Conference Agenda

Regional application, topographic influence, and mixed pixel decomposition have become the three major scientific problems in the retrieval of forest parameters by multi-source remote sensing data. This project has proposed some methods for the prediction of the mountain forest height, the canopy closure, and the effective leaf area index. Then, the forest above ground biomass model was constructed based on vegetation indices, topographic indices and these structure parameters with physical significance.

In the aspect of three-dimensional parameter inversion of forest based on SAR, the quantitative processing methods of satellite-airborne PolSAR and InSAR data is innovated, which reduce the influence of terrain and improve the estimation accuracy of forest structure parameters. Moreover, a feature selection method for PolSAR based on genetic algorithm is proposed, which reduces the impact of feature redundancy on the accuracy of PolSAR classification and quantitative estimation. In addition, the adaptive TomoSAR spectrum analysis method is innovated, which effectively improve the quality of TomoSAR profile imaging. And a series of PolSAR/PolInSAR calibration and application research are carried out, the development of the PolSARpro software module and the translation of PolSAR classics are completed.

Biodiversity underpins the health of ecosystems and the services they provide to society. With the help of forest vertical structure parameters derived from LiDAR data, the SVR model for species diversity estimation show better result than using GF-2 multi-spectral or hyperspectral data only. The merging derived waveform parameters from synthesize waveform and the variation of spectral indices and texture that derived from GF-2 imagery showed a satisfied result for four different species diversity estimation. Synergizing passive and active remote sensing offers tremendous potential for forest species diversity estimation at regional scale.

In Dragon 4 project 32396, it involves the use of geomatic methods on remotely sensed data from both ESA and Chinese side and other geospatial databases for explored the land degradation surveillance of drylands in china. This aim frames the following concrete objectives:1)To develop methods of vegetation & soil bio-physical variables retrieval based on satellite data in drylands, at local and regional scale;2)To enhance, benchmark and validate two novel approaches, i.e. Rain Use Efficiency (2dRUE) method and Climate response in Net Primary Productivity (CRNPP)method to land degradation surveillance by remote sensing 3)To use the said approaches to map land degradation in a study area defined by the Potential Extent of Desertification in China (PEDC). After four years joint-research, following result have been achieved. 1) It is found that incorporating the red-edge bands of Sentinel-2 in LSMM could improve the accuracy of Non-Photosynthetic Vegetation Fraction estimation, utiliz-ing the VV/VH bands of Sentinel-1 was helpful for distinguishing shrub and grassland coverage and combing time series of photosynthetic and non-photosynthetic vegetation could effectively estimate the SOM in topsoil of desertified land.(2) Both two methods of land degradation could reflect the spatial distribution, driving force and the rate of degradation / restoration in the PEDC well.

Bistatic SAR interferometry is an essential technique of satellite geodesy and is capable of observing glacier height changes. Most recent studies focus on the decadal scale due to limitations of data acquisition and precision. Eastern Nyainqentanglha distributes the majority parts of maritime glaciers and presents with the quickest glacier mass loss rate at High-mountains Asia. Here we obtained two epochs of TerraSAR-X/TanDEM-X SAR images observed in ~2012 and ~2017. Together with the SRTM topography data that observed in 2000 we derived geodetic glacier mass balances in two periods (2000 - ~2012 / ~2012 - ~2017). We proposed three InSAR procedures for deriving the second period, which yields basically identical results of geodetic glacier mass balance. Topography differencing between DEMs derived by TerraSAR-X/TanDEM-X shows better precision than between TerraSAR-X/TanDEM-X and SRTM, and are capable of providing geodetic glacier mass balance information at sub-decadal scale. The patterns of glacier mass balance are almost identical before and after ~2012 and show with slightly increasing glacier loss rate. The increase of glacier height downwasting was more severe at lower sections. Glaciers distribute at the southeastern present with quicker lost rates than the northwestern part.

The cryosphere in high elevation regions is sensitive to the effects of climate change. Meltwater from the cryosphere contributes a significant fraction of the freshwater resources in China and in the countries receiving water from the Third Pole. Within the ESA Dragon 4 project we investigated glacier mass changes in different regions using multi-temporal stereo satellite imagery since the 1960s. Detailed investigations including mapping of rock glaciers have been performed in the Poiqu basin in central Himalaya. Results reveal that in most regions glacier mass loss continuously accelerated and that even in regions where glaciers have been in balance with climate mass loss now prevails. The greatest mass loss has been found in Northern Tien Shan and the Central Himalaya with mass loss rates between 0.35 and 0.40 mw.e.a^-1 and the lowest in eastern Pamir (~0.05 mw.e.a^-1). The mass loss has been primarily driven by an increase in summer temperature and is further accelerated by proglacial lakes which have become abundant in most regions with the ongoing glacier recession. The glacial lake area in Poiqu basin more than doubled between 1964 and 2017 from about 9.7 km² to more than 20 km². At the same time glaciers were shrinking at a rate of more than 0.5% per year. Rock glaciers are abundant in Poiqu region covering about 21 km², with is more than 10% of the glacier area (about 190 km²) in 2015. With ongoing glacier wastage the rock glaciers can become an increasingly important water resource.

The United Nations Sustainable Development Goals (SDGs) identify the sustainable management of freshwater as crucial for providing the economic, social and environmental well-being of the present and future generations. Lakes in the intermediate basin of the Yangtze River (YIB), play a fundamental role in regional bio-geochemical cycles and provide major services to the YIB communities including water supply, water purification, flood regulation (SDG 6.1.1 &6.3.2), climate regulation (SDG 13), food (SDG 2), recreational opportunities (SDG 3) and biodiversity (SDG 15). However, the extreme temporal and spatial variability of these massive but extremely shallow ecosystems prevents a reliable quantification of their dynamics with respect to changes in climate and land use.

Over these complex landscapes, a priority task is improving the monitoring and assessment capacity of authorities of these important resources. By exploiting the Sentinel HR constellation, well as Chinese VHR satellites, Dragon researchers developed automated or semi-automated methods to explore spatial and temporal changes to the complex ecosystems of Dongting and Poyang Lakes, as well as more than 80 water bodies in the YIB. Sentinel systematic acquisitions were used to create a dense time series, making analysis more consistent, also to over a year of a prioritised S1 acquisition. Now it is a 20 years monitoring of Poyang lake water surfaces that have been achieved. Water height was also monitored over the intermediate Yangtze basin, and combined with Hydroweb data in 3 lakes, and a dozen of virtual stations on the major rivers. One task consisted in addition to verify that all most sensitive areas would be covered by the OLTC procedure. This Sentinel3 on-board command served as the base to validate the reception window on the a priori elevation of the study lakes.

The phenology of wetland vegetation was shown to link to the dynamics of water level of the lake across the year and have important impacts on the migratory birds. Understanding the relationship between hydrological regimes and the spatial-temporal pattern of wetland vegetation is vital to collaborate the economic development and the protection of the migratory birds in the Poyang Lake, even the low reaches of Yangzi River. It was demonstrated that the White-naped cranes Antigone vipio wintering at Poyang Lake wetlands, southeast of China, mainly used the habitats created by the hydrological variations, i.e. seasonal water level fluctuation. Based on the results of the Dragon project, it was identified that White-naped cranes follow the water level recession process, keeping close to the boundary of water patches at most of the time. Similar approach has been followed also for swan geese and diving ducks.

The second pilar of the project was focused on water quality assessment and monitoring. In the Yangtze area, lakes supply freshwater and ecosystem services to nearly a billion people. Lake transparency, usually denoted by Secchi disk depth (SSD), is responsive to catchment characteristics, regulates many ecosystem features, and serves as a sentinel of water quality. Understanding spatio-temporal variations in water quality allow for the identification of major drivers of change and possible management and mitigation alternatives. Water transparency was retrieved over lakes for 2000-2018 models were developed to explore changes in catchment conditions, climate and human activities. The results showed that hydrological and climate related drivers strong influence water quality in these sensitive but vital lakes. Lake depth plays a mjor role in seasonal variations of sediment resuspension, and therefore transparency in the YIB. Likewise, wind speed and direction also influence lake water quality. During the summer, weak wind speeds and increased precipitation increase lake depth, leading to maximum transparency. Using the monthly climatological data, lakes showed two possible trends, based on the hydrological conditions of the lake. For the three largest lakes, the Dongting, Poyang, and Chaohu, in-situ water levels were higher in summer, limiting resuspension of lake sediments, often with high concentrations of phosphorus and other contaminants.

Dragon 4 results highlights the benefits of interdisciplinary approaches to gain a better understanding of managing complex freshwater ecosystems and ecosystem services. The results of the Dragon studies are being used to improve water resource monitoring and show the potential for collaborative research in Earth Observation.

Soil moisture and snow water monitoring using microwave remote sensing data has gained wide interests in recent years. In order to improve the accuracy of soil moisture and snow water estimations, in the Shandian River and Xiaoluan River basin, multi-resolution, multi-angle and multi-spectral airborne data were obtained. The near surface soil moisture (0 cm ~ 5 cm) ,200 m by 200 m quadrats and two soil moisture and temperature profile and precipitation networks were established. The result shows that the L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm³/cm³ to 0.5 cm³/cm³; In Altay National Reference Meteorological station, L, C, and X, Ku and Ka band radiometers were installed and snow process model was validated. The result shows that the SNTHERM+MEMLS simulated T_B showed an RMSE of 2.56 K and 3.3 K, respectively at 18.7 and 36.5 GHz vertical polarization, compared to ground-based radiometer measurements; Based on Global Navigation Satellite System-Interferometry and Reflectometry (GNSS-IR) technique, two methods were proposed to reconstruct the power of the direct and reflected signal. Experiment data were used to validate the proposed method, which demonstrated the feasibility of the proposed methods. The results showed that the retrieval error is within ±0.1 cm³/cm³ in most of the cases. Another experiment was carried out to measure the penetration depth of GNSS signal directly. The results showed that the maximum penetration depth under 0.1577 ~ 0.3394 cm³/cm³ soil moisture is less than 21 cm. To determine the snow water content (SWE) a new set-up using refracted signals and path-delay estimation methods were devised, tested and validated. The comparison with state-of-art snow pillow, snow scale, and manual measurements showed an agreement below 5%. It reveals that radiometer, radar observation and GNSS-IR multiple microwave remote sensing technologies have great potentials for global water cycle key components retrievals.

The project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia and to clarify the roles of the interactions between the land surface and the atmosphere over the Tibetan Plateau in the Asian monsoon system. Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems.

Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in a distributed river basin model. A pilot study was carried out on the Red River basin.

Multiple hydrological data products were generated using the data collected by Chinese satellites. Total Precipitable Water was retrieved with FY-3D/MWRI data, Snow Cover Area with FY-4A and Himawari-8 and Snow Water Equivalent with FY-3B/MWRI data. A data processing chain was developed to produce the Chinese 16 m GF-1 Analysis Ready Data (ARD). These data were used to retrieve land surface reflectance and to map land cover. A global LAI product at 1 km spatial and 5-day temporal resolution was generated with FY3/MERSI data. LAI was retrieved at higher spatial resolution with the GF1/WFV data at 16 m spatial and 10-day temporal resolution. A new ET dataset from 2000 to 2018 was generated, including rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GF, S2/MSI and L8/OLI data. The geodetic mass balance was estimated between 2000 and 2017 with ZY-3 Stereo Images and the SRTM DEM. Surface velocity was studied with L5/TM, L8/OLI and S2/MSI data over the period 2013 – 2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy and a new data set on glacier albedo was generated for the period 2001–2020.

A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung 4 and the No. 24 Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier the accumulated glacier melt during the whole period was between 1.5 m w.e. and 2.5 m w.e. in the accumulation zone and between 4.5 m w.e. and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The glacier mass balance over a period of time hides the seasonality in forcing by precipitation and temperature and the gradient in precipitation within the glaciers. These were observed by combining intensive field campaigns with continuous automatic observations.

The linkage of the glacier and snow-pack mass balance with water resources in a river basin was analysed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties, i.e. soil hydraulic conductivity, Brooks-Corey index, soil depth, minimum stomatal and soil resistances. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modelling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (AMSR), LST (MODIS), precipitation (TRMM and FY-2D) and in-situ measurements.

In the case-study on the Red River Basin a new algorithm has been applied to disaggregate the SMOS soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Abstract: The Northern China was hit by a severe dust storm on March 15, 2021, involving a large range and affecting a deep degree, which was unprecedented in more than a decade. In the study, we carried out a day and night continuous monitoring of the dust storm path, using multi spectral data from the Chinese FY-4A satellite combined with the Japanese Himawary-8 from the visible to near-infrared, mid-infrared and far-infrared band. We monitored .the whole process of the dust storm from the occurrence, developed, transportation and extinction. The HYSPLIT backward tracking results show two main dust sources: one is the west of Mongolian Republic, and the other is western Inner Mongolia Gobi and Ordos and other areas. Analysis shows that the preconditioning and favourable weather conditions were the main reason for the severe dust storm. Continuous high temperature without precipitation in early period led to abundant sources of sand and dust over the west of Mongolia and Inner Mongolia. On March 14-15, strong winds behind the Mongolian cyclone blew a great deal of dust into the air and they were transported over a long distance by the cyclone, resulting in the strong dust storm, which severely affected the vast area of northern China. The strong dust storm occurred under the background of significantly increase frequency and intensity of extreme weather over northern China in recent years, which may be related to the development of global climate change.

Figure 1 shows the process of dust blowing into the air, transportation and developed into dust storm in Mongolia on March 14, 2021.

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Fig1. Monitoring of the process of sand blowing into the air, transportation and developed into dust storm in Mongolia on March 14, 2021.

Figure 2 show the process of dust storm moved towards China and arrived in Beijing at 21:00 March 14, 2021.

Fig2. Monitoring of the process of dust storm moved towards China and arrived in Beijing at 21:00 March 14, 2021.

At the same time of about 22:00 March 14,2021, events of sand and storm blowing into the air occurred from the Hexi corridor, to the western inner Mongolia Gobi and Ordos, and the great deal of dust storm was transported east ward by strong wind behind the Mongolian cyclone, arrived in Beijing at 2:00 March 15 and affected the atmosphere combined with these from Mongolia.

Fig 3. Monitoring of dust storm from the Hexi corridor and western inner Mongolia Gobi and Ordos arrived in Beijing, and affect the atmosphere combined with these from Mongolia.

From 4:00 to 6:00 on March 15, 2021, dust from Mongolia and west China jointly formed the strong dust storm over northern China.

Fig 4. Monitoring the forming of strong dust storm over northern China during 4:00 to 6:00 March 15, 2021.

Figure 5 show the whole process and path Mongolia and combined with the other source of west China, and formed the severe dust storm over northern China and finally disappeared over the Korean Peninsula and Japanese Sea. The whole process lasted more than 40 hours, and transported a distance of about 3900km with a mean speed of 95km per hour.

Fig 5. The process and path of the severe dust storm over northern China

Keywords: the FY-4A Satellite, the Himawary-8 satellite, dust storm, climate change

It is crucial to monitor and understand regional sea-level changes that can differ from Global Mean Sea Level (GMSL) both in terms of magnitude as well as governing forcing and mechanisms (Stammer et al., 2013). 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 Beaufort Gyre (BG) in the Arctic Ocean to natural climate variability of the Pacific Ocean.

We highlight results of three studies: The first study investigates the benefits of the reprocessed altimetry dataset at 5 Hz with augmented signal resolution to study the sea level variability of the Arctic and Nordic Seas (Bonaduce et al., 2021, in prep). In particular, we compare the ability of this dataset to improve the mesoscale details in the forecasts in comparison to the conventional altimetry sampling (1 Hz) dataset and to the altimetry-blind experiments (e.g. without assimilation of altimeter data), in order to assess the added value of the enhanced altimetry reprocessing as well as the assimilation of the high-resolution altimetry data in ocean re-analysis for the Arctic. The second study highlights the non-stationary nature of the Barents Sea SST response to ENSO (Chatterjee et al., 2021, to be submitted). The causal link is established via the ENSO-related changes in north Atlantic atmospheric circulation which modulate the East Atlantic Pattern (EAP) and thus the Atlantic Water intrusion and SST in the Barents Sea. The third study investigates the impact of Pacific Decadal Oscillation-like (hereafter PDO-like) SST anomaly on surface air temperature over the mid-to-high latitude Northern Hemisphere. After removing the ENSO’s signal, the PDO-like SST anomaly is related to the surface air temperature over northern Europe and the Chukchi Peninsula and Kamchatka Peninsula through changing the atmospheric circulations.

The Third Pole Environment centred on the Tibetan plateau and the Himalayas feeds Asia’s largest rivers which provide water to 1.5 billion people across ten countries. Due to its high elevation, TPE plays a significant role in global atmospheric circulation and is highly sensitive to climate change. Intensive exchanges of water and energy fluxes take place between the Asian monsoon, the plateau land surface (lakes, glaciers, snow and permafrost) and the plateau atmosphere at various temporal and spatial scales, but a fundamental understanding of the details of the coupling is lacking especially at the climate scale. Expanding westward from the Third Pole, the Pan-Third Pole region covers 20 million km², encompassing the Tibetan Plateau, Pamir, Hindu Kush, Iran Plateau, the Caucasians, the Carpathians, etc. and is home to over 3 billion people. Climate change is expected to dramatically impact the water and energy as well as carbon cycles and exchanges in the Pan-TPE area and consequently alter the water resources, food security, energy transition and ecosystems as well as other related societal challenges. Monitoring and modelling climate change in Pan-TPE reflect key societal issues and contribute to the science component to other international initiatives, e.g. UN sustainable development goals (SDG), GEO societal benefit areas and the ESA EO science for society strategy.

The objective of this CLIMATE-Pan-TPE project is: To improve the process understanding of the interactions between the Asian monsoon, the plateau surface (including its permafrost and lakes) and the Tibetan plateau atmosphere in terms of water, energy and carbon budgets; To assess and monitor changes in cryosphere and hydrosphere; and to model and predict climate change impacts on water resources and ecosystems in the Pan-Third Pole Environment.

A core innovation of the CLIMATE-Pan-TPE project is to verify or falsify recent climate change hypotheses (e.g. links between plateau heating and monsoon circulation, snow cover and monsoon strength, soil moisture and timing of monsoon) and projections of the changes of glaciers and permafrost in relation to surface and tropospheric heating on the Tibetan plateau as precursors of monsoon pattern changes and glaciers retreat, and their impacts on water resources and ecosystems.

Method: We will use earth observation, in-situ measurements and modelling to advance process understanding relevant to monsoon scale predictions, and improve and develop coupled regional scale observation and hydroclimatic models to explain different physical links and scenarios that cannot be observed directly.

Deliverables: The deliverables will be scientific outputs in terms of peer reviewed journal publications, PhD theses and data sets in terms of novel data records and modelling tools of essential climate variables for quantification of water, energy and carbon cycle dynamics in the Pan-Third Pole Environment.

Coastal marine environments, being invaluable ecosystems and host to many species, are under increasing pressure caused by anthropogenic impacts such as, among others, growing economic use, coastline changes and recreational activities. A continuous monitoring of those environments is of key importance for the identification of natural and manmade hazards, for an understanding of oceanic and atmospheric coastal processes, and eventually for a sustainable use of those vulnerable areas.

The project “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME) addresses research and development activities that focus on the way, in which the rapidly increasing amount of high-resolution EO data can be used for the surveillance of marine coastal environments, and how EO sensors can detect and quantify processes and phenomena that are crucial for the local fauna and flora, for coastal residents and local authorities.

ReSSCoME is organized into five research packages (RP), each addressing a relevant aspect of changing coastal marine environments: the state of vulnerable coastal regions and their changes (addressed in the RP on intertidal regions and coastline changes), the impact of growing economic use on coastal environments (offshore wind farms and oil pollution), and the growing threat of plastic debris and green tides (coastal pollution).

Intertidal regions are particularly sensitive to natural and anthropogenic hazards. Hence, RP ‘Intertidal regions’ focuses on an optimization of the monitoring of those regions by including multi-modal SAR data into existing monitoring schemes that are based on optical EO data and in-situ observations.

China and Northern Europe are hot-spots for actual and future developments of offshore wind energy. As such, RP ‘Offshore wind farms’ will provide information on wind resources, wake effects and environmental impacts, which are needed by wind energy industries during the entire lifecycle of a wind farm.

The detection and quantification of marine oil pollution and the identification of its sources are crucial for the pollution monitoring in coastal marine waters. RP ‘Offshore oil pollution’ addresses these tasks through a synoptical use of EO data and the automated processing of large quantities of SAR data (Big Data).

Floating marine litter is a global problem, with millions of plastic items ending up in the sea. In addition, harmful algal blooms such as green tides are posing a threat to coastal marine environments. RP ‘Coastal pollution’ addresses both aspects and will help in both optimizing the detection and quantification of marine litter, and understanding the dynamics of green tides.

Finally, coastlines are changing rapidly worldwide as a result of both (quasi-) natural and anthropogenic pressures. RP ‘Coastline changes’ will demonstrate the use of EO data for an accurate long-term quantification of coastline changes, which is needed by coastal managers for a sustainable development of coastal environments.

The project consortium is formed by internationally renowned experts in each of the research fields. In order to ensure a high degree of cross-fertilization and synergy effects among the partners, five cross-cutting themes were identified, the synergism of EO data, handling and processing of Big Data, identification of coastal stress factors, support of Young Scientists, and dissemination and outreach. Responsibilities for each RP and cross-cutting theme are equally distributed among all partners.

The partner affiliations are based on, or close to, five European (Norwegian, North, Baltic, Black, and Mediterranean Sea) and three Chinese marginal seas (Bohai, Yellow and South China Sea). These marginal seas host five areas of interest, of which large quantities of EO data are being analysed, and in which complementing in-situ campaigns will be run. In addition, the western Java Sea will serve as a test and validation area for newly developed algorithms.

This study provides the first-year progress advancements achieved within the framework of the ESA-MOST Dragon-5 project ID 57979. The latter aims at demonstrating the benefits of radar products for coastal area monitoring. Within this context, improving the understanding of the marine environment, advancing the analysis of sea surface properties and boosting the exploitation of EO satellites for the management of marine disasters is addressed. In detail, the study provides innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions, by means of multi-polarization and multi-frequency Synthetic Aperture Radar (SAR) satellite imagery together with complementary microwave satellite instruments as scatterometers and radiometers on-board of operational and planned missions operated by ESA, ESA TPM and Chinese EO.

The main phenomena to be investigated include coastal water pollution, coastal erosion, in-land water body observation, metallic target detection and typhoon/cyclone monitoring. The proposed piece of research is focused on the development of tailored models combined with AI methodologies that allow the interpretation and the processing of polarimetric SAR measurements,collected under different imaging modes. As a result, user-friendly outputs are generated that include detection maps of metallic targets as aquacultures, ships and wind farms, wetland coastal erosion/accretion trends due to both anthropogenic and natural phenomena, mapping marine pollutants, modelling, tracking and forecasting extreme weather events as cyclones/typhoons.

As a first-year progress, the following articles were published on peer-reviewed international journals under the framework of the ESA-MOST Dragon-5 project ID 57979:

• T. Meng, X. Yang, K.-S. Chen, F. Nunziata, D. Xie and A. Buono, “Radar Backscattering Over Sea Surface Oil Emulsions: Simulation and Observation," IEEE Transactions on Geoscience and Remote Sensing, in print., 2021.
• V. Corcione, A. Buono, F. Nunziata and M. Migliaccio, “A Sensitivity Analysis on the Spectral Signatures of Low-backscattering Sea Areas in Sentinel-1 SAR Images," MDPI Remote Sensing, no. 13, pp. 1183, 2021.
• E. Ferrentino, A. Buono, F. Nunziata, A. Marino and M. Migliaccio, “On the use of multi-polarization satellite SAR data for coastline extraction in harsh coastal environments: the case of Solway Firth," IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 14, pp. 249-257, 2021.

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Our aquatic environments are critical for our wellbeing as well as providing vital resources for established maritime industries (fisheries, coastal tourism, energy and mineral production, boat building, shipping and ports activity) and emerging new industries of the Blue Economy (aquaculture, marine renewable energy, bio-products (pharmaceutical and agrichemicals), sea-bed mineral exploration and extraction, Blue Carbon (carbon storage in mangroves, seagrass and saltmarsh) and desalination. The mounting and conflicting pressures from the number of users and uses, coupled with population growth, industrialisation, land use intensification and climate change bring into focus the urgent need for the sustainable management of our aquatic resources and space.

The increasing availability of satellite data from Copernicus Sentinels (Sentinel-3) and Chinese Earth observation missions (e.g. from HY-1) has radically transformed the approaches to monitor and sustainably manage coastal, inland and transitional systems and has stimulated the development of innovative products. This project aims to develop and validate innovative products for transitional and coastal waters to support and improve the water ecosystem services, sustainable management and security.

The optical diversity that characterise many of these aquatic systems often challenges the applicability of EO approaches. As a first step, we investigated the optical diversity in coastal and transitional systems. We used in-situ bio-optical data collected between 2015 and 2019 in coastal waters around Europe. We compared the hyperspectral data with optical water types previously suggested for inland and coastal waters and proposed new frameworks for applications in coastal waters. This information is important for the applicability of retrieval algorithms in coastal and transitional waters. We also investigated the performance of different atmospheric correction models (e.g. Polymer, C2RCC, iCOR for Sentinel-3 OLCI and Acolite, C2RCC, Polymer, Sen2Cor) over coastal waters.

During the reporting period we worked on the development of Harmful Algal Blooms detection algorithms from Sentinel-3 OLCI data. This included a preliminary study of the detection of Pseudo-nitzschia blooms in Galician coast (NW Spain). We employed support vector machines (SVM) and in-situ data of Pseudo-nitzschia abundances to develop a set of novel algorithms for the detection of this diatom in the area. The models showed a robust performance in the independent test data set.

Ongoing work is focused on the testing of algorithms for the retrieval of phytoplankton size classes (PSC) in coastal and transitional systems. A dataset of in-situ fractionated chlorophyll-a data from water bodies around Europe was put together to test algorithms based on pigment analysis, chlorophyll a concentrations and phytoplankton absorption. We will present some initial findings from this benchmarking and will show ways of calibrating existing PSC global algorithms for operational use in local circumstances for Sentinel-3 data. Future work will include testing the detection capabilities of the PSC product using coincident datasets of in-situ Apparent optical properties (AOPs), Inherent Optical Properties (IOPs) and phytoplankton characteristics.

Water in oil (WO) and oil in water (OW) emulsions from marine oil spills have different physical properties, volume concentrations, and spectral characteristics. Identification and quantification of these different types of oil emulsions are important for oil spill response and post-spill assessment. Based on image statistics, we proposed here a decision tree method to classify oil type, and oil quantification is further attempted, with results partially validated through spectral analysis and spatial coherence test. The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse-resolution oiled pixels are first classified to WO and OW types, and 1295 nm is the optimal wavelength to perform spectral unmixing of mixed coarse-resolution pixels.

We applied MODIS surface reflectance data to analyse the temporal and spatial distribution characteristics of water clarity (Zsd) in the Jiaozhou Bay, Yellow Sea from 2000 to 2018. Zsd retrieval models were regionally optimized using in-situ data with coincident MODIS images, and then were used to retrieve the Zsd products in Jiaozhou Bay from 2000–2018. The analysis of the Zsd results suggests that the spatial distribution of relative Zsd spatial characteristics in Jiaozhou Bay was stable, being higher Zsd in the southeast and a lower Zsd in the northwest. The annual mean Zsd in Jiaozhou Bay showed a significant upward trend, with an annual increase of approximately 0.02 m. Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Zsd in Jiaozhou Bay, respectively.

Coastal zones are essential for the socio-economic well-being of many nations. Coastal regions, which are the location of large population centres, 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 to coastal, river delta, and inland water zones, including water management, which in turn lead to cascading and unpredictable impacts on other sectors.

The GREENISH project aims to provide extensive research and development analyses of areas in Europe and China subject to climate change induced (e.g., SLR, 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. Of great relevance is a detailed understanding of the combined risk of SLR, tidal evolution, storm surges, and ground subsidence in coastal areas and lake-river systems. 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). Inland water bodies such as lake and river system also experience substantial degradation with rapid economic development. The use of optical, SAR, InSAR, and hyper-spectral data products will be fostered. Some selected case-study areas have been identified, including the Yangtze and Pearl river deltas, Poyang Lake, the Bohai Rim Region (China), the city of Istanbul (Turkey), the Po river delta and the Venice Lagoon (Italy). Flood hazards will be investigated by using satellite SAR and altimeter data, tide gauge data, and by developing proper hydrodynamic models.

The main goal of the project is the well-use of Earth Observation (EO) data and in-situ monitoring information, to detect the long-term evolution of coastal, deltaic and lake-river systems. More specifically, the project aims:

- To study the ground deformation in coastal/deltaic regions with conventional and novel interferometric SAR approaches.

- To monitor changes of urbanized areas via coherent and incoherent change detection analyses.

- To study interactions between ocean currents and coasts, such as coastal erosion, using high resolution optical and SAR satellite images.

- To assess SLR, tidal evolution, and hydrogeological risks in urban coastal areas.

- To study the interactions between Poyang Lake and its connecting rivers.

- To study atmosphere/surface interactions and develop atmospheric phase screen correction methods in multi-temporal SAR images.

- To develop methods to integrate satellite- and ground-based RADAR systems to monitor public infrastructures in Shanghai

- To develop interactive maps of coastal, urban, and inland zones susceptible to primary and secondary risks via GIS.

Use of Earth Observation data represents the key asset of the project and expected results will contribute to further developments and analyses in the years to come, from both the theoretical and experiemtal point of view.

ID.59257: Mapping forest parameters and forest damage for sustainable forest management from data fusion of satellite data

PI Europe: Dr. Johan Fransson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SWEDEN

PI China: Prof. Xiaoli Zhang, Beijing Forestry University, CHINA

Forests play a critical role in the Earth’s ecosystem and have a strong impact on the environment. Under the threat of global climate change, remote sensing techniques provide information for a better understanding of the forest ecosystems, early detection of forest diseases, and both rapid and continuous monitoring of forest disasters. This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring. The aim is to study and explore remote sensing techniques in forest applications, especially on data fusion of satellite images, laser scanning, and hyperspectral drone images. The research contents are mainly tree species classification, forest parameter estimation and forest insect damage detection.

1. Work carried out during the current year:

(1) Satellite images:

We applied for and acquired ESA, Copernicus Sentinel and Chinese EO data, such as RADARSAT-2, SPOT, WorldView, Sentinel and Gaofen series, combined with hyperspectral data of UAV and LiDAR data to carry out relevant research. The satellite images acquired for the study area in China are:

RADARSAT-2, two scenes altogether. One scene data covers Fushun County, one scene covers Qingyuan County.

• SPOT, two images altogether. One scene data covers Wangyedian, one scene covers Gaofeng.
• Sentinel-1, time-series images from 2019 to 2020, covering Wangyedian.
• Sentinel-2, time-series cloud-free images from 2019 to 2020, covering Wangyedian.
• Gaofen-2, two images acquired 2020, that cover Gaofeng.

The satellite images acquired for the study area in Sweden are:

• Sentinel-2, time-series cloud-free images from 2018 to 2021, covering Remningstorp in Sweden.
• RADARSAT-2, one image acquired 2020 and a second to be acquired during the summer 2021, that cover Remningstorp in Sweden.
• Pleiades, one image (per 29 Apr 2021), of Remningstorp, Sweden.

(2) Survey data:

• One field investigation of forest parameters was conducted in Nanning, Guangxi, China, and the parameters including diameter at breast height (>5cm), tree height, under branch height, environmental parameters and the coordinates of four corners were obtained.
• For forest pests and diseases, a field survey of forests damaged by pine wood nematodes was carried out in Fushun, Liaoning, China. The spectral information of the damaged trees and healthy trees in different susceptible stages were collected.
• The forest information of the field sample plots in the study area Remningstorp were updated and stand-level information about forest managements were noticed. We have previously used the same field sample plots for analysis of tree species from Sentinel-1 and Sentinel-2 data.
• A controlled experiment is conducting at Remningstorp in 2021. Pheromone dispensers was set in 24 plots, expecting the bark beetles attacking around 180 trees. The process of infestations will are recorded during April 2021 to June 2021.

(3) Technical progress:

• In the section on tree species classification, we proposed two deep learning models, an improved prototypical networks (IPrNET) and a new prototypical networks combined with attention mechanism CPAM-P-NET model, using UAVs hyperspectral data, and obtained good classification results for eight major tree species in southern China.
• For forest parameters such as height, biomass, we combined the ZY-3 stereo image and DEM to automatically extract a high resolution spatially continuous tree height product. In addition, the acquired tree height product combined with Sentinel-2 data were used to achieve the forest above-ground biomass distribution map.
• In the section on forest biotic disturbance detection, pine wood nematode (Bursaphelenchus xylophilus) and Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) and the European spruce bark beetle (Ips typographus [L.]) three kinds of forest insect damage were studied.

For D. tabulaeformis, we proposed a spectral-spatial classification framework combining UAV-based hyperspectral images and digital images to achieve more detailed classification and automatically extract damaged tree crowns. For Bursaphelenchus xylophilus, we analyzed the spectral characteristics of three tree species (Pinus tabulaeformis, Pinus koraiensis and Pinus tabulaeformis) in Weihai, Shandong Province and Fushun, Liaoning Province during different susceptible stages, and explored the spectral response characteristics of plants under stress. For spruce bark beetles, we have been developing algorithms to detect infestations using Sentinel-2 images in 2018 and 2019.

2.Plan for next year

(1) Data acquisition:

We would like to apply for TanDEM data covering Wangyedian and Weihai, as well as WorldView-3 data covering Wangyedian, Anhui and Fushun.

We have ordered one WorldView-2 image and one WorldView-3 image covering Remningstorp in May 2021. We will also acquire one more RADARSAT-2 image in September, and one or two RADARSAT-2 images in the winter 2021-2022.

(2) The research content:

• For tree species classification, we will acquire WorldView-3 and Sentinel-2 images for repeated experiments in southern China, and further analyze tree species classification based on deep learning model. And in the study area Remningstorp, the planned analysis from WorldView-3 images will be done for individual trees thanks to the higher resolution of the images.
• In the section on forest parameters, we will combine the Sentinel-1 SAR data for biomass estimation, and combine the satellite images and LiDAR data for tree crown extraction.
• We will use the RADARSAT-2 images for developing change detection methods of forest biomass.
• In the section on forest insect damage detection, early detection of Bursaphelenchus xylophilus based on spectral characteristics of different stages combined with remote sensing data will be studied. And for spruce bark beetles, we will acquire WorldView-3 images and field data from a controlled experiment in 2021. Early detection using WorldView-3 images will be studied.

(3) Cooperation plan:

Our two teams share similar interests in forest remote sensing, conduct similar projects to communicate and collaborate more in scientific research. Annual seminars are planned in 2021 to 2024. The two team will collaborate in joint studies and publications. Niwen Li, a PhD student in BFU, will have a research exchange to SLU from August 2021 to July 2022. And more exchange of PhD students is under consideration. The scientific communication will be conducted every month by the researchers.

The European Space Agency (ESA) will launch in 2023 the BIOMASS spaceborne mission, based on a SAR device operating over several polarizations at P band, and whose main objective is the characterization of dense forests. One of the original features of this mission concerns its ability to provide 3-D SAR information through polarimetric and tomographic SAR (PolTomoSAR) data processing. The whole mission lifetime will be split into two phases: during the first phase (15 months) stacks of 7 coherent images will be acquired over all forested areas, whereas during the second phase (4 years), data will be measured in dual-baseline Polarimetric SAR Interferometry (PolInSAR) configuration, with 3 images every 7 months. This contribution proposes to compare the performance of the different observations modes of the future BIOMASS mission for the characterization of tropical forests, and to evaluate the gain in performance conferred by a synergistic combination of the two phases of the sensor’s life and by the use external information, such Lidar acquisitions etc. In particular, it provides indicators of variability for different typical descriptors of the SAR response of a forest, and tests several scenari for the fusion of multi-source information using SAR images acquired at P band in the frame of the TropiSAR campaign and auxiliary data.

This joint project combines the use of field and airborne remote sensing data to validate and calibrate innovative new satellite sensors from CNAS, ESA and NASA for forest inventory, assessment and monitoring applications in China and the UK.

The Pu’er airborne remote sensing experiment was conducted in December 2020, which covered a subtropical forest area near the city of Pu’er in the Yunnan province of China. The multi-sensor airborne remote sensing data covered 900 km² area were acquired using two Chinese Academy of Forestry’s Airborne Observation Systems (CAF-AOS). The LiDAR point cloud has an average footprint density of 2.3 pts/m². The hyperspectral image has a spatial resolution of 2 m and 125 bands with the spectral range spanning from 400 to 990 nm. The spatial resolution of CCD image is 0.5 m. The spatial resolution of the middle-wave infrared image and long-wave infrared image is 1.87 m and 0.85 m, respectively. Moreover, 100 forest plots, and other ground measurements such as land-use/land-cover (LULC) ground truth points, Leaf Area Index (LAI), soil moisture, and chlorophyll content were conducted during that time. These high quality airborne and ground observed data provide a great support for the validation of high resolution satellite remote sensing products.

Field data, ground based structural surveys and UAV remote sensing campaigns took place at the Aberfoyle Research Forest, Loch Lomond and Trossachs National Park, Scotland, during October – November 2019. More field data collection is planned in this area for the summer of 2021. The majority of this site comprises conifer plantations with a predominance of Sitka spruce (60%), Scots pine, Japanese larch and Norway spruce. Additionally, there are some stands of native broadleaf species. Monitoring plots for yield assessment and windthrow risk modelling were surveyed using a handheld laser scanner, enabling the beneath canopy structure and density to be recorded. Sites were overflown with UAV RGB sensors, enabling a 3D reconstruction of the canopy using structure from motion (SfM) analysis. The Research Forest was additionally flown using airborne LiDAR during April 2021, updating a sequence of previous data captured during 2002, 2006, and 2012. All the areas covered in the field and by airborne methods constitute a set of fiducial sites for the constant calibration and validation of satellite imagery. The main goals of the Scottish experiments are the creation of superior and more intensive forest inventory products, improved growth models and a more accurate production forecast system.

The airborne hyperspectral images at meter-scale spatial resolution, and centimetre resolution from UAV multispectral data, provide the capability to describe the canopy structure and monitor the physiological conditions through some narrow bands spectrum, which has great potential for forest health monitoring, forest type/tree species classification. The reflectance from airborne hyperspectral images was firstly compared with the satellite remote sensing products from Gaofen-1/2/6 and sentinel-2. Then, the forest type/tree species was classified based on the composite Sentinel-2 image with 10/20 m spatial resolution on Google Earth Engine (GEE). An automated training dataset was built based on several published land cover products. Finally, forest type/tree species classification results are generated using random forest classifier. The final forest type/tree species classification results will be evaluated using the LULC ground truth data and plots information for further analysis.

The airborne LiDAR data and UAV SfM data were used to evaluate the performances of forest vertical parameters estimation algorithms using Gaofen-7, GEDI and ICESat-2 data. Both terrain and forest parameters will be analyzed.

As grassland is the largest terrestrial ecosystem in China, as well as the sources of many major rivers and key areas of water and soil conservation, it plays an irreplaceable role in ensuring national scale ecological security and promoting ecological civilization construction. However, as an important ecological resource, grassland ecosystem in China has been greatly degrading caused by climate change, overgrazing and other human activities. Therefore, grassland degradation monitoring and assessment have become an extremely urgent work for grassland ecological conservation and restoration of degraded grassland. Some remote sensing mapping methods of grassland types and remote sensing inversion of grassland biomass at regional scale have been put forward in previous studies, but most of them have poor universality and may not meet the needs of grassland dynamic and accurate monitoring.

In Dragon 5 project 59313, we did some scientific studies based on the geomatics methods on remotely sensed data from both European and Chinese side and other geospatial databases. In the first year of Dragon 5, joint research results have been achieved in the following two aspects:

(1) Fully employing the potential of time series sentinel-1 and sentinel-2 of ESA and the observation data of grassland shrubbery sample were used as the data sources. The shrub coverage of Xilingol grassland was estimated by correlation analysis and random forest model methods. It is of great significance for the sustainable utilization and management of grassland and the response analysis of climate change to grasp the information of large-scale grassland shrub accurately.

(2) Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil quality. On the Google Earth Engine (GEE) cloud computing platform, we choose three machine learning methods: random forest (RF), support vector machine (SVM) and Multi-layer perceptron neural networks (MLP Neural Nets) models. Using Sentinel-2 of ESA, topographic factors and climatic factors carry out 30-meter resolution high-precision mapping of the 0-20cm SOC and STN content of the soil surface of grassland. High-resolution SOC and STN digital maps are of great significance for soil quality assessment and land degradation monitoring.