Conference Agenda

The Northern China was hit by a severe dust storm on March 15, 2021, covering a large area and bring devastating impact to a degree that was unprecedented in more than a decade. In the study, we carried out a day-and-night continuous monitoring to the dust moving path, using multi-spectral data from the Chinese FY-4A satellite combined with the Japanese Himawary-8 from visible to near-infrared, mid-infrared and far-infrared bands. We monitored the whole process of the dust weather from the occurrence, development, transportation and extinction. The HYSPLIT backward tracking results showed two main sources of dust affecting Beijing during the north China dust storm: one is from western Mongolia; the other is from arid and semi-arid region of northwest of China. Along with the dust storm, the upper air mass, mainly from Siberia, brought a significant decrease in temperature. The transport path of the dust shown by the HYSPLIT backward tracking is consistent with that revealed by the satellite monitoring. The dust weather, which originated in western Mongolia, developed into the “3.15 dust storm” in north China, lasting more than 40 hours with a transport distance of 3900km andcaused severe decline in air qualityin northern China, Korean peninsula and other regions. It is the most severe dust weather in past 20 years in east Asia.

Arctic region has experienced the most rapid climate change impacts in the entire globe during the recent decades. Sea level is a key climate change indicator is which integrates the response of different components of the earth’s system to natural and anthropogenic forcings. Moreover, monitoring sea level change in the Arctic, of high importance since it has a wide range of economic and social consequences. Sea level changes in regional systems such as the Arctic Ocean can differ from Global Mean Sea Level (GMSL) both in terms of magnitude as well as governing forcing and mechanisms. For instance, while changes in salinity can have significant distinct impact on regional sea level change, such as in the Arctic Ocean, it has minor effect on GMSL.

Quantifying the natural variability in the regional sea level change is also urgent in order to distinguish it from a potentially forced (anthropogenic) signal. Furthermore, the role of remote impact of climate variability from one region to another needs to be well-understood. Natural climate variability in the Pacific Ocean can, for instance, impact the Arctic Amplification and thus the sea ice conditions (Li et al., 2015; Svendsen et al., 2018; Yang et al., 2020). The way in which this translates into sea level change, on the other hand, remains unclear. The aim of this study is to examine and relate the sea level variability of the Arctic fresh water reservoir, the Beaufort Gyre (BG), to natural climate variability of the Pacific Ocean.

First, we present the recent advancements in the Arctic Sea level research from space. In particular, the focus is on the results obtained from the analysis of the recent ESA CryoTEMPO data and CNES AltiDoppler data. One of the findings is that the continuous westward extension of the BG observed during the time-period 2003-2014 (Regan et al., 2019) is no longer evident after 2016. In fact, the spatial extent of the gyre during the past decade is the lowest in 2020. Our analysis reiterates the role of large-scale atmospheric circulation on BG sea level variability. Next, we present that the boreal autumn Eurasian snow cover can influence the following winter Arctic sea ice, which further modulates the Rossby wave propagating from troposphere to stratosphere and finally impacts on stratosphere polar vortex. The stratosphere signals further propagate downward to the troposphere, leading to Arctic Oscillation-type circulation anomalies, consequently induce co-variability of climate between Pacific and Arctic. Finally, we present preliminary results of a study analysing the role of Pacific Ocean on the sea level variability of the Beaufort Sea using satellite altimetry, CMIP6 models (20+) and a 10 km resolution NEMO-NAA10km model (dynamical downscaling of NorESM climate model).

In the past two years, based on in-situ observations, reanalysis data, satellite remote sensing, and numerical model, we have made the following research progress in the energy and water cycles of the Tibetan Plateau:
1. Field observation: a) A three-dimensional comprehensive observation and research platform for water and heat exchange between ground and air on the Tibetan Plateau has been established. It has built 27 integrated observation stations for the interaction of the land and the atmosphere (atmospheric boundary layer tower system, eddy correlation observation system), 10 microwave radiometers and 5 radiosonde systems, 3 soil temperature and humidity observation networks, The "Three-dimensional Comprehensive Observation and Research Platform of Water and Heat Exchange between Land and Air on the Tibetan Plateau" consists of 11 observation points of land-atmosphere interaction and 10 sets of wind blowing snow instruments. The platform can realize multi-element, weather comprehensive integrated observation of the near-surface layer and troposphere of the Tibetan Plateau, and provide comprehensive observation data, research basis and decision-making basis for weather monitoring and forecasting, severe weather warning and climate environment prediction in the region and its surrounding areas. b) The monthly average evapotranspiration (ET) of the Tibetan Plateau was obtained by precise calculation. Based on the long-term continuous observation data of the three-dimensional comprehensive observation network system of multi-layer ground-atmosphere interaction in the third pole region built by scientific research, using the improved SEBS model, combined with the MODIS satellite remote sensing data and reanalysis data, the calculation results of the 2001-2018 period were obtained. The monthly average ET of the Tibetan Plateau was verified using the observation data of 6 turbulent flux stations. The new research results show that the average annual total ET (2001-2018) of the Tibetan Plateau is 1.238 ± 0.058 trillion tons, but there are large spatial differences in its annual trend. At the same time, it was found that the evapotranspiration of the plateau increased in the east and decreased in the west, but the overall trend was decreasing.
2. Remote sensing: Using the domestic FY-4A/AGRI satellite data, a set of inversion methods for surface characteristic parameters of the Tibetan Plateau and a surface flux estimation scheme were developed, and hourly resolution ground gas flux data was established, revealing surface temperature and Spatiotemporal characteristics of surface turbulent fluxes.
3. Reanalysis data: revealed the interdecadal variation characteristics of atmospheric heat sources over the Tibetan Plateau in summer. Based on five reanalysis datasets from 1979 to 2019, the characteristics of interdecadal variability of atmospheric heat sources in the Tibetan Plateau region in summer and their relationship with the teleconnection of the Eurasian Silk Road were analyzed. The development of this research work has revealed the interdecadal variation characteristics of the Tibetan Plateau's atmospheric heat source in summer, and deepened the understanding of the influence of the Tibetan Plateau's dynamic and thermodynamic effects on the summer's atmospheric heat source.
4. Numerical simulation: understanding of the development and evolution mechanism of the heavy snowfall process on the Tibetan Plateau. The circulation background, water vapor conditions, thermal conditions and dynamic conditions of a large-scale heavy snowfall process on the Tibetan Plateau were systematically analyzed by using a comprehensive analysis method combining quantitative diagnosis of physical quantities with WRF simulation. This research not only deepens the understanding of the development and evolution mechanism of the heavy snowfall process on the Tibetan Plateau, but also provides a theoretical reference and scientific basis for accurately forecasting short-term heavy snowfall in the Tibetan Plateau and developing sustainable animal husbandry, so as to improve the ability of disaster prevention and mitigation.
5. Cultivate young scientists in the field of climate and environment. A doctoral student went to the University of Twente for the Dragon Program joint training. 5 graduate students (including 2 doctors and 3 masters).

Shadows from clouds and buildings often present in satellite images, especially in high spatial resolution satellite imagery. The Gaofen-2 (GF-2) high-resolution imaging satellite launched in 2014 has two panchromatic multispectral cameras, which is capable of collecting images with a Ground Sampling Distance of 0.8 m and 4 m in the multispectral bands on a swath of 23 km. The GF-2 provides services for high-precision land use survey. TROPOMI launched in 2017 is a satellite spectrometer with a spatial resolution of 3.5 km x 5.5 km. TROPOMI is mainly used to derive atmospheric composition products. Cloud shadows can be identified in the TROPOMI images. In atmospheric composition retrievals, clouds are usually screened and/or corrected before deriving atmospheric and surface properties. However, the cloud shadows are not flagged or corrected. On one hand, cloud shadows could lead to a bias in the atmospheric composition products if they are not corrected. On the other hand, the shadows can be used to retrieve aerosol and surface properties simultaneously.

We have developed a cloud shadow detection algorithm for TROPOMI called DARCLOS. The DARCLOS algorithm provides potential cloud shadow flags and actual cloud shadow flags. The TROPOMI actual cloud shadow flags have been verified using VIIRS images.

Because of the cloud shadow flags, we could analyse the TROPOMI NO₂ products in the shadowed pixels and in the cloud-free, shadow-free pixels to quantify the impacts of shadows on the NO₂ product. We have focused on the TROPOMI NO₂ products over Europe and China because of relatively high tropospheric NO₂ column densities in these two regions.

For the aerosol optical thickness retrievals we started with GF-2 images over Beijing. Due to the high spatial resolution of GF-2, it is possible to identify the shadows from buildings. The aerosol optical thickness is retrieved using the contrast between shadowed pixels and bright pixels and compared with AERONET data. If surface types are the same for the shadow and non-shadow pixels, surface contributions in the measured reflectances can be cancelled at these pixels. Therefore, surface albedo is not important in this algorithm, which is beneficial for the aerosol retrievals in city scale where surface albedo has large uncertainties. In principle, this algorithm can also be adapted to retrieve aerosol optical thickness using cloud shadow and non-shadow pixels.

In the presentation we will report the progresses on the cloud shadow detection, impacts of cloud shadows on the TROPOMI NO₂ products, and the aerosol retrievals using shadows.

The UK is aiming to achieve net zero emissions of GHG’s (greenhouse gas emissions) by 2050 (the Committee on Climate Change (CCC) advises in May 2019). Northern Ireland's contribution to the UK's fifth carbon budget mandates a reduction at least 35% of emissions by 2030 compared to the 1990 level. In the first phase of the project we have conducted investigations into the evolution and current status of carbon emission along with electricity generation with different type of renewable energy resources in Northern Ireland (NI). According to the national statistics, the total emissions 22 MtCO2e in 2013 across the NI was approximately 4% of the total greenhouse gas emissions in the UK, however NI accounts for 2.8% of the UK population and 2.1% of the UK GDP, hence it was concluded that the total emission of NI was more than the rest of the UK. The further results show that the NI has relatively high percentages per capita emission in the agricultural, transportation, residential, LULUCF (land use, land use change, and forestry) and power sector. In the past decades a large number of the renewable energy sites have been established across the UK and they are currently in the operation. This report will present the performance against the commitments set in the Northern Ireland Energy Strategy ‘Path to Net Zero Energy’ which includes a target to meet 70% of electricity generation from diverse renewable sources by 2030. We will present the details of the percentage of electricity generated in the NI from renewable sources as well as information about the types of these renewable sources. The report also presents a study on the relationship between the CO2 emission reduction with the power generated by renewable energy with different types of renewable energy in the NI, possible approaches of capturing CO2 emission by GHGSat.

EMPAC addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical influences. Satellite and ground-based remote sensing together with detailed in situ measurements provide complimentary information on the contributions from different sources and processes affecting AQ, with scales varying from the whole of China to local studies and from the surface to the top of the boundary layer and above. Different species contributing to air quality are studied, i.e. aerosols, in AQ studies often represented as PM2.5, trace gases such as NO2, NH3, Volatile Organic Compounds (VOCs) and O3. The primary source of information in these studies is the use of a variety of satellite-based instruments providing data on atmospheric composition using different techniques. However, satellite observations provide column-integrated quantities, rather than near-surface concentrations. The relation between column-integrated and near-surface quantities depends on various processes. This relationship and the implications for the application of satellite observations in AQ studies are the focus of the EMPAC project. Detailed process studies are planned to be undertaken, using ground/based in situ measurements, instrumented towers, as well as remote sensing using lidar and Max-DOAS. A unique source of information on the vertical variation of NO2, O3, PM2.5 and BC is obtained from the use of an instrumented drone.
The results of the second year will be presented, including trend studies of air pollutants, newly derived NOx emissions using Sentinel 5p and the effect of COVID on the air quality in the YRD.

The aim of this project consists in the development and application of processing methodologies to address two specific Sub-topics relevant for stack-based spaceborne applications. Sub-topic 1 concerns the internal structure of natural media, and it is mapped to Dragon topic Solid Earth - Subsurface target detection. Subtopic 2 concerns joint estimation of deformation and water vapour maps, and it is mapped to Dragon topic Solid Earth - Monitoring of surface deformation of large landslides. The topics above are of fundamental importance in the context of present and future spaceborne missions, which will allow increasingly more systematic use of multiple acquisitions thanks to improved hardware stability and orbital control. Indeed, the proposed activities are intended to support use of multi-pass data stacks from: the upcoming P-Band mission BIOMASS; future L-Band missions, such as the SAOCOM constellation, the upcoming Chinese L-Band bistatic Mission Lu-Tan1, and potentially Tandem-L and Rose-L; the C-Band Sentinel Missions.

The main results until now are summarized into four contributions:

1. The performance of three typical TomoSAR super-resolution algorithms was evaluated (i.e., Capon, MUSIC and CS methods) in reconstructing tropical forest tomographic profile and in obtaining the forest height and underlying topography based on the scattering characteristics of the forest. Furthermore, the effects of different baseline designs and filters on the results were discussed. The experimental results show that: (1) All the algorithms have the ability to reconstruct tomographic profile. Considering the robustness and time-liness of the algorithm, Capon algorithm performs well and is recommended. (2) Under the same conditions, the more baselines, the more uniform distribution baselines, the better recon-struction of the tomographic profile. (3) In order to obtain forest height and underlying topography, it is necessary to select the appropriate filter window size and filters. Smaller Windows fail to suppress side lobes, and larger ones tend to lose detail. With the experimental result, Hamming window filter performs well and is recommended.

2. Single-image polarimetric SAR backscatter coefficient information has great advantages in regional or national scale forest height and biomass extraction, since it is not limited by interferometric geometry. However, in forested areas with large topographic relief, the SAR backscatter echo signal is easily affected by the terrain, which limits its effective association with the vertical structure parameters of the forest. In view of this fact, we established a multi-stage SAR backscatter coefficient correction strategy in complex terrain forest scenes, including:（1）For the influence of the azimuth slope, the polarization orientation angle and correct the polarimetric covariance matrix were calculated. (2) Considering the heteromorphic relationship between the SAR slant range image space and the geographic coordinate space, a hybrid projection angle (HPA) approach was used to correct the SAR effective scattering area; (3) To solve the residual terrain effect caused by SAR observation and target geometry in the forest scene, we adopt a LUT correction method based on SAR look angle and range slope. After applying the above-mentioned SAR terrain radiometric comprehensive correction for forest scene, we further used an RVoG semi-empirical model method to obtain the forest height. Finally, we validate the algorithm with Full- polarimetric UAVSAR data covering a mountainous forest area.

3) A new technique will be presented 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. 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

4) Different approaches to structural analyses of forested areas are presented based on the bistatic and multi-frequency data-set TomoSense, collected in the context of an ESA study in 2020/21. In the study here reported, the data are processed in two different fashions. 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. 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. The two approaches are here compared concerning their capability to correctly estimate forest structure and forest height.

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The forecasts require accurate information about the sources and sinks of natural and anthropogenic GHGs, in particular, carbon dioxide (CO₂) and methane (CH₄). Presently, GHG concentrations are observed using ground-based and satellite observations. While local sources can be observed using accurate in-situ measurements, remote sensing methods from satellites are needed to obtain global and regional coverage, which are important for climate research. A number of studies have indicated that uncertainties in regional CO₂ and CH₄ surface fluxes can be significantly reduced with global, unbiased, precise space-borne measurements which can lead to a more complete understanding of the CO₂ and CH₄ budget. The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, GHGs are challenging. Validation of measurements and their uncertainties and continuous development of retrieval methods are important for the success of satellite remote sensing systems, especially for GHGs where error requirements are demanding. Furthermore, sophisticated data assimilation methods and atmospheric transport models are needed to link atmospheric concentration to the underlying surface fluxes.

In this project we use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval intercomparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. The main geographic focus is China but we will also take advantage of the global view provided by the space-borne data.

We will show validation results from the TCCON network and Chinese ground-based measurements complemented with AirCore profile observations of GHGs at Sodankylä. We will also present the outcome of an intercomparison of two independent retrieval algorithms available at University of Leicester and IAP that have been applied to TanSat. Furthermore, we discuss CO2 and CH4 surface flux results obtained with the GEOS-Chem atmospheric transport model combined with Ensemble Kalman Filter. We will conclude the presentation with an outlook towards future satellite missions for greenhouse gases.

spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission’s requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm−1 for Band 1, 0.003 cm−1 for Band 2, 0.002 cm−1 for Band 3, and 0.004 cm−1 for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO2 results yields a value of 0.869 for R2 (goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for standard deviation of error. Similarly, the XCH4 comparison yields values of 0.841 for R2, 4.64 ppb for bias, and 4.66 ppb for standard deviation of error.

This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3, Sentinel-6 and the Chinese HY-2 satellite altimeters using two permanent Cal/Val facilities: (1) the Permanent Facility for Altimetry Calibration established by ESA in Crete, Greece and (2) the National Altimetry Calibration Cooperation Plan of China. Other satellites, such as the Guanlan, CryoSat-2, CFOSAT, CRISTAL, etc., may also be supported by these Cal/Val infrastructures.

Satellites are being calibrated and monitored using uniform, standardized procedures and protocols while exploiting trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. At present, the PFAC, Greece implements the action plan established by ESA for Fiducial Reference Measurements for Altimetry and reports its Cal/Val results along with their FRM uncertainty.

Through the ESA Dragon-5 project, the FRM procedures, protocols and best practices, will be updated, upgraded and followed at both Cal/Val facilities in Europe and China. Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere) all defined by Cal/Val sites. The final uncertainty (FRM status) for altimeter bias will be attributed to several individual error sources, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc.

During this second Dragon5 year, the following tasks are being carried out:

• Two European Cal/Val sites in the west and south coast of Crete, Greece have been employed for the calibration of HY-2B satellite altimeter;
• Both sites already attain the FRM strategy in terms of their infrastructure, instrumentation, operations, and data processing and analysis;
• The Wanshan Cal/Val facility comprises four sites at Wailingding, Dangan, Zhiwan and Miaowan islands. Multi-disciplinary scientific and ancillary equipment (i.e., GNSS receivers, tide gauges, GNSS buoys, meterorological stations, solar photometers, etc.) support the calibration and validation services;
• Absolute calibration of the HY-2B sea-surface height bias has been determined by European and Chinese Cal/Val infrastructures following diverse and independent methodologies;
• The ESA FRM4ALT guidelines are put into action to estimate the FRM uncertainty for each Cal/Val site;
• Relative calibration of the HY-2B altimeter is carried out with respect to Sentinel-6A MF and Sentinel-3 altimetry mission via crossover analysis.
• Cal/Val results are cross-examined at the two Cal/Val facilities.

The main outcomes and conclusions of this Dragon5 joint work for the 2^nd year of collaboration, are:

• Both European and Chinese Cal/Val sites have become operational and implement independent calibration methodologies for European and Chinese satellite altimetry missions (i.e., Sentinel-6A MF, Sentinel-3A/B, HY-2B/C, etc.);
• Standardization of operations and data processing are followed by both Cal/Val infrastructures in Europe and China using different instrumentation/models and processing algorithms/strategies, while deviations because of geographic location (i.e., bathymetry, tides, etc.) are reported;
• Products of HY-2B satellite altimeter differ depending on the data repository used.;
• The performance of the HY-2B and HY-2C satellite altimeter is within its mission requirements.

Respect to the objectives and schedule of our project, the mid-term report will include on-going activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. The presentation has three parts. Firstly, a short introduction will be given about Bufeng-1 and recent Chinese GNSS-R missions. Secondly, by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements, the preliminary results show that BuFeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. In this presentation, the measurements of Bufeng-1 will be aligned with SFMR collected hurricanes, SMAP derived soil moisture, and DTU18 sea level models. Then, the validations of the accuracy and correlation coefficients will be analyzed to discuss the limitations and issues for the future research. For the last part, we will give the outlook about our future works of the objectives and the future plan of Chinese GNSS-R missiions.

This report provides an overview of the recent progress on the cross-calibration and validation of CSES/Swarm satellite magnetic field and plasma measurements. The main results are as follows:

(1) The first comprehensive comparison of ion density (Ni) in the topside ionosphere measured by the Langmuir probe (LP) and faceplate (FP) of the thermal ion imager on board Swarm satellites were performed. Results show a systematic difference between the LP and FP derived Ni values, and the systematic difference shows prominent dependences on solar flux, local time, and season. Although both Ni datasets show generally good linear regression with electron density (Ne) measurements from the incoherent scatter radar (ISR) located at Jicamarca, the Ni derived from LP shows an additional dependence on the solar flux, while such dependence cannot be seen in the FP-derived Ni. More light ions (e.g., H +), diffusing down from the plasmasphere to the Swarm altitude, seem to cause the overestimation of Ni from LP during low solar activity. A linear relation between the Swarm LP-derived Ni and ISR Ne is derived, and such a function is recommended to be implemented into further updates of the Swarm LP plasma density data.

(2) A detailed analysis for the correlation between electron density (Ne) and temperature (Te) at the topside ionosphere were carried out. In situ measurements from four satellites have been utilized, including the China Seismo-Electromagnetic Satellite (CSES), Swarm A and B, as well as the earlier Challenging Minisatellite Payload (CHAMP) satellite. Observations from the four satellites show generally consistent relationship between the Ne and Te at the topside ionosphere. When Ne is low, the Te is negative correlated with Ne, while the slop of negative relation becomes shallower or even reverses to a positive relation after Ne exceeds a certain threshold. Interestingly, two abnormal features of the Swarm Te measurements are observed: a) when Ne is lower than 1×10¹¹m⁻³, Te sometimes becomes very scatter at low and middle latitudes; b) when Ne is larger than 1×10¹¹m⁻³, Te is grouped into two branches at the equatorial and low latitudes. Further analysis reveals that the flags used in the Swarm Level-1 B plasma density product cannot well distinguish the two abnormal features of Te, implying further efforts are needed for the Swarm Te data calibration.

(3) Based on the in-orbit magnetic field data of China Seismo-Electromagnetic Satellite (CSES) and Swarm Bravo satellite, some researches on the cross calibration and correction technology were carried out. The condition applied is that two satellites pass by in a relatively short period of time and through spatial location at a relatively close range, and set different spatial-temporal scale standards, combined with Kp index to screen for geomagnetic quiet periods. Then with the help of CHAOS model, indirect analysis was realized. Furthermore, the difference between the in-orbit data and model value was visualized, and the phenomenon and possible reason of data variation with time and geomagnetic latitude variation were analyzed. According to the analysis results from 2019 to 2020, the scalar magnetic field detection payloads of the two satellites have maintained long-term stability in-orbit. Both scalar magnetic field data are in good agreement with CHAOS model and relatively consistent and stable. The difference between the data and the model is mainly distributed in the geomagnetic high latitude region. The results of the study can evaluate the reliability of the satellite magnetic field data and the consistency of multiple satellites detection results. Applying them to the field of in-orbit data processing and analysis may improve data accuracy and reliability, and further optimize the data processing method, which may provide a methodological reference for doing similar evaluation and analyzation subsequently.

Past year still saw that COVID restrictions hampered FTIR and MAX-DOAS operations, yet we reached some very important milestones. Foremost, after passing the TCCON application and evaluation process, Xianghe has become an official operational TCCON site in September 2021. Data from June 2018 onwards are publicly available from the TCCON data archive (https://tccondata.org/). Also the teams readily implemented the transition from the GG2014 to the GGG2020 TCCON retrieval processing suite, data of which became publicly available in May 2022.

Xianghe FTIR (both TCCON and NDACC-type), and UV-VIS ground-based remote sensing measurements have been and continue to be used for a wide array of research topics, be it satellite validation (S5P, OCO-2/3, TANSAT, etc.), finding novel retrieval strategies (for instance for O₃) or network retrieval strategy harmonisation studies (NDACC HCHO and NO₂). Its location next to Beijing provides an excellent testing ground for retrieval algorithms (satellite and ground-based) as one wants to test ones product under as many conditions as possible (from remote pristine to heavily urbanized, across all continents). This is important for uncovering imperfections in the algorithms, which can then be evaluated and remedied. For instance, when comparing Xianghe TCCON CO₂ dry air mole fractions with OCO-2 and OCO-3 measurements in North-China, we found that the mean bias is close to 0, but that the OCO-3 snapshot area mode (SAM) is about 1.0 ppm overestimated.

In recent years, Europe, USA, and China have started to implement the concept of creating an SI-traceable Satellite – SITSat. A key objective is to enable high-accuracy on-orbit radiometric calibration of other satellite sensors traceable to the SITSat. Cross-calibration is considered as an effective approach to transfer the radiometric reference from the high-accuracy satellite (e.g., SITSat) sensor to other satellite sensors which require periodical calibration. The Simultaneous Nadir Overpass (SNO) method based on strict matching of observations is the ideal and most accurate way to cross-calibrate and compare satellite sensors. However, high-resolution spaceborne sensors tend to have relatively small swaths and thus fewer opportunities to have ideal matching conditions with a reference sensors. However, relaxing the matching conditions; times, view/illumination angles etc to increase the number of matches inevitably leads to increased uncertainty.

The key step in cross-calibration is to establish the proper reference i.e. Top-of-Atmosphere (TOA) radiance/reflectance, corresponding to the observation value of the satellite being monitored. For pseudo invariant calibration sites (PICS), the relatively stable surface and atmospheric conditions help ensure the accuracy of cross-calibration results. Similarly, TOA reflectance models established for PICS sites can also ensure high-repeatability of such sites as a radiometric reference in cross-calibration. For RadCalNet sites, autonomous observation instruments are deployed to obtain surface and atmospheric parameters at the time of overpass of a satellite. RadCalNet can provide satellite operators with SI-traceable TOA spectrally-resolved reflectance derived over a network of sites, with associated uncertainties. Therefore, RadCalNet sites are able to serve as calibration references for high-medium resolution satellite, with uncertainties limited by the radiometric values assigned to them from characterization against another sensor, at present ground based observations. However, with the concept of SITSats the opportunity arises to provide the reference calibration to the RadCalNet site from a spaceborne sensor and use the surface instrumentation to provide a monitor/correction function instead of the absolute reference. Here we describe a new benchmark transfer calibration method for high-resolution spaceborne sensors, which uses RadCalNet sites measurements as the intermediate radiometric reference value.

In this study, the TOA reflectance models of RadCalNet sites were constructed using satellite observation data with high radiometric calibration accuracy. Then the model was used to correct the RadCalNet standard TOA reflectance products. The corrected RadCalNet TOA reflectance was then used as an intermediate radiometric reference, which can be traced back to the reference satellite sensor. The corrected RadCalNet TOA reflectance was then used to calibrate the monitored satellite sensors. Through the uncertainty analysis of this method, the uncertainty of cross-calibration between the reference satellite and the satellite to be calibrated caused by the relaxation of time matching constraint can be reduced.

Taking Baotou site in China and RVP site in US, the proposed method is validated, and the application demonstration is carried out by using the Chinese and European satellites (i.e., Sentinel-2A/2B, GF-1, GF-6 and SV-1). The preliminary uncertainty analysis results show that this method can achieve obtain high-precision calibration coefficients, and the calibration uncertainty is 3.5%-4%.

Plant canopy characteristics are essential indicators of plant growth status, of which the two most commonly reported are the leaf area index (LAI) and leaf chlorophyll content (LCC). They determine the interception and absorption rates of solar radiation by vegetation, thereby implying the plant productivity and yield. They are also the two main driving variables in several ecological and crop models that provide practical guidance for precision agriculture and aid monitor the state and functioning of terrestrial environments. Such biophysical products could indirectly be estimated from the optical Earth Observation dataset through surface reflectance. Nevertheless, the non-destructive estimation from different satellites have varying levels of spatial and spectral resolution. Therefore, the validation of such biophysical products parameters and improving remote sensed characterization of vegetation biophysical properties are of great importance to ensure they meet the requirements for specific applications. The UAV4VAL project objective is to evaluate the capability of UAVs as a source of reference data for validating decametric surface reflectance and vegetation biophysical products like LAI, with a specific focus on the European Sentinel-2 and Chinese Gaofen-6 missions.

In the first year, we focused on the LAI product retrieval from both Sentinel2 and GF6 data and validated with in-situ LAI collection and UAV image. We collected 17 ground LAI measurements at Taizi Mountain, China (30.916°, 112.866°) on 31th October 2020, and gathered the GF6 and Sentinel2 imagery over the same period. LAI-2200C plant canopy analyser and digital hemispherical photography was used for obtaining in-situ LAI. In addition, the drone images were collected by a P4 Multispectral camera with 5 bands. To evaluate whether UVA imagery can well bridge the scale gap between ground measurements and satellite imagery, we first generate LAI validation maps from UAV by constructing a regression model between in-situ LAI and UAV vegetation index(VI). Second, the Sentinel-2 retrieved LAI maps was implemented using SNAP and a similar hybrid LAI retrieval process was applied on GF6, the hybrid LAI retrieval combine the advantages of physical-methods with the learning accuracy and flexibility of non-parametric regression algorithms. Finally, we compared the traditional in-situ LAI validation and the validation using UAV LAI maps on Sentinel-2 imagery.

The results of this study showed Atmospherically resistant vegetation index (ARVI) is the best VI for UAV-based LAI retrieval with R2 of 0.66 and RMSE of 1.00. The validation of Sentinel-2 LAI products using the UAV LAI map outperformed traditional in-situ validation. The UVA validation on LAI has RMSE of 1.566 and MAE of 1.238 while the in-situ validation has higher RMSE and MAE of 2.17 and 1.61, respectively. The ground validation of GF6-derived LAI also got poor result with low R2, i.e., ~0.1 to ~0.15 and high RMSE, i.e., ~1.4 to ~1.6 between GF6-derived LAI and in-situ LAI. However, the GF6 image of the study area was highly influenced by cloud and the LAI retrieval from GF6 still needs further exploration. In addition, a vegetation indices (VIs) sensitivity test between NDVI, MTCI and MCARI based on PROSAIL simulated dataset. MTCI showed the highest correlation with Canopy Chlorophyll Content (CCC), with 0.8577 for UAV sensors and 0.8493 for GF6 sensor. MTCI was recommended for Chlorophyll retrieval in the future. The next step will focus on the in-situ biophysical parameters and UVA image collection in the UK site and compare the LAI-retrieval results between GF6 and Sentinel2.

In August 2018, ESA’s Earth Explorer mission Aeolus has been successfully launched to space. Since then Aeolus has been demonstrating its capability to accurately measure atmospheric wind profiles from the ground to the lower stratosphere on a global scale deploying the first ever satellite borne wind lidar system ALADIN (Atmospheric Laser Doppler Instrument).

In order to identify and correct the systematic error sources, enhance the performance of ALADIN and the data quality of the wind products, several calibration and validation campaigns were implemented.

In the aspect of ALADIN calibration, the ALADIN laser frequency stability and its impact on wind measurement was assessed and the correction of wind bias for ALADIN using telescope temperatures was conducted. By monitoring the ALADIN laser frequency over more than 2 years, excellent frequency stability with pluse-to-pluse variations of about 10MHz (root mean square) is evident despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). Analysis of the Aeolus wind error with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) model winds shows that the temporally degraded frequency stability of the ALADIN laser transmitter has only a minor influence on the wind data quality on a global scale, which is primarily due to the small percentage of wind measurements for which the frequency fluctuations are considerably enhanced. Another systematic error source is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s⁻¹. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %.

Shortly after the launch of Aeolus, co-located airborne wind lidar observations, which employed a prototype of the satellite instrument – the ALADIN (Atmospheric LAser Doppler INstrument) Airborne Demonstrator (A2D), were performed in central Europe, meanwhile ground-based coherent Doppler wind lidars (CDLs) net was established over China, to verify the wind observations from Aeolus. In the first airborne validation campaign after the launch and still during the commissioning phase, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. Owing to the different measurement grids and LOS viewing directions of the satellite and the airborne instrument, intercomparison with the Aeolus wind product requires adequate averaging as well as conversion of the measured A2D LOS wind speeds to the satellite LOS (LOS*). The statistical comparison of the two instruments shows a positive bias (of 2.6 m s⁻¹) of the Aeolus Rayleigh winds (measured along its LOS*) with respect to the A2D Rayleigh winds as well as a standard deviation of 3.6 m s⁻¹. In China, by the simultaneous wind measurements with CDLs at 17 stations, 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. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. From the comparison results of respective Baselines, 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, 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. 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.

Global observations of column carbon dioxide concentrations and aerosol optical properties profiles are important for climate study and environment monitoring which is why China decided to implement the lidar mission ACDL (Aerosol and Carbon dioxide Detection Lidar) to measure CO2 and aerosol from space – has been launched to space successfully on 16 April 2022. The commissioning phase of ACDL is scheduled to be 6 months, during which the calibration and validation campaigns are implemented and the retrieval algorithms of column carbon dioxide concentration and aerosol optical properties profiles are improved. It is expected that with the calibrations and validations of ACDL and the updates of retrieval algorithms, the products of ACDL will be accurate and robust for science applications.

The main scientific objectives concern the monitoring of the quality of the French and Chinese coastal waters using OLCI and COCTS/CZI space-borne sensors. The project is divided into different tasks: (1) Characterization of uncertainty of OLCI and COCTS/CZI ocean color products in coastal waters; (2) Development of novel regional EO datasets in coastal waters. The first task aims at evaluating the atmospheric correction and bio-optical algorithms of OLCI and COCTS/CZI in our two areas of interest using in-situ measurements collected by both teams and the second task aims at developing regional bio-optical algorithms for the Chinese/French coastal waters according to specific spectral configuration of COCTS and OLCI.

In the last year, even though COVID-19 hindered certain field work and reduced physical contact, in-situ atmospheric and oceanic optical data has been continuously collected in both Chinese and European coastal waters, e.g., the Bohai and East China Sea, the English Channel and Cabo Verde, thanks to field campaign and also permanent observing systems including several AERONET-OC sites. Level 2 products of OLCI/Sentinel 3 as well as those of COCTS/HY-1 were comprehensively validated by in-situ measurements. In-situ data are well quality controlled. Also, depending on local cross time, these L2 products in above-mentioned regions are compared with those provided by MODIS onboard AQUA annd TERRA, and VIIRS onboard SNPP and NOAA 20 satellites correspondingly. Temporal and spatial match-up follows protocols commonly accepted by the ocean color community.

In this report, detailed validation results will be presented, which give an overall quality assessment of operational ocean color products in the Chinese and European coastal waters. Uncertainty patterns will be analyzed and compared among different water mass. Suggestion regarding to improvement of these products are finally recommended.

Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between surface and atmosphere. On the extensive Tibetan Plateau (TP), where in-situ observations are usually extremely sparse, accurate knowledge of the land surface energy balance is crucial for understanding and simulating regional processes of meteorology, hydrology and ecology. More specifically, all-weather LST products are required for accurately simulating soil heat transfer, which provides insights into changes in TP permafrost / seasonally frozen ground and regional climate change. However, LST products based on thermal infrared (TIR) remote sensing are limited to clear sky conditions. Within the Dragon 5 project ‘All-weather land surface temperature at high spatial resolution: validation and applications’, two recently developed all-weather satellite LST products are compared against in-situ measurements from LST validation stations and LST extracted from ERA5-Land data provided by the Copernicus Climate Change Service (C3S).

The two LST satellite products investigated here provide (nearly) gap-free all-weather LST and are based on two different retrieval approaches: 1) reanalysis and thermal infrared remote sensing merging (RTM) (Zhang et al., 2021), the idea of which is the temporal component decomposition method for merging TIR LST with passive microwave (PMW) LST (Zhang et al., 2019) and 2) merging of clear-sky MSG/SEVIRI LST with the surface temperature of a Soil-Vegetation-Atmosphere (SVAT) model (Martins et al., 2019). The in-situ LST for validating these two LST products are obtained from radiometric measurement obtained at the permanent LST validation sites ‘KIT-Forest’ (mixed forest; Germany), ‘Lake Constance’ (water surface; Germany - Switzerland), ‘Evora’ (cork oak tree forest; Portugal) and ‘Gobabeb’ (gravel plains; Namibia). For years 2019 to 2021 the research teams generated all-weather LST products over Europe and Africa and extracted spatial subsets centred on the four validation stations (51 x 51 pixel for 1 km satellite data; 11 x 11 pixel for 5 km satellite data). In order to ease LST product inter-comparisons and validations, all data, i.e. satellite LST and in-situ LST, are transformed into netCDF format before they are spatially and temporally matched.

The presentation provides a brief summary of the two all-weather LST retrieval algorithms, gives an overview of the progress made within the project so far and presents and discusses some examples of the two all-weather LST satellite data sets. Validation results obtained over the four validation stations will be presented and observed differences between the two all-weather LST data sets as well as their respective spatial variability over the validation sites will be discussed. Some examples of the first all-weather LST product, e.g. urban heat island analysis and surface evapotranspiration will also be presented.

References:
Martins, J. P. A., Trigo, I. F., Ghilain, N., Jimenez, C., Göttsche, F.-M., Ermida, S. L., Olesen, F.-S., Gellens-Meulenberghs, F., and Arboleda, A. (2019), An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations. Remote Sensing, vol. 11, no. 24, pp. 3044, doi: 10.3390/rs11243044

Zhang, X., Zhou, J., Gottsche, F.-M., Zhan, W., Liu, S., and Cao, R. (2019), A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4670 – 4691, doi: 10.1109/tgrs.2019.2892417

Zhang, X., Zhou, J., Liang, S., and Wang, D. (2021). A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment, vol. 260, 112437, doi: 10.1016/j.rse.2021.112437