Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model

Verhoef, W.

doi:10.1016/0034-4257(84)90057-9

Cited by 1,658 publications

(795 citation statements)

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“…At the canopy scale, SAIL is the most popular canopy RTM; also likely due to its availability and rather low number of input variables [15]. SAIL is based on a four-stream approximation of the RT equation, in which case one distinguishes two direct fluxes (incident solar flux and radiance in the viewing direction) and two diffuse fluxes (upward and downward hemispherical flux) [58].…”

Section: Prosailmentioning

confidence: 99%

“…And secondly, the introduction of leaf elements enables the interactions with leaf optical properties, i.e., a higher LAI implies more light absorption due to the properties of leaf elements and scattering effects. LAD controls the orientation of the leaves [15]. LAD, like LAI, regulates to an extent whether bidirectional TOC reflectance originates from vegetation only (e.g., in case of erectophile leaves; LAD: 90 • ) or is also mixed with soil reflectance (e.g., in case of planophile leaves; LAD: 0 • ).…”

Section: Prosailmentioning

confidence: 99%

“…These terms refer to the model complexity and associated processing speed constraining the inversion of such models: Economically invertible models are models with relatively few input parameters and fast processing that enables fast calculations and so applications such as model inversion or rendering of simulated scenes. Examples of this category include the widely used leaf RTM PROSPECT [14], canopy RTM SAIL [15] and atmospheric RTM 6S [16] and OSS [17].…”

Section: Introductionmentioning

confidence: 99%

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Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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Abstract:Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance (E di f , E dir ), direct and diffuse upward transmittance (T dir , T di f ), spherical albedo (S) and path radiance (L 0 ), the most accurate MLRA's were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400-2500 nm spectral range took no more than a few minutes on a contemporary computer-in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E di f and T di f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E dir and T dir atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

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Section: Prosailmentioning

confidence: 99%

Section: Prosailmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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“…PROSAIL is a combination of the PROSPECT leaf RT model (Jacquemoud and Baret 1990) and the SAIL canopy RT model (Verhoef 1984), which has been used for a variety of applications (Jacquemoud et al 2009). PROSPECT-5 was used in this study, simulating leaf reflectance and transmittance at a 1 nm spectral sampling interval (Feret et al 2008).…”

Section: Prosailmentioning

confidence: 99%

Estimating potato leaf chlorophyll content using ratio vegetation indices

Kooistra

Clevers

2016

Remote Sensing Letters

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Chlorophyll content at leaf level is an important variable because of its crucial role in photosynthesis and in understanding plant functioning. In this study, we tested the hypothesis that the ratio of a vegetation index (VI) for estimating canopy chlorophyll content (CCC) and one for estimating leaf area index (LAI) can be used to derive chlorophyll content at the leaf level. This hypothesis for estimating chlorophyll content at the leaf level was tested using simulations with the PROSAIL radiative transfer model and field spectroradiometry measurements in five consecutive years (2010)(2011)(2012)(2013)(2014) for potato crops on experimental fields. During the growing season, in-situ field measurements of LAI and leaf chlorophyll content (LCC) were performed. Results showed that good estimates of LCC were feasible using ratio vegetation indices (VIs). This was tested at satellite level using RapidEye images. This letter presents a proof of concept for estimating LCC using Sentinel-2 data. Results confirm the importance of the red-edge bands for agricultural applications, but also showed that indices using the red-edge bands may be replaced by indices using green bands. It should now be tested with real Sentinel-2 data whether its spectral bands at 10 m spatial resolution are suitable for estimating LCC, avoiding the need for red-edge bands that only are available at 20 m. ARTICLE HISTORY

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“…By inverting the PROSAIL radiative transfer model, we expect to detect seasonal variations of the vegetation signal as a function of the Leaf Area Index (LAI). PROSAIL is the short name of two coupled models that are very popular in the remote sensing community: PROSPECT, a leaf optical properties model (Jacquemoud & Baret 1990), and SAIL, a canopy reflectance model (Verhoef 1984). It could be possible to estimate the average amount of vegetation present on Earth.…”

mentioning

confidence: 99%

Present and Future Detection of Terrestrial Biomarkers on Earthshine

Briot¹,

Arnold²,

Jacquemoud³

2012

Proc. IAU

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Abstract. In the context of life detection on terrestrial exoplanets, new methods of search for spectral signatures of chlorophyll and other biomarkers in the Earthshine have been developed in the last few decades. Astronomical observations made at OHP and ESO (NTT) showed a significant signal when continents are facing the Moon. This signal, called the Vegetation Red Edge (VRE), is undoubtedly due to chlorophyll absorption properties. In order to strengthen these results, the LUCAS (LUmière Cendrée en Antarctique par Spectroscopie) project dedicated to the measurement of the Earthshine from the Concordia Research Station (C Dome, Antarctica) has been set up. One of the objectives of LUCAS was to observe prolonged variations of the VRE corresponding to various parts of the Earth facing the Moon. An extension of this project, called LUCAS II, would allow long-term observations to detect seasonal variations of the vegetation signal. These data, together with accurate measurements of the Earth's albedo, will help validate a model of global and spectral albedo of our planet.

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Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model

Cited by 1,658 publications

References 4 publications

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Estimating potato leaf chlorophyll content using ratio vegetation indices

Present and Future Detection of Terrestrial Biomarkers on Earthshine

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