**4. Conclusions**

LCar in simulations. In ANGERS data, LCar and LChl were significantly linear correlated (R2 = 0.908) and RBRI showed strong relationship with LChl (R<sup>2</sup> = 0.785); therefore, RBRI showed high estimation accuracy in LCar retrieval. The RBRI was established on the equation R672/ (R550 × R708), which was different from the normalization and ratio form that most indices adopted. The form of the denominator (R<sup>550</sup> × R708) might help to increase the numerical range of RBRI, making it more sensitive to large values of LCar. However, RBRI might not be sensi-

were obviously overestimated. In the experimental data of winter wheat, although LCar and LChl were significantly linear related (R<sup>2</sup> = 0.888), RBRI showed poor estimation results for LCar retrieval. This suggested that RBRI might not be stable when used in various datasets

Blackburn [14] pointed out that the overlaps between spectral absorption features of carotenoids and chlorophylls might affect the relationship between LCar and PSNDc (or PSSRc). Moreover, 470 nm waveband was used in these spectral indices, which was not the best absorption band for carotenoids. Their performance with the leaf-simulated data and the ANGERS data supported this viewpoint. However, PSNDc and PSSRc showed rather good estimation accuracy in LCar retrieval with winter wheat data. This may be due to the fact that LCar values of the measured foliar data of winter wheat were in the range of

tive to LCar changes in this range. PRI was successfully applied to a variety of studies [38, 39]. In this chapter, PRI showed poor performance in LCar estimation with leaf-simulated data and the ANGERS data. The 531 nm waveband of PRI was used to detect variations of xanthophyll cycle components [31], PRI's relationship with LCar may be overly influenced by a single carotenoid component [40].Compared with the estimated results in simulated data and the ANGERS data, PRI showed the best estimation accuracy in LCar retrieval with winter wheat data. Previous study had also shown that PRI was accurate in LCar estimation in cotton plants [37]. These results indicated that PRI may be suitable for LCar estimation in single-species vegetation. Unlike PRI, PRIm1 showed poor estimation results in all the used datasets. This may be because PRIm1 was devised to reduce the effect of the canopy structure effect and indicated water stress [32]. These results indicated that PRIm1 was not suitable for LCar estimation. Similarly, PSRI was devised to indicate leaf senescence and fruit ripening, it was sensitive to changes of the content ratio of carotenoids to chlorophylls [8]. The assessment results also indicated that PSRI was not suitable for LCar estimation. SRcar showed poor correlation with LCar in simulated data, this was mainly because that the parameters of the leaf-simulated data in this chapter were more complicated than that of Hernandez-Clemente et al. [16]. Similarly, the poor estimation results of LCar with SRcar in the ANGERS data may be related to the diversity of vegetation types. However, SRcar showed good estimation accuracy in LCar assessment with winter wheat data, which indicated that it might be suitable for LCar retrieval with single-species

Although the new index CARI showed good estimation results for LCar retrieval with different foliar datasets, the simulation results with canopy level data suggested that CARI was not sensitive to LCar variations when LAI was low (e.g., LAI = 1). Moreover, soil moisture

. Unlike the ANGERS data numerical range, these indices may be more sensi-

), as samples with LCar values lower than <3 μg/cm<sup>2</sup>

tive to low values of LCar (<3 μg/cm<sup>2</sup>

for LCar estimation.

212 Progress in Carotenoid Research

4–12 μg/cm2

vegetation.

This chapter mainly focused on leaf and canopy level radiative transfer model PROSPECT-5 and 4SAIL to simulate abundant leaf and canopy synthetic data and to establish a new carotenoid index (CARI) for LCar assessment based on the spectral absorption features of carotenoids. Abundant measured data, including the ANGERS data and experimental data of winter wheat, were then used to comprehensively evaluate the capability and robustness of CARI in LCar retrieval. Results showed that CARI correlated best with LCar among all the selected spectral indices with leaf-simulated data. Moreover, CARI showed accurate and robust estimation results of LCar with the ANGERS data and experimental data of winter wheat. Further investigation of CARI in LCar retrieval with simulated and measured canopy level data showed that CARI was insensitive to LCar variations when LAI values were low. In these conditions, soil moisture parameters affected the estimation accuracy of LCar with CARI. Overall, we suggest that CARI is suitable for LCar assessment, which could provide basis for LCar nondestructive estimation with remote sensing techniques.
