Improving leaf area index retrieval using spectral characteristic parameters and data splitting

Lin, Yinghao; Shen, Huaifei; Tian, Qingjiu; Gu, Xingfa

doi:10.1080/01431161.2019.1674461

Cited by 4 publications

(6 citation statements)

References 48 publications

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“…The slope of the linear regression between the reflectance and wavelength in red-edge (680-760 nm [23,29]; from R segmentation to NIR segmentation in this study).…”

Section: The Slope Of Red-edgementioning

confidence: 77%

“…The spectral reflectance of the vegetation in the 600-900 nm region appears as three broken-lines, representing yellow-edge, red-edge and NIR shoulder (Figure 2). Previous studies reported slightly different wavelength regions of yellow-edge and red-edge (e.g., 560-640 nm [23] or 550-580 nm [29,44] for yellow-edge, and 680-760 nm [23,29], 670-780 nm [18,34,39] or 670-750 nm [44] for red-edge). Therefore, it is important to capture the accurate turning points between yellow-edge and red-edge, and between red-edge and NIR shoulder before extracting the spectral parameters in these three wavelength regions.…”

Section: Automatic Extraction Of Spectral Parameters Of Yellow-edge R...mentioning

confidence: 81%

“…Red-edge parameters, including red-edge position (REP), red-edge amplitude (REA) and red-edge area [26,27], are the most popular indices for detecting the biophysical characteristics of vegetation growth, including chlorophyll content [27], LAI [24], biomass [28], nitrogen status [29], growth stage [30,31] and vegetation response to stress and disturbance [27,32,33]. Red-edge parameters also enhance the separation among vegetation types [28,34] and different ground features [28].…”

Section: Introductionmentioning

confidence: 99%

“…The satellite (RapidEye, WorldView-2/3, Sentinel-2 and Gaofen-6) imagery, designed with red-edge bands, is helpful to improve classification accuracy and enhance LAI estimation by migrating saturation issues [28,[35][36][37][38]. Although, comparing to vegetation indices, red-edge parameters are less sensitive to the changes in specific biophysical factors (vegetation type, canopy structure and soil cover) and environment conditions (site condition, atmospheric effects, irradiance and solar zenith angle) [29,39]. Wei et al's research indicates that the red-edge parameters are affected by the underlying surface in arid grasslands [23].…”

Section: Introductionmentioning

confidence: 99%

“…However, it remains unclear exactly how dead materials, a large component of native grasslands, influence red-edge parameters. In addition to red-edge and yellow-edge parameters [29], the NIR shoulder [20] and red absorption valley [33] are rarely used in vegetation monitoring.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Liu

et al. 2022

Remote Sensing

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Even though research has shown that the spectral parameters of yellow-edge, red-edge and NIR (near-infrared) shoulder wavelength regions are able to estimate green cover and leaf area index (LAI), a large amount of dead materials in grasslands challenges the accuracy of their estimation using hyperspectral remote sensing. However, the exact impact of dead vegetation cover on these spectral parameters remains unclear. Therefore, we evaluated the influences of dead materials on the spectral parameters in the wavelength regions of yellow-edge, red-edge and NIR shoulder by comparing normalized difference vegetation indices (NDVI) including the common red valley at 670 nm and NDVI using the red valley extracted by a new statistical method. This method, based on the concept of segmented linear regression, was developed to extract the spectral parameters and calculate NDVI automatically from the hyper-spectra. To fully understand the impact of dead cover on the spectral parameters (i.e., consider full coverage combinations of green vegetation, dead materials and bare soil), both in situ measured and simulated hyper-spectra were analyzed. The impact of dead cover on LAI estimation by those spectral parameters and NDVI were also evaluated. The results show that: (i) without considering the influence of bare soil, dead materials decreases the slope of red-edge, the slope of NIR shoulder and NDVI, while dead materials increases the slope of yellow-edge; (ii) the spectral characteristics of red valley disappear when dead cover exceeds 67%; (iii) large amount of dead materials also result in a blue shift of the red-edge position; (iv) accurate extraction of the red valley position enhances LAI estimation and reduces the influences of dead materials using hyperspectral NDVI; (v) the accuracy of LAI estimation using the slope of yellow-edge, the slope of red-edge, red-edge position and NDVI significantly drops when dead cover exceeds 72.3–74.5% (variation among indices).

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“…The slope of the linear regression between the reflectance and wavelength in red-edge (680-760 nm [23,29]; from R segmentation to NIR segmentation in this study).…”

Section: The Slope Of Red-edgementioning

confidence: 77%

Section: Automatic Extraction Of Spectral Parameters Of Yellow-edge R...mentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Liu

et al. 2022

Remote Sensing

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Inversion study of the meadow steppe above-ground biomass based on ground and airborne hyperspectral data

Wen,

Zhang,

Wang

et al. 2024

Geocarto International

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A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data

et al. 2022

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High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring vegetation via remote sensing (RS). However, the observation angle and solar angle of high-frequency GSS RS data usually differ, and the differences in bidirectional reflectance from the reflectance spectra of the vegetation canopy are significant, which makes it necessary to normalize angles for GSS RS data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library is effective for the angle normalization of RS data. However, its spatiotemporal applicability and error propagation are currently unclear. To resolve this problem, we herein propose a synthetic angle normalization model (SANM) for RS vegetation canopy reflectance; this model exploits the GSS imaging characteristics, whereby each pixel has a fixed observation angle. The established model references a topographic correction method for vegetation canopies based on path-length correction, solar zenith angle normalization, and the Minnaert model. It also considers the characteristics of diurnal variations in vegetation canopy reflectance spectra by setting the time window. Experiments were carried out on the eight Geostationary Ocean Color Imager (GOCI) images obtained on 22 April 2015 to validate the performance of the proposed SANM. The results show that SANM significantly improves the phase-to-phase correlation of the GOCI band reflectance in the morning time window and retains the instability of vegetation canopy spectra in the noon time window. The SANM provides a preliminary solution for normalizing the angles for the GSS RS data and makes the quantitative comparison of spatiotemporal RS data possible.

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Improving leaf area index retrieval using spectral characteristic parameters and data splitting

Cited by 4 publications

References 48 publications

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Inversion study of the meadow steppe above-ground biomass based on ground and airborne hyperspectral data

A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data

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