Applicability of the PROSPECT model for Norway spruce needles

Malenovský, Zbyněk; Albrechtová, Jana; Lhotáková, Zuzana; Zurita-Milla, R.; Clevers, J.G.P.W.; Schaepman, Michael E.; Cudlín, Pavel

doi:10.1080/01431160600762990

Cited by 113 publications

(82 citation statements)

References 55 publications

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“…Our estimated values of the structural parameter N fit well within the known range (1.0-2.5) for a wide variety of species (Jacquemoud and Baret 1990). However, Malenovsky et al (2006) found higher values (1.72-2.63) of N for Norway spruce; this might be because of sitespecific nature of the parameter. The mean value of 1.42 for beech trees also agrees with the results of Demarez et al (1999), who studied the seasonal variation of N for selected broadleaf tree species.…”

Section: Discussionmentioning

confidence: 49%

“…Although leaf structure parameter N corresponds to the number of leaf layers and is most plausible as a whole number, to take account of the subtle variations in leaf structure, N can be considered as a real number with continuous values (Jacquemoud et al 1996). To start the simulation the maximum and minimum values found in the literature were used for the range of N. Therefore, N was set between 0.5 and 3.0 ( Combal et al (2003), Malenovsky et al (2006), Feret et al (2011)). The structural parameter N was then retrieved using the simulated spectra which best fit the measured spectra of each sample.…”

Section: Generation Of Look-up Tablesmentioning

confidence: 99%

“…Therefore, the technique first developed by Daughtry et al (1989) and later revised by (Mesarch et al 1999) was applied to measure the spectral property of the conifer needles. A universal sample holder that could accommodate all lengths of conifer needles was designed, following Malenovsky et al (2006). Needles were detached from each sample shoot, placed on the sample holder, secured with scotch tape and leaving a space of approximately one needle's width between needles to avoid multiple reflectance from adjacent needles (Daughtry et al 1989).…”

Section: Spectral Measurementsmentioning

confidence: 99%

“…Several studies have reported significant improvement to the accuracy of parameter retrieval after using prior information (e.g. Malenovsky et al 2006, Dasgupta et al 2009); others (Feret et al 2011, Romero et al 2012) have tried to exclude unrealistic combinations of input parameters by applying a linear regression equation derived from correlating the input parameters.…”

Section: Introductionmentioning

confidence: 99%

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Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Ali

Darvishzadeh

Skidmore

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf samples were measured during a field campaign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf samples were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R 2 and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R 2 values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.

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Section: Discussionmentioning

confidence: 49%

Section: Generation Of Look-up Tablesmentioning

confidence: 99%

Section: Spectral Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Ali

Darvishzadeh

Skidmore

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Leaf albedo was calculated as sum of DHRF and DHTF. To measure conifer needle optical properties, the needles were secured in a special holder developed by Malenovský et al (2006) at distances equal to or smaller than their thickness. The needle samples were scanned using a desktop document scanner, from which between-needle gap fractions were estimated using Otsu's automatic threshold method (Otsu, 1975).…”

Section: Field Datamentioning

confidence: 99%

Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: Theoretical basis

Yang

Knyazikhin

Mõttus

et al. 2017

Remote Sensing of Environment

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This paper presents the theoretical basis of the algorithm designed for the generation of leaf area index and diurnal course of its sunlit portion from NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR). The Look-up- Table (LUT) approach implemented in the MODIS operational LAI/FPAR algorithm is adopted. The LUT, which is the heart of the approach, has been significantly modified. First, its parameterization incorporates the canopy hot spot phenomenon and recent advances in the theory of canopy spectral invariants. This allows more accurate decoupling of the structural and radiometric components of the measured Bidirectional Reflectance Factor (BRF), improves scaling properties of the LUT and consequently simplifies adjustments of the algorithm for data spatial resolution and spectral band compositions. Second, the stochastic radiative transfer equations are used to generate the LUT for all biome types. The equations naturally account for radiative effects of the three-dimensional canopy structure on the BRF and allow for an accurate discrimination between sunlit and shaded leaf areas. Third, the LUT entries are measurable, i.e., they can be independently derived from both below canopy measurements of the transmitted and above canopy measurements of reflected radiation fields. This feature makes possible direct validation of the LUT, facilitates identification of its deficiencies and development of refinements. Analyses of field data on canopy structure and leaf optics collected at 18 sites in the Hyytiälä forest in southern boreal zone in Finland and hyperspectral images acquired by the EO-1 Hyperion sensor support the theoretical basis.

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Remote Detection and Monitoring of Plant Traits: Theory and Practice

Angel

Shiklomanov

2022

Annual Plant Reviews Online

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A means for tracking global plant biodiversity and understanding ecological processes is to identify general functioning principles and quantify underpinning common traits across species. Cumulative knowledge from applied spectroscopy in plant functioning has demonstrated the use of hyperspectral remote sensing in quantifying leaf and canopy biochemical and physiological traits. This article aims to provide an overview of the existing theory, research, and applications of remote sensing of plant functioning traits and wraps up future directions and challenges for ecologists and remote sensing users in assessing global biodiversity and ecological functions. It covers the physics, chemistry, and physiological ecology underlying the relationships between leaf and canopy optical properties with functional traits. Within the scope of the article, descriptions and commonly used physical and empirical trait retrieval approaches are included for both leaf and canopy scales. The last section considers the advancing of retrieving frameworks through emerging hyperspectral remote sensing technology and data‐driven analytics. The article concludes by calling for a greater infusion of biological and ecological principles into remote trait retrieval techniques to ultimately improve understanding of ecosystem function and dynamics across different spatial and temporal scenarios.

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Applicability of the PROSPECT model for Norway spruce needles

Cited by 113 publications

References 55 publications

Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: Theoretical basis

Remote Detection and Monitoring of Plant Traits: Theory and Practice

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