Consistent EO Land Surface Products including Uncertainty Estimates

Abstract

<p><strong>Abstract.</strong> Earth Observation (EO) land products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation-soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a one dimensional representation of the radiative transfer within the canopy-soil system such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to validation of JRC-TIP products, including the comparison against in-situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. As many aspects of JRC-TIP are generic the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.</p>

Reviews and Syntheses: Flying the Satellite into Your Model

“…Close collaboration with AD tool developers has proven beneficial in the efficient setup of robust AD-compliant systems for modelling (see e.g. Rayner et al, 2005;Forget et al, 2015;Schürmann et al, 2016;Kaminski et al, 2016b) or retrieval (see e.g. Pinty et al, 2007;Lauvernet et al, 2008Lauvernet et al, , 2012Lewis et al, 2012).…”

“…An example of the global terrestrial vegetation is provided by the Carbon Cycle Data Assimilation System (CCDAS). Initially set up for the assimilation of in situ observations of the atmospheric CO 2 concentration (Rayner et al, 2005), the system was extended step by step with observation operators for several level 2 or 3 products, namely fraction of absorbed photosynthetically active radiation (FAPAR) products (Knorr et al, 2010;Kaminski et al, 2012a), the column-integrated atmospheric carbon dioxide concentration (XCO 2 ) (Kaminski et al, 2010(Kaminski et al, , 2016b, and the surface layer soil moisture (Scholze et al, 2016). The observation operator for FAPAR was a considerable extension of the previous system because it required modules for the simulation of vegetation phenology and hydrology, which were previously provided by an offline calculation.…”

Evaluation of Sentinel-3A and Sentinel-3B ocean land colour instrument green instantaneous fraction of absorbed photosynthetically active radiation

“…The slight bias is also explained by the non-linearity of radiative transfer theory: it makes a difference whether we aggregate FAPAR products generated at the native resolution (e.g. 60 m to 300 m) or compute the FAPAR on the aggregated spectral bands (Kaminski et al, 2017). Times of acquisition of both instruments are not the same as the S3 orbit is a near-polar, sun-synchronous orbit with a descending node equatorial crossing at 10:00 Mean Local Solar time (MLST) whereas the MLST of S2 at the descending node is 10:30.…”

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

“…the spatio-temporal structure in the FAPAR data set is solely imposed by observations from space. The use of the two-stream model ensures physical consistency of all derived variables, as long as the products are used in the native resolution of the albedo input product [ 68 ], which in our case is 1km. In the temporal domain, as for the output of the terrestrial model (see below) we use monthly averages.…”