Conference Agenda

Emission and backscattering signals of land surfaces at different frequencies have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. Such a capability also improves our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content for ecosystem monitoring. We present here a prototype Community Land Active Passive Microwave Radiative Transfer Modelling platform (CLAP) for simulating both backscatter (T_B) and emission (σ⁰) signals of land surfaces, in which the CLAP is backboned by an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering, and the Tor Vergata model for modelling vegetation scattering and the interaction between vegetation and soil parts. The CLAP was used to simulate both ground-based and space-borne multi-frequency microwave measurements collected at the Maqu observatory on the eastern Tibetan plateau. The ground-based systems include a scatterometer system (1-10 GHz) and an L-band microwave radiometer. The space-borne measurements are obtained from the X-band and C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) radiation observations. The impact of different vegetation properties (i.e., structure, water and temperature dynamics) and soil conditions (i.e., different moisture and temperature profiles) on the microwave signals were investigated as well as by CLAP simulation for understanding factors that account for diurnal variations of the observed signals. The CLAP is expected to improve our capability for understanding and applying current and future multi-frequency space-borne microwave systems (e.g. those from ROSE-L and CIMR) for land monitoring.

As the spatial resolution of space-borne spectrometers is rapidly improving and moving towards sub-kilometer scale, three dimensional (3D) cloud effects become more prominent in the retrieval of atmospheric trace gases. Currently in the Sentinel-5P (S5P) nitrogen dioxide (NO₂) product (3.6 km x 5.6 km² resolution) the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm is used to retrieve a one dimensional (1D) horizontal homogeneous Lambertian cloud layer for cloud correction. However, in reality clouds are 3D objects, they are not spatially homogeneous in brightness and they can have effects on neighboring clear-sky pixels by casting shadows on lower clouds or on the ground surface or by scattering light into the pixels.

In the S5P NO₂ retrieval algorithm the retrieved slant column density is translated to a vertical column density (VCD) by correcting for the light path using pre-calculated air-mass factors (AMF) from a radiative transfer model, using surface and cloud parameters as input. When a cloud shadow is cast over a clear-sky pixel the downward light intensity is reduced, altering the average observed light path. This lowers the sensitivity of the measurement for the lower atmospheric layers and thus changes the AMF. As this effect is not accounted for in the current 1D AMF calculation, the AMF used in the retrieval is different from the true AMF. The effect would be similar to an overestimation of the surface albedo in the AMF calculation, in turn potentially leading to an overestimation of the vertically resolved AMF at surface levels and an underestimation of the NO₂ VCD, when sufficient NO₂ is present in the lower troposphere.

This study attempts to observe such cloud shadow effects in the AMF and VCD fields in the S5P NO₂ data with focus on hot-spot areas during winter when generally more clouds are present and the cast cloud shadow surface areas are relatively high due to higher solar zenith angles. SUOMI-NPP VIIRS data can be used to identify the pixels affected by cloud shadows. Further steps are to design a correction method for these cloud shadow effects in the NO₂ algorithm.

With the rapid economic growth, China's ports and shipping industry has achieved unprecedented development, while aggravating air pollution. Due to the large number of domestic inland river vessels, limited legislation for emission control and no monitoring infrastructure, information on inland river vessel emissions is very limited. Taking the Yangtze River in the region of Nanjing as research area, a ship emission inventory was compiled based on real-time information received from Automatic Identification System (AIS) signals, ship-related basic data provided by China Classification Society (CCS) database and relevant data from field research. A method is developed to use AIS signals to calculate ship emissions per vessel. The total estimated ship emissions have been calculated in the observation area from September 2018 to August 2019 for NO_x, SO₂, PM₁₀ and PM_2.5. The calculated inland ship NOx emissions have been compared to the total NOx emissions in the same region derived from TROPOMI observations using DECSO. The DECSO algorithm (Daily Emissions Constrained by Satellite Observations) is an inversion algorithm using TROPOMI data. By comparing the ship emission inventory with DECSO, the result shows a consistent spatial distribution with riverine cities having higher NO_x pollution than non-riverine cities. With this comparison we analyzed the relative impact of ship emissions on densely populated regions around the river. The same method of deriving ship emissions will be used for the port of Rotterdam, to derive the impact of ship emission in this region.

The Arctic is experiencing rapid climate change. The increasing temperature not only reduces the sea-ice extent but will also have doubled the number of lightning flashes by the end of the century. The increase of lightning will cause more wildfires. Both contributions give rise to nitrogen oxides (NO_x) emissions.

In this study, we track and estimate three-year (2019-2021) Arctic NO₂ emissions by combing the TROPOspheric Monitoring Instrument (TROPOMI) observations, Visible Infrared Imaging Radiometer Suite (VIIRS) data, and the Vaisala’s Global Lightning Dataset (GLD360).

The consecutive overlapping orbits of TROPOMI passing over the Arctic provide unique opportunities for tracking the lightning NO₂ (LNO₂) and calculating both LNO₂ lifetime and production efficiency. Previous studies focused on the LNO₂ emissions in the tropical and mid-latitude regions and estimated the global LNO_x within the range of 2 to 8 T N yr^-1. This study can add the missing LNO₂ productions in high latitudes and highlight the potential of TROPOMI as well as future satellite missions for monitoring Arctic NO_x emissions.

During the Research on the Simulation and Mechanism of the impacts of Black Carbon on Climate and Environment atmospheric measurement campaign carried out near Nanjing, China in June 2018, a lightweight, accurate nitrogen dioxide (NO₂) sensor was attached to a quadcopter to measure vertical profiles of NO₂. Between 1 and 14 June 2018, ∼50 vertical NO₂ profiles were measured inside the planetary boundary layer up to an altitude of 900-1300 meters during 13 subsequent measurement days. Six NO₂ soundings were conducted on a daily basis at approximately 8 AM (morning), 12 & 4 PM (afternoon), 8 PM (evening) and 12 & 4 AM (night). The NO₂ measurements were calibrated using a scaling factor derived from a side-by-side inter comparison with a commercial NO₂ analyzer operated by NUIST prior to the start of the campaign. These measurements clearly demonstrate the diurnal cycle of NO₂, including the emergence of elevated concentrations close to the surface during the night and early morning and the mixing of the boundary layer from sunrise onward resulting in flat NO₂ vertical profile shapes with lower concentrations. As a result, this type of measurement could play an important role in the validation of future geostationary satellites since the diurnal cycle of NO₂ will have an impact on the accuracy of the satellite retrievals.

An accurate weather forecast requires abundant and reliable input data in the process of ingestion into Numerical Weather Prediction Model (NWPM). The sources of data are heterogeneous: ground-based radars, Global Navigation Satellite System (GNSS) products, radiosonde, in-situ weather stations, and many more.

Unluckily, in several countries, meteorological data are scarce. One example is sub-Saharan Africa, where there is, on average, only one weather station every 26,000 square kilometers. This number is eight times lower than the World Meteorological Organization (WMO) recommendation. Moreover, According to the World Bank Organization, 54% of African weather stations cannot produce reliable results, while 71% of upper-air weather stations cannot capture accurate data.

One solution can be provided by exploiting space-borne Synthetic Aperture Radar (SAR) images. In the field of Interferometric SAR (InSAR), particularly for deformation monitoring, the atmospheric delay on the radar signal has always been considered a disturbance to be mitigated. However, this additional delay is a direct consequence of different conditions in pressure, temperature, and humidity of the medium traveled by the signal (the troposphere). Thus, it might be helpful information for meteorological models.

The advantages of SAR in meteorology are manifolds. First of all, large-scale measurements spanning thousands of square kilometers are beneficial to NWPM. Moreover, SAR can provide maps at very high resolution, in the order of half a kilometer, which is currently unmatched by any other instrument.

This poster will present a technique to estimate a set of Atmospheric Phase Screens (APS) from a stack of C-Band Sentinel-1 SAR data. The algorithm exploits the so-called Phase Linking algorithm, which can optimally estimate the interferometric phase over distributed and permanent targets. The joint exploitation of the two is the critical factor enabling the generation of dense and uniform maps spanning thousands of kilometers, even over highly decorrelating areas such as tropical forests.

An error in the knowledge of the satellite’s orbits during the acquisitions will induce an aberration in the estimated APS called Orbital Phase Screen (OPS). The OPS will corrupt the low spatial frequencies of the APS, therefore it must be removed. The proposed workflow includes a cross-calibration that relies on a network of GNSS stations installed on the imaged area. The cooperation of SAR and GNSS allows for a reliable estimation of the OPS and its subsequent removal from the atmospheric product. This procedure leads to an error-free APS without the risk of filtering the low spatial frequency components of the APS itself.

The procedure is first tested in South Africa by generating delay maps as large as 210,000 square kilometers. The area shows severe decorrelation and steep topography. Still, the proposed algorithm could produce a reliable estimate of the differential atmospheric delay. The orbital correction routine is also employed, and the derived APS are validated using an external NWPM (GACOS). Some spatial statistics are derived from the delay maps, and we show that they follow theoretical models in the literature.

Finally, we propose a second validation site: Sweden. In this area, the seasons and in particular snow formation and melting determine the correlation level between SAR images. Nevertheless, our approach is still able to produce satisfactory results. Several tens of GNSS stations are available in this area, allowing for a very accurate OPS estimation and subsequent validation of the APS maps.

Synthetic aperture radar (SAR) remote sensing has gained a prominent position concerning remote sensing of forest scenarios, largely due to its all-weather observation capabilities, large coverage, and accuracy [1]–[3]. Long wavelength fully polarimetric SAR systems are particularly interesting concerning the exploration of the vertical structure of the illuminated media, like forests, ice, and snow, by virtue of the combination of penetration capabilities and sensitivity to different scattering mechanisms [4]–[10]. SAR tomography (TomoSAR) technology can obtain three-dimensional vertical backscattering power distribution and reconstruct the vertical structure of the illuminated media[5], [7]. Different from the common SAR imaging mode, with the aid of a certain of SAR acquisitions, TomoSAR can resynthesize aperture perpendicular to the slant-range direction and providing a powerful technical tool for reconstructing the three-dimensional structure. That is also a key technology for the forthcoming Earth Explorer mission BIOMASS to map global forest height and above over ground biomass (AGB), as well as underlying topography[11]. In this context, the TomoSense experiment was organized by the European Space Agency (ESA) to provide the scientific community with unprecedented data to study the features of radar scattering from temperate forests, comprising tomographic and fully polarimetric SAR surveys at P-, L-, and C-band, acquired in mono- and bistatic mode by simultaneously flying two aircraft [10], [12]. The data were acquired at the Kermeter site in the National Park Eifel in North-Rine Westphalia in Germany. The TomoSense dataset also includes Terrestrial Laser Scanning (TLS) and Airborne Lidar Scanning (ALS) products to provide a reliable reference for validation.

In this paper, we analyze TomoSense data to present an experimental study related to the use of two different approaches to structural analyses of forested areas. The first one consists in using multi-baseline data to form a tomographic reconstruction of the forest vertical profile, using well known methods from SAR tomographic processing. [5]-[7]. The second one takes advantage of the phase histogram approach, which under some circumstances allows for an estimation of forest structure using single-baseline data [13]. The two approaches are here compared concerning their capability to correctly estimate forest structure and forest height.

[1] R. F. Hanssen, Radar interferometry: data interpretation and error analysis, vol. 2. Springer Science & Business Media, 2001.

[2] M. Liao, T. Balz, F. Rocca, and D. Li, “Paradigm changes in Surface-Motion estimation from SAR: Lessons from 16 years of Sino-European cooperation in the dragon program,” IEEE Geosci. Remote Sens. Mag., vol. 8, no. 1, pp. 8–21, 2020.

[3] F. Rocca et al., “Three-and Four-Dimensional Topographic Measurement and Validation,” Remote Sens., vol. 13, no. 15, p. 2861, 2021.

[4] S. R. Cloude, Polarisation: Applications in Remote Sensing. Oxford University Press, 2009.

[5] S. Tebaldini and F. Rocca, “Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 1, pp. 232–246, 2011.

[6] S. Tebaldini, T. Nagler, H. Rott, and A. Heilig, “Imaging the internal structure of an alpine glacier via L-band airborne SAR tomography,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7197–7209, 2016.

[7] S. Tebaldini, “Algebraic Synthesis of Forest Scenarios From Multibaseline PolInSAR Data,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp. 4132–4142, 2009.

[8] N. Labriere et al., “In situ reference datasets from the TropiSAR and AfriSAR campaigns in support of upcoming spaceborne biomass missions,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 11, no. 10, pp. 3617–3627, 2018.

[9] T. Fatoyinbo et al., “The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions,” Remote Sens. Environ., vol. 264, p. 112533, 2021.

[10] S. Tebaldini et al., “The Tomosense Experiment: Mono-and Bistatic Sar Tomography of Forested Areas At P-, L-, and C-Band,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 7955–7958.

[11] S. Quegan et al., “The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space,” Remote Sens. Environ., vol. 227, pp. 44–60, 2019.

[12] M. M. d’Alessandro and S. Tebaldini, “SAR Correlation Tomography for Vegetation Analysis with ESA TomoSense Data,” in 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1–6.

[13] G. H. X. Shiroma and M. Lavalle, “Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 6, pp. 3754–3777, 2020.

Satellite observations of greenhouse gases over the Arctic and boreal regions are important for a better understanding of the changing natural carbon cycle and monitoring anthropogenic emissions. However, high latitudes pose significant challenges to reliable space-based observations of carbon dioxide (CO2). In addition to large solar zenith angles and frequent cloud coverage, snow-covered surfaces absorb strongly in the near-infrared wavelengths that are used for retrievals of CO2. Because of the resulting low radiances of the reflection measured by the satellite in nadir geometry, the retrievals over snow may be less reliable and are typically filtered or flagged for potentially poor quality.
We present the results of a feasibility study for examining how to improve satellite-based remote sensing of CO2 over snow-covered surfaces. Our primary goal is to support the development of the upcoming Copernicus Anthropogenic CO2 Monitoring Mission (CO2M). CO2M is planned to be operational in 2025 and it will provide quantitative information of anthropogenic CO2 emissions from cities and large production facilities to help meet the carbon emission reduction targets agreed in the Paris Agreement. Our findings are also applicable to other missions that retrieve CO2 from reflected sunlight, for example OCO-2 and TanSat.
As a part of the feasibility study, extensive radiative transfer (RT) simulator development is also undertaken. RaySca, a novel RT simulator aims to be a computationally fast model of polarized radiation within planetary atmospheres in visual, near-infrared and shortwave-infrared wavelength bands. Modeling the polarization of the radiation will enable further research of atmospheric remote sensing with planetary surfaces and atmospheric aerosols with complex reflection and scattering properties. Within the ESA Dragon co-operation, we plan to continue the research and development of atmospheric RT models in the context of further improving greenhouse gas retrievals from space, specifically in polluted conditions.

The Greenhouse Gases Monitoring Instrument (GMI) on board the Chinese Gaofen-5 (GF-5) satellite provides rich observation data for the global remote sensing of atmospheric CO₂. To meet the high-precision satellite retrieval needs of atmospheric CO₂, this paper designs a coupled bidirectional reflectance distribution function (BRDF) CO₂ retrieval (CBCR) method, which describes the surface reflectance characteristics by the BRDF, corrects for atmospheric scattering based on full physics retrieval theory, and ensures the stable retrieval of multiple parameters and atmospheric CO₂ by enriching prior constraints. Theoretical analysis shows that the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics is significantly related to the aerosol optical depth (AOD), solar zenith angle (SZA) and viewing zenith angle (VZA). The validation of GMI CO₂ retrievals shows that the CBCR method significantly reduces the influence of the surface bidirectional reflectance characteristics under high AOD and high SZA conditions, decreasing the atmospheric CO₂ retrieval error from 0.58±5.64 ppm to -1.33±3.13 ppm, and the correlation with the temporal variation of actual atmospheric CO₂ increases from 34.7% to 76.8%. Our CBCR method can correct the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics on atmospheric CO₂ retrievals, and this work demonstrates that describing the surface reflectance characteristics by the BRDF is a promising idea in the field of satellite CO₂ retrievals.

After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s⁻¹, 2.64 m s⁻¹ and −0.25 m s⁻¹, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 0.93, 0.92 and −0.33 m s⁻¹. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s⁻¹, 5.77 m s⁻¹ and −1.15 m s⁻¹, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 1.00, 0.96 and −1.2 m s⁻¹. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Aeolus has the capability to measurement wind profiles and aerosol optical properties profiles synchronously, which provide the possibility of the observation of aerosol transport and advection. Based on the observation of ALADIN (Atmospheric Laser Doppler Instrument), combined with the data of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), ECMWF (European Centre for Medium-Range Forecasts) and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model), a long-term large-scale Saharan dust transport event which occurred between 14 and 27 June 2020 is tracked and the possibility of calculating the dust mass advection is explored. We evaluate the performance of ALADIN and CALIOP on the observations of dust optical properties and wind fields and explore the possibility of tracking the dust events and calculating the dust mass advection with the combination of satellite and model data. The dust plumes are identified with the AIRS/Aqua Dust Score Index and with the vertical feature mask product from CALIOP. The emission, dispersion, transport and deposition of the dust event are monitored using the data from AIRS/Aqua, CALIOP and HYSPLIT. With the quasi-synchronized observations by ALADIN and CALIOP, combined with the wind field and relative humidity, the dust advection values are calculated. From this study, it is found that the dust event generated on 14 and 15 June 2020 from the Sahara in North Africa dispersed and moved westward over the Atlantic Ocean, finally being deposited in the western Atlantic Ocean, the Americas and the Caribbean Sea. During the transport and deposition processes, the dust plumes are trapped in the northeasterly trade-wind zone between latitudes of 5^∘ and 30^∘ N and altitudes of 0 and 6 km. Aeolus provided the observations of the dynamics of this dust transport event in the Saharan Air Layer (SAL). From the measurement results on 19 June 2020, the dust plumes are captured quasi-simultaneously over the emission region (Western Sahara), the transport region (middle Atlantic) and the deposition region (western Atlantic) individually, which indicates that the dust plume area over the Atlantic on the morning of this day is quite enormous and that this dust transport event is massive and extensive. The quasi-synchronization observation results of 15, 16, 19, 24 and 27 June by ALADIN and CALIOP during the entire transport process show good agreement with the Dust Score Index data and the HYSPLIT trajectories, which indicates that the transport process of the same dust event is tracked by ALADIN and CALIOP, verifies that the dust transport spent around 2 weeks from the emission to the deposition and achieved the respective observations of this dust event's emission phase, development phase, transport phase, descent phase and deposition phase. Finally, the advection values for different dust parts and heights on 19 June and on the entire transport routine during transportation are computed. On 19 June, the mean dust advection values are about 1.91±1.21 mg m⁻² s⁻¹ over the emission region, 1.38±1.28 mg m⁻² s⁻¹ over the transport region and 0.75±0.68 mg m⁻² s⁻¹ over the deposition region. In the whole lifetime of the dust event, the mean dust advection values were about 1.51±1.03 mg m⁻² s⁻¹ on 15 June 2020, 2.19±1.72 mg m⁻² s⁻¹ on 16 June 2020, 1.38±1.28 mg m⁻² s⁻¹ on 19 June 2020, 1.60±1.08 mg m⁻² s⁻¹ on 24 June 2020 and 1.03±0.60 mg m⁻² s⁻¹ on 27 June 2020. During the dust development stage, the mean advection values gradually increased and reached their maximum on 16 June with the enhancement of the dust event. Then, the mean advection values decreased during the transport and the deposition of the dust over the Atlantic Ocean, the Americas and the Caribbean Sea.

Satellite altimetry provides the means for global monitoring of sea level, sea ice and
inland waters with accuracy of mm/yr. Absolute calibration of satellite altimeters by
external, permanent and independent ground facilities is necessary to safeguard
accuracy and reliability of those satellite observations for climate change monitoring.

The main objective of the Dragon V project (ID 59198) is to standardize procedures
for calibration/validation of European and Chinese satellite altimeters. Such
procedures are to follow the guidelines prescribed by the strategy for Fiducial
Reference Measurements for Altimetry, developed by the European Space Agency.

One of the fundamental quantities that needs to be calibrated in satellite altimetry is
the backscatter coefficient (sigma-naught); a parameter related to wind observations
at sea and which constitutes an indispensable parameter in climate change models.
At the moment, there is no European or Chinese Cal/Val facility exclusively dedicated
to sigma-naught calibration.

This work presents the first steps towards design, implementation and validation of
corner reflectors for absolute and direct sigma-naught calibration of satellite
altimeters. First, the pros and cons of corner reflectors are given and compared
against active transponders. Then, the geometrical shape of the corner reflector is
examined as it controls performance characteristics, such as radar cross section with
respect to radar elevation and azimuth, maximum gain achieved, side lobe
attenuation, its durability to outdoor conditions, etc. Finally, the need for designing a
corner reflector capable to support sigma-naught calibration for multi-mission and
multi-frequency satellite altimeters is presented.

Understanding the temporal dynamics of vegetation biophysical and structural characteristics such as the fraction of absorbed photosynthetically active radiation (FAPAR) and plant area index (PAI) is valuable for agricultural and forestry applications. The installation of automated in situ measurement networks can better characterise these parameters over a complete growing season in comparison to ad-hoc field campaigns.

For Component 2 of the Copernicus Ground Based Observations for Validation (GBOV) service, a wireless quantum sensor network have been installed at Mediterranean vineyard vegetation (Valencia Anchor Station, Spain). This network will supplement manual field data collections (DHP and LI-COR LAI-2200C measurements), which have been periodically collected throughout the growing season, allowing the performance of the automated systems to be assessed against established and accepted in situ measurement techniques.

The instrument deployment, data processing and filtering methods and performance against manual measurements are presented. An evaluation of the temporal dynamics of the biophysical variables from the automated systems and the implications of a temporally dense, but spatially limited, dataset for the validation of moderate spatial resolution satellite products are also discussed.