Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies

Verhoef, W.; Li, Jia; Xiao, Qing; Su, Zhongbo

doi:10.1109/tgrs.2007.895844

Cited by 340 publications

(179 citation statements)

References 43 publications

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“…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]. SAIL inputs consist of leaf area index (LAI), leaf angle distribution (LAD), ratio of diffuse and direct radiation (skyl), soil coefficient (soil coeff.…”

Section: Prosailmentioning

confidence: 99%

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%

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

et al. 2016

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“…The effects of canopy heterogeneity on reflectance have not yet been fully addressed in RT models, which require specific geometrical and structural parameters. The 4SAIL model [22] is typical of this type based on the four-stream differential equation of RT in a layer, which cannot properly describe the effects of canopy heterogeneity on the bidirectional reflectance of the row-planted crops. The reflectance output of this model comprises sunlit and shadow surfaces in which the field of view is divided into four components: (1) sunlit and shaded vegetation surface; (2) sunlit and shaded soil surface [23].…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat

et al. 2014

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Rice and wheat are mainly planted in a row structure in China. Radiative transfer models have the potential to provide an accurate description of the bidirectional reflectance characteristics of the canopies of row-planted crops, but few of them have addressed the problem of row-planted structures. In this paper, a new 4SAIL-RowCrop model for row-planted rice and wheat canopies was developed by integrating the 4SAIL model and the Kimes geometric model. The Kimes model and the Kimes-Porous geometric optics (GO) module were used to simulate different scene component proportions. Spectral reflectance and transmittance were subsequently calculated using the 4SAIL model to determine the reflectance of crucial scene components: the illuminated canopy, illuminated background and shadowed background. The model was validated by measuring the reflectance of rice and wheat cultivars at different growth stages, planting densities and nitrogen fertilization rates. The directional and nadir reflectance simulated by the model agreed well with experimental data, with squared correlation coefficients of 0.69 and 0.98, root mean square errors of 0.013 and 0.009 and normalized root mean square errors of 15.8% and 12.4%, respectively. The results indicate that the 4SAIL-RowCrop model is suitable for simulating the spectral reflectance of the canopy of row-planted rice and wheat.

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“…Although the clumping index is directional, the average clumping index can be used for crop scenes, which is defined as the ratio between the effective LAI and actual LAI values. It can be owing to the fact that the 4SAIL model performed well over many crop canopies with effective LAI values [34]. Currently, several algorithms have been proposed to calculate the average vegetation clumping information [35].…”

Section: Vegetation Clumping Indexmentioning

confidence: 99%

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Bian

Cao

et al. 2017

Remote Sensing

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Abstract:The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 • C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.

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Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies

Cited by 340 publications

References 43 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

Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

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