Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS)

This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset. Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001– 0.04. LAI is retrieved with r2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs

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

Earth observation (EO) land surface 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 1-D 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 they 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 the 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. Since 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

Land cover classification using multi-temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands

The interannual variability of Africa's ecosystem productivity: a multi-model analysis

We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.JRC.DDG.H.3-Global environement monitorin

Organisational climates for diversity and their impacts on managerial attitudes and perceptions in the NHS and retail industry

Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 to 2.96 g C MJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were considerably higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models

Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana

Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities

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

This article presents the evaluation of the Copernicus Sentinel-3 Ocean Land Colour Instrument (OLCI) operational terrestrial products corresponding to the green instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and its associated rectified channels. These products are estimated using OLCI spectral measurements acquired at the top of the atmosphere by a physically-based approach and are available operationally at full (300 m) and reduced (1.2 km) spatial resolution daily. The evaluation of the quality of the FAPAR OLCI values was based on the availability of data acquired over several years by Sentinel-3A (S3A) and Sentinel-3B (S3B). The evaluation exercise consisted of several stages: first, an overall comparison of the two S3 platform products was carried out during the tandem phase; second, comparison with an FAPAR climatology derived from the Medium Resolution Imaging Spectrometer (MERIS) provided information on the seasonality of various types of land cover. Then, direct comparisons were made with the same type of FAPAR products retrieved from two sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sentinel-2 (S2) Multispectral Instrument (MSI), and with several ground-based estimates. In addition, an analysis of the efficiency of the retrieval algorithm with 3D radiative transfer simulations was performed. The results indicated that the consistency between daily and monthly S3A and S3B on a global scale was very good during the tandem phase (RMSD = 0.01 and a correlation R2 of 0.99 with a bias of 0.003); we found an agreement with a correlation of 0.95 and 0.93 (RMSD = 0.07 and 0.09) with JRC FAPAR S2 and JRC FAPAR MODIS, respectively. Compatibility with the ground-based data was between 0.056 and 0.24 in term of RMSD depending on the type of vegetation with an overall R2 of 0.89. Immler diagrams demonstrate that their variances were lower than the total uncertainties. The quality assurance using 3D radiative transfer model has shown that the apparent performance of the algorithm depends strongly on the type of in-situ measurement and canopy type

An Earth Observation Land Data Assimilation System (EO-LDAS)

Current methods for estimating vegetation parameters are generally sub-optimal in the way they exploit information and do not generally consider uncertainties. We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Data assimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area. The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational data assimilation system. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and Earth Observation data (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes. In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (> 2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data. The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps

Enteroendocrine K-cells exert complementary effects to control bone quality and mass in mice

International audienceThe involvement of a gut-bone axis in controlling bone physiology has been long suspected, although the exact mechanisms are unclear. We explored whether glucose-dependent insulinotropic polypeptide (GIP)-producing enteroendocrine K-cells were involved in this process. The bone phenotype of transgenic mouse models lacking GIP secretion (GIP-GFP-KI) or enteroendocrine K-cells (GIP-DT) was investigated. Mice deficient in GIP secretion exhibited lower bone strength, trabecular bone mass, trabecula number and cortical thickness, notably due to higher bone resorption. Alterations of microstructure, modifications of bone compositional parameters, represented by lower collagen cross-linking were also apparent. None of these alterations were observed in GIP-DT mice lacking enteroendocrine K-cells, suggesting that other K-cell secretory product acts to counteract GIP action. To assess this, stable analogues of the known K-cell peptide hormones, xenin and GIP, were administered to mature NIH Swiss male mice. Both were capable of modulating bone strength mostly by altering bone microstructure, bone gene expression and bone compositional parameters. However, the two molecules exhibited opposite actions on bone physiology, with evidence that xenin effects are mediated indirectly, possibly via neural networks. Our data highlight a previously unknown interaction between GIP and xenin, which both moderate gut-bone connectivity

GPGPU computation and visualization of three-dimensional cellular automata

This paper presents a general-purpose simulation approach integrating a set of technological developments and algorithmic methods in cellular automata (CA) domain. The approach provides a general-purpose computing on graphics processor units (GPGPU) implementation for computing and multiple rendering of any direct-neighbor three-dimensional (3D) CA. The major contributions of this paper are: the CA processing and the visualization of large 3D matrices computed in real time; the proposal of an original method to encode and transmit large CA functions to the graphics processor units in real time; and clarification of the notion of top-down and bottom-up approaches to CA that non-CA experts often confuse. Additionally a practical technique to simplify the finding of CA functions is implemented using a 3D symmetric configuration on an interactive user interface with simultaneous inside and surface visualizations. The interactive user interface allows for testing the system with different project ideas and serves as a test bed for performance evaluation. To illustrate the flexibility of the proposed method, visual outputs from diverse areas are demonstrated. Computational performance data are also provided to demonstrate the method’s efficiency. Results indicate that when large matrices are processed, computations using GPU are two to three hundred times faster than the identical algorithms using CPU