Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

Wang, Quan; Li, Pingheng

doi:10.1002/hyp.8221

Cited by 26 publications

(28 citation statements)

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“…NDVI and CIred edge, which are sensitive to greenness of vegetation rather than water content, were not significantly related with EWT, but they had significant relationships with GWCD, GWCF, and RWC. Although some studies demonstrated significant relationships between GWCD and NDII/NDMI [17,40], NDII/NDMI was not related to any of the measured variables in this study. To explore the vertical differences in the relationships between VIs and water-related properties at leaf level, we analyzed the relationships for each leaf layer from the top to the bottom of the canopy (Table 6).…”

Section: Estimation Of Water-related Properties At Leaf Levelcontrasting

confidence: 87%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.

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Section: Estimation Of Water-related Properties At Leaf Levelcontrasting

confidence: 87%

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Liu

et al. 2015

Remote Sensing

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“…Most published indices are expressed as reflectance or a first-order derivative at a given wavelength (R) [30][31][32], wavelength difference (D) [33,34], simple ratio (SR) [2,31,[35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], normalized difference (ND) [35,36,[50][51][52] or double differences (DDn) [52,53]. Inverse reflectance differences (ID) such as the anthocyanin reflectance index (ARI) or carotenoid reflectance index (CRI) [54,55] proposed as well.…”

Section: Development Of New Indicesmentioning

confidence: 99%

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

Sonobe

Wang

2017

Remote Sensing

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Abstract:The reflectance properties of leaves are influenced by diverse biochemical components including chlorophyll, one of the key indicators related to plant photosynthesis and plant stress. Although a number of hyperspectral indices have been proposed for quantifying leaf chlorophyll concentrations, their applications are largely restricted to where they were developed and can hardly provide satisfactory results in other cases. In this study, universally applicable hyperspectral indices calculated from both original and first-order derivative spectra were identified for quantifying leaf chlorophyll concentrations in deciduous forests. Using the main criteria of the ratio of performance to deviation (RPD) and the widely applicable information criterion (WAIC), new hyperspectral indices were proposed for quantifying chlorophyll concentrations in four independent datasets. The results revealed that the normalized derivative difference between the green peak (520-540 nm) and the end of the red edge (720-740 nm) were effective. The normalized difference type of index using reflectance derivatives at 522 and 728 nm, dND (522, 728), was the most effective index for quantifying chlorophyll concentrations, with an R 2 of 0.807 and a lowest root mean square error of 8.67 µg/cm 2 , n = 816. This index was also validated based on a simulated dataset generated from the model of PROpriétés SPECTrales Version 5 (PROSPECT 5). Its applicability for assessing chlorophyll content in various deciduous forests was hence demonstrated. We foresee its wide application in the future.

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“…Many important advances in hyperspectral assessments of forest properties have been made using hyperspectral physiologic indices, ‘continuum removal’ methods applied over biochemical absorption centers, and SMA (Asner et al. 2011; Wang and Li 2012).…”

Section: Spectral Vegetation Indicesmentioning

confidence: 99%

Vegetation Indices, Remote Sensing and Forest Monitoring

Huete¹

2012

Geography Compass

132

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With increasing threats and pressure exerted on Earth’s forest resources, there are greater demands for more quantitative, timely, and accurate information on their state, functioning, and sustainability. Satellite remote sensing offers an effective way of measuring and monitoring vast forest areas in a consistent and robust manner. This complements ground forest surveys and overcomes the spatial limitations of in situ sampling of forest biophysical properties. Among the various remote sensing tools used in characterizing forests, spectral vegetation indices (VIs) are widely adopted for monitoring forest states and canopy processes. In this article I provide a brief overview on VI applications and advances made in the assessment and monitoring of forest biophysical states, functioning, phenology, and disturbance. I also address current and future challenges, demands, and limitations of VIs for long term forest monitoring and applications in climate science, hydrology, and biogeochemistry.

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Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements

Cited by 26 publications

References 50 publications

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

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

Vegetation Indices, Remote Sensing and Forest Monitoring

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