Investigation of product accuracy as a function of input and model uncertainties

Wang, Yujie; Tian, Yuhong; Zhang, Yu; Saleous, Nazmi; Knyazikhin, Yuri; Vermote, Éric; Myneni, Ranga B.

doi:10.1016/s0034-4257(01)00225-5

Cited by 109 publications

(66 citation statements)

References 27 publications

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“…It allows the algorithm to admit only those LAI values for which the modeled BRFs agree with the energy conservation law at any wavelength of solar spectrum, thus allowing a significant reduction in the number of retrieved solutions. Extensive prototyping of the algorithm with data from different sensors (AVHRR, LANDSAT, SeaWiFS) indicates that this constraint significantly enhances the accuracy of LAI/FPAR retrievals Wang et al, 2001). …”

Section: Energy Conservation As a Constraintmentioning

confidence: 99%

“…In general, this is not the case, especially with respect to model uncertainties. Convergence can be assured by introducing a stabilization parameter, as described in Wang et al (2001), which allows a correct specification of the overall uncertainty in the inverse prob- lem. The quality of retrieval is a function of the overall uncertainty, denoted here as e.…”

Section: Input and Model Uncertaintiesmentioning

confidence: 99%

“…A critical value of e is one that optimally approximates the unknown true overall uncertainty. This is estimated as the value of e for which 95% of all land pixels for which the algorithm fails were non-vegetated areas or regions where the data were corrupted due clouds or atmospheric effect Wang et al, 2001). …”

Section: Input and Model Uncertaintiesmentioning

confidence: 99%

See 2 more Smart Citations

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

Myneni

Hoffman

Knyazikhin

et al. 2002

Remote Sensing of Environment

1,679

1,150

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An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. D

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Section: Energy Conservation As a Constraintmentioning

confidence: 99%

Section: Input and Model Uncertaintiesmentioning

confidence: 99%

See 1 more Smart Citation

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

Myneni

Hoffman

Knyazikhin

et al. 2002

Remote Sensing of Environment

1,679

1,150

View full text Add to dashboard Cite

show abstract

“…The measurement uncertainty degrades the retrieval accuracy, as manifested by the increasing RMSE and the decreasing R . It is difficult to quantify the real measurement uncertainty, but it is assumed to be around 20% [69]. This value may be higher when considering the differences between sensor-specific characteristics in the context of the concurrent use of multi-sensor data.…”

Section: Robustness To Uncertainty In Reflectancesmentioning

confidence: 99%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

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“…A global LAI product (MOD15) is being routinely generated with 1-km resolution at 8-day intervals and can be downloaded from the National Aeronautics and Space Administration (NASA) data center (Myneni et al, 2002). It is derived from the red (648 nm) and near-infrared (858 nm) bands of the surface reflectance product (MOD09), using land cover type product (MOD12) and ancillary information on surface characteristics as background (Wang et al, 2001). The retrievals are performed by comparing observed and modeled surface reflectances for a suite of canopy structures and soil patterns that covers a range of expected natural conditions.…”

Section: Article In Pressmentioning

confidence: 99%

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

Liu

Chen

Liu

et al. 2007

Journal of Environmental Management

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Investigation of product accuracy as a function of input and model uncertainties

Cited by 109 publications

References 27 publications

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

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