**3.3. Car retrieval with leaf level experimental data**

Leaf level experimental data of winter wheat were used to further investigate the capability of the above spectral indices in LCar estimation. Results in **Table 4** showed that the estimation accuracy of CRI550 and CRI700 is the worst. CARred-edge and CARgreen have slightly improved LCar estimating accuracy. RARSc presented an excellent estimation result (R<sup>2</sup> = 0.674, RMSE = 1.443 μg/cm<sup>2</sup> ), which is consistent with its estimation results with the ANGERS data. Different from its good estimation accuracy in ANGERS data, RBRI showed poor estimation accuracy in the experimental data (R2 = 0.222, RMSE = 2.234 μg/cm<sup>2</sup> ). PSNDc and PSSRc have a performance with good results in LCar estimation with the experimental data (R<sup>2</sup> > 0.57, RMSE < 1.65 μg/cm<sup>2</sup> ). Compared with its poor estimation results in the ANGERS data, PRI showed the highest estimation accuracy of LCar in the experimental data (R<sup>2</sup> = 0.710, RMSE = 1.369 μg/cm<sup>2</sup> ). PRIm1 showed poor estimation results in the experimental data, which was consistent with the ANGERS data. Compared with the ANGERS data, the estimation accuracy of PSRI and SRcar in LCar with the experimental data had slightly improved. Similar to its performance with the ANGERS data, CARI had high estimation accuracy in LCar retrieval with the experimental data (R<sup>2</sup> = 0.639, RMSE = 0.639 μg/cm<sup>2</sup> ), showing that CARI was accurate and robust for LCar estimation with different leaf level datasets.

**3.4. Assessing CARI for LCar retrieval with canopy spectra**

ship between CARI and LCar was the worst (R<sup>2</sup>

RMSE = 0.675 μg/cm<sup>2</sup>

Dashed lines indicate 1:1 lines.

(R2 = 0.922, RMSE = 1.398 μg/cm<sup>2</sup>

The above results with leaf level measured data showed that CARI was accurate and robust in LCar estimation. Canopy level simulations and measured data were then used to further explore the effect of LAI and soil moisture on CARI for LCar retrieval. Canopy simulation results in **Figure 5a** showed that the overall correlation between CARI and LCar was high (R<sup>2</sup> = 0.675,

**Figure 4.** Scatterplots of measured LCar versus predicted LCar for spectral indices with leaf level experimental data.

were around 1, which suggests that when LAI values were small, CARI was not sensitive to LCar variations. Indeed, when LAI values were around 1, the information obtained by the canopy spectrum was mostly related to soil background, thus it affected the estimation of LCar. The influence of soil moisture parameter on LCar retrieval with CARI was then investigated when LAI values were 1. Results in **Figure 6** suggested that variations of soil moisture parameter did affect the correlation between CARI and LCar. When the value of soil moisture parameter was 1 (i.e., simulated dry soil), CARI correlated worst with LCar (R<sup>2</sup> = 0.614, RMSE = 0.614 μg/cm<sup>2</sup>

When its value was 0 (i.e., simulated wet soil), CARI showed the best correlation with LCar

between CARI and LCar increased, and when LAI exceeded 4, the correlation reached 0.89 and remained unchanged. When the LAI values exceeded 4, the fitting equations between CARI and LCar hardly changed, suggesting that when LAI values were larger than 4, CARI

might be less sensitive to LCar variations based on canopy spectral data.

); however, the correlation differed when LAI values varied. The relation-

= 0.455, RMSE = 0.455 μg/cm<sup>2</sup>

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209

). In general, with the increase of LAI values, the correlation

) when LAI values

).

Similar to the results with the ANGERS dataset, scatter diagrams of the best four ranking spectral indices were presented in **Figure 4**. The results showed that the estimation results obtained by PRI were the best. LCar values estimated by PRI and the measured values were concentrated near the 1:1 line, and the slope of the scatter plot was 0.76. In addition, RARSc, CARI and PSNDc also showed good estimation results. Unlike the ANGERS data (**Figure 3**), The LCar values of the leaf level experimental data were in the range from 3.05 to 12.59 μg/cm2 , and LCar was in low to moderate numerical range. According to **Figure 4**, the majority of LCar values of the samples were around 10 μg/cm2 , this was mainly because most of the samples that were collected at the booting, head emergence and pollination stages had little LCar variation.

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**Figure 4.** Scatterplots of measured LCar versus predicted LCar for spectral indices with leaf level experimental data. Dashed lines indicate 1:1 lines.

#### **3.4. Assessing CARI for LCar retrieval with canopy spectra**

Among all the indices, PSRI had the lowest estimation accuracy, possibly due to its insensitive

fifth in all estimation results. Compared with these existing spectral indices, the estimation

Based on the estimation results of these spectral indices in LCar retrieval with the ANGERS dataset, the scatter diagrams of the best four ranking spectral indices were presented in **Figure 3**. The results showed that compared with other indices, the fitting line of the scatterplot of RBRI is closer to the 1:1 straight line (the slope of the fitting line is 0.730).In addition,

also showed good estimation results, except for the samples that had LCar values greater than

the 1:1 straight line with the measured values. Compared with RBRI, CARI was more sensi-

Leaf level experimental data of winter wheat were used to further investigate the capability of the above spectral indices in LCar estimation. Results in **Table 4** showed that the estimation accuracy of CRI550 and CRI700 is the worst. CARred-edge and CARgreen have slightly improved LCar estimat-

which is consistent with its estimation results with the ANGERS data. Different from its good estimation accuracy in ANGERS data, RBRI showed poor estimation accuracy in the experi-

good results in LCar estimation with the experimental data (R<sup>2</sup> > 0.57, RMSE < 1.65 μg/cm<sup>2</sup>

tion accuracy of LCar in the experimental data (R<sup>2</sup> = 0.710, RMSE = 1.369 μg/cm<sup>2</sup>

Compared with its poor estimation results in the ANGERS data, PRI showed the highest estima-

poor estimation results in the experimental data, which was consistent with the ANGERS data. Compared with the ANGERS data, the estimation accuracy of PSRI and SRcar in LCar with the experimental data had slightly improved. Similar to its performance with the ANGERS data, CARI had high estimation accuracy in LCar retrieval with the experimental data (R<sup>2</sup> = 0.639,

Similar to the results with the ANGERS dataset, scatter diagrams of the best four ranking spectral indices were presented in **Figure 4**. The results showed that the estimation results obtained by PRI were the best. LCar values estimated by PRI and the measured values were concentrated near the 1:1 line, and the slope of the scatter plot was 0.76. In addition, RARSc, CARI and PSNDc also showed good estimation results. Unlike the ANGERS data (**Figure 3**), The LCar

was in low to moderate numerical range. According to **Figure 4**, the majority of LCar values

values of the leaf level experimental data were in the range from 3.05 to 12.59 μg/cm2

collected at the booting, head emergence and pollination stages had little LCar variation.

Similar to CARI, these indices were not sensitive to higher LCar values (>15 μg/cm<sup>2</sup>

, and the estimated values of most sample points were evenly distributed around

). RARS and PSSRc indices also showed satisfactory estimation results.

), showing that CARI was accurate and robust for LCar estimation with

, this was mainly because most of the samples that were

), but it showed a slight "saturation effect" on high LCar

) ranks

), second to RBRI, showing

). The CARI index

).

),

).

). PRIm1 showed

, and LCar

= 0.674, RMSE = 1.443 μg/cm<sup>2</sup>

). PSNDc and PSSRc have a performance with

to LCar. The estimation accuracy of SRcar generally (R<sup>2</sup> = 0.213, RMSE = 4.489 μg/cm<sup>2</sup>

accuracy of CARI was accurate (R<sup>2</sup> = 0.545, RMSE = 0.545 μg/cm<sup>2</sup>

15 μg/cm2

values (>15 μg/cm2

208 Progress in Carotenoid Research

RMSE = 0.639 μg/cm<sup>2</sup>

different leaf level datasets.

of the samples were around 10 μg/cm2

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

**3.3. Car retrieval with leaf level experimental data**

mental data (R2 = 0.222, RMSE = 2.234 μg/cm<sup>2</sup>

ing accuracy. RARSc presented an excellent estimation result (R<sup>2</sup>

that CARI data can be used to accurately estimate LCar in the ANGERS data.

RBRI index was more sensitive to higher leaf carotenoid content (>15 μg/cm<sup>2</sup>

The above results with leaf level measured data showed that CARI was accurate and robust in LCar estimation. Canopy level simulations and measured data were then used to further explore the effect of LAI and soil moisture on CARI for LCar retrieval. Canopy simulation results in **Figure 5a** showed that the overall correlation between CARI and LCar was high (R<sup>2</sup> = 0.675, RMSE = 0.675 μg/cm<sup>2</sup> ); however, the correlation differed when LAI values varied. The relationship between CARI and LCar was the worst (R<sup>2</sup> = 0.455, RMSE = 0.455 μg/cm<sup>2</sup> ) when LAI values were around 1, which suggests that when LAI values were small, CARI was not sensitive to LCar variations. Indeed, when LAI values were around 1, the information obtained by the canopy spectrum was mostly related to soil background, thus it affected the estimation of LCar. The influence of soil moisture parameter on LCar retrieval with CARI was then investigated when LAI values were 1. Results in **Figure 6** suggested that variations of soil moisture parameter did affect the correlation between CARI and LCar. When the value of soil moisture parameter was 1 (i.e., simulated dry soil), CARI correlated worst with LCar (R<sup>2</sup> = 0.614, RMSE = 0.614 μg/cm<sup>2</sup> ). When its value was 0 (i.e., simulated wet soil), CARI showed the best correlation with LCar (R2 = 0.922, RMSE = 1.398 μg/cm<sup>2</sup> ). In general, with the increase of LAI values, the correlation between CARI and LCar increased, and when LAI exceeded 4, the correlation reached 0.89 and remained unchanged. When the LAI values exceeded 4, the fitting equations between CARI and LCar hardly changed, suggesting that when LAI values were larger than 4, CARI might be less sensitive to LCar variations based on canopy spectral data.

it challenging to assess LCar with its own absorption features [16]. Based on the reviewed studies on LCar estimation with remote sensing techniques, this chapter established a new carotenoid index (CARI) based on the spectral absorption features of carotenoids. Abundant synthetic data simulated from leaf and canopy models, and measured dataset, including the ANGERS and winter wheat data, were then used to comprehensively investigate its capability in LCar assessment. CARI was established in the form of chlorophyll indices, that is, CIrededge and CIgreen. These chlorophyll indices proposed by Gitelson et al. [15] utilized red-edge (or green) band that was sensitive to chlorophyll variation. Meanwhile, a near infrared waveband was also considered to eliminate the effect of other pigments and backward scattering effect. Many studies had shown that CIred-edge and CIgreen can be used to estimate leaf chlorophylls content accurately [34, 35]. Through analyzing the correlation between LCar and reflectance in the visible range from 400 to 800 nm wavelength, we utilized the reflectance of 521 nm band to establish the CARI index. The 521 nm waveband was located in the spectral absorption band of carotenoids and was significantly related to LCar. However, strong correlation between reflectance of 521 nm waveband and LChl existed. In order to eliminate the effect of chlorophylls on carotenoids retrieval, 720 nm waveband was also used in CARI; owing to that, the reflectance of 720 nm band was the most correlated with LChl. With PROSPECT-5 simulations, the new CARI showed a significantly strong linear relationship with LCar. Moreover, CARI showed low correlation with LChl (R<sup>2</sup> = 0.315), showing that it was less sensitive to LChl variations and the use of 720 nm band obviously decreased the effect of LChl on LCar estimation to some extent. In addition, CARI showed good estimation of LCar with both the ANGERS data and leaf level experimental data of winter wheat, indicating that it was

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With different foliar datasets (leaf simulations, the ANGERS data and winter wheat experimental data), performance of published spectral indices in LCar estimation varied. Carotenoids index, including CRI550, CRI700, CARred-edge and CARgreen, showed significant linear relationship with LCar with foliar simulated data. However, those indices exhibited poor results for LCar estimation with the ANGERS data and experimental data of winter wheat, suggesting that the accuracy and robustness of these indices in LCar estimation needed to be improved. Compared with CRI550 and CRI700, the estimation accuracy in LCar retrieval with CARred-edge and CARgreen slightly improved. This suggested that adding of a near infrared band (770 nm) in CRI550 and CRI700 could improve the estimation accuracy [15]. Based on foliar measured data, RARSc showed accurate estimation of LCar (ranking third with the ANGERS data, while ranking second with winter wheat data). These results were consistent with previous studies using RARSc to estimate LCar with measured data [36, 37], suggesting that RARSc was robust in LCar estimation. Performance of RBRI in LCar estimation with simulated and measured data significantly varied: it showed low correlation with LCar in foliar simulations, best results in LCar retrieval with the ANGERS data and poor results with winter wheat experimental data. Based on the spectral absorption features of chlorophylls, Datt [13] proposed the RBRI spectral index, which was used for chlorophylls and carotenoids retrieval. In his study, chlorophylls and carotenoids were significantly correlated. However, in the foliar simulations, their correlation was low (R2 = 0.235), although LChl was significantly related with RBRI (R<sup>2</sup> = 0.847). This could explain the correlation between RBRI and

accurate and robust for LCar assessment with CARI.

**Figure 5.** (a) Correlation between CARI and LCar at different LAI values, from all canopy simulations with 4SAIL model (n = 40,800). (b) Scatterplots of measured LCar versus predicted LCar for CARI with canopy reflectance obtained from field data (n = 44). Dashed lines indicate 1:1 lines.

**Figure 6.** Relationships between CARI and LCar using canopy reflectance simulations with LAI value fixed to 1 at different soil moisture levels. Psoil value set as (a) 0, (b) 0.5 and (c) 1. All other parameters for 4SAIL were fixed based on **Table 3** (n = 1700).

Based on the canopy level measured data of winter wheat, LCar estimation results with CARI is shown in **Figure 5b**. Compared to the results that used leaf level data, the estimation accuracy was rather low for LCar retrieval with canopy level spectrum (R<sup>2</sup> = 0.366, RMSE = 0.366 μg/cm<sup>2</sup> ), and LCar values lower than 5 μg/cm<sup>2</sup> were obviously overestimated (**Figure 5b**). However, it should be noted that these low LCar samples were collected at the wheat kernel milk stage, when leaves were close to senescence and LAI values were less than 1. The inaccurate estimation of these low LCar samples would confirm to use caution in the assessment of LCar from CARI, using canopy reflectance, when LAI values are low.

#### **3.5. Discussion**

The spectral absorption features of carotenoids in the visible range make it possible for analysis of nondestructive estimation of leaf carotenoids content. However, the overlaps of spectral absorption characteristics of carotenoids and chlorophylls in the visible band making it challenging to assess LCar with its own absorption features [16]. Based on the reviewed studies on LCar estimation with remote sensing techniques, this chapter established a new carotenoid index (CARI) based on the spectral absorption features of carotenoids. Abundant synthetic data simulated from leaf and canopy models, and measured dataset, including the ANGERS and winter wheat data, were then used to comprehensively investigate its capability in LCar assessment. CARI was established in the form of chlorophyll indices, that is, CIrededge and CIgreen. These chlorophyll indices proposed by Gitelson et al. [15] utilized red-edge (or green) band that was sensitive to chlorophyll variation. Meanwhile, a near infrared waveband was also considered to eliminate the effect of other pigments and backward scattering effect. Many studies had shown that CIred-edge and CIgreen can be used to estimate leaf chlorophylls content accurately [34, 35]. Through analyzing the correlation between LCar and reflectance in the visible range from 400 to 800 nm wavelength, we utilized the reflectance of 521 nm band to establish the CARI index. The 521 nm waveband was located in the spectral absorption band of carotenoids and was significantly related to LCar. However, strong correlation between reflectance of 521 nm waveband and LChl existed. In order to eliminate the effect of chlorophylls on carotenoids retrieval, 720 nm waveband was also used in CARI; owing to that, the reflectance of 720 nm band was the most correlated with LChl. With PROSPECT-5 simulations, the new CARI showed a significantly strong linear relationship with LCar. Moreover, CARI showed low correlation with LChl (R<sup>2</sup> = 0.315), showing that it was less sensitive to LChl variations and the use of 720 nm band obviously decreased the effect of LChl on LCar estimation to some extent. In addition, CARI showed good estimation of LCar with both the ANGERS data and leaf level experimental data of winter wheat, indicating that it was accurate and robust for LCar assessment with CARI.

With different foliar datasets (leaf simulations, the ANGERS data and winter wheat experimental data), performance of published spectral indices in LCar estimation varied. Carotenoids index, including CRI550, CRI700, CARred-edge and CARgreen, showed significant linear relationship with LCar with foliar simulated data. However, those indices exhibited poor results for LCar estimation with the ANGERS data and experimental data of winter wheat, suggesting that the accuracy and robustness of these indices in LCar estimation needed to be improved. Compared with CRI550 and CRI700, the estimation accuracy in LCar retrieval with CARred-edge and CARgreen slightly improved. This suggested that adding of a near infrared band (770 nm) in CRI550 and CRI700 could improve the estimation accuracy [15]. Based on foliar measured data, RARSc showed accurate estimation of LCar (ranking third with the ANGERS data, while ranking second with winter wheat data). These results were consistent with previous studies using RARSc to estimate LCar with measured data [36, 37], suggesting that RARSc was robust in LCar estimation. Performance of RBRI in LCar estimation with simulated and measured data significantly varied: it showed low correlation with LCar in foliar simulations, best results in LCar retrieval with the ANGERS data and poor results with winter wheat experimental data. Based on the spectral absorption features of chlorophylls, Datt [13] proposed the RBRI spectral index, which was used for chlorophylls and carotenoids retrieval. In his study, chlorophylls and carotenoids were significantly correlated. However, in the foliar simulations, their correlation was low (R2 = 0.235), although LChl was significantly related with RBRI (R<sup>2</sup> = 0.847). This could explain the correlation between RBRI and

Based on the canopy level measured data of winter wheat, LCar estimation results with CARI is shown in **Figure 5b**. Compared to the results that used leaf level data, the estimation accuracy

**Figure 6.** Relationships between CARI and LCar using canopy reflectance simulations with LAI value fixed to 1 at different soil moisture levels. Psoil value set as (a) 0, (b) 0.5 and (c) 1. All other parameters for 4SAIL were fixed based on

**Figure 5.** (a) Correlation between CARI and LCar at different LAI values, from all canopy simulations with 4SAIL model (n = 40,800). (b) Scatterplots of measured LCar versus predicted LCar for CARI with canopy reflectance obtained from

should be noted that these low LCar samples were collected at the wheat kernel milk stage, when leaves were close to senescence and LAI values were less than 1. The inaccurate estimation of these low LCar samples would confirm to use caution in the assessment of LCar from

The spectral absorption features of carotenoids in the visible range make it possible for analysis of nondestructive estimation of leaf carotenoids content. However, the overlaps of spectral absorption characteristics of carotenoids and chlorophylls in the visible band making

= 0.366, RMSE = 0.366 μg/cm<sup>2</sup>

were obviously overestimated (**Figure 5b**). However, it

),

was rather low for LCar retrieval with canopy level spectrum (R<sup>2</sup>

CARI, using canopy reflectance, when LAI values are low.

and LCar values lower than 5 μg/cm<sup>2</sup>

field data (n = 44). Dashed lines indicate 1:1 lines.

210 Progress in Carotenoid Research

**3.5. Discussion**

**Table 3** (n = 1700).

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 sensitive to low values of LCar (<3 μg/cm<sup>2</sup> ), as samples with LCar values lower than <3 μg/cm<sup>2</sup> 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 for LCar estimation.

parameters affected the estimation accuracy of LCar with CARI. When LAI values are low, and soil is in a dry condition, canopy spectral reflectance of plants is mainly controlled by soil reflection; this could weaken plant canopy information, thus reducing LCar estimation accuracy (**Figure 6c**). When the soil is in a wet condition, the overall soil reflectance is lower, thus its confounding effect on LCar estimation seems to be reduced (**Figure 6a**). Our results with measured datasets thus supported the insensitivity of CARI to LCar detection using canopy reflectance when LAI is low. Further investigations on CARI using canopy reflectance acquired with hyper- or multispectral sensors (such as Sentinel-2), are still needed to achieve accurate and robust LCar calibrations, thus providing a promising new tool for assessing

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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

The content in this chapter was supported by the National Key Research and Development Program of China (2016YFD0300601) and the National Natural Science Foundation of China (41571354, 41501468, and 41301389) and the Innovation Foundation of Director of Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China (Grant No. Y6XS560030, Y5ZZ01101B). The authors would like to thank those who helped with the field

campaign at the National Experimental Station for Precision Agriculture.

information on plant physiological status at the regional scale.

**4. Conclusions**

techniques.

**Acknowledgements**

**Conflict of interest**

The authors declare no conflict of interest.

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 4–12 μg/cm2 . Unlike the ANGERS data numerical range, these indices may be more sensitive 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 vegetation.

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 parameters affected the estimation accuracy of LCar with CARI. When LAI values are low, and soil is in a dry condition, canopy spectral reflectance of plants is mainly controlled by soil reflection; this could weaken plant canopy information, thus reducing LCar estimation accuracy (**Figure 6c**). When the soil is in a wet condition, the overall soil reflectance is lower, thus its confounding effect on LCar estimation seems to be reduced (**Figure 6a**). Our results with measured datasets thus supported the insensitivity of CARI to LCar detection using canopy reflectance when LAI is low. Further investigations on CARI using canopy reflectance acquired with hyper- or multispectral sensors (such as Sentinel-2), are still needed to achieve accurate and robust LCar calibrations, thus providing a promising new tool for assessing information on plant physiological status at the regional scale.
