls content (LChl), leaf mass per area (LMA) and leaf carotenoids content (LCar), with spectral indices methods and RTMs inversion [18, 22–24]. However, less attention was given to the application of RTMs in Car content retrieval than to Chl content assessment. For foliar Car content estimation, leaf model PROSPECT could simulate abundant leaf level data through combining plant biochemical parameters, which could be used for the investigation of optical characteristic of Car and other pigments and for quantitative evaluation of estimating results of foliar Car content with different spectral indices as well. In addition, for assessment of leaf Car content with plant canopy spectra, canopy spectra were influenced by more than biochemical parameters, canopy structure, illumination and observation geometry, and soil background properties affected canopy spectrum as well [25]. Among these factors, leaf area index (LAI), one of the key parameters describing the canopy structure, and the soil background, has a large effect on canopy reflectance signals [26, 27]. Utilization of PROSAIL model (coupled by leaf model PROSPECT and canopy model SAIL) could generate an extensive canopy level dataset useful for better understanding the relationship between canopy geometry, background environment and canopy reflectance, thus it could shed light on the effect of LAI and soil background on foliar Car content assessment and provide basis for an accurate and robust LCar estimation

Therefore, the goal of this chapter is to propose a nondestructive method to assess LCar with remote sensing techniques, through developing an accurate and robust LCar estimation index, using simulated and measured datasets based on their absorption features in the visible spectrum. The specific objectives were to: (1) establish a new carotenoid index (CARI) for LCar estimation, assess and compare its performance with published carotenoid indices using leaf level simulated data obtained from PROSPECT-5; (2) evaluate the capability and robustness of the new CARI and published carotenoid indices with various leaf level measured data including the widely used ANGERS dataset and field survey data; (3) clarify the effect of LAI and soil background on LCar assessment with the CARI using an extensive synthetic dataset

obtained from PROSAIL and measured data at the canopy scale.

are critical [17].

with spectral index methods.

The absorption features of Car in the visible range make it possible for Car content retrieval with remote sensing techniques. Based on its absorption features, researches on Car content estimation at both the leaf and canopy level with spectroscopic analysis have been conducted in recent years [11]. With ratio analysis of reflectance spectra (RARS) method, Chappelle et al. [12] suggested that reflectance at the 500 nm wavelength correlated best with Car content, and the reflectance was less affected by other pigments. Thus, they proposed a ratio analysis of reflectance spectra ((RARSc, R760/R500) for Car assessment. Research conducted by Datt [13] indicated that the maximum sensitivity of reflectance to variation in pigment content was in the green band region at 550 nm and in the red-edge region at 708 nm; a reflectance band ratio index (RBRI) (R672/(R550 × R708)) was then proposed for pigment content estimation, which had a good correlation with Car content. Based on the reflectance of the Car absorption band at 470 nm, Blackburn [14] put forward two spectral indices with the optimal wavebands 470 and 800 nm, that is, pigment specific simple ratio (PSSRc) and pigment specific normalized difference (PSNDc), for Car estimation at the leaf level. Gitelson et al. [11] found that the first-order derivative reflectance around 510 nm was the most sensitive to Car content. They established two spectral indices, that is, carotenoid reflectance index 550 (CRI550) and carotenoid reflectance index 700 (CRI700), for foliar Car content assessment. Based on a conceptual three-band model, Gitelson et al. [15] further put forward green carotenoid index (CARgreen) and rededge carotenoid index (CARred-edge) with three bands located at 510–520 nm, 690–710 nm (560– 570 nm for CARgreen) and a NIR band, for Car retrieval at the leaf level. Research conducted by Hernández-Clemente et al. [16] indicated that vegetation canopy structure severely affected the performance of CRI550 for Car content assessment at the canopy level. A simple ratio index (SRcar, R515/R570) was then proposed and it showed good correlation with Car content at both leaf and canopy levels.

Overall, previous studies have indeed made much progress in Car content estimation both at the leaf and canopy level; nevertheless, most of the research focused on establishing spectral indices or models for Car content retrieval, with limited measured datasets. These limited data might not be generic enough in order to provide a robust method of assessing Car composition and distribution, at a range of phenological stages and leaf structures. Spectral indices or models based on these datasets might be site- or species-specific, their robustness and capability deserve further investigation when applied to a wide variety of plant leaves and conditions. Thus, to develop robust spectral indices or models for Car content retrieval with spectroscopic techniques, the quality of the training dataset, the selection of the optimal wavelengths and the availability of an independent dataset for the validation are critical [17].

chlorophylls, composed of chlorophyll a (Chl a) and chlorophyll b (Chl b), represent the principal class of pigments responsible for light absorption in photosynthesis [2]. Carotenoids, that include carotenes and xanthophylls, are the second major group of plant pigments [1]. They are part of the essential structures of the photosynthetic antenna and reaction center and help stabilize chlorophyll-protein complexes [3, 4]. Besides their function in photosynthesis, previous studies suggest that the assessment of the variation of Car and of their ratio to Chl could shed light on the understanding of photoprotection, photosynthetic acclimation and photosynthetic efficiency in plants [5–10]. Within the plant growth cycle, a normal decrease in Chl indicates that plants are affected by environmental stresses, while the variation of Car reflects the physiological status of vegetation [10]. For instance, it has been observed that Car content would change when plants are in sun-intense and high-temperature conditions, or when nitrogen availability is low or at the onset of leaf senescence [5]. Therefore, quantitative estimation of Car content is extremely useful in order to clarify the mechanisms of photopro-

The absorption features of Car in the visible range make it possible for Car content retrieval with remote sensing techniques. Based on its absorption features, researches on Car content estimation at both the leaf and canopy level with spectroscopic analysis have been conducted in recent years [11]. With ratio analysis of reflectance spectra (RARS) method, Chappelle et al. [12] suggested that reflectance at the 500 nm wavelength correlated best with Car content, and the reflectance was less affected by other pigments. Thus, they proposed a ratio analysis of reflectance spectra ((RARSc, R760/R500) for Car assessment. Research conducted by Datt [13] indicated that the maximum sensitivity of reflectance to variation in pigment content was in the green band region at 550 nm and in the red-edge region at 708 nm; a reflectance band ratio index (RBRI) (R672/(R550 × R708)) was then proposed for pigment content estimation, which had a good correlation with Car content. Based on the reflectance of the Car absorption band at 470 nm, Blackburn [14] put forward two spectral indices with the optimal wavebands 470 and 800 nm, that is, pigment specific simple ratio (PSSRc) and pigment specific normalized difference (PSNDc), for Car estimation at the leaf level. Gitelson et al. [11] found that the first-order derivative reflectance around 510 nm was the most sensitive to Car content. They established two spectral indices, that is, carotenoid reflectance index 550 (CRI550) and carotenoid reflectance index 700 (CRI700), for foliar Car content assessment. Based on a conceptual three-band model, Gitelson et al. [15] further put forward green carotenoid index (CARgreen) and rededge carotenoid index (CARred-edge) with three bands located at 510–520 nm, 690–710 nm (560– 570 nm for CARgreen) and a NIR band, for Car retrieval at the leaf level. Research conducted by Hernández-Clemente et al. [16] indicated that vegetation canopy structure severely affected the performance of CRI550 for Car content assessment at the canopy level. A simple ratio index (SRcar, R515/R570) was then proposed and it showed good correlation with Car content at both

Overall, previous studies have indeed made much progress in Car content estimation both at the leaf and canopy level; nevertheless, most of the research focused on establishing spectral indices or models for Car content retrieval, with limited measured datasets. These limited data might not be generic enough in order to provide a robust method of assessing

tection and light-adaption and for early diagnosis of stress in plants.

leaf and canopy levels.

198 Progress in Carotenoid Research

Radiative transfer models (RTMs) are effective tools to clarify the mechanism describing the relationships between spectral reflectance and plant parameters. They provide an analysis of the remote sensing signal based on a robust understanding of the physical, chemical and biological processes, allowing to assemble rapidly abundant simulation datasets [18]. In recent years, the RTMs have been used extensively for various applications on the vegetation studies [19]. Based on simulated data at the leaf and canopy level with leaf model PROPSECT [20] and multilayer canopy model Scattering by Arbitrary Inclined Leaves (SAIL) [21], there are researches on leaf biochemical parameters retrieval, such as leaf chlorophylls content (LChl), leaf mass per area (LMA) and leaf carotenoids content (LCar), with spectral indices methods and RTMs inversion [18, 22–24]. However, less attention was given to the application of RTMs in Car content retrieval than to Chl content assessment. For foliar Car content estimation, leaf model PROSPECT could simulate abundant leaf level data through combining plant biochemical parameters, which could be used for the investigation of optical characteristic of Car and other pigments and for quantitative evaluation of estimating results of foliar Car content with different spectral indices as well. In addition, for assessment of leaf Car content with plant canopy spectra, canopy spectra were influenced by more than biochemical parameters, canopy structure, illumination and observation geometry, and soil background properties affected canopy spectrum as well [25]. Among these factors, leaf area index (LAI), one of the key parameters describing the canopy structure, and the soil background, has a large effect on canopy reflectance signals [26, 27]. Utilization of PROSAIL model (coupled by leaf model PROSPECT and canopy model SAIL) could generate an extensive canopy level dataset useful for better understanding the relationship between canopy geometry, background environment and canopy reflectance, thus it could shed light on the effect of LAI and soil background on foliar Car content assessment and provide basis for an accurate and robust LCar estimation with spectral index methods.

Therefore, the goal of this chapter is to propose a nondestructive method to assess LCar with remote sensing techniques, through developing an accurate and robust LCar estimation index, using simulated and measured datasets based on their absorption features in the visible spectrum. The specific objectives were to: (1) establish a new carotenoid index (CARI) for LCar estimation, assess and compare its performance with published carotenoid indices using leaf level simulated data obtained from PROSPECT-5; (2) evaluate the capability and robustness of the new CARI and published carotenoid indices with various leaf level measured data including the widely used ANGERS dataset and field survey data; (3) clarify the effect of LAI and soil background on LCar assessment with the CARI using an extensive synthetic dataset obtained from PROSAIL and measured data at the canopy scale.
