Conference Agenda

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

The objective of this project is to assess the feasibility of remotely sensed products of key cryospheric and hydrological elements (snow, evapotranspiration, soil moisture and precipitation) in representative regions across the Third Pole region and the Heihe River Basin of China and selected test sites in other regions, e.g. northern Finland. The in-situ measurements used to validate remotely sensed products have been collected from several ground-based observation networks including the Finnish Meteorological Institute (FMI), the TERrestrial ENvironmental Observatories (TERENO), the Agrosphere institute (IBG-3) and The Qilian Mountain Observatories (QMO). Essential remote sensing products e.g. the GlobSnow data sets covering northern hemisphere and the soil moisture data set from SMOS, were evaluated by referencing ground-based observations in representative regions. The upscaling methods were developed to improve the representativeness of ground-based observations to remote sensing pixels. The validated products were also inter-compared with other gridded products, and the spatiotemporal trends were diagnosed by statistical indexes, e.g., RMSE and correlation coefficient. The performance of each product will be further evaluated in different landscapes, topographic conditions in the representative regions selected in China and Europe. The research results have been submitted to or published in international journals such as IEEE TGARS, Remote Sensing, and the Cryosphere. In addition, young scientists on this project made considerable efforts to observe snow, evapotranspiration, soil moisture and precipitation. They also assist with the validation of remotely sensed products on preprocessing data, developing validation algorithms and writing validation reports.

Driven by climate change and large-scale forestry projects, the vegetation coverage has been significantly afforested in China. An increase in vegetation greenness improves ecosystem productivity and reduce the water supply, which leads to potential conflict of water demands between ecosystems and humans. This problem has been well-assessed in dry environments with significant evidence, but there are a few studies in humid areas. Therefore, this study will focus on the Poyang Lake Basin in the humid areas of southern China. This study analyzed the change in global vegetation greenness reflected by the satellite-derived growing season LAI (LAIgs). The causes of vegetation dynamic change are firstly analyzed in combination with climate and land use data. The impact of vegetation greenness change on watershed water yield is then investigated based on the modified Water Supply Stress Index (WaSSI) model.

Results show that the vegetation in Poyang Lake Basin grows well. During the study period, the NDVI of the basin increased significantly with a trend of 0.0031/a, in which 78% of the regional vegetation showed a greening trend, while 22% of the regional vegetation showed a browning trend. Temperature rise and afforestation promote regional vegetation greening, but urbanization is the main driving factor of vegetation browning. The partial correlation coefficient between temperature and NDVI was 0.959 (p<0.01), while the partial correlation coefficient between precipitation and NDVI was -0.647 (p<0.05). The correlation between climatic factors and NDVI showed obvious spatial heterogeneity, which indicated the vegetation in the central basin was more vulnerable to climate change than that in other regions. During the study period, frequent droughts occurred in the Poyang Lake basin. The increase of vegetation greenness by 20-80% under different drought intensities resulted in a decrease in water yield by 3-27%. At the scale of sub-basins, the increase of vegetation greenness had a negative effect on water yield. In addition, the decrease of water yield caused by increasing vegetation greenness under persistent high-intensity drought was 2-3 times that under short-term moderate drought. The effect of vegetation greenness increase on water yield under drought conditions is related to vegetation type, duration, and intensity of drought.

The rapid increase of forest greenness caused by massive afforestation may lead to new environmental problems under the condition of continuous high-intensity drought in humid areas such as the Poyang Lake basin. Therefore, given the increasing frequency of extreme climatic events, afforestation with a targeted approach should be implemented as it would provide the most benefits. In addition, selective harvesting in forested areas with high density could be an effective strategy to maintain water supply in humid regions.

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of accumulation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this mid-term project report, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR and optical techniques to investigate seismic hazard and risk, detect potential landslides on wide regions, and demonstrate EO-based landslide early warning system over selected landslides. Regarding the landslide hazard, in order to achieve these objectives, next tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) improvement of a cross-platform SAR offset tracking method for the retrieval of ground displacements; c) InSAR and PolInSAR monitoring and semiautomatic mapping of active displacement areas on wide regions, identification of triggering factors and modelling; d) application of InSAR-based landslide early warning system on selected sites. The achieved results, which mainly focus on selected sensitive regions including the Tibet Plateau and the Three Gorges in China and the Alcoy valley in Spain, provide essential assets for planning present and future scientific activities devoted to monitoring landslides. These analyses are crucial for an optimal prevention and management of these geohazards, as well as for a rapid response after their occurrence.

In the framework of Dragon-5 project, Northeastern University (NEU) from China and the National Institute of Geophysics and Volcanology (INGV) from Italy analyzed the multiple geohazards over the heavy industrial base in Northeast China using time Series SAR images. Moreover, we have also considered a new study site, the Changbaishan active volcano (Jilin Province, ~300 km east from Shenyang). This volcano last erupted in 1903 and was responsible for the largest eruption of the last millennium in 946 CE. Changbaishan is affected by landslides, earthquakes, degassing, and ground deformation. Deformations occurred during the 2002-2006 unrest episode and in 2017, when a nuclear test in North Korea triggered landslides. The multi-hazard exposure of Changbaishan is relevant because a population of ~135000 in China and 31000 in North Korea lives within 50 km from the volcano. We analyze the Changbaishan 2018–2020 deformations by using remote sensing data and detect an up to 20 mm/yr, NW-SE elongated, Line of Sight movement on the southeastern flank and a −20 mm/yr Line of Sight movement on the southwestern flank.

These data reveal an unrest occurring during 2018–2020. Modeling results suggest that three active sources are responsible for the observed ground velocities: a deep tabular deflating source, a shallower inflating NW-SE elongated spheroid source, and a NW-SE striking dip-slip fault. The depth and geometry of the inferred sources are consistent with independent petrological and geophysical data. Our results reveal an upward magma migration from 14 to 7.7 km. The modeling of the leveling data of the 2002–2005 uplift and 2009–2011 subsidence depicts sources consistent with those responsible for the 2018–2020 unrest. The past uplift is interpreted as related to pressurization of the upper portion of a spheroid magma chamber, whereas the subsidence is consistent with the crystallization of its floor, this latter reactivated in 2018–2020. Therefore, Changbaishan is affected by an active magma recharge reactivating a NW-SE striking fault system.

The analysis and modelling of the Changbaishan volcano has been the topic of a joint published paper on Frontiers in Earth Sciences (doi: 10.3389/feart.2021.741287).

Concerning the Fushun open pit mine, in these first 2 years of the project, the 2 research teams have collaborated to following the MT-InSAR processing updating the results from DRAGON-4 project until the end of 2021. We have also performed new processing technique as the OT time series analysis during 2013 to 2016 for this area.

Fushun west Opencast coal mine (FWOCM), located in the southwest of Fushun city, China, is the largest opencast mine in Asia. Since the 1920s, more than 90 landslides have been reported in FWOCM, especially the huge landslide on the south slope, which named Qiantaishan landslide. The Qiantaishan landslide has experienced a fast moving period during 2013 to 2016, and has stabilized since 2017. During the fast moving period, the landslide mass has moved approximately 90 meters. However, since 2017, displacements of the Qiantaishan landslide is less than 150 millimeters per year. In order to analyze the spatial pattern and temporal evolution of different periods of the Qiantaishan landslide, both MT-InSAR and multi-temporal pixel offset tracking has been conducted. Multi-temporal pixel offset tracking is conducted based on 53 Cosmo SkyMed SAR images collected from 2013-07-03 to 2016-12-18, to monitor displacement of the fast moving period of Qiantaishan landslide. The results show that the landslide moves very fast during 2014, and slows down during 2015 to 2016. Besides, displacement of the Qiantaishan landslide shows very strong correlation with precipitation, which accelerates in rainy season and decelerates in dry season. Starting from the beginning of 2017, the Qiantianshan landslide gradually stabilized. The MT-InSAR analysis is conducted based on Sentinel-1 images collected during 2017-01-11 to 2021-12-16, to monitor the slow-moving period of Qiantaishan landslide. The MT-InSAR results show that the displacements rate of the Qiantaishan landslide is within 150 mm/year, which has basically stabilized. The area with the largest displacement is located near the former Liushan old river channel, and the maximum displacement rate is approximately 120mm/yr. This is due to the undercutting of the bedrock near the old river channel and the existence of river pebble layer, which has good permeability, allowing rainwater to penetrate through the cracks on the slope, reducing the tensile strength and increasing the mobility of the landslide body.

With the key goal to exploit satellite Synthetic Aperture Radar (SAR) imagery and advanced processing methods for archaeological prospection and heritage sites protection, the Dragon-5 SARchaeology international collaboration project is making step changes to demonstrate the capability of SAR to detect objects of archaeological significance, and monitor the status and stability of cultural and natural heritage sites and their assets. These are Earth observation applications of paramount importance in the field of land monitoring and Earth system science. The project focuses on a range of study sites in China, Russia, Mongolia, Italy, Norway and Bulgaria, including a wealth of heritage asset types, namely burial mounds, partly buried archaeological ruins, standing monuments within urban centres, natural reserves, paleo-channels and ice patches with organic remains.

This work reports on the key achievements from the first two years of the project, during which the research activities focused on: state-of-the-art review of heritage applications of imaging radar; multi-sensor SAR and optical data collection and tailored tasking of new acquisition campaigns over the study sites; SAR image processing with feature extraction, image classification, change detection and Interferometric SAR (InSAR) methods; analysis and interpretation; field data collection, ground truthing and validation of EO-based evidence and observations.

In the wider Province of Rome (Italy), a long-term InSAR ground deformation analysis was carried out with big data stacks of Sentinel-1 IW SAR imagery, and land subsidence hotspots that may represent a potential threat to heritage assets were identified. An initial investigation on the detectability of buried archaeological features was also performed across sub-urban and rural landscapes of the province by analysing multi-frequency SAR data collected in C-band in RADARSAT-2 Fine Beam and Sentinel-1 IW dual-pol. and in X-band by COSMO-SkyMed. The interpretation of SAR imagery has been aided by very high resolution optical data from DEIMOS-2, WorldView-3, Pléiades-1 and Google Earth, and validated by evidence collected in the field.

In Wuhan (China) long-term SAR and InSAR analyses were carried out to estimate risks for local cultural heritage sites due to urbanization and surface motion. Long time series of COSMO-SkyMed data, acquired via the Wuhan-CSK project – a cooperation between Wuhan University and the Italian Space Agency (ASI) – as well as TerraSAR-X data were used for long-term deformation estimation and to survey the urbanization development. Additionally, ERS-1/2 and ENVISAT ASAR data acquired via the Dragon-5 project were processed. The available historical data from Keyhole sensors allowed for manual mapping of the urban areas into the mid-1960s. The 3D development of the urban area was in the focus of the processing of high resolution SAR data, so that the detailed 2D and 3D urbanization analysis allowed for identification of the urban development and therefore a better risk assessment for cultural heritage sites in Wuhan.

For the research on burial mounds, the work focused on improving the methodologies and better monitoring the sites with respect to climatological factors. This is important as the most valuable burial mounds are to be found in or close to permafrost areas. Global warming and thawing of permafrost endanger the organic remains in some of the sites in question that are currently still frozen and therefore extremely valuable for archaeological analysis. Learning more about the current extent of permafrost, monitoring spatial changes and hopefully being able to predict the spatio-temporal patterns of future changes is of crucial importance for the planning and prioritization of future archaeological excavations.

The detection of looting activities is an important task for cultural heritage protection. SAR interferometric coherence is a very sensitive change detection tool and, in combination with the high temporal availability of SAR data, could make for a good approach of looting detection. The main challenge is the very high sensitivity of the coherence to change and other factors, such as soil moisture and spatial baseline differences. Successful change detection with low false alarm rates is therefore difficult, especially when looking for changes in sub-pixel dimensions. To test new approaches based on detecting statistical inhomogeneities within adaptive local and non-local neighbourhoods, a test is prepared at the satellite receiving station of LIESMARS, Wuhan University. On the test site, looting structures will be artificially created to conduct tests of detectability and support method development using high resolution TerraSAR-X data and medium resolution Sentinel-1 data.