Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Sonobe, Rei; Wang, Quan

doi:10.3390/rs9030191

Cited by 56 publications

(9 citation statements)

References 55 publications

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“…Most previous studies developed spectral indices based on visible bands ranging from 400 to 760 nm, on either original reflectance or derivative value‐based indices (Peng et al., ). This is because chlorophyll strongly absorbs light at blue (400–500 nm) and red (600–700 nm) regions, and does not include light in the green, orange (500–600 nm), and near‐infrared regions (Houborg et al., ; Beck et al., ; Sonobe & Wang, ). Wider spectra may capture more information of leaf physiological status.…”

Section: Discussionmentioning

confidence: 99%

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Peng

Min

Wang

et al. 2018

Ecology and Evolution

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Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference (D ij), normalized differences (ND), first‐order derivative (FD), and first‐order derivative difference (FD(D)) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content (R 2 > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.

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Section: Discussionmentioning

confidence: 99%

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Peng

Min

Wang

et al. 2018

Ecology and Evolution

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“…However, it has been reported that the outputs from these instruments can be obscured depending on the leaf thickness, as it affects light transmission and scattering [20]. Hyperspectral remote sensing, which mainly concentrates on visible-near infrared (400-1000 nm) light and sometimes contains short-wave infrared ranges (1000-2500 nm), offers some alternative methods to monitor biochemical properties such as chl [21][22][23][24]. Besides the biochemical properties, some narrow wavebands possess high sensitivity to subtle changes in plants caused by stress or diseases, effectively detecting various stress or disease indicators [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

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“…The study site is located at Nakagawane forest (138 o 06′ E, 35 o 04′ N) ( Figure 1), one of the research forests of Shizuoka University, Japan [53]. This research site has a typical alpine cold-temperate climate with a mean annual air temperature of 16°C and a mean annual precipitation of approximately 2500 mm [54], and an elevation from 390 to 1560 m.…”

Section: Study Sitementioning

confidence: 99%

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

Song

Wang

Jin

2020

Forests

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A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests.

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Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests

Cited by 56 publications

References 55 publications

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

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