The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) sub-group of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAIe) (RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = -0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods.

Accurate monitoring of albedo trends over snow is essential to evaluate the consequences of the global snow cover retreat on Earth’s energy budget. Satellite observations provide the best way to monitor these trends globally, but their uncertainty increases over snow. Besides, different products sometimes show diverging trends. A better assessment of the fitness of satellite products for monitoring snow albedo trends is needed. We analyze the consistency of black-sky albedo estimates from global long-term products over snow: AVHRR-based (CLARA-A2.1, GLASS-v4.2), MODIS-based (MCD43C3-v6.1/v6, GLASS-v4.2), MISR-based (MIL3MLSN-v4) and multi-sensor (C3S-v1/v2). We use MCD43C3-6.1 as the reference based on a previous comparison against in-situ measurements. CLARA-A2.1 is the one most consistent with MCD43C3, but has a low coverage in high latitudes and an artificial albedo decrease since 2015. The study shows the limitations of MIL3MLSN, GLASS, and C3S multi-sensor products over snow. MIL3MLSN has a too-low coverage of albedo over snow. GLASS-AVHRR overestimates albedo in regions with seasonal snow due to delayed snowmelt and underestimates it in permanently snow-covered regions. GLASS-MODIS is more consistent with MCD43C3 at mid-latitudes, but also underestimates albedo in regions with permanent snow and has an increase of missing values after 2011. Both GLASS datasets are temporally inconsistent with the other products. Despite the improvements from v1 to v2, C3S-v2 has the largest negative bias over snow and discontinuities in the transitions between sensors. The study evidences the difficulties of AVHRR products to provide stable snow albedo estimates in polar regions, particularly before 2000.

This presentation will focus on GBOV activities, introducing the service, its distribution system, and its growing network as well as its achievements after the first operation phase. Some examples from the GBOV ground stations will be reported to illustrate the instrument setup and datasets and more importantly the lessons learnt from the first phase. GBOV phase 1 has been a success thanks to a large community of scientist providing high quality dataset. GBOV phase 2 is willing to strengthen its relationship with ground network and go further in stimulating interactions with the global community.

To better characterize the temporal dynamics of vegetation biophysical variables, a variety of automated in situ measurement techniques have been developed in recent years. In this study, we investigated automated digital hemispherical photography (DHP) and wireless quantum sensors, which were installed at two sites under the Copernicus Ground Based Observations for Validation (GBOV) project. Daily estimates of plant area index (PAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) were obtained, which realistically described expected vegetation dynamics. Good correspondence with manual DHP and LAI-2000 data (RMSE = 0.39 to 0.90 for PAI, RMSE = 0.07 for FAPAR) provided confidence that the investigated approaches can deliver data of comparable quality to traditional in situ measurement techniques.

The Copernicus Global Land Service (CGLS) ensures operational monitoring of the continental ecosystems through the provision of global terrestrial geophysical variables. The CGLS portfolio covers several satellite-based land products at different spatial and temporal resolution related to energy, vegetation, water, and cryosphere. Before delivering the products to the users a quality assessment is conducted to assure that the products meet the user requirements and to inform the users on the quality, uncertainties and limitations attached to them. The exhaustive quality assessment follows international validation good practices guidelines over an evaluation period (typically 2 years) and is complemented by an operational quality evaluation of the latest delivered products. This paper describes the overall strategy and criteria used to perform the quality assessment of the CGLS land vegetation and water products to reach the highest level of validation and hence confidence in the quality of the CGLS products.

The Copernicus Ground-Based Observations for Validation (GBOV) service aims to develop and distribute robust in-situ datasets from a selection of ground-based monitoring sites for a systematic and quantitative validation of Earth Observation (EO) land products. The EO land products of particular interest are those from the Copernicus Global Land Service (CGLS), but GBOV data usage is not restricted to CGLS products and is fully open to the entire community, following the general Copernicus data policy. In this paper, a global overview of GBOV service is shown, and the attention is focused on the vegetation type products: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR) and the Fraction of Covered ground (FCover). An introduction of the algorithm implemented to compute these products, mainly from upscaling of ground-based observation with high resolution satellite images, is presented.

Land surface albedo defines the fraction of short-wave radiation reflected by the Earth's surface and controls the surface energy balance; thus, it is important for environmental and climate scientific communities. Remote sensing is the only means to globally map land surface albedo, however for it to be of use to the aforementioned communities, it must be accurate with respect to Global Climate Observing System (GCOS) requirements. Sources of error are introduced in each step of the provision of land surface albedo products, whereby this letter intends to investigate sources of error introduced by the narrow-band-to-broad-band conversion formula step. The radiative transfer modeling of vegetation is used to simulate spectral albedo over complex 3-D vegetation canopies; then narrow-band-to-broad-band conversion formulas for numerous sensors are applied on the spectral albedo to compute the broad-band albedo (BBA), and the accuracy of formulas is investigated. Results indicate that the effectiveness of conversion formulas is determined by the sensor, depending on the placement and number of the sensor wavebands, the ecosystem complexity, and the broad-band range of the BBA.

Satellite-derived time series of the fraction of absorbed photosynthetically active radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global data sets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyzes three multitemporal fAPAR data sets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two data sets are currently used by the Joint Research Centre—Monitoring Agricultural ResourceS Unit (JRC-MARS) for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three$10^{\circ} \times 10^{\circ}$regions with different eco-climatic characteristics (Niger, Brazil, and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between data sets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover-related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between data sets were identified in differences in the fAPAR retrieval algorithm definitions.

Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquired multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces for a decade at global scale. For the last ten years, scientists have used multi-spectral data or terrestrial geophysical products for characterizing the state of the global system and its variability. Our paper shows highlights of several achievements of the use of MERIS data over terrestrial surfaces but specifically focuses on regional to global scale applications. We first summarize daily operational biophysical parameters and present examples of their uses for the monitoring of land surface states and changes, especially related to ECVs. In addition, specific projects for deriving a series of land cover maps will be presented and we conclude on the MERIS data exploitation and highlights future applications.

The study has developed an interactive mission benefit analysis (MBA) tool that allows instantaneous evaluation of a range of potential mission designs. The designs are evaluated in terms of their constraint on carbon and water fluxes through calibration of a terrestrial bisphere model. The constraint is quantified by methematically rigorous uncertainty propagation in CCDAS. Applying the MBA tool, the study showed that the benefit of FAPAR data is most pronounced for hydrological quantities and moderate for quantities related to carbon fluxes from ecosystems. In semi-arid regions, where vegetation is strongly water limited, the constraint delivered by FAPAR for hydrological quantities was especially large, as documented by the results for Africa and Australia. Sensor resolution is less critical for successful data assimilation, and with even relatively short time series of only a few years, significant uncertainty reduction can be achieved.

This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This biophysical variable acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product includes (1) the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), (2) the assessment of the performance for detecting various events (verisimilitude) against the time-series of well-known land surfaces and (3) the direct comparisons of the FAPAR MERIS values to similar products generated by other independent sensors, like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectroradiometer (MODIS), and against ground-estimates of FAPAR performed over various vegetation types.

This paper evaluates 1) to what extent one-dimensional (1-D) models can be used to represent the magnitude and directionality of the surface reflectance field of heterogeneous canopy targets at different spatial resolutions, and 2) whether this usage results in significant biases in the estimation of the corresponding state variables. It will be seen that when both the 1-D and three-dimensional (3-D) models account for all features of the measured radiation field, then-in the absence of further information regarding the nature and structure of the target-the use of a 3-D model may amount to an over-interpretation of the available data. The simplified surface structure formulation contained within the 1-D model, on the other hand, may affect the values of the state variables that such models will retrieve. This is because the shape of the reflectance anisotropy of the 3-D target is almost always different from that of a structurally homogeneous (1-D) canopy with the same state variable values but no foliage clumping. By consequence the 1-D canopies that are capable of mimicking the bell (or bowl) shaped reflectance anisotropy of 3-D targets will tend to feature lower leaf area index, higher soil albedo and, in particular, predominantly erectophile (or plagiophile) leaf normal distributions.

For pt.I see ibid., vol.40, no.7, p.1560-73 (2002). The Multi-angle Imaging SpectroRadiometer (MISR) instrument on board the Terra platform offers the capability of acquiring reflectance data on any Earth target in four spectral bands, from nine different directions, in at most seven minutes, at a spatial resolution adequate for the monitoring of the status of terrestrial surfaces. This paper describes the implementation of a physical and mathematical approach to design a simple two-dimensional algorithm dedicated to the interpretation of data collected by this instrument. One dimension fully exploits the spectral information in the blue, red and near-infrared bands while the other dimension capitalizes on the multiangular capability of MISR to assess the anisotropic behavior of terrestrial surfaces with respect to solar radiation. The spectral information is derived following an approach proposed for single angle instruments, such as the MEdium Resolution Imaging Spectrometer (MERIS), the Global Imager (GLI), the Sea-viewing Wide Field-of-view Sensor (SeaWIFS) and VEGETATION. The access to simultaneous multiangular observations from MISR allows extending this approach. This strategy delivers an estimate of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which pertains to vegetation photosynthetic activity and is a measure of the presence and density of vegetation.

The recent availability of quasi-simultaneous multispectral and multidirectional measurements from space, as provided by the Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra platform, offers new and unique opportunities to document the anisotropy of land surfaces at critical solar wavelengths. This paper presents simple physical principles supporting the interpretation of the anisotropy of spectral radiances exiting terrestrial surfaces in terms of a signature of surface heterogeneity. The shape of the anisotropy function is represented with two model parameter values which may be mapped and interpreted in their own right. The value of one of these parameters also permits identifying geophysical conditions where the surface heterogeneity becomes significant and where three-dimensional (3D) radiation transfer effects have to be explicitly accounted for. This paper documents these findings on the basis of results from a number of 3D radiation transfer model simulations. The latter are used to perform an extensive sensitivity study which includes issues related to the scale of investigation. A preliminary validation of these results, conducted with a dataset collected by the AirMISR instrument over the Konza prairie, is also discussed.

This paper describes the implementation of a physical and mathematical approach to designing advanced vegetation indices optimized for future sensors operating in the solar domain such as the medium resolution imaging spectrometer (MERIS), the global imager (GLI), and the VEGETATION instrument, and proposes an initial evaluation of such indices. These optimized indices address sensor-specific issues such as dependencies with respect to the actual spectral response of the sensor as well as the natural sensitivity of remote sensing measurements to illumination and observing geometry, to atmospheric absorption and scattering effects, and to soil color or brightness changes. The derivation of vegetation index formulae optimized to estimate the same vegetation property fraction of absorbed photosynthetically active radiation (FAPAR) from data generated by different sensors allows the comparison of their relative performances compared with existing vegetation indices, both from a theoretical and experimental point of view and permits the creation of global products, as well as the constitution of long time series from multiple sensors.

The Leaf Area Index (LAI) of a plant canopy is an important environmental parameter required by various applications. It would be highly desirable to be able to estimate this parameter on the basis of satellite remote sensing data in the optical spectral range. However, LAI affects the propagation of light in a plant canopy (and therefore its measurable reflectance factor) exclusively through a boundary condition of the equation of radiation transfer. It is shown that LAI may be retrievable accurately and reliably only when the canopy is optically thin enough to allow a significant illumination of the underlying soil, and when the optical properties of this soil are such that the radiance field emerging from this level is sufficiently different from that which would be exhibited by a deeper canopy. The combinations of radiative conditions (soil and plant properties) necessary for the reliable and accurate retrieval of the LAI on the basis of remote sensing reflectance data acquired above the canopy in the red and near-infrared spectral regions are investigated and documented with the help of simulation studies. These results show the retrievability of LAI from remote sensing data in optimal situations, however.