Conference Agenda

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.

Coastal zones are essential for the socio-economic well-being of many nations [1] Coastal regions have multiple uses, needs and opportunities, and are particularly exposed to extreme events and climate change. Many key sectors are affected by long-term effects in these zones, such as the monitoring of public/private infrastructures, cultural/natural heritage preservation, risk management, and agriculture. The combined effects of sea level rise (SLR), tidal evolution, modulated ocean currents and extreme events can have numerous impacts on coastal, river delta, and inland water zones, including water management, which in turn lead to cascading and unpredictable impacts on other sectors. The ESA-DRAGON V GREENISH project [2] aims to provide extensive research and development analyses of areas in Europe and China subject to climate change induced (e.g., Sea Level Rise, flooding, and urban climate threats) and anthropogenic disasters (e.g., ground subsidence over reclaimed-land platforms), with the goal to improve the knowledge and develop new remote-sensing methods. Global sea-level is rising, and tides are also changing worldwide, and these risks are accompanied by increasing concerns about the growing urbanization of the world’s low-lying coastal regions and related coastal hazards (e.g., flooding). On the other hand, Inland water bodies such as lake and river system also experience substantial degradation with rapid economic development.

The main project goals are: i) To study the ground deformation in coastal/deltaic regions with conventional and novel interferometric SAR approaches; ii) To monitor changes in urbanized areas via coherent and incoherent change detection analyses; iii) To study interactions between ocean currents and coasts, such as coastal erosion, using high resolution optical and SAR satellite images; iv) To properly assess SLR, tidal evolution, and hydrogeological risks in urban coastal areas; v) To study the interactions between Poyang Lake and its connecting rivers. vi) To develop atmospheric phase screen correction methods in multi-temporal SAR images. vi) To develop interactive maps of coastal, urban, and inland zones susceptible to primary and secondary risks via GIS, and finally vii) To train Young Scientists.

A number of planned activities have already started and some results have already been achieved, which will be presented at the on-line event scheduled for October 2022. Specifically, we processed a sequence of SAR data related to the area of Venice Lagoon and the Po ‘river system to create a base for further analyses devoted to analyzing the effect of extreme weather conditions and sea level rise in the lagoon [3]. To this aim, we investigated the impact of a recent flood event that occurred in the area in 2019. In this context, we applied/tested methods of incoherent change detection [4]-[5]. Some experiments have also been carried out in the city of Shanghai to apply artificial intelligence methods with TerraSAR-X image time-series in urban context to reveal changes triggered by human activities. Assessment and analysis of capability of disaster reduction and crucial index at district Level in Shanghai have also been made. We also investigated the recent decade deformation time-series in Chongming Island of Shanghai by using four space-borne Synthetic Aperture Radar (SAR) satellite datasets.

The risk of flooding in the coastal area of the Shanghai megacity was further characterized. To this aim, two independent sets of synthetic aperture radar (SAR) data collected at the X- and C-band through the COSMO-SkyMed (CSK) and the European Copernicus Sentinel-1 (S-1) sensors have been exploited. By assuming that the still extreme seawater depth is chi-square distributed, the probability of waves overtopping the coast was estimated. We also evaluated the impact on the territory of potential extreme flood events by counting the number of very-coherent objects (at most anthropic, such as buildings and public infrastructures) that could be seriously affected by a flood. To forecast possible inundation patterns, we used the LISFLOOD-FP hydrodynamic model [6]. Experimental results, which are detailed in [7], showed that two coastline segments located in the southern districts of Shanghai, where the height of the seawall is lower, had the highest probability of wave overtopping and the most significant density of coherent objects potentially subjected to severe flood impacts. The slowly developing landslides in the districts of Istanbul have also been investigated using S-1 sensors [8].

Other planned activities are in course for: i) the analysis of the Istanbul/Marmara-Sea coastal environment, ii) the investigation of large-scale coverage of Bohai Rim Region ground subsidence caused by underground resources extraction such as underground water, oil, gas and brine over Bohai rim region, iii) the analysis of ocean currents and the SLR impact.

References

1. Sengupta, D.; Chen, R.; Meadows, M.E. Building beyond land: An overview of coastal land reclamation in 16 global megacities. Appl. Geogr. 2018, 90, 229-238.
2. https://dragon5.esa.int/projects/
3. Mastro P, Calò F., Giordan D., Notti D., Pepe A., “On Monitoring the Impact of Floods and Extreme Weather Events in Protected Cultural Heritage Areas: The Venice Lagoon Case Study”, proceedings of Living Planet Symposium, 23 – 27 May, 2022, Bonn, Germany.
4. Lu, D.; Mausel, P.; Brondizio, E.; Moran, E. Change Detection Techniques. Int. J. Remote Sens. 2004, 25, 2365–2407
5. Mastro, P.; Masiello, G.; Serio, C.; Pepe, A. Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations. Remote Sens. 2022, 14, 3323. https://doi.org/10.3390/rs1414332.
6. Bates, P.D.; De Roo, A.P.J. A simple raster-based model for flood inundation simulation. J. Hydrol. 2000, 236, 54–77
7. Tang, M.; Zhao, Q.; Pepe, A.; Devlin, A.T.; Falabella, F.; Yao, C.; Li, Z. Changes of Chinese Coastal Regions Induced by Land Reclamation as Revealed through TanDEM-X DEM and InSAR Analyses. Remote Sens. 2022, 14, 637. https://doi.org/10.3390/rs14030637.
8. Bayik, C.; Abdikan, S.; Ozdemir, A.; Arikan M.; Balik Sanli F.; Dogan U. Investigation of the landslides in Beylikdüzü-Esenyurt Districts of Istanbul from InSAR and GNSS observations. Nat Hazards 109, 1201–1220 (2021).

It is presented in this paper some recent progresses of ESA-MOST China Dragon Cooperation Program “Synergistic Monitoring of Ocean Dynamic Environment from Multi-Sensors (ID. 58009)” including: (1) Assessment of ocean swell height observations from Sentinel-1A/B Wave Mode against buoy in situ and modeling hindcasts; (2) Quantifying uncertainties in the partitioned swell heights observed from CFOSAT SWIM and Sentinel-1 SAR via triple collocation; (3) Up-to-Downwave asymmetry of the CFOSAT SWIM fluctuation spectrum for wave direction ambiguity removal; and (4) Validation of wave spectral partitions from SWIM instrument on-board CFOSAT against in situ data.

This is the first presentation of quasi-synchronous spaceborne synthetic aperture radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission (RCM) and RADARSAT-2 consisting of quad-polarization (HH+HV+VH+VV) wide swath observations of Hurricane Epsilon. These measurements clearly show that the denoised HV- and VH-polarized normalized radar cross sections (NRCSs) have great consistency. NRCS values at HV- and VH-polarizations are more sensitive to wind speeds and less sensitive to incidence angles or wind directions than those at HH and VV for hurricane-force winds. For large incidence angles and high wind speeds, the sensitivity of HH-polarized NRCS to wind speed is higher than that of VV. HH- and VV-polarized NRCS gradually lose wind direction dependency at high winds. It is notable that the time interval between the two SAR acquisitions is only 3 minutes. This allows for a direct comparison of HV- and VH-polarized images to investigate the variations of high-resolution backscattering within the hurricane vortex, thereby revealing the most dynamical areas. An asymmetric dynamic is observed around the eye of Hurricane Epsilon, based on positive and negative differences (VH–HV) in the western and eastern parts of the eye. The impacts of rain on quad-polarized NRCS are also examined using collocated rain rates from the Global Precipitation Mission (GPM) and wind speeds from the Soil Moisture Active Passive (SMAP). Significant rain-induced NRCS attenuations are about 1.7 dB for HH and VV, and 2.2 dB for HV and VH, when the rain rate is 20 mm/hr. These attenuations are associated with rain-induced turbulence and atmospheric absorption. This work shows that the collocated RCM and RADARSAT-2 hurricane observations provide a unique analysis of synoptic and joint C-band measurements of the ocean surface in quad-polarization; this is noteworthy in view of preparations for the next generation of dual-polarization scatterometer (SCA) onboard MetOp-SG.

The first two years of this project focused on retrieval of biophysical variables of vegetation, estimation of bare soil properties, and monitoring pest and diseases. The remote sensing data used include non-imaging hyperspectral data and image data from satellites such as Sentinel-2, GF-1 and PRISMA. The field experiments were carried out at Maccarese farm in Central Italy and farms in the Quzhou district in China.

For vegetation, field campaigns were primarily performed contemporary to the Sentinel-2 or PRISMA acquisitions on the Maccarese (IT) farm along the winter wheat and maize growing season. A Decision Trees Ensembles (DTE) model has been developed by training a database of plant canopy spectral and directional reflectance generated by using PROSAIL-PRO. A reduction of dimensionality performed by selecting a subset of PRISMA bands (i.e. excluding water vapor absorption bands and noisy bands) were applied prior to the training phase. DTE model has been selected among the tested algorithms as appears a robust method in dealing with noise, less prone to overfitting and less sensitive to the difference between simulated and real data. LAI, LCC, FCOVER and FAPAR were retrieved on the Maccarese test site with a RMSE value of 1.15 m² m⁻², 11.45 μg cm⁻², 0.16% and 0.16%, respectively. RMSE values are always lower than the ones derived by Sentinel-2 with SNAP. Further steps will consider the retrieval of additional biophysical variables as carotenoid, nitrogen and leaf mass area.

For topsoil characterization the test site is located in the Quzhou district and more than 100 soil samples were collected and analyzed in the lab to assess SOC. PRISMA data preprocessing was similar to the one for vegetation with the inclusion of data pre-treatment i.e. Savitzy–Golay filter (SG), Standard Normal Variate (SNV) and Continuum Removal (CR). PLSR and Cubist algorithms were applied only to bare soil pixel by using a 10-fold cross validation. Results show a significant accuracy for SOC showing a RPD higher than 2. The result for Quzhou reflects the small field size with respect to the PRISMA GSD [30m]. To minimize the field size effects, an optimized pan sharpening procedure by using Sentinel-2 bands and the 5m/pixel PRISMA PAN, is under development.

For what concerns crop threats among which pests & diseases, Italian and Chinese research groups are working on the development of a high resolution crop mapping and a crop early warning to be combined with the pest/diseases monitoring model to accurately estimate its possible effect in terms of production loss and food security. To this purpose, the activity carried out in the framework of Dragon 5 will take into account results coming from other initiatives in the context of the GEO work programme (CROP_PEST_MONITORING and AFRICULTURES). High resolution (10m) crop mapping based on Sentinel-2 image series methodology has been developed and it will be integrated with pest/diseases monitoring model in order to be able to forecast/estimate the potential affected area.

For desert locust presence risk forecasting, we have proposed a dynamic prediction method of desert locust presence risk at Somalia-Ethiopia-Kenya. First, we obtained six static environmental factors, including DEM, land cover, soil sand content, clay content, silt content, and coarse fragment content. Then the time lag variables of four dynamic environmental factors, namely rainfall, soil moisture, NDVI, and LST, were then extracted based on multi-source time-series data. Finally, we have constructed a dynamic remote sensing-based risk forecast model of desert locust presence combining a multivariate time lag sliding window technique and machine learning algorithms. Monthly prediction experiments from February to December 2020 were then conducted, extracting high, medium and low risk areas of desert locust occurrence in the study area. Results demonstrated that the overall accuracy was 77.46%, and the model enables daily dynamic forecasting of desert locust risk up to 16 days in advance, providing early warning and decision support for preventive ground control measures for the desert locust.

The project, therefore, at the Mid Term event, is well aligned to the timetable described at the time of the submission and most of the activities have been started. Moreover, the PhD candidate, selected within the YS program, is working on the project using common data.

The recognition of intra-urban heat islands depends on the capacity to identify, at varying temporal and spatial scales, of variations in urban form and urban fabric. In this paper Earth Observation is used to define Land Surface Temperature, recognize intra-urban heat islands and identify the impact of urban form/fabric characteristics [land cover (greenery), the impervious fraction, the building density and height and the type of construction materials) to surface thermal dynamics. Particular attention is given to the relation between high/medium/low density areas and LST on the one hand, and on the cooling potential of greenery (in the above areas) on the other, in view of defining urban patterns to be taken into consideration once adaptation plans to urban heat are developed. Results are examined against spatial resolution, time of day and season, whereas special attention is given to the impact of climate extremes, namely heat waves, to any recognized patterns as well as to the presence of intra-urban heat islands. Landscape metrics are also used in support of the extraction of urban patterns and the understanding of their links to surface thermal dynamics. Finally, results are rolled out as a climate service for Climate friendly and smart cities.

Urbanization is continuing at an unprecedented rate in many cities across globe. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution, urban heat island, loss of biodiversity and ecosystem services, and making cities more vulnerable to disasters. Therefore, timely and accurate information on urban changing patterns is of crucial importance to support sustainable and resilient urban planning and monitoring of the UN 2030 Urban Sustainable Development Goal (SDG). The overall objective of this project is to develop innovative, robust and globally applicable methods, based on Earth observation (EO) big data and AI, for urban land cover mapping and urbanization monitoring.

Using ESA Sentinel-1 SAR, Sentinel-2 MSI and Chinese GaoFen-1 images, the EO-AI4Urban team has developed varous deep learning-based methods for urban mapping and change detection during the past two year. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for urban extraction. The DA approach jointly exploits Sentinel-1 SAR and Sentinel-2 MSI data to improve across-region generalization for built-up area mapping [1]. For urban change detection, several novel methods have been developed including a dual-stream U-Net [2] and a Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization [3]. Further, a high-resolution feature difference attention network (HDANet) is proposed to detect changes using the Siamese network structure. To tackle the loss of the spatial features of buildings caused by the multiple successive down-sampling operations using fully convolutional networks (FCNs), a multi-resolution parallel structure is introduced in HDANet, and the image information with different resolutions is comprehensively employed Using Landsat time series, a machine learning method was developed to map built-up areas and to analyze changes during 2000 to 2020 [4]. In addition, techniques for the characterization of urban changes exploiting sequences of SAR and/or optical data, with focus on Chinese cities are further developed. The temporal fitting of the coherence sequence using Sentinel-1 SAR data has been explored. This method has shown potential to discriminate and label accordingly the changes in time for intra-urban areas [5]. Another approach has been developed to focus on an automatic labeling of these temporal sequences based on a statistical analysis of the changes in time, clustered into different behaviors (e. g., stable clusters, fast changes, etc.). This approach has been tested in Chinese cities and achieved significant results [6]. In consideration of the difficulties to find an object of interest in large-scale scenes, a deep learning-based visual grounding method in remote sensing images has been developed to automatically locate a target object from large-scale scenes by a language description [7]. A robust cloud detection approach has also been developed [8]. Finally, using Landsat time series, a machine learning method was developed to map built-up areas and to analyze changes during 2000 to 2020 [9].

Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements upon fully supervised learning from Sentinel-1 SAR data, Sentinel-2 MSI data and their input-level fusion. The fusion DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale [1]. Using the Onera Satellite Change Detection (OSCD) dataset, the results showed that the dual-stream U-Net outperformed other U-Net-based approaches together with SAR or optical data and feature level fusion of SAR and optical data [2]. Using bi-temporal SAR and MSI image pairs as input, the Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization have been tested in the 60 sites of the SpaceNet7 dataset. The method achieved higher F1 score than that of several supervised models when applied to the sites located outside of the source domain [3]. Using the LEVIR-CD dataset, the results confirmed that HDANet can improve the differential feature representation for building change detection [4].

Using Landsat time series, the results show that machine learning method could extract built-up areas effectively. To analyze urbanization in 13 cities in the Beijing–Tianjin–Hebei region, SDG indicator 11.3.1, the ratio of land consumption rate to population growth rate (LCRPGR) is calculated and the results show that the LCRPGR in Beijing–Tianjin–Hebei region fluctuated from 1.142 in 2000–2005 to 0.946 in 2005–2010, 2.232 in 2010–2015 and 1.538 in 2015–2020. Apart from the megacities of Beijing and Tianjin, the LCRPGR values were greater than 2 in all cities in the region after 2010, indicating inefficient urban land use [9].

References:

[1] Hafner, S., Y. Ban and A. Nascetti, 2022. Unsupervised Domain Adaptation for Global Urban Extraction Using Sentinel-1 and Sentinel-2 Data. Remote Sensing of Environment (in press).

[2] Hafner, S., A. Nascetti, H. Azizpour and Y. Ban, 2022. Sentinel-1 and Sentinel- 2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net. IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5.

[3] Hafner, S., Y. Ban and A. Nascetti, 2022. Multi-Modal Consistency Regular- ization Using Sentinel-1/2 Data for Urban Change Detection. Submitted to ISPRS Journal of Photogrammetry and Remote Sensing.

[4] Wang, X., J. Du, K. Tan et al., 2022. A High-Resolution Feature Difference Attention Network for Change Detection. To be submitted.

[5] Che, M. and P. Gamba, Temporal and Spatial Change Pattern Recognition by Means of Sentinel-1 SAR Time-Series. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 160-163.

[6] Che, M., A. Vizziello, P. Gamba, "Spatio-temporal Change Mapping with Coherence Time-Series", submitted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7] Sun, Y., S. Feng, X. Li, Y. Ye, J. Kang, X. Huang. 2022. Visual Grounding in Remote Sensing Images. Proceedings of the ACM International Conference on Multimedia, 2022.

[8] Li, X., X. Yang, X. Li, S. Lu, Y. Ye, Y. Ban. 2022. GCDB-UNet: A novel robust cloud detection approach for remote sensing images. Knowledge-Based Systems, 238: 107890.

[9] Zhou, M., Lu, L., Guo, H., Weng, Q., Cao, S., Zhang, S., & Li, Q. (2021). Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data. Remote Sensing, 13(15).

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.

Ocean wave is one of the important objects of ocean observation by satellite microwave remote sensing. Since the successful launch of TOPEX/Poseidon in 1992, the satellite altimeters had provided the abundant global ocean wave height observations. But the altimeters can only observe the ocean wave height of the points under the satellite along track. Synthetic Aperture Radar (SAR) can obtain ocean wave spectrum data with a certain swath observation, but SAR ocean wave data have the issue of wave wavelength truncation. China-France Oceanography Satellite (CFOSAT) was launched on 20^th Oct. 2018, and the equipped SWIM on CFOSAT provided a new means for global ocean remote sensing observation. In this study, CFOSAT SWIM ocean wave observation data are evaluated by buoy and altimeter data firstly. The nadir and non-nadir ocean wave data of SWIM are compared to buoys and altimeter data. Then the study on ocean wave data fusion based on multi-source satellite remote sensing is carried out, and the global ocean wave remote sensing data from 2016 to 2020 are generated by using HY-2 series, sentinel-3 series, jason-3 altimeter, Sentinel-1 SAR and CFOSAT SWIM ocean wave data. In addition, the components of ocean waves are identified according to the wave age by combining the sea surface wind data, and the swell remote sensing fusion is carried out to generate global ocean swell products with the period more than one year. Finally, the preliminary analysis of ocean wave characteristics is carried out with the global ocean wave products produced in this study.

A SAR internal wave amplitude inversion algorithm based on Euler numerical simulation is proposed. The traditional satellite SAR internal wave amplitude inversion algorithm is based on the analytic relationship between the half width and the amplitude of the internal solitary wave revealed by the KdV equation or its variants. Those methods often underestimate the internal wave amplitude. There are at least two reasons for this problem: 1) KdV and its variants are insufficient to accurately describe the nonlinear behavior of large-amplitude internal waves; 2) The half-width of internal waves on the sea surface observed by SAR are different from that on the water layer where the maximum vertical displacement is located. The proposed new method iteratively conducts the numerical simulation of internal waves with different amplitudes. The best amplitude is obtained when the simulated half-width of the internal waves apprearing on the sea surface is most close to the half-width observed by SAR.

However, there are two possible amplitudes for one half-width. The inversion algorithm has to choose one of the two amplitudes. Such selection is done by comparing the simulated SAR NRCS modulation corresponding to the two amplitudes with the observed SAR NRCS modulation.

Case studies in multiple sea areas around the world show that the amplitude accuracy obtained by the new SAR internal wave amplitude inversion algorithm is significantly better than the KdV algorithm. In addition, in order to accelerate the convergence of the model at large amplitudes, the Miyata equation was used to calculate the initial flow field.

HY-1C and HY-1D are the two ocean color satellites in China which play the important role in routine work of global marine environment monitoring launched separately in 2018 and 2020. The overall objective of HY-1 serial satellite is to monitor global ocean color and SST (Sea Surface Temperature), as well as the coastal zones’ environment. The China France Oceanography Satellite (CFOSAT) and Haiyang-2B (HY-2B) satellites were successively launched in China in 2018. As missions for measuring the dynamic marine environment, both satellites can measure the nadir significant wave height (SWH). Sentinel-2A/B satellites were launched in 2015 and 2017 separately. In this project, all these satellites data have been used to monitor marine disaster and environmental changes. Based on the various methods and different data types, satellite remote sensing monitoring research have been conducted in several typical marine disasters and dynamic environment changes. The results show the advantages both in new algorithms and multiple satellite data applications. The main developments in the mid-term of the project are as follows:
1) Using HY-1C/D and Sentinel-2 satellite data, this project investigates the sea ice, oil spill and green tide disaster in Bohai Sea and the Yellow Sea, as well as the East China Sea. The results show that both HY-1
C/D and Sentinel satellite data have played an important role in ocean ecological disaster monitoring. It’s deserved to point out that the Coastal Zone Imager (CZI) on-board HY-1C/D displays much powerful performance in operational monitoring of marine spills, green-tides and sea-ice disasters because of the large width, rapid coverage and high signal-to noise ratio of data. According to the characteristics of different spatial resolution data, we develop a comprehensive method to classify the difference of monitoring results using various satellite data which could improve the accuracy of greed-tide detection and coherence the green-tide bio-mass evaluations resulted from different satellite data.
2) Based on the time series HY-1C/D satellite data in 2019-2021, the long-term oil spills detection has been conducted in China Seas and coherent areas. The results show that it’s possible to distinguish the various spill types, for example the emulsified and non-emulsified oils, using the CZI satellite data in the condition of different sun-glint reflections which also displays the outstanding advantages of HY-1C/D data applications. According to the 3 years data analysis, the spatial patterns of oil spill distributions have been conducted for the first time in the China Seas.
3）In this project, the HY-2B altimeter and CFOSAT nadir SWHs have been validated against the National Data Buoy Center (NDBC) buoys and the Jason-3 altimeter SWH data, respectively, which resulted in CFOSAT nadir SWH having the best accuracy and HY-2B having the best precision. The SWHs of the two missions are also calibrated by Jason-3 and NDBC buoys. Following calibration, the root mean square error (RMSE) of CFOSAT and HY-2B are 0.21 and 0.27 m, respectively, when compared to Jason-3, and 0.23 and 0.30 m, respectively, compared to the buoys. Our results show that the two missions can provide good-quality SWH and can be relied upon as a new data resource of global SWH.
4）Using simultaneous observations of wind and wave fields by the CFOSAT, this project reports preliminary investigation results of the typhoon waves during the passage of super typhoon Lingling (2019) over the China offshore waters. The results show that the significant wave heights (SWHs) are over 5 m on the right side of the typhoon track for wind speeds over 14 m s-1, agreeing with the theoretical estimates. The dominant waves have wavelengths of 150 – 180 m, and propagate eastward for northwestward blowing winds. The misalignments of the wind and wave directions increase with the distance from the typhoon center, agreeing with theoretical prediction. We also present the typhoon monitoring results with multiple satellites such as CFOSAT, HY-2B and ASCAT.

Better prediction of sea state integrated parameters has a key role in the estimate of momentum and heat fluxes exchanges between ocean and atmosphere. By using deep learning technique we are now able to retrieve Significant Wave Height on the wide swath of scatterometer, as proposed by Wang et al. (2021). The objective of this work is to assess the impact of assimilating wide swath SWH and directional wave spectra from CFOSAT and Seninel-1 on the wave forecasting. We also investigated the impact of improved wave forcing on the ocean mixed layer in a coupled experiment of wave model and ocean model.

During the DRAGON-5 project we have processed two years of wide swath SWH from HY-2B-2C and CFOSAT mission. Wave model runs have been performed with data assimilation and control run for this long period. The validation of the results have been implemented with independent wave data from altimeters and also from buoys networks.

The results show the capacity of using wide swath SWH and directional wave spectra to track and well capture the initial conditions of swell generated in severe storms. We also highlight the complementary of using SWIM and SAR wave spactra for different wavelength scales. This significantly improves the wind-wave growth in critical ocean regions such as the Southern ocean.

Furtehr comments and conclusions will be given during the oral presentation.

In recent years, deep neural networks have become a hype tool for various optical and SAR remote sensing image interpretations including object detection, recognition, land use land cover classification, change detection, and multi-temporal image analysis. However, in real-world scenarios, there still are many challenges, such as lack of large annotation data, heterogeneous data transferring, open set recognition, etc. In this talk, we will present our latest efforts to address such problems with the support of the Dragon Project, including： 1) Self-supervised learning for remote sensing image classification and recognition; 2) Heterogeneous SAR domain adaptive object detection; 3) open set recognition and new class discovery for remote sensing images. Moreover, we will introduce an interactive deep learning remote sensing image annotation system and an explainable analysis system for the remote sensing imagery system we are developing.

As an important oceanic physical process, mesoscale eddies play a key role in the processes of ocean mass transport and energy exchange, such as biogeochemical cycles, marine ecosystems, and marine heat balance.

In recent years, these automated ocean eddy identification and tracking algorithms can be divided into two categories: Eulerian- and Lagrangian-based approaches. Based on instantaneous sea surface height (SSH) or velocity field, the major circular structures of mesoscale eddies can be detected, which are called as Eulerian eddies. Previous SSH-based and sea level anomaly-based (SLA-based) methods have shown the best performance due to their ability to avoid extra noise and excess eddy detections. Although SSH-/SLA-based methods worked well at the basin scale, the calculation efficiency decreases distinctly at the global scale, mainly due to the high-order computation complexity on contour iterations. With the improvement of data resolution, existing hardware fails to meet the requirement of long-time-scale global eddy recognition due to the increase of the number of SSH/SLA contours. Thus, based on satellite altimeter data, a highly effective orthogonal parallel algorithm for identifying and tracking global eddies is proposed. Surprisingly, this algorithm is ~100 times faster than the previous SSH-based method on global eddy detection without reducing the accuracy of mesoscale eddy recognition. According to this orthogonal parallel algorithm, the global mesoscale eddy dataset for the past 28 years (1993-2020) was generated, which provides a data foundation for the subsequent study of mesoscale eddies. In terms of the mesoscale eddy dataset, an automatic recognition method of global dipole eddy pairs that consist of two counter-rotating eddies moving together for a period of time within a limited space distance is proposed by using the K–D tree for cutting space. Simultaneously, the transmission modes and characteristics of dipoles are analyzed, including the characteristics of long life, high propagation speed, and entanglement trajectory. In addition, an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events is proposed and the corresponding data set of eddy trajectories in the Northwest Pacific is available, which would fill the gaps in data sets to support studies on eddy splitting and merging in the Northwest Pacific.

Different from other methods based on instantaneous flow fields, Lagrangian eddies are the cumulative results of the state of the fluid within a given time scale, which can maintain material coherence over the specified time intervals. Firstly, by using the elliptic Lagrangian Coherent Structures, the boundary of a black-hole eddy was extracted based on the data of the geostrophic flow velocity field in the Western Pacific Ocean. Combined with multi-source satellite remote sensing data and in-situ data, the consistency of material transport in the horizontal direction and the coherence of material in the vertical direction of the vortex are analyzed and verified. The results show that the black hole vortex boundary can describe material transport more objectively than the Euler vortex boundary on a longer time scale. Then, the Lagrangian eddies in the western Pacific Ocean are identified and analyzed. By introducing the Niño coefficient, the lag response of the Lagrangian eddy to El Niño is found. Through normalized chlorophyll data, it is observed that Lagrangian eddies can cause chlorophyll aggregation and hole effects. These findings demonstrate the important role of Lagrangian eddies in material transport. Nevertheless, although Lagrangian eddies work well at estimating material transport, the high calculation cost during the integration process has become a bottleneck, especially when the data resolution is improved or the study area is enlarged. Therefore, SLA-based orthogonal parallel detection of global rotationally coherent Lagrangian eddies is built, whose runtime is much faster than that of a previous nonparallel method. Finally, a data set of long-time-scale global Lagrangian eddies is established.

Furthermore, an integrated marine visualization system, named i4Ocean, has been presented. The system is designed and implemented to investigate and study physical marine processes by visualizing and analyzing spatiotemporal marine data. Notably, these actions are realized by providing various GPU-based interaction and visualization techniques for displaying multidimensional data. The system achieves three goals: high visibility, good performance and interactive capabilities. The techniques of z-coordinate calibration and sphere rendering, which restore the most authentic marine environment, provide excellent feedback for oceanographers. The efficient ray sampling technique including a preintegrated transfer function and adaptive sampling methods, increases the rendering efficiency of ocean data. By further introducing a transfer function, users can extract the region of interest in the system and analyze diverse marine phenomena. A data-centric approach was adopted to guide the design of the transfer function by analyzing the scalar field and its properties. The best parameters of the transfer function were obtained to maximize the visibility of important features, which helps to analyze mesoscale eddies of typical ocean phenomena.

Orbital sensors have the capability to provide global repeated observations that can cover the whole world, so they have the potential to observe the earth in a large range, and are widely used in monitoring key phenological events of forest vegetation, monitoring ecosystem environment and other fields. However, due to the limitation of time and space resolution, remote sensing technology faces many challenges in the application of Earth observation. For example, the current satellite image data cannot directly reflect the occurrence and development process of key phenological events. In addition, different methods are used to indirectly estimate ecological indicators to obtain estimation results that are far different, which brings great difficulties to correctly explain and understand the correlation between phenological change and ecosystem and climate change. In a word, the monitoring and research of ecological events based on satellite images need reliable ground reference data, and the lack of relevant ground phenological event observation reference information is the recognized main bottleneck in the field.
In the field of forest phenological monitoring, there are many technical challenges to assess the exact date of phenological events such as the germination and abscission of leaves based on satellite observations. This study aims to explore the feasibility of studying the change characteristics of light reflectivity of vegetation during the occurrence period of key forest phenological events based on the high spatial and temporal resolution long-term forest observation timing of laser radar, and optimizing the monitoring accuracy of satellite phenological events based on the relevant characteristics. The research relies on LiPhe (Lidar Phenological Monitoring) platform and relevant data established by Geospatial Information Research (FGI) of the Finnish National Surveying and Mapping Bureau. In February 2020, LiPhe platform was established in Finland's 111 year old national experimental forest area (Hyytiälä 61°51 ́N,24°17 ́E)。The Liphe platform mainly comprises a Riegl VZ-2000 ground-based lidar scanner (TLS), as well as relevant edge computing and remote control facilities. The scanner is installed on the SMEAR II meteorological observation tower established in 1995, 30 meters from the ground, and the scanning range covers about 10 hectares of forest around the tower. Since April 2020, the scanner will collect data every half an hour and continuously provide point cloud data with high spatial resolution (100 meter distance and 1 cm three-dimensional point distance).

As of October 2021, a year and a half of high spatial and temporal resolution 3D point cloud observation sequence has been obtained.
This study shows the important potential of relevant data as accurate phenological ground observation reference data to support the study of tree level phenological change mechanism and optimize satellite image phenological event monitoring results. With the help of machine learning method, the expected results of the occurrence time of key phenological events in the same forest area based on LiPhe data and satellite data are compared, and the key parameters affecting the prediction of the occurrence time of phenological events are analyzed. The correlation comparison reveals the characteristics of forest light reflectivity changes caused by the dynamic changes of forest structure and the changes of its related physical properties, and demonstrates the optimization role of the relevant characteristics in the interpretation of satellite spectral information phenological events.
In the field of ecological environment quality assessment, scholars have learned the complex and diverse characteristics of the ground ecosystem through in-depth research, and it has natural defects to simply use a single indicator to evaluate the ecological quality. Relying on the advantages of remote sensing data, such as easy access, large area and multi temporal observation, scholars have proposed a variety of comprehensive ecological remote sensing inversion models, coupling a variety of ecological indicators to reflect the quality of the ecological environment. Among them, the RSEI model proposed by Xu Hanqiu couples the four indicators of greenness, humidity, heat and dryness, and uses the principal component analysis method to adaptively determine the weight of the indicators, eliminating the risk of too strong subjective factors of AHP experts' experience to confirm the weight, which has been widely used in the field of ecological remote sensing. This paper studies the ecological environment quality of Shanxi Province in 2013-2019 based on the RSEI model. Specifically, through the Google Earth Engine (GEE) cloud platform coding, the winter RSEI results of 2013-2019 were obtained, and the spatiotemporal differentiation characteristics of the ecological environment in winter in Shanxi Province were studied. The experimental process was mostly based on the GEE cloud platform, which has important research significance for the ecological environment protection in Shanxi Province and even the whole country.