Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Verrelst, Jochem; Camps‐Valls, Gustau; Muñoz-Marí, Jordi; Rivera, Juan Pablo; Veroustraete, Frank; Clevers, J.G.P.W.; Moreno, José

doi:10.1016/j.isprsjprs.2015.05.005

Cited by 522 publications

(422 citation statements)

References 208 publications

(259 reference statements)

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“…However, the regression methods should be able to implement nonlinear and smooth (regularized) functions, in order to deal with complex, possibly ill-posed input-output relations. Earlier experiments on emulation revealed that nonparametric regression methods limited to linear transformations, such as partial least square regression (PLSR), are unable to reconstruct spectral signals with sufficient accuracy [37], while nonlinear regression methods in the families of NNs and kernel-based MLRAs have proven to be powerful adaptive estimators (e.g., [5,37,46]). …”

Section: Interpreting Emulator Resultsmentioning

confidence: 99%

“…These methods have been amply described in earlier publications (e.g., [5,38,44,46,49,50]). The interested reader can find more details therein.…”

Section: Emulator Theorymentioning

confidence: 99%

“…This method has never been a viable option for computationally intensive RTMs, and even for computationally cheap RTMs the method is slow and not appealing. Making use of pre-generated LUTs is preferred in inversion schemes (see [5] for a review). For instance, canopy RTM LUTs are used in the MODIS LAI retrieval products [82] and also atmospheric correction algorithms make use of LUT-based inversion methods (e.g., [83][84][85][86][87]).…”

Section: New Processing Opportunities With Emulatorsmentioning

confidence: 99%

“…RTMs are deterministic models that describe absorption and scattering, and some of them even describe the microwave region, thermal emission or sun-induced chlorophyll fluorescence emitted by vegetation (e.g., [3,4]). They are useful in a wide range of applications including (i) sensitivity analysis; (ii) developing inversion models to accurately retrieve atmospheric and vegetation properties from remotely sensed data (see [5] for a review); and (iii) to generate artificial scenes as would be observed by an optical sensor (e.g., [6,7]). Plant and atmospheric RTMs are currently used in an end-to-end simulator that functions as a virtual laboratory in the development of next-generation optical missions [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Although these advanced models serve perfectly as virtual laboratories for fundamental research on light-vegetation and atmosphere interactions (see e.g., [18,25]), their high computational cost make them impractical for applications such as inversion. Consequently, when it comes to selecting an RTM for applications that demand many simulations, the current pragmatic approach is to search for a good balance between acceptable accuracy and computational complexity [5].…”

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: Interpreting Emulator Resultsmentioning

confidence: 99%

“…These methods have been amply described in earlier publications (e.g., [5,38,44,46,49,50]). The interested reader can find more details therein.…”

Section: Emulator Theorymentioning

confidence: 99%

Section: New Processing Opportunities With Emulatorsmentioning

confidence: 99%

Section: Introductionmentioning

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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Generic Algorithms for Estimating Foliar Pigment Content

Gitelson

Solovchenko

2017

Geophysical Research Letters

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Foliar pigment contents and composition are main factors governing absorbed photosynthetically active radiation, photosynthetic activity, and physiological status of vegetation. In this study the performance of nondestructive techniques based on leaf reflectance were tested for estimating chlorophyll (Chl) and anthocyanin (AnC) contents in species with widely variable leaf structure, pigment content, and composition. Only three spectral bands (green, red edge, and near‐infrared) are required for nondestructive Chl and AnC estimation with normalized root‐mean‐square error (NRMSE) below 4.5% and 6.1%, respectively. The algorithms developed are generic, not requiring reparameterization for each species allowing for accurate nondestructive Chl and AnC estimation using simple handheld field/lab instrumentation. They also have potential in interpretation of airborne and satellite data.

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2021

Deep Learning for the Earth Sciences

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Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

Cited by 522 publications

References 208 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

Generic Algorithms for Estimating Foliar Pigment Content

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