Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Kaminski, Thomas W.; Pinty, B.; Voßbeck, Michael; Lopatka, Maciej; Gobron, Nadine; Robustelli, Monica

doi:10.5194/bg-14-2527-2017

Cited by 19 publications

(12 citation statements)

References 39 publications

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“…This advantage of hyperspectral over multispectral data was also found by other studies using hyperspectral field data for LCC as well as for LAI estimations [44][45][46]. Whatever the system used, full uncertainty information should be provided along with the biochemical and biophysical products [47], since even small margins of increased accuracy may significantly reduce uncertainties in an operational processing chain [3].…”

Section: Biophysical and Biochemical Variable Estimationsmentioning

confidence: 58%

Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

et al. 2018

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Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables.

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Section: Biophysical and Biochemical Variable Estimationsmentioning

confidence: 58%

Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

et al. 2018

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“…The results are compared with other products available over the same sites. Two recent papers provide an approachable and non-specialist overview of this area [18,19].…”

Section: Introductionmentioning

confidence: 99%

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

et al. 2017

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Abstract:The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r 2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r 2 > 0.85); the RMSE is lower by 4-5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

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“…The benefit of using multiple data streams in a CCDAS lies in the complementarity of the data and thus in the ability to constrain different components of the underlying process model. In fact, because of the model internal interactions and feedbacks among the components the simultaneous assimilation of complementary observations has synergistic effects such that the constraint is larger than the sum of the individual constraints, as shown for instance by Kato et al (2013), who assimilated observations of FAPAR and latent heat flux.…”

Section: Discussionmentioning

confidence: 99%

“…Knorr et al, 2010) or in combination with other data streams (e.g. Kaminski et al, 2012;Kato et al, 2013;Forkel et al, 2014). SIF is a promising observation for constraining the gross uptake of CO 2 by plant photosynthesis.…”

Section: Examples Of Systematic Observations From Satellite Eo Datamentioning

confidence: 99%

Final response to the referees' comments

Scholze¹

2017

Preprint

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The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well errorcharacterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO 2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties). We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005), emphasising the rapid advance in relevant space-based observations.

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Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Cited by 19 publications

References 39 publications

Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Final response to the referees' comments

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