Influence of small‐scale structure on radiative transfer and photosynthesis in vegetation canopies

Knyazikhin, Yuri; Kranigk, J.; Myneni, Ranga B.; Panfyorov, Oleg; Gravenhorst, G.

doi:10.1029/97jd03380

Cited by 76 publications

(50 citation statements)

References 14 publications

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“…The problem of inverting the RTM model from observations to obtain estimates of input parameters (the inverse problem) is said to be well posed if a solution to the inverse problem exists, is unique and depends continuously (and smoothly) on the data [2,9]. In most cases, these conditions are not met, and most model inversions are said to be ill posed.…”

Section: Introductionmentioning

confidence: 99%

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An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

et al. 2015

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Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an "Emulator toolbox" that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO's MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, Remote Sens. 2015, 7 9348 sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together.

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Section: Introductionmentioning

confidence: 99%

“…In order to produce accurate mappings, LUTs need to have fine sampling in parameter space, which results in a very large number of model runs and, consequently, involving a computationally-demanding task, albeit one that is trivial to parallelise. A good example of this approach is taken for the MODIS LAI product [9].…”

Section: Introductionmentioning

confidence: 99%

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

et al. 2015

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“…But this approach is not straightforward. According to Hadamarad postulates, mathematical models of physical phenomena are mathematically invertible if the solution of the inverse problem to be solved exists, is unique and depends continuously on variables [20]. Unfortunately this assumption is not met.…”

Section: Introductionmentioning

confidence: 99%

Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model

et al. 2013

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Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized "L 1 -estimate" function with a relative error of 17.6% (r 2 : 0.73), while best LAI retrievals were obtained through non-normalized "least-squares estimator" (LSE) with

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“…To define a realization of a vegetation distribution within the canopy in the discrete leaf case, Knyazikhin et al (1998) propose the definition of an indicator function:…”

Section: B Vegetation Local Densitymentioning

confidence: 99%

Optical and Infrared Modeling

Kallel¹

2010

Geoscience and Remote Sensing New Achievements

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Influence of small‐scale structure on radiative transfer and photosynthesis in vegetation canopies

Cited by 76 publications

References 14 publications

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model

Optical and Infrared Modeling

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