Conference Agenda

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

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

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

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

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

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. During the first project phase, we focussed on the SAR monitoring of exposed intertidal flats, of nonlinear oceanic internal waves, and of offshore wind installations near the coast.

SAR data acquired from intertidal flats are being used to assess their economic use through intensive aquacultures and to identify areas of strong morphodynamics. Polarimetric decomposition of Gaofen-3 SAR data of various spots on the Chinese coast revealed that aquacultural rafts show different backscattering mechanisms than the surrounding mud flats. Moreover, Sentinel-1 SAR-C data are used to generate topographic maps of extended intertidal flats on the German North Sea coast. Those maps, derived for different time periods, will be used to assess morphodynamic changes.

Nonlinear internal waves (NLIW) around Hainan Island in the South China Sea play a critical role in local mixing, thermohaline circulation and nearshore ecosystem. However, the generation of NLIWs around Hainan Island are rarely studied. We have been investigating the source sites and generation mechanisms of NLIWs around Hainan Island based on the synergistic analysis of SAR observations, field measurements and numerical analyses. It is found that the NLIWs in different regions around Hainan Island have different generation regimes.

Offshore wind installations in the South China Sea near the coast, which trigger a new demand for studying the effects of horizontal wind speed gradients and the wind power variation within the coastal zone. We have retrieved wind maps at 10 m height from Sentinel-1 SAR-C and Envisat ASAR observations and found that generally, the speed of the prevailing south-easterly winds and wind power declined by about 8% and 22%, respectively.

Apart from the various ongoing research activities, efforts have been undertaken to stimulate the exchange of Young Scientists and to educate them through dedicated Summer Schools. The efforts, however, were strongly affected by the ongoing pandemic.

The project aims at exploiting microwave satellite measurements to generate innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions. The following added-values products are addressed: water pollution, coast classification and erosion monitoring, ship and metallic target observation, typhoon monitoring.

Up to the mid-term deadline the following activities have been addressed:

Water pollution: Theoretical scattering models (under monostatic and bistatic configurations) have been developed to predict sea surface scattering with or without surfactants. In the monostatic case, theoretical predictions have been contrasted with actual measurements collected by the Synthetic Aperture Radar.

Target detection: Multi-polarization backscattering from a known ship observed at different incidence angles. The analysis is carried on using metrics based on both power and phase information.

Extreme weather events: SAR and ancillary scatterometer and model-based information are used to estimate the wind vector from SAR scenes under moderate and extreme weather conditions.

All this matter will be detailed in the proposed piece of study.

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. 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 last decades are witnessing a revolution in Earth observation (EO) capabilities for characterising coastal and inland water dynamics through the Copernicus programme of satellite missions (Sentinel 1/2/3) and Chinese Earth observation missions (e.g. from HY-1). The increasing monitoring capabilities are now extending satellite applications to near-shore and transitional systems such as lagoons and estuaries.

Our 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. It is widely recognised that the successful uptake of new Earth observation (EO) products and services is predicated by reliable and robust calibration and validation activities. Inland, transitional and near-shore coastal waters are spatially and temporally heterogeneous regarding their optical properties. It is further widely recognised within the EO and user communities, that there exists a significant lack in high quality data for calibration, validation, and uncertainty assessment. As part of the H2020 CoastObs, CERTO, NSFC and CAS projects, dedicated effort was made to collect high quality in-situ data from coastal and transitional ecosystems, including ria de Vigo, lagoons in the Danube Delta, Taihu Lake, Yuqiao Reservoir, and Danjiangkou Reservoir. The collection of new in-situ data has been challenged by the Covid-19 related travel restrictions and cruise operation difficulties in the Black Sea, which is one of our case study areas. Nevertheless, during this reporting period in-situ data were collected in the Razelm-Sinoe lagoon system,rivers and transitional waters in Hainan Island and Yantai coastal region in Shandong Peninsula coast in 2021 and 2022. Typically, the in-situ data collected included above-water Remote Sensing reflectance (Rrs), Inherent optical properties (absorption, scattering, attenuation, backscatter), chloroplastic pigments, suspended sediments (inorganic, total), coloured dissolved organic material, suspended particles composition, primary production, and size fractionated pigments. Radiometric data collected here were used in the Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity (Pahlevan et al. 2021). Detection algorithms and products for Harmful Algal Blooms (HABs) products were developed based on Sentinel-3 OLCI, HY-1C and ground-based multispectral remote sensing images respectively. HABs detected by Sentinel-3 OLCI included a regional neural network model for the retrieval of chlorophyll-a concentration and innovative species indicators based on machine learning methods (i.e. support vector machines) for Pseudo-nitzschia spp. and Alexandrium minutum. According to the validation results, Pseudo-nitzschia spp. products are quite robust and reliable, detecting over 90 % of the blooms with a false alarm rate around 10 %. A manuscript on the detection of Pseudo-nitzschia spp. has been submitted. HABs detected by HY-1C investigated the ultraviolet (UV) reflection spectra of floating cyanobacteria blooms and identified that the blooms have significant UV reflection features associated with the floating status. HABs detected by ground-based multispectral remote sensing represent a new technical method to dynamically monitoring cyanobacteria blooms which can operate under cloud cover and can provide accurate and continuous spatiotemporal patterns of the blooms. Two articles have been published in regards to the detection of HABs (Suo et al., 2021; Zhao et al., 2021). For the marine oil spills (MOS), a new lab-derived HSV colour model was proposed for the optical quantification of oil emulsions using multi-band coarse-resolution imagery and one article has been published (Jiao et al., 2021). Other relevant work during this reporting period included the development of a meta-classification framework (Werther et al. 2021) to assess the trophic status of nearshore and inland waters, and development of water clarity products for Taihu Lake (Yin et al., 2021), Yangtze River (Zhao et al., 2021) and lakes across Sri Lanka (Somasundaram et al., 2021). These will provide the basis the estimation of phytoplankton size classes and the primary production in transitional systems. We also worked on the development of new frameworks for optical conditions found in transitional systems. In-situ data of biogeochemical and inherent optical properties were used to characterise the relationships among optical clusters in inland, coastal, and transitional systems. We provide new information on the optics in these systems, which is important for optical models and remote sensing of coastal and transitional waters.

Monitoring of agricultural crops is of vital importance for efficient food production and sustainable development of agricultural sector. The main objective of the present study was to evaluate the possibilities for crop recognition, using Sentinel-2 data in Parvomay test area in Bulgaria. For that purpose, the classification methods Support Vector Machines (SVM) and Random Forest (RF) were evaluated. These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites, for the growing season 2020-2021. Main crops grown in the research area are winter wheat, rapeseed, sunflower and maize. In accordance with their development cycles, we developed temporal image composites for the suitable moments of time when each crop is most accurately distinguished from other crops. Ground truth data was available from the integrated administration and control system (IACS) - a vector database containing information about crops sown in individual agricultural parcels for the territory of Bulgaria. The IACS data was used for both training the classifiers and accuracy assessments of the final maps.

The sentinel series satellite in Europe and GF series satellite in China are providing the data options for agricultural monitoring as well as enhancing the capability of agricultural monitoring in general. This project has made the great progress since the inception of this project. In Heilongjiang Sanjiang site, the 15 farms in the plain were selected as the study area. The sentinel 2 time series images during the growing season of 2020 to 2022 were downloaded from the official website https://scihub.copernicus.eu. The samples were collected from field by the queries with farmers and field survey. The Random Forest algorithm was applied to implement the classification. Finally, the field preparation, different rice planting methods and the harvested fields were identified from the images. In Jinzhong site, a heave flood happened in early Oct. 2021. In the middle Oct., the research team was sent to the field and collected the crop types in the field, crop types in the flooded water and others harvested already in the field. The 3 good images in September were available to retrieved the crop types before the flood. The image on Oct 17, 2021 was used to retrieved the crop types and flood crops. Then the results were layered and used to analysis the flood situation. The field situation in winter also was investigated. The bare fields, fruit tree fields, vegetated fields and others were classified with a Random Forest classifier. In Shanxi site, the apple fields and winter wheat/maize fields were also identified from the satellite image. The Venus satellite images are agreed by the team to provide this year. But it seems that there are some issues and the data are not made available right now. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm is being exchanged and it is expected to bring a step forwards to support agricultural monitoring at fine scale.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global fresh water withdrawals. Irrigation efficiency and crop water use efficiency are key concerns for agricultural water management. The objective of the project is to assess irrigation water needs and crop water productivity based on the integrated use of satellite data with high resolution, ground hydro-meteorological data and numerical modelling, which is particularly significant for large un gauged agricultural areas. In such studies, satellite observation-based products or information with high accuracy and continuously spatial and temporal coverage are essential to support monitoring and modelling of agricultural water use and efficiency at farm and basin scales.

The following progresses have been made in the two years of project implementation:

1) FEST-EWB model improvement in evapotranspiration estimates over crop trees areas for optimizing crop irrigation efficiency.

Remotely- sensed data at different temporal and spatial resolution of vegetation parameters (leaf area index (LAI), fractional coverage of vegetation, albedo) which are used as inputs to hydrological model are obtained at high spatial and temporal resolution merging Sentinel 2 data with Landsat 8 and 9. Satellite LST is further retrieved from Landsat 8 and 9 at 30 m spatial resolution to be used for the hydrological model calibration.

Indeed, the energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations.

The model can work both in the single-source version (Corbari et al., 2011) and in its double-source one (Paciolla et al., 2022). The former uses a single balance equation for the pixel, while the latter, although requiring the same amount of input data, distinguishes between the vegetated and non-vegetated areas in the pixel. This improvement may result crucial in analysing heterogeneous agricultural areas such as those of fruit tree crops, where arboreal canopy is interspersed with bare soil or low-cut grass land cover.

The FEST-EWB model uses as input data the meteorological information, soil type data and satellite vegetation information (as LAI, fv and albedo from Sentinel-2 data). The comparison between modelled and observed LST was used to calibrate the model soil parameters with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration.

Preliminary results of the amount of precision irrigation water supply and the Evapotranspiration deficit at pixel scale will also be shown.

The FEST-EWB modelling approach has been applied, both in its single- and two-source structure, over field sites featuring walnut (Italy, 2019-21), vineyard (Spain, 2012 and Italy, 2008) and pear trees (Italy, 2022).

2) Estimation of Cropland Gross Primary Production by Integrating Water Availability Variable in Light-Use-Efficiency Model. A light-use-efficiency (LUE) model for cropland Gross Primary Production (GPP) estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. According to the site-level evaluation, the proposed EF-LUE model explained 82% of the temporal variations of GPP across crop sites, which is much better than original LUE model. The fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data were applied to EF-LUE model For the global cropland GPP estimation. The results showed overall better accuracy than other existing global GPP products, and it could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the soil water stress on cropland GPP. This work was published in Remote Sensing.

3) Calibration and validation of SWAT model in ungauged basins. To meet the challenge of model calibration and validation in ungauged basins, we developed a new approach to calibrate SWAT hydrological model using remote sensing evapotranspiration data. This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. Different remote sensing ET datasets were tested in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR, and the calibration results based on ETMonitor ET showed the best performance with R² > 0.9 and Nash–Sutcliffe Efficiency (NSE) > 0.8. The calibrated SWAT model simulation was validated against remote sensing data on total water storage change with acceptable performance (R² = 0.57, NSE = 0.55), and remote sensing soil moisture data from ESA CCI product with R² and NSE greater than 0.85. Based on the proposed procedure, a case study focused on Lake Chad Basin in Africa was carried out and the paper was published in Remote Sensing.

4) A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data. Microwave retrieval of soil moisture is an underdetermined issue, as microwave emission from the land surface is affected by various surface parameters. Increasing observation information is an effective means to make retrievals more robust. We developed a multi-temporal and multi-angular (MTMA) approach using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for systematically retrieving vegetation optical depth (VOD_p , p indicates the polarization ─ H: horizontal, V: vertical), effective scattering albedo (ω_{p_eff}), soil surface roughness (Z_{p_s}), and soil moisture (SM_p). The retrieved by MTMA shows generally good agreement with in-situ measurements with overall correlation coefficients of larger than 0.75, and the overall ubRMSE of (0.050 m³/m³) and (0.054 m³/m³) which are lower than that of SMOS-IC Version 2 (V2) (referred to as SMOS-IC) (0.058 m³/m³) and SMOS-L3 (SMOS Level 3) (0.066 m³/m³) products. This work was published in Remote Sensing of Environment.

Net primary productivity (NPP) and Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this study, based on the long-time series NPP data products, the global NPP spatiotemporal dynamic change analysis is realized by using the methods of trend analysis, catastrophe analysis and wavelet analysis. And then, the change trend and law of NPP in different regions are analyzed, which can provide a reference for the study of global carbon cycle. In addition, on this basis, by coupling the improved CASA model, geoscience statistical model (GSMSR) and the soil respiration–soil heterotrophic respiration (R_s-R_h) relationship model, the NEP of terrestrial ecosystems at intercontinental, national and regional scales is estimated, which provides scientific basis and technical conditions for carbon balance assessment and carbon neutralization policy formulation at different scales.