Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Verhoef, W.; Bach, Heike

doi:10.1016/j.rse.2006.12.013

Cited by 359 publications

(248 citation statements)

References 39 publications

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“…We varied LAI from 1.0 to 8.0 units (Table 2), which not only incorporates a global range of LAI values [23], but is also far more variable than what is typically encountered in closed-canopy tropical forests [38]. Our leaf angle distributions and other model parameters were also structurally very wide ranging [18,20]. Surprisingly, we found that leaf NSC estimates from modeled canopy spectroscopy remained demonstrably good, although precision and accuracy did suffer under such extreme canopy structural variability compared to the leaf-level analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Twenty-five spectra per sample were averaged and then referenced to a calibration block within the integrating sphere (Spectralon, Labsphere Inc., Durham, NH). An integrating sphere and collimated light source are required to obtain directional-hemispherical reflectance and transmittance measurements, which are subsequently required for use in scaling up to the canopy level with radiative transfer models [18][19][20]. The high-fidelity measurement capability of our field instruments resulted in leaf spectra that did not require smoothing or other filters commonly used in leaf optical studies.…”

Section: Leaf Spectral Propertiesmentioning

confidence: 99%

“…We projected the mean leaf reflectance and transmittance spectra (n = 6136 pairs) to the canopy level using the 4SAIL2 (4-Stream Scattering by Arbitrarily Inclined Leaves) radiative transfer model [20]. This model simulates top-of-canopy spectral reflectance based on the measured leaf hemispherical-directional reflectance and transmittance spectra, along with the variation of leaf area index (LAI), leaf angle distribution and other crown geometric-optical properties.…”

Section: Canopy Reflectance Modelingmentioning

confidence: 99%

“…The modeled LAI variation is considered extreme for tropical canopies [22]. Leaf angle distribution (LAD) is described by a two-parameter model, with the first parameter controlling average leaf inclination and the second parameter controlling the bimodality of the leaf angle distribution [20]. The ranges shown here are extremely wide for tropical vegetation canopies.…”

Section: Canopy Reflectance Modelingmentioning

confidence: 99%

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Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

Asner

Martin

2015

Remote Sensing

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Non-structural carbohydrates (NSC) are products of photosynthesis, and leaf NSC concentration may be a prognostic indicator of climate-change tolerance in woody plants. However, measurement of leaf NSC is prohibitively labor intensive, especially in tropical forests, where foliage is difficult to access and where NSC concentrations vary enormously by species and across environments. Imaging spectroscopy may allow quantitative mapping of leaf NSC, but this possibility remains unproven. We tested the accuracy of NSC remote sensing at leaf, canopy and stand levels using visible-to-shortwave infrared (VSWIR) spectroscopy with partial least squares regression (PLSR) techniques. Leaf-level analyses demonstrated the high precision (R 2 = 0.69-0.73) and accuracy (%RMSE = 13%-14%) of NSC estimates in 6136 live samples taken from 4222 forest canopy species worldwide. The leaf spectral data were combined with a radiative transfer model to simulate the role of canopy structural variability, which led to a reduction in the precision and accuracy of leaf NSC estimation (R 2 = 0.56; %RMSE = 16%). Application of the approach to 79 one-hectare plots in Amazonia using the Carnegie Airborne Observatory VSWIR spectrometer indicated the good precision and accuracy of leaf NSC estimates at the forest stand level (R 2 = 0.49; %RMSE = 9.1%). Spectral analyses indicated strong contributions of the shortwave-IR (1300-2500 nm) region to leaf NSC determination at all scales. We conclude that leaf NSC can be remotely sensed, opening doors to monitoring forest canopy physiological responses to environmental stress and climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Leaf Spectral Propertiesmentioning

confidence: 99%

Section: Canopy Reflectance Modelingmentioning

confidence: 99%

Section: Canopy Reflectance Modelingmentioning

confidence: 99%

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Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

Asner

Martin

2015

Remote Sensing

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“…Based on the Four-Stream Radiative Transfer theory developed by Verhoef and Bach [37], the FAPAR, FAPAR dir and FAPAR dif are calculated from PROSAIL simulations in this study ( Figure 3). The direct and diffuse directional transmittance and reflectance calculated by PROSAIL are first considered in order to assess the absorption efficiency of light by the canopy.…”

Section: Fapar Calculation Based On Prosail Modelmentioning

confidence: 99%

Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress

Zhou¹,

Liu²,

Zhao³

et al. 2017

Remote Sensing

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Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth.

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Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground‐based estimates over six FLUXNET sites

Fang

2015

JGR Biogeosciences

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The fraction of photosynthetically active radiation (PAR) absorbed by green elements (FPAR) is an essential climate variable in quantifying canopy absorbed PAR (APAR) and gross and net primary production. Current satellite FPAR products typically correspond to black-sky FPAR under direct illumination only, but the radiation transfer and vegetation absorption processes differ for direct and diffuse PARs. To address this, the present study developed a new approach to estimate direct, diffuse, and total FPARs, separately, from Landsat surface reflectance data. Field-measured direct and diffuse FPARs were first derived for crops, deciduous broadleaf forests, and evergreen needleleaf forests at six FLUXNET sites. Then, a coupled soil-leaf-canopy radiative transfer model (SLC) was used to simulate surface reflectance under direct and diffuse illumination conditions. Direct, diffuse, and total FPARs were estimated by comparing Landsat-5 Thematic Mapper (TM) data and simulated surface reflectances using a lookup table approach. The differences between the Landsat-estimated and the field-measured FPARs are less than 0.05 (10%). The diffuse FPAR is higher than the direct FPAR by up to 19.38%, whereas the total FPAR is larger than the direct FPAR by up to 16.07%. The direct APAR is higher than the diffuse APAR under clear-sky conditions, but underestimates the total APAR by À277.72 μmol s À1 m À2 on average. The approach described here can be extended to estimate direct, diffuse, and total FPARs from other satellite data and the obtained FPAR variables could be helpful to improve modeling of vegetation processes.

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Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Cited by 359 publications

References 39 publications

Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress

Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground‐based estimates over six FLUXNET sites

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