Attractive properties of Sentinel-2 MSI for monitoring vegetation dynamics include its reasonable spatial resolution, spectral sampling and revisit capabilities. But the large number of factors that affect vegetation reflectance spectra and the impact of clouds mean that the likely uncertainties when monitoring crops and forest resources are likely still relative high. One way of improving on this is to use data assimilation with optical radiative transfer models as observation operators. This allows for fuller use of the measured signals (rather than e.g. vegetation indices using only a few channels), for better use of multi-temporal data and ultimately reduced uncertainties in vegetation and soil state. In this paper, we discuss state estimation approaches and data assimilation in particular. We present an Earth Observation Data Assimilation System (EO-LDAS) which is an implementation of such ideas. EO-LDAS, developed under ESA funding, uses the semi-discrete radiative transfer model as observation operator and a temporal regularisation constraint as dynamic model and allows state variables to be solved at a daily time step with associated uncertainty. As well as temporal interpolation, the system provides a reduction in uncertainty of around 2 over a scenario MSI data alone. The outlook for the application of such methods is also discussed.

Simultaneous observations of thermal radiative fluxes and radiances from the surface (Atmospheric Radiation Measurement Mobile Facility, Niamey) and top of atmosphere (Geostationary Earth Radiation Budget (GERB) instrument) during the Radiative Atmospheric Divergence using ARM Mobile Facility, GERB data, and AMMA Stations experiment are compared with results from a radiative transfer model (Edwards‐Slingo). Emphasis is placed on diagnosing the accuracy of the cloud‐free radiation measurements using multiple instruments at the surface. The surface forcing from aerosol is found to regularly exceed 20 Wm−2, and reached ∼100 Wm−2 during the March 2006 dust storm. Equivalent comparisons are made with top of atmosphere (TOA) measurements but here radiance closure is not achieved. A disagreement is found between the angular anisotropy derived from GERB products and that from radiative transfer (RT) calculations. A hybrid TOA radiative flux time series is created using RT‐calculated TOA anisotropy and GERB‐observed TOA radiance. At 1100 UT (local noon), this hybrid flux differs from the Edition 1 GERB product by a positive difference in the range ∼0–10 Wm−2. Three collections of fluxes exist to calculate column‐integrated atmospheric heating (divergence) from surface and TOA fluxes. The first two are fluxes from observations only or from RT calculations only. The third is a combination of RT calculation and observed fluxes that includes the hybrid flux. The resulting divergences are binned by sonde launch times and averaged over the year. The range of divergence during a day depends on the flux collection used (−200 to −111 Wm−2, −212 to −116 Wm−2, or −205 to −112 Wm−2) for observations only, for RT calculations only, or for observation‐calculation fluxes. All estimates agree as to the interday variation being larger than that of intraday variability.

In the Radiative Atmospheric Divergence Using ARM Mobile Facility GERB and AMMA Stations (RADAGAST) project we calculate the divergence of radiative flux across the atmosphere by comparing fluxes measured at each end of an atmospheric column above Niamey, in the African Sahel region. The combination of broadband flux measurements from geostationary orbit and the deployment for over 12 months of a comprehensive suite of active and passive instrumentation at the surface eliminates a number of sampling issues that could otherwise affect divergence calculations of this sort. However, one sampling issue that challenges the project is the fact that the surface flux data are essentially measurements made at a point, while the top‐of‐atmosphere values are taken over a solid angle that corresponds to an area at the surface of some 2500 km2. Variability of cloud cover and aerosol loading in the atmosphere mean that the downwelling fluxes, even when averaged over a day, will not be an exact match to the area‐averaged value over that larger area, although we might expect that it is an unbiased estimate thereof. The heterogeneity of the surface, for example, fixed variations in albedo, further means that there is a likely systematic difference in the corresponding upwelling fluxes. In this paper we characterize and quantify this spatial sampling problem. We bound the root‐mean‐square error in the downwelling fluxes by exploiting a second set of surface flux measurements from a site that was run in parallel with the main deployment. The differences in the two sets of fluxes lead us to an upper bound to the sampling uncertainty, and their correlation leads to another which is probably optimistic as it requires certain other conditions to be met. For the upwelling fluxes we use data products from a number of satellite instruments to characterize the relevant heterogeneities and so estimate the systematic effects that arise from the flux measurements having to be taken at a single point. The sampling uncertainties vary with the season, being higher during the monsoon period. We find that the sampling errors for the daily average flux are small for the shortwave irradiance, generally less than 5 W m−2, under relatively clear skies, but these increase to about 10 W m−2 during the monsoon. For the upwelling fluxes, again taking daily averages, systematic errors are of order 10 W m−2 as a result of albedo variability. The uncertainty on the longwave component of the surface radiation budget is smaller than that on the shortwave component, in all conditions, but a bias of 4 W m−2 is calculated to exist in the surface leaving longwave flux.

An overview is presented of the meteorological and thermodynamic data obtained during the Radiative Atmospheric Divergence using Atmospheric Radiation Measurement (ARM) Mobile Facility, Geostationary Earth Radiation Budget (GERB) data, and African Monsoon Multidisciplinary Analysis (AMMA) stations (RADAGAST) experiment in Niamey, Niger, in 2006. RADAGAST combined data from the ARM Program Mobile Facility (AMF) at Niamey airport with broadband satellite data from the GERB instrument on Meteosat‐8. The experiment was conducted in collaboration with the AMMA project. The focus in this paper is on the variations through the year of key surface and atmospheric variables. The seasonal advance and retreat of the Intertropical Front and the seasonal changes in near‐surface variables and precipitation in 2006 are discussed and contrasted with the behavior in 2005 and with long‐term averages. Observations from the AMF at Niamey airport are used to document the evolution of near‐surface variables and of the atmosphere above the site. There are large seasonal changes in these variables, from the arid and dusty conditions typical of the dry season to the much moister and more cloudy wet season accompanying the arrival and intensification of the West African monsoon. Back trajectories show the origin of the air sampled at Niamey and profiles for selected case studies from rawinsondes and from a micropulse lidar at the AMF site reveal details of typical atmospheric structures. Radiative fluxes and divergences are discussed in the second part of this overview, and the subsequent papers in this special section explore other aspects of the measurements and of the associated modeling.

The FLuorescence EXplorer (FLEX) mission proposes to launch a satellite for the global monitoring of steady-state chlorophyll fluorescence in terrestrial vegetation. Fluorescence is a sensitive probe of photosynthetic function in both healthy and physiologically perturbed vegetation, and a powerful non-invasive tool to track the status, resilience, and recovery of photochemical processes and moreover provides important information on overall photosynthetic performance with implications for related carbon sequestration. The early responsiveness of fluorescence to atmospheric, soil and plant water balance, as well as to atmospheric chemistry and human intervention in land usage makes it an obvious biological indicator in improving our understanding of Earth system dynamics. The amenability of fluorescence to remote, even space-basedobservation qualifies it to join the emerging suite of space-based technologies for Earth observation. FLEX would encompass a three-instrument array for measurement of the interrelated features of fluorescence, hyperspectral reflectance, and canopy temperature. FLEX would involve a space and ground-truthing program of 3-years duration and would provide data formats for research and applied science.

The FLuorescence EXplorer (FLEX) mission proposes to launch a satellite for the global monitoring of steady-state chlorophyll fluorescence in terrestrial vegetation. Fluorescence is a sensitive probe of photosynthetic function in both healthy and physiologically perturbed vegetation, and a powerful non-invasive tool to track the status, resilience, and recovery of photochemical processes and moreover provides important information on overall photosynthetic performance with implications for related carbon sequestration. The early responsiveness of fluorescence to atmospheric, soil and plant water balance, as well as to atmospheric chemistry and human intervention in land usage makes it an obvious biological indicator in improving our understanding of Earth system dynamics. The amenability of fluorescence to remote, even space-basedobservation qualifies it to join the emerging suite of space-based technologies for Earth observation. FLEX would encompass a three-instrument array for measurement of the interrelated features of fluorescence, hyperspectral reflectance, and canopy temperature. FLEX would involve a space and ground-truthing program of 3-years duration and would provide data formats for research and applied science.

The linear mixture model has been extensively used in the analysis of satellite data, especially for characterization of surface cover at subpixel scales. In the model, a multispectral signal is assumed to comprise a weighted sum of characteristic spectra, the weights corresponding to fractional coverage. The residual is almost invariably assumed to be independent of the weights, and usually taken to arise from Gaussian noise; the maximum-likelihood estimate of the abundances is then found by minimizing a quadratic objective function. Nonuniform sampling of the radiance distribution within the field of view means, however, that there is some dependence of the residual on the true surface abundances, when it is understood that these are simple area averages. We account for this signal-dependent noise by incorporating the modified variance-covariance matrix of the residual into the quadratic objective function. It is shown that, despite the increased complexity of the new objective function, it is minimized by the traditional estimator. This is true whether or not we require the estimated abundances to sum to unity, i.e., whether the minimization is constrained or unconstrained. The constrained and unconstrained estimators are here treated together by deploying a "sum-to-one" parameter.

The European Space Agency's Project for On-Board Autonomy is intended to demonstrate a range of innovations in the design, construction, and operation of small satellites. It carries a number of scientific instruments, the most advanced of which is the Compact High-Resolution Imaging Spectrometer. A typical nadir image is 13 km /spl times/13 km in size and has 18 narrow spectral channels at 17-m spatial resolution. When operated at 34-m spatial resolution, the instrument can capture data in 62, almost contiguous, spectral channels. The platform is highly manoeuvrable: along-track pointing allows a given site to be imaged five times during a single overpass, while across-track pointing ensures that the revisit time for a site of interest is less than a week. This unique combination of spectral and angular sampling provides a rich source of data with which to study environmental processes in the atmosphere and at the Earth's surface.

Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions.

Current Earth Observation (EO) satellites have limited ability to discriminate variables necessary for predicting the impact of nutrient loss on water quality. This project investigates whether new hyperspectral EO sensors provide enhanced accuracy and more detailed spatial context to aid the characterization of crop type and fractional ground cover. These are key input parameters in models of field and catchment scale nitrogen, phosphorus and sediment loss to streams. Such contemporary information, when coupled with supporting (e.g. digital elevation model) data, will help identify vulnerable areas and provide a framework for developing pollution risk assessment tools based on the spatial co-location of risk factors (slope, erodable soils, crop type).

Spectral matching and linear mixture modeling techniques have been applied to synthetic imagery and AVIRIS SWIR imagery of a semiarid rangeland in order to determine their effectiveness as mapping tools, the synergism between the two methods, and their advantages, and limitations for rangeland resource exploitation and management. Spectral matching of pure library spectra was found to be an effective method of locating and identifying endmembers for mixture modeling although some problems were found with the false identification of gypsum. Mixture modeling could accurately estimate proportions for a large number of materials in synthetic imagery; however, it produced high variance estimates and high error estimates when presented with all nine AVIRIS endmembers because of high noise levels in the imagery. The problem of which endmembers to select was addressed by implementing a mixture model that allowed estimation of the errors on the proportions estimates, discarding the endmembers with the highest errors, recomputing the errors, and the proportions estimates, and iterating this process until the mixture maps were relatively free from noise. This methodology ensured that the lowest contrast materials were discarded. The inevitable confusion that followed was monitored the using the maps produced by spectral matching. Spectral matching was more effective than mixture modeling for geological mapping because it allowed identification and mapping of the relatively pure regions of all the surficial materials that exert an influence on the spectral response. The maps of the different clay minerals were of considerable value for mineral exploration purposes. Conversely, spectral matching was less useful than mixture modeling for rangeland vegetation studies because a classification of all pixels is needed and abundance estimates are required for many applications. Mixture modeling allowed identification of both nonphotosynthetic and green vegetation cover and thus total cover. Though the green vegetation mixture map appears to be very precise, the nonphotosynthetic vegetation estimates were poor.

New satellite instruments that sample top-of-atmosphere radiance at a number of view angles offer the potential for improved retrieval of atmospheric aerosol opacity, land surface bidirectional reflectance, and biophysical parameters. This paper presents a method for simultaneous retrieval of aerosol opacity and land surface bidirectional reflectance, which utilizes the dual view capability of the second Along-Track Scanning Radiometer (ATSR-2). Analysis of a physically based model of light scattering results in two simple equations defining possible spectral variation of land surface bidirectional reflectance distribution function (BRDF). These are used as constraints to anew inversion of a model of atmospheric scattering to simultaneously retrieve atmospheric aerosol opacity and bidirectional reflectance from top-of-atmosphere radiance. The inversion assumes no a priori knowledge of the land surface cover. Sensitivity is evaluated using both simulated and field-measured data to reproduce expected ATSR-2 observations. Where an atmosphere of known aerosol scattering properties, but of unknown optical depth, is available, results show mean absolute error in retrieval of aerosol opacity of the greater of 0.02 or 15% relative error and bidirectional reflectance retrieval at 55 nm to an accuracy of <0.01. Where a number of candidate aerosol models are available, results show discrimination of dominant aerosol type is possible in 95% of cases considered. The methods perform best over dark surfaces, such as vegetation, but show accurate retrieval over soil and pixels containing a number of cover types.

A method for atmospheric correction of ATSR-2 optical imagery is presented which exploits the sensor's dual view capability. A general model of land surface bidirectional reflectance is developed and used as a constraint to simultaneously retrieve atmospheric aerosol opacity and bidirectional reflectance from top of atmosphere radiance. The inversion assumes no a priori knowledge of the land surface cover, Validation has been performed over boreal forest, showing close agreement between ATSR-2 optical depth retrieval and field measurements.

A linear mixture model was applied to Landsat Thematic Mapper data to map iron-oxide content over the northern part of the Namib Sand Sea, Namibia. Field samples were collected to calibrate the remotely sensed iron oxide proportions to dithionite extractable Fe in mg g−1, the in situ proportions correlating strongly with the remotely sensed estimates (r=0.91). The results reveal a region of high Fe-oxide concentration in the east, an area of low Fe-oxide concentration in the west, separated by a mixing zone. We interpret this to indicate that there is more than one sand source contributing to the sand sea.

The problem of a plane parallel atmosphere bounded below by a reflecting surface is considered. It is well known that when that surface reflects isotropically, the radiation distribution emerging from the top of the atmosphere may be expressed in terms of the scattering and transmission functions of that atmosphere. For more general non-Lambertian surfaces this is not usually the case. However, it will be shown that for a certain class of surfaces this can still be done, and the problem reduced to evaluating certain integrals; the integrands of which are the product of ground reflectance terms and either the scattering or transmission functions of the atmosphere. The models described in this note were derived in order to provide “closed form” solutions with which to validate coupled atmospheric-surface radiation code, but they have a certain intrinsic interest beyond that, and may find application in sensitivity studies of retrieval algorithms for data from multi-view satellite instruments.

A linear transformation was recently recommended for application on hyperspectral imagery. This note shows that the method is completely equivalent to extracting proportional ground cover by standard means, but is less efficient than the more usual methods of spectral unmixing.

This paper presents a method for retrieval of aerosol opacity and land surface bi-directional reflectance using data from the second Along-Track Scanning Radiometer (ATSR-2). The method is based on inversion of a physically based model of land surface reflectance to provide a constraint on the spectral variation of reflectance. Validation is performed for a range of cover types