Conference Agenda

A large number of studies explored the mechanisms of synoptic-scale forming of ozone mini holes/highs: far-range meridional transport of air masses from regions with different climatological ozone mixing ratios (called “mechanism A”) and adiabatic vertical displacement of isentropes (mechanism B). In the paper, we investigate ozone mini holes/highs events over the North China Plain in winter during 1979-2019. The analysis shows that most ozone mini holes/highs events conform to the mechanism A and B and two typical weather change processes accompanying with these events: rapid cooling weather processes accompanies with ozone minihighs, while abnormal rapid warming weather processes accompanies with ozone miniholes. However, we also find a significant proportion of anomalous events do not conform to this rule: rapid cooling processes accompanies with ozone minihighs, while rapid warming processes accompanies with ozone minihighs. Behind these abnormal phenomena, there may exists a new ozone mini holes/highs forming mechanism: rapid cooling weather processes accompanies with ozone miniholes, while abnormal warming weather processes accompanies with ozone minihighs. The new mechanism may be related to the land and sea position of the North China Plain in the east and its landform features in the west.

ID: 59376/DRAGON 5
Title: Pacific modulation of the Sea level variability in the Arctic Ocean and Nordic Seas.

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 in one region on the other needs to be well-understood. Climate change in the Pacific can, for instance, impact Arctic warming and the sea ice (Li et al., 2015; Svendsen et al., 2018; Yang et al., 2020). How this translates to sea level change 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.

In so doing, results of three distinct analyses are reported here: (i) The variability of the BG as estimated using the state-of-the-art ESA Cryo-TEMPO altimeter data, while freshwater content estimates are derived from in-situ observations, ocean reanalysis and satellite sea surface salinity, satellite altimeter and gradiometer data; (ii) The benefits of the reprocessed altimetry dataset at 5 Hz with augmented signal resolution to study the mesoscale-based sea level variability of the Arctic and Nordic Seas; (iii) The usefulness of estimating a Multiparameter Eddy Significant Index in the Nordic Seas; and (iv) The remarkable role of North Pacific Oscillation in the Beaufort Sea level change.

龙计划5 ID：59376
题目：太平洋气候系统对北冰洋和北欧海海平面变化的影响

监测和理解区域海平面变化极为重要。区域海平面变化的幅度以及影响因子和机制方面均有别于全球海平面的变化（Stammer等，2013）。例如，盐度变化在北冰洋海平面的变化中具有重要作用，但其对全球海平面变化的影响则很小。定量研究区域海平面变化对自然变率和人为强迫的响应具有重要意义。此外，一个地区的气候变化对另一个地区的远距离影响需要深入探究。例如，太平洋气候系统变化可以影响北极地区的气候变化（Li等，2015；Svendsen等，2018；Yang等，2020）。然而，太平洋气候变化如何影响北冰洋的海平面变化尚不明晰。本研究旨在探究波弗特海的海平面变化与太平洋气候系统之间的联系。

本次报告主要介绍四项相关的研究内容：（1）利用先进的ESA Cryo-TEMPO高度计数据估算波弗特海平面的变化，其中数据来自于实地观测、海洋再分析、卫星海表盐度、卫星高度计和梯度计；（2）重新处理的z具有增强信号分辨率的5 Hz下的高度计数据，用于研究北冰洋和北欧海域的中尺度海平面变化；（3）在北欧海域估算多参数涡度显著指数的作用；（4）北太平洋涛动模态可以显著影响波弗特海平面变化

Satellite altimeter is one of the important means for remote sensing observation of ocean dynamic processes. Satellite altimetry data is abnormal in the coastal areas where the echo waveform of radar altimeter is affected by the land and the accuracy of the geophysical correction of sea surface height calculation is low in the coastal areas. The second ocean dynamic environment monitoring satellite of China named HY-2B is equipped the radar altimeter. The waveforms are retracked and the range corrections are reprocessed in the coastal areas in order to solve the issues that the accuarcy of HY-2B altimetry data degrades and have few effective measurements in coastal areas. According to the characteristics of echo waveform of HY-2B radar altimeter in the coastal areas, a coastal waveform retracking algorithm based on effective trailing edge and small noise leading edge is developed to reduce the influence of land pollution on HY-2B altimetry waveform retracking. The comparisons of waveform retracking results of HY-2B altimeter by different retracking algorithms show that the proposed coastal waveform retracking algorithm based on effective trailing edge and small noise leading edge has obvious advantages in waveform retracking and can obtain more accurate sea surface height observation in the coastal areas. For the degraded low accuracy of HY-2B altimetry range corrections in the coastal areas, the sea state bias correction, wet tropospheric correction, ionospheric correction and ocean tide in the coastal areas are improved, and the errors of range corrections are reduced. The comparison between the results with range correction reprocessing and the original data shows that the coastal range correction reprocessing presented in this study can effectively improve the accuracy of HY-2B altimeter data. According to the proposed coastal waveform retracking algorithm based on effective trailing edge and small noise leading edge and the range correction reprocessing method in the coastal areas, the HY-2B altimeter data in the China seas and their adjacent waters (105~135 °E, 0~42 °N) from December 2018 to May 2022 is processed. Comparisons and analyses of the sea surface height difference between the processed results and standard products show that the precision and availability of the processed HY-2B altimeter data are improved compared with the standard product.

Detection of rain on C-band synthetic aperture radar (SAR) images of the ocean is a challenging task, since several processes contribute to the radar signature of rain, which are often ambiguous: 1) surface scattering from the sea surface whose roughness is modified by impinging raindrops, and 2) volume scattering and attenuation by hydrometeors in the atmosphere. Understanding the signature that rain imposes on SAR images of the sea surface is of relevance for interpreting other features visible on SAR images of the sea surface correctly. Rain disturbs other radar signatures, e.g., those of wind patterns and of internal waves. While the contribution of surface scattering to the radar signatures of rain over the ocean has been studied intensively, the contribution of volume was often considered negligible at C-band. One mechanism that was identified only recently as an important contributor to radar signatures of convective precipitation system over the ocean, is radar scattering at hydrometeors in the melting layer (ML). Building on a previous paper, we investigate this contribution in more detail by analyzing Sentinel-1 SAR images showing radar signatures of different types of convective rain over the northern part of the tropical South China Sea. We compare them with the dual-polarized weather radar data of the Hong Kong Observatory (HKO), with and data of the Global Precipitation Mission (GPM) and with radiosonde data. The comparison shows the radar signatures due to radar scattering at hydrometeors in the ML occurs in areas where updraft has carried moist air up to the freezing level. This occurs usually near the center of the rain cell, but in one case, we have observed it also at the rim of a downdraft pattern. Here, the updraft is so strong that it reaches the height of the freezing layer, which in this case had a height of 5325 m. Our analysis has also revealed that radar scattering at hydrometeors in the melting layer does not only give rise to the often observed patches or blobs of strongly increased NRCS values at co-and crosspolarization, but also to less strong increased NRC values which lie in the range of NRCS values caused by wind. Thus, such ML-related radar signatures can easily confounded with wind signatures. Furthermore, we point out that the theory describing the radar scattering at hydrometeors in the ML, which is applied in this paper to the C-band on board the Sentinel-1 satellites, is also applicable to L-band SARs, like the one flown on Seasat. Finally, we show examples how rain disturbs the radar signature of internal waves

The monitoring and forecasting of global water cycle under climate changes indeed require enhancement of satellite remote sensing products in both of spatial resolution and accuracy and to seek for new opportunities of satellite missions. We have developed new soil moisture and vegetation optical depth datasets from current sensors including the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), AMSR2, and Soil Moisture Active Passive (SMAP). we applied the Multi-Channel Collaborative Algorithm (MCCA) to those microwave sensors operating at different frequencies possess differentiated vegetation penetration capabilities and might provide significant information of the Soil-Plant-Atmosphere-Continuum (SPAC) system.

The SMAP MCCA retrievals are inter-compared with other SSM and VOD products (MT-DCA version 5, and DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB), showing an analogous spatial pattern. The MCCA derived SSM had the lowest unbiased root mean square error ubRMSE of 0.055 m3/m3 followed by SMAP-IB and DCA (0.061 m3/m3), and an overall Pearson’s correlation coefficient of 0.744 (SMAP-IB performed best with R=0.764) when evaluated against in situ observations from the International Soil Moisture Network (ISMN). Comparable accuracy also found in widely used validation spare network SCAN. The MCCA generates VOD at both vertical and horizontal polarization. While the magnitude of the polarized VODs is lower than other products. MCCA polarized VODs were found to have a good linearity with live biomass and canopy height, though partial saturation exists in the relationship with live biomass of tropical forests but not canopy height. The polarization difference of L-band VODs is mainly located at densely vegetated and arid areas.

The AMSR-E/2 MCCA retrievals are inter-compared with other SSM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) at ISMN soil moisture networks. Although the R-value of MCCA (0.709) was slightly lower than that of LPRM-X (0.735), MCCA achieved the best scores in terms of RMSE=0.074 m3/m3, ubRMSE=0.073 m3/m3 and bias=0.007 m3/m3. For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions, especially for the H-polarized VOD. MCCA-derived VODs can physically present reasonable variations across the microwave spectrum, which is superior to the LPRM and VODCA.

Overall, MCCA products developed in this study showed good performance on both SSM and VOD. It is crucial for studies that consider the effects of paired SSM and VOD simultaneously, such as water fluxes in the SPAC system. In addition, the retrieval is implemented on snapshot observations, and MCCA can provide continuous daily data once the daily Tb is updated. It is expected that the MCCA algorithm can be extended to the observations of the upcoming Copernicus Imaging Microwave Radiometer (CMIR) mission.

The main objective of the project is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale. The objective will be achieved through the following sub-objectives: i) Retrieval of key water cycle variables from multi-source RS data (WP1); ii) Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2); iii) Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3); iv) Parameter estimations for terrestrial system models based on the LDAS (WP3); v) Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).

Two LDAS will be developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and one for EUROCORDEX-domain (LDAS_EU). LDAS_Silk will be based on the recently developed watershed system model and a common software for nonlinear and non-Gaussian land data assimilation (ComDA). LDAS_EU will be based on the recently developed Terrestrial System Modeling Platform (TSMP) and Parallel Data Assimilation Framework (PDAF). Multi-source RS data, from visible to thermal infrared and microwave, will be used to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. These data will be used as forcing data, calibration and validation data, and for assimilation into the two LDAS.

In this presentation, the progress of the project in the past three years will be reported.

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

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

In the aspect of ALADIN calibration, the ALADIN laser frequency stability and its impact on wind measurement was assessed and the correction of wind bias for ALADIN using telescope temperatures was conducted. By monitoring the ALADIN laser frequency over more than 2 years in space, excellent frequency stability with pluse-to-pluse variations of about 10MHz (root mean square) is evident despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). Another systematic error source is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %.

Shortly after the launch of Aeolus, co-located airborne wind lidar observations, which employed a prototype of the satellite instrument – the ALADIN (Atmospheric LAser Doppler INstrument) Airborne Demonstrator (A2D), were performed in central Europe, meanwhile ground-based coherent Doppler wind lidars (CDLs) net was established over China, to verify the wind observations from Aeolus. In the first airborne validation campaign after the launch and still during the commissioning phase of the mission, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. The statistical comparison of the two instruments shows a positive bias (of 2.6 m s−1) of the Aeolus Rayleigh winds (measured along its LOS*) with respect to the A2D Rayleigh winds as well as a standard deviation of 3.6 m s−1. In the validation campaign over China, by the simultaneous wind measurements with CDLs at 17 stations, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. From the comparison results of respective Baselines, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. The vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Aeolus has the capability to measure wind profiles and aerosol optical properties profiles synchronously, which provides the possibility for studying the wind-driven evolution of aerosol. Combining the measurements of ALADIN/Aeolus and the data from other spaceborne sensors, together with NWP models, wind-driven dust aerosol transport and marine aerosol production are discussed, respectively.

Based on the observation of ALADIN, combined with the data of CALIOP, AIRS, ECMWF and HYSPLIT, a long-term large-scale Saharan dust transport event which occurred between 14 and 27 June 2020 is tracked and the possibility of calculating the dust mass advection is explored. The dust event's emission phase, development phase, transport phase, descent phase and deposition phase on 15, 16, 19, 24 and 27 June are captured by the quasi-synchronization observations of ALADIN and CALIOP, which is verified with the AIRS Dust Score Index data and the HYSPLIT trajectories. The dust mass advection of each transport phase is calculated.

Based on the observation of ALADIN and CALIOP, combined with the data from ECMWF, three remote ocean areas are selected and the optical properties at 355 nm of pure marine aerosol are derived. Then the optical properties are analyzed and discussed combined with the wind speeds. Eventually, the relationships between the marine aerosol optical properties and the wind speeds are explored at two sperate vertical atmospheric layers (0-1 km and 1-2 km, correspond to the heights within and above marine atmospheric boundary layer), revealing the marine aerosol related atmospheric background states. The optical properties present increasing trends and fitted by power law function with wind speed in all cases, implying that the atmosphere of the two vertical layers will both receive the marine aerosol input produced and transported by the wind and the turbulence. As derived data, the averaged marine aerosol optical depth and the averaged lidar ratio are acquired and discussed along wind speed bins.

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

Remote sensing of ocean color over coastal waters is challenging and these difficulties can be placed in 3 categories: i) adverse atmospheric conditions associated with the presence of thin clouds or thick aerosol plumes (sometimes biomass burning), ii) challenging environments found over or around the water target (boundary conditions); iii) extreme conditions associated with the water content in optically active constituents (high concentrations of sediments). Evaluation and improvements of the estimation of bio-optical and biogeochemical parameters is an indispensable task for accurately monitoring the dynamics and the quality of coastal waters through the use of ocean color remote sensing. Especially, with the improvement of sensor ability and the advent of novel retrieval algorithms/models, ocean color is playing a more and more important role in understanding the utilization, the protection and the management of coastal environments. Ocean color data can thus provide biogeochemical data with known uncertainty, which is of great importance for quantitatively characterizing variation of key elements in coastal ecosystem and is required for input in modelling. Sentinel 3A/3B is new generation ocean color missions in Copernicus program in Europe, while HY-1C/1D is the first operational ocean color satellites in China. Their optical sensors (OLCI for Sentinel 3 and COCTS for HY-1) provide invaluable knowledge of ocean ecosystems due to their large swath and frequent coverage.

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

During the symposium, we will present the validation results of OLCI and COCTS L2 products over different coastal waters across Europe and China. In this report, reference data including both aerosol and sea-water reflectance are acquired by an automatic photometer (CE318-TV12-OC, also called SeaPRISM) manufactured by CIMEL corporation (France). It measures the sun, sky and sea surface periodically, from which aerosol optical thickness (AOT) and Remote-sensing reflectance (Rrs) can be derived. This instrument has already been deployed in AERONET-OC network. Four CE318 are selected across Europe and China, two in Europe and two in China. They are all deployed on offshore platforms where sea water demonstrates different optical signatures. With temporal coverage spanning between January 2020 and December 2022, validation results show that (1) OLCI can provide AOT generally in agreement with in-situ data but tends to over-estimate AOT in both European and Chinese waters. Such over-estimation is more notable in Europe. (2) Irrespective of water types, AOT from COCTS shows no obvious over-/under-estimation in general, but demonstrates significant uncertainty (i.e., big dispersion). (3) Rrs from OLCI agrees very well with in-situ measurements in most visible-infrared bands. (4) COCTS tends to under-estimate Rrs across various waters. Furthermore, validation results for NASA L2 ocean color products (e.g., MODIS/AQUA) with same CE318 dataset will also be presented for inter-comparison. Also, consistency will be checked among ocean color products.

Finally, spatial-temporal analysis of the variability of the concentration of chlorophyll-a concentration is analyzed for the OLCI sensor over European coastal waters. This analysis is part of the PhD thesis of a young scientist. The trend, seasonal and intra-seasonal patterns are analyzed between 2016 and 2023. Future work plan and young scientist training will also be presented.

Geohazard monitoring is essential to anticipate and alleviate the hazards of natural disasters, safeguard human lives and critical infrastructure, and promote the sustainable growth of communities located in areas prone to such events. The increasing incidence of land subsidence and landslides poses a significant threat to human settlements and critical infrastructure worldwide, requiring urgent attention and mitigation measures. To effectively manage the risks associated with geohazards and minimize their impacts, it is of utmost importance to map their displacement rates and gain a comprehensive understanding of their mechanics. In this work, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. During last year, the research team has been mainly working on: a) EO monitoring, automatic mapping and classification of active displacement areas related to land subsidence and landslides on wide regions; and b) identification of triggering factors and modelling of specific landslides and land subsidence based on InSAR and in situ data. The results obtained from the study, which primarily concentrate on selected vulnerable areas in China and Spain, offer valuable insights for planning current and future scientific efforts aimed at monitoring landslides and land subsidence. The comprehensive analyses of these geohazards are essential for effective prevention and management, as well as enabling prompt response in the aftermath of their occurrence.

In the framework of the ESA-MOST Dragon-5 project, the National Institute of Geophysics and Volcanology (INGV) from Italy, Northeastern University (NEU) and Jilin Earthquake Agency from China conduct collaborative research on the multiple mining-induced geohazards in Northeast China using time Series SAR images. Moreover, we have also considered a new study site, the Changbaishan active volcano (Jilin Province, ~300 km east from Shenyang), which was responsible for the largest eruption of the last millennium in 946 CE.

Our first study site is the Fushun west Opencast coal mine (FWOCM), located in the southwest of Fushun city, China, which is the largest opencast mine in Asia. Since the 1920s, more than 90 landslides have been reported in FWOCM, especially the huge landslide on the south slope, which named Qiantaishan landslide. The Qiantaishan landslide has experienced a fast moving period during 2013 to 2016, and has stabilized since 2017. During the fast moving period, the landslide mass has moved approximately 90 m. However, since 2017, displacements of the Qiantaishan landslide is less than 150 mm/year. In order to analyze the spatial pattern and temporal evolution of different periods of the Qiantaishan landslide, both MT-InSAR and multi-temporal pixel offset tracking have been performed. Multi-temporal pixel offset tracking has been conducted considering 53 Cosmo SkyMed SAR images collected from 2013-07-03 to 2016-12-18 for the descending track, to monitor displacement of the fast moving period of Qiantaishan landslide. The results show that the landslide moves very fast during 2014, and slows down during 2015 to 2016. The MT-InSAR analysis has been carried out based on Sentinel-1 images collected during 2017 to 2022, to track the slow-moving period of Qiantaishan landslide. MT-InSAR results highlight that the displacements rate of the Qiantaishan landslide is up to 150 mm/year, which has basically stabilized. Comparison with ground measurements and cross correlation analysis via cross wavelet transform with monthly precipitation data are also computed, to analyze the influence factors of displacements in FWOCM.

The second study site, Changbaishan volcano complex is affected by landslides, earthquakes, degassing, and ground deformation. Deformations occurred during the 2002-2006 unrest episode and in 2020-2022. Analysis on the multi-hazards of Changbaishan is very important because a population of ~135000 in China and 31000 in North Korea lives within 50 km far from the volcano. Using 33 Envisat ASAR images acquired during 2004-2010 along the descending orbit, the accurate surface deformation parameters of Changbaishan Tianchi volcano has been extracted through a modified multi-temporal InSAR approach which involves point selection based on the Normalized Difference Vegetation Index (NDVI), to minimize the volume decorrelation problem. Then, based on three-dimensional geometric relationship between the volcanic surface deformation field and the radar line of sight (LOS) deformation, Mogi point source modeling has been calculated, revealing the inflation-deflation-stabilization process of the magma chamber during the end of the 2002-2005 unrest episode. Furthermore, we analyze the deformation of Changbaishan volcano during 2018–2022 processing by means of the SBAS technique a dataset consisting of 23 ALOS-2 images (L-Band, StripMap mode), acquired along the ascending orbit and revealing a low-level unrest occurred during 2020.12-2021.6. Modeling results suggest that three active sources are responsible for the observed ground velocities: a deep tabular deflating source, a shallower inflating NW-SE elongated spheroid source, and a NW-SE striking dip-slip fault. The depth and geometry of the inferred sources are consistent with independent petrological and geophysical data.

Acknowledgments

The Sentinel-1 data are free of charge distributed by the European Space Agency.

The COSMO-SkyMed data are provided by ASI through the ASI-ESA Dragon5 Project ID. 58029.

Forests play a critical role in the Earth's ecosystem and strongly impact the environment. Under the threat of global climate change, remote sensing techniques can 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 of ecosystems and spans the subtopics estimation of forest quality parameters and forest and grassland disaster monitoring. The aim is to study and explore the application of multi-source remote sensing technology in forest parameter extraction and forest disaster monitoring using data fusion of satellite images, drone-based laser scanning and drone-based hyperspectral images. The research contents include tree species classification, forest parameters estimation, and forest disturbance detection.

1. Work performed

(1) Satellite image data

We applied for satellite images through ESA and MOST of China, including RADARSAT-2 (2020 and 2021), WorldView-3 (June 2021), Sentinel-1/2 (from 2018 to 2022), and Gaofen-1/2/6 (from 2020 to 2022). These data cover several study areas including Gaofeng, Weihai, Fushun, Lu'an, Wangyedian, Genhe and Pu'er in China and Remningstorp in Sweden.

(2) Field investigation data

For different research contents, field investigations were carried out in Gaofeng, Fushun, Lu'an, Genhe, Pu'er and Remningstorp. The details are as follows:

l The forest information of the sample plots in Gaofeng and Genhe in China was updated in 2021 and 2022.

l Spectral information from healthy and pine nematode-infested forests at different stages of the Fushun and Lu'an study areas in China was collected in 2021.

l Forest tree species types, forest changes and disturbance information of Pu'er study area in China were collected in 2023. The occurrence status and geographical distribution of Simao pine bollworm pests and diseases were recorded.

l The forest information of the sample plots in Remningstorp, Sweden was updated in 2019 and 2021. Controlled experiments were conducted for bark beetle infestation in 2021 and 2023.

(3) Technical progress

l Tree species classification. We proposed four pixel-based deep learning tree species classification models using drone-based hyperspectral data: an improved prototype network (IPrNet), a CBAM-P-Net model of the prototype network combined with an attention mechanism, a Proto-MaxUp+CBAM-P-Net model of the CBAM-P-Net combined with a data enhancement strategy, and SCL-P-Net introducing contrast supervised learning. We evaluated and screened low-cost and efficient UAV optical image acquisition solutions for individual tree species identification，and developed an instance segmentation algorithm, ACE R-CNN, for individual-tree species identification using UAV LiDAR and RGB images. The performance of these models was demonstrated in the Gaofeng study area. A tree species classification method based on multi-temporal Sentinel-2 data was developed and the performance was verified at Remningstorp.

l Forest parameters extraction. We proposed a method for extracting crown parameters considering inter-tree competition using terrestrial close-range observation data with missing canopy information. We proposed a mean-shift individual-tree crown segmentation algorithm based on canopy attributes using UAV oblique photography data, and developed an individual-tree biomass estimation model fusing multidimensional features. A three-level stratified feature screening method fusing airborne hyperspectral and LiDAR data was innovated to construct regional AGB estimation models for different tree species, which has good performance in the Gaofeng study area. A high spatial resolution tree height extraction method combining ZY-3 stereo images and DEM was proposed, and a forest AGB estimation model using Sentinel-2 data and tree height data was developed to obtain accurate forest AGB maps in the Wangyedian study area. We proposed a quantitative method for thinning and clear-cutting phase height for detecting silvicultural treatment using the phase-height data from time-series TanDEM-X. In addtion, we investigated the use of interferometry (InSAR) of TanDEM-X images for estimation of forest changes (height, biomass and biomass change), and mapped smaller forest height changes (increase) in a boreal forest in Sweden.

l Forest disturbance detection. For Bursaphelenchus xylophilus, we analyzed the spectral characteristics of two tree species (Pinus tabulaeformis and Pinus koraiensis) in the study areas of Weihai and Fushun during different infection stages. Sensitive bands were selected and a detection model was constructed to identify the infection stages of Bursaphelenchus xylophilus. A conifer information extraction index (NDFI) based on time-series Landsat images was constructed to assist remote sensing monitoring of pine wood nematode disease. For European spruce bark beetles (Ips typographus [L.]) infestation, methods of early detecting infestations were proposed using drone-based multispectral images. We investigated how early the infestation can be detected after an attack. We also compared the machine-learning- and vegetation-index-based methods for the early detection of bark beetle infestations, and found the machine-learning-based methods had overfitting issues with low transferability for the untrained areas. For forest disturbance, a CCDC disturbance detection algorithm incorporating spectral indices and seasonal features was proposed to robustly map forest disturbances over the past 30 years in the Genhe study area.

(4) Collaborative Research

l One visiting PhD student from BFU to SLU from 2022 to 2023.

l Co-supervising 1 PhD student.

l One joint research paper published in Ecological Indicators. One joint research paper under view by IEEE Transactions on Geoscience and Remote Sensing. Two conference papers were published in IGARSS 2022, and one joint conference paper was accepted by IGARSS 2023.

2. Future Plans

(1) The research contents

l For tree species classification, we will explore deep learning models for individual-tree and stand-scale tree species classification using WorldView-3 and Sentinel-2 imagery.

l For tree forest parameters, we will explore crown extraction methods combining satellite imagery and LiDAR, and monitor regional biomass dynamics using Sentinel-1 data under multi-factors disturbance.

l For forest insect damage detection, we will study early identification methods of Bursaphelenchus xylophilus and Ips typographus [L.] based on multispectral and hyperspectral images from UAVs. The improved CCDC algorithm will be used to further explore the spatial and temporal distribution patterns of forest disturbance in China.

(2) Cooperation plan:

l Co-research on Cooperation project between China and Europe in Earth Observation on forest monitoring technology and demonstration applications.

l Co-publishing 1~2 research papers.

Co-organizing an international summer school on forest parameters and deforestation mapping using remote sensing data.

The airborne multi-dimensional SAR flight experiment in the forest area of Genhe district was organized in 2021, and the PolSAR dataset of P, L, S, C and X bands, P-band TomoSAR dataset and dual antenna InSAR dataset of C band were obtained. Based on this dataset, we analyzed and evaluated the performance of PolSAR data of 5 bands and different band combinations in estimating forest volume.

In the case of PolSAR data radiometric calibration method development, using space-borne GF-3 data and airborne UAVSAR data, we proposed the cross-co-polarization radio coefficient, which can be used to obtain the truth value of polarization scattering of any distributed targets. The calibration method can effectively reduce the constraints on targets of existing methods. In case of terrain radiometric correction (RTC), one RTC method for PolSAR based on RPC model has been proposed, which reduces the technical threshold for geometric and radiometric correction of PolSAR. In addition, a RTC method suitable for supervised classification of PolSAR was proposed, which can improve the accuracy of forest type classification by about 20%.

A series of land cover classification method studies were carried out using spaceborne Radarsat-2 and airborne UAVSAR time-series polarimetric SAR data. We constructed the time-polarization features, reflected the degree of feature variance, and constructed the foundation for effective feature selection; proposed the polarimetric and time dimension feature selection algorithms IESSM and SSV, designed a classifier based on Transformer, and reduced the feature redundancy and enhanced the adaptability of the classifier; extracted the time-variant scattering features based on the time-series polarimetric SAR data characterization model, enhanced the expression ability of scattering variation, and improved the classification accuracy.

In terms of InSAR, the multi-layer model suitable for short wavelength InSAR is innovated, and the retrieval accuracy of forest height is improved by realizing that the observation calculation and theoretical model of InSAR coherence obey the same assumption. Moreover, the algorithm for jointly measuring forest height using P/X dual-frequency InSAR has been proposed, which effectively improves the extraction accuracy of DTM/DSM/CHM in forest areas.

Most current TomoSAR methods use a local means value of the sample covariance matrix, which may get the poorly refined spectrum, and lose some detailed information. In addition, the spectrum will inevitably produce sidelobe effects. To address the above issues, a non-local means method is applied to identify neighboring pixels with high similarity to the target pixel, thereby comprehensively reflecting its feature information. Moreover, G-Pisarenko method is introduced in TomoSAR to reduce the spurious interference signal. These two methods have been respectively verified that the feasibility and effectiveness with BioSAR 2008 L-band data and AfriSAR 2016 P-band datasets, respectively. In addition, we propose a TomoSAR algorithm based on atomic norm minimization(ANM) to solve the scatterer location error caused by elevation discretization in traditional TomoSAR methods. The performance of the algorithm has been verified by TerraSAR-X data. TomoSAR baseline correction and phase compensation methods for multi baseline interferometric SAR assisted by DEM have been developed, improving the imaging quality of TomoSAR over forested areas. Additionally, we have developed a multi feature collaborative forest biomass estimation method based on TomoSAR profile fitting, which has achieved high accuracy (>90%) in tropical rainforest regions.

PolTomoSAR techniques have been developed to characterize tropical forests at P band using adaptive parametric signal processing approaches, and over temperate forests at L band using a minimal number of images. The benefits of a synergistic use of the different modes of the upcoming BIOMASS missions have been evaluated by computing the ultimate performance limits of this mission for different forest characteristics, and according to various temporal scenarios. The gain provided by external sources of information, such as GEDI, has been evaluated by coupling this estimation with Bayesian principles. The case of estimation techniques using models of forest vertical profiles that differ from actual ones has been investigated. Some of the developed techniques will contribute to the BIOMASS processing group of methods Multi-Mission Algorithm and Analysis Platform (MAAP). Some work also been done regarding the definition of BOMASS level 3 product processing chain, i.e. Forest Height, Above Ground Biomass and Forest Disturbance.

Times series of Sentinel 1 measurements were used for mapping deforestation at large scale (see https://www.tropisco.org/ )and new techniques, based on Bayesian processing are being developed.

Successful monitoring and modelling climate change in water, energy and carbon cycles in the Pan-Third Pole Environment (CLIMATE-Pan-TPE) demands improved understanding of the interaction between the Asian monsoon, the Tibetan Plateau surface, and the plateau atmosphere in terms of the water and energy budget. CLIMATE-Pan-TPE aims to verify or falsify recent hypotheses, which include the links between plateau heating and monsoon circulation, snow cover and monsoon strength, soil moisture and timing of monsoon. We analysed the mechanistic bases for projections of the changes of glaciers and permafrost in relation to surface and tropospheric heating on the Tibetan Plateau, and their impacts on water resources in South East Asia. More specifically we will report the following results: (1) a long-term (2005-2016) hourly dataset of the integrated land-atmosphere interaction observations from six field stations over the Tibetan Plateau; (2) monthly actual evapotranspiration and its spatial distribution on the TP (2001-2018) using the Surface Energy Balance System (SEBS) model with satellite products and meteorological reanalysis data as input; (3) hourly land surface heat fluxes and evapotranspiration estimated based on multisource remote sensing data; (4) a monthly 0.01° terrestrial evapotranspiration product for the TP (1982-2018) using the MOD16-STM equation; (5) methods for estimating surface soil moisture, monitoring and predicting freeze-thaw states and quantifying soil ice content with microwave remote sensing data, (6) estimation of the total annual evaporation amounts over the entire TP lakes as 51.7±2.1 km³ year^-1, with a plausible hypothesis of near-zero heat storage during ice-free season and near-constant ice sublimation during winter; and (7) the water vapor channel of the Yarlung Zangbo Grand Canyon (YGC) in the southeastern TP was investigated by establishing a three-dimensional comprehensive observation system of mountain land-air interaction, water vapor transport, cloud cover, and rainfall activity. The observation datasets will benefit future research on mountain meteorology.

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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 2023 of the project are as follows:

1) 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.

2)Using HY-1C/D and MODIS satellite data, this project investigates the green tide biomass in the Yellow Sea and East China Sea. 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. The results show that : 1)Compared with both of pixel area and cover area, the uncertainty of biomass estimations is the least one which could reduce the scale differences involved in the area estimations evidently and could be used to quantify the green-tide monitoring . 2) Based on the CZI and MODIS data in 2021, the comprehensive monitoring of green-tide using biomass-like method has been conducted to display the reasonable spatial distributions as well as the evolution tendency with high accuracy.

3) A new method is proposed to compare and verify ocean wave spectrum by remote sensing and in situ measurements at the spectral level. Under different sea conditions and sea surface conditions，mean directional wave height spectra from surface waves investigation and monitoring (SWIM/CFOSAT) are compared at the spectral level to the buoy counterparts, in different classes of the sea state. Under medium and high sea conditions, 8 ° and 10 °SWIM spectra have a high consistency with buoy observations.Under low sea conditions, bias between SWIM and buoy observation mainly due to parasitic peak, non-linear surfboard effect and a slight underestimation of speckle noise spectral density.

4）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.

5）Icebergs are big chunks of ice floating on the ocean surface, and melting of the icebergs contribute for the major part of the freshwater flux into ocean. Dynamic monitoring of the icebergs and accurate estimation of their volume are of great importance to predict the trend of freshwater budget of the Southern Ocean. Prydz Bay in Antarctica with a large number of icebergs is selected as the study area. In this work, a normalized shadow pixel index (NSPI) is designed to identify iceberg shadows with different shapes in HY-1C/D CZI and Sentinel-2 MSI images. Besides, the iceberg freeboard can be determined with considerable precision (~1.13 m). Moreover, the basal melting of icebergs has been preliminarily assessed according to the variation of iceberg freeboard using repeated MSI observations. The results indicate that icebergs in Prydz Bay were with a mean freeboard of ~56 m in early December 2022, and experienced a reduction in freeboard of ~1.89 m within two months, in correspondence with the Antarctic seasonal trend. The new methodological framework, therefore, turns out to be a reliable complementary approach to studying the iceberg freeboard in polar regions.

The use of Significant Wave Heights (SWH) on the swath of scatterometers satellite missions has been shown to be of great interest for monitoring wave propagation in storm conditions and improving wave forecasting in coastal areas. In this work, the production of swath significant wave heights for all HY2B, 2C and 2D satellite missions is pursued and assimilation tests of these data have been implemented and evaluated with independent buoys and altimeters wave data. Morever combined assimilation experiments of swath SWH jointly with CFOSAT and Sentinel-1 directional spectra have been performed with the latest CFOSAT level 2 processing (IPF-6). This latter provides improved antenna gain and directional wave spectra with better data quality filtering.
We will also present in this work the development of maximum wave height retrieval using deep learning technique for HY2 missions and their use for the detection of dangerous waves by combining with parameters computed on the directional wave spectra of CFOSAT and Sentinel-1. Extreme events with strong wave-current interactions and wave propagation in cyclone conditions have been investigated.

This work investigates the recent implementation of significant wave height from SAR wave spectra by machine learnig technique. Assimilation experiments have been performed by using SWH from Sentinel-1 and SAR and SWIM directional wave spectra. The validation with buoy wave data indicates very good consistency in terms of bias and scatter index of SWH.

Further results related to impact of the assimilation of these new wave products on the coupling with ocean model and what consequences on upper ocean mixed layer.

This report will present the recent developments and accuracy validation of our project's snow remote sensing data products. In terms of snow cover area, we focused on analyzing the consistency between two snow products, VIRSS and MODIS. We found that although the NDSI obtained by the two sensors were very consistent, there were significant differences between the final snow cover area products due to differences in cloud identification algorithms. This study suggests that we should develop a cloud identification algorithm that can be applied to both VIRSS and MODIS to ensure the consistency of snow cover area data products and provide reliable data for further research on snow changes and related studies. In terms of snow depth remote sensing, we developed a machine learning-based fusion method for snow depth in the northern hemisphere. This method combined six existing snow depth remote sensing and reanalysis data, and through learning observations from nearly 20,000 stations in the northern hemisphere, obtained the fused snow depth data, which is much more accurate than existing data.

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The 2030 SDGs identify water (SDG 6), as well as vegetation/land use (SDG15) as keys parameters for providing the economic, social, and environmental well-being of the present and future generations. Remote sensing can be a powerful tool to reach these objectives and support SDG indicator analysis at different scales.

At regional and local scale, the consortium has is exploring tools to identify changes in sensitive ecosystems of the Yangtze River basin and surrounding regions. These include new approaches to study Poyang and Dongting lakes, as well as the Anhui’s small lakes. These have taken advantage of ICEYE and Radarsat data in synergy with Sentinel2 to ensure the monitoring of water extent, while IceSat and Sentinel3, Sentinel6 altimetric data have been exploited to monitor water bodies’ altitude. This acquired knowledge is crucial for the exploitation of SWOT products which first delivery will occur in summer 2023. Over Poyang, an increase in earlier draw off of the water since 2000 has an important effect on the mudflat evolution. Based on MR EO imagery, the mudflat has been increasing and the main distribution area is shifted from North to South. In the Northern area affected by sand mining, the water surface rate of the inlet channel has increased, and the overall outer edge of the mudflat is more fragmented than before. In the South side of the Ganjiang River, the delta area on the is affected by the water and sand entering the lake and is growing steadily, with the front edge of the delta extending outward for about 1.84 km.

Water quality assessment, a pilar for SDG6, requests to develop and validate processing protocols for multiple sensor systems. New advances have been done for the cCO2 estimation based on model using Sentinel-3-derived lake environmental variables and field data. Works done over sixteen lakes on the middle and lower reaches of the Yangtze basins shown that CO2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations. The annual mean CO2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO2 sinks while the rest were sources. CO2 concentrations decreased with increasing eutrophication and decreasing lake size. With this problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it’s not clear how lake transitions occur at regional and global scales. To answer this point, a long-term monitoring of 22 lakes of the Yangtze watershed have been carried out, exploiting a novel innovative and efficient three steps algorithm that can distinguish, aquatic vegetation, floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). The AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the middle Yangtze watershed. In addition, over large lakes (>500 km2), including the Taihu and Chaohu, daily MR satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics. Chaohu and Taihu lakes are presenting perennial blooms with an increasing trend. Climate factors were found to be linked to changes in annual initial bloom time; while an increase in human activities was associated to bloom duration, area and frequency.

At a larger scale, an important work has been done on the distribution and dynamics of vegetation related to vegetation growth and carbon cycling, with an analysis of the impact of global climate change, changes in temperature and precipitation. Based on meteorological and remote sensing data; critical soil moisture (CSM) was used as a proxy of the land-atmosphere coupling to study the interaction process between land and atmosphere and its impact on land vegetation. Then, single models of CMIP6 were optimized through machine learning methods to develop future climate and Gross Primary Production (GPP) datasets and analyze the temporal and spatial changes of climate and GPP.

The vegetation in China has significantly increase over the last four decades, 1982-2020, with 72.34% of the regional greening. At regional scale, precipitation is appearing as a key factor affecting vegetation growth in arid and semi-arid areas such as the Mongolian Plateau, the Qinghai-Tibet Plateau, and the Loess Plateau. Precipitation resources are abundant in southeast China, and temperature is the dominant factor of regional vegetation growth. In another hand, it must be noticed that drought stress is also an important factor affecting vegetation growth. Short-term cumulative drought promotes regional vegetation greening (1-4 months), while medium-term and long-term cumulative drought inhibits vegetation greening (5-12 months). The carbon sequestration function of vegetation in southwestern and southeastern China will be affected under continuous drought conditions.

Drought conditions were analyzed through an innovative approach based on the differential correlation measure (∆Corr) that characterizes the strength of water or energy limitations. The ∆Corr detecting the response of surface water and energy to short-term surface processes. Based on climate and vegetation characteristics, sufficient variation was found in both global and grid unit CSM content. CSM content is wetter in areas with less annual rainfall, shorter root systems, and lower vegetation coverage. It was found that under three SPP scenarios in the future (2021-2100), the high-latitude regions of the Northern Hemisphere will warm significantly, while the spatiotemporal distribution pattern of precipitation shows no significant difference. Under different scenario models, GPP changes show obvious spatiotemporal heterogeneity.

Obtained results shown how EO data exploitation can provide important insight into the knowledge and understanding of keys parameters such as water resources and quality, eutrophication purposes and this from local to global scale.

Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between the surface and atmosphere. This abstract provides a summary of the latest progress of Dragon-5 project (59318), including (1) all-weather LST generating methods and its implementation; (2) all-weather LST downscaling methods; and (3) satellite LST validation against in-situ LST.

To investigate the temporal and spatial variations of LST in China, long-term, high-quality, and all-weather LST datasets are urgently needed. However, the publicly reported all-weather LSTs are not available during the temporal gaps of MODIS between 2000 and 2002. Therefore, the enhanced RTM (E-RTM) method was proposed to produce a daily 1-km all-weather LST dataset for the Chinese landmass and surrounding areas, i.e. TRIMS LST (Tang et al., 2023). Validation against in-situ LST shows a MBE range of -2.26~1.73 K and a RMSE range of 0.80~3.68 K, with slightly better accuracy than the MODIS. The TRIMS LST has already been used by scientific communities in various applications, e.g. evapotranspiration estimation, and urban heat island (UHI) modelling.

A method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly all-weather LST (Ding et al., 2022). The method was implemented over Tibetan Plateau with the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation against in-situ LST shows that the accuracy of the all-weather LST is better than FY-4A LST and CLDAS LST. The mean RMSE is better than 3.94 K for all conditions, respectively. The reconstructed all-weather LST also has good image quality and provides reliable spatial patterns.

To obtain high spatial resolution all-weather LST, two downscaling methods were developed. The first is downscaling TRIMS LST using LightGBM from 1 km to 250 m over Southeast Tibet (Huang et al., 2021). Validation against in-situ temperature shows a MBE of 0.74 K (-0.01 K) and RMSE of 2.25 K (2.15 K) for daytime and nighttime, respectively. The second is a method, which is proposed based on the geographically weighted regression (GWR) and random forest (RF), and considering the weights of LST descriptors (Ding et al., 2023). The method was tested to downscale 1000-m aggregated ASTER LST to 100 m. Validation against in-situ LST shows that the MBE and RMSE can be reduced more than 0.22 K and 0.1 K in Beijing and Zhangye. The explorations in downscaling provide the basis for obtaining high-resolution all-weather LST.

To validate the satellite-retrieved LST at the kilometer scale, we proposed a temporal variation method for evaluating the ground station’s spatial representativeness (Ma et al., 2021). The spatial representativeness indicator is defined as the LST difference between the in-situ radiometer’s FOV and satellite pixel scale and extended in temporal with the temporal variation of LST and related parameters. Then, the in-situ LST was convert to pixel scale to validate the MODIS and AATSR LST. Results show that, among the selected stations, a systematic bias of -1.95~5.60 K and a random error of 0.07~3.72 K can be found for the validation results if the station’s spatial representativeness is ignored. Therefore, it is suggested that the ground station’s spatial representativeness over inhomogeneous surfaces should be considered in LST validation, as well as other related parameters. In further research, the spatial representativeness of KIT’s station and HiWATER station will be evaluated and then used in all-weather LST validation.

Since 2008 KIT operates a permanent LST validation station near Gobabeb, Namibia (Göttsche et al., 2022). In the rainy season of 2010/2011 the largest amount of rainfall in recorded history was measured at Gobabeb’s meteorogical station (Eckhardt et al., 2013), which resulted in an exceptionally strong growth of grass over large parts of the gravel plains. Due to the extreme atmospheric conditions and the changes in biophysical surface properties, LST retrievals for this period can provide interesting insights into the performance of LST products. Here, two all-weather LST products are compared over the gravel plains: 1) all-weather LST obtained with Reanalysis and Thermal infrared remote sensing Merging (RTM) (Zhang et al., 2021), which uses reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data to estimate LSTs at cloudy MODIS overpasses, and 2) the operational all-weather LST product of the Land Surface Analysis (LSA) Satellite Application Facility (SAF), which merges clear-sky MSG/SEVIRI LST with surface temperatures from a Soil-Vegetation-Atmosphere (SVAT) model driven by LSA SAF satellite products (Martins et al., 2019). The two all-weather LST products are validated with in-situ LST and their spatial variation over the gravel plains is investigated, thereby providing a comprehensive analysis of their performance over a broad range of atmospheric and surface conditions encountered at Gobabeb.

Eckardt, F.D., Soderberg, K., Coop, L.J., et al., 2013. The nature of moisture at Gobabeb, in the central Namib Desert. J ARID ENVIRON, 93, 7–19.

Ding, L., Zhou, J., Li, Z.-L., et al., 2022. Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data From Geostationary Satellites (RTG). IEEE Trans. Geosci. Remote Sensing, 60, 1–17.

Ding, L., Zhou, J., Ma, J., et al., 2023. A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight. IEEE Geosci. Remote Sensing Lett., 20, 1–5.

Göttsche, F.-M., Cermak, J., Marais, E., et al., 2022. Validation of Satellite-Retrieved Land SurfaceTemperature (LST) Products at Gobabeb, Namibia. Journal Namibia Scientific Society, 69, 43–61.

Huang Z., Zhou J, Ding L., et al., 2021. Toward the method for generating 250-m all-weather land surface temperature for glacier regions in Southeast Tibet. Journal of Remote Sensing, 25, 1873–1888.

Ma, J., Zhou, J., Liu, S., et al., 2021. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment, 265, 112669.

Martins, J. P. A., Trigo, I. F., Ghilain, N., et al., 2019. An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations. Remote Sensing, 11(24), 3044.

Tang, W., Zhou, J., Ma, J., et al., 2023. TRIMS LST: A daily 1-km all-weather land surface temperature dataset for the Chinese landmass and surrounding areas (2000–2021), Earth Syst. Sci. Data Discuss. in review.

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In the Dragon-5 project SARchaeology, we are working on using satellite SAR data and developing methods to support archaeological prospections and heritage site protection. SAR offers unique advantages, but also several challenges in this field. During Dragon-5, our work focused so far on study sites in China, Russia, Italy, Norway, and Turkey. However, due to the sanctions imposed on Russia, the cooperation on this test area came to a stop, so that the team members who were working on this site (i.e. University of Sydney) are currently focusing their attention on the other areas. In terms of employed methodologies, a strong focus is on the use of long-term multi-baseline SAR interferometry for continuous surface motion stability analysis on cultural heritage sites, as well as change detection methods. In terms of change detection, various approaches are under development, ranging from PSInSAR-based detection of urban developments, automatic coherence and amplitude-based change detection for looting mapping, and coherence change detection for damage assessment. Additionally, multi-sensor / multi-angle image analysis for post-earthquake damage detection in high-resolution SAR images has been undertaken for damage detection after the devastating earthquake that hit Turkey and Syria on 2023-2-6, damaged a vast area and led to immense loss in lives. The area is also well-known for its richness in cultural heritage und unfortunately, widespread damages to cultural heritage has been witnessed. To support the identification of damages at sites of archaeological interest, the team used data from the Dragon-5 project as well as several Third-Party Mission (TPM) data sources. Not all damages to cultural heritage are clearly visible from remote sensing imagery and the situation gets significantly worse when using SAR data. Even using very high-resolution TerraSAR-X staring spotlight datasets, damages are often hard to identify without the availability of similar images acquired before the disaster, which are missing. This proves the importance of continuous observation missions, like Sentinel-1, from which (albeit the very low spatial resolution) damage maps can be derived through coherence change detection analysis, which than can be used as a starting point for visual inspection on high-resolution optical images or very-high resolution SAR images. With optical imagery, the weather conditions play a central role in the detectability of damages, while on SAR images the image configuration, for example orbit direction and looking angles, can determine if a damage is visible or not in a given image.

Looting provides a global threat to cultural heritage, but in the aftermath of natural or man-made disasters, looting unfortunately strives even more. Looting activities are detectable from remote sensing. However, small-scale looting pits/holes are not always identified as such. In an experiment on the detectability of looting activities within SAR data, we conduct an experiment in Wuhan (China), where we create an experimental looting site of two different sizes and monitor the area with SAR data before and after the ‘looting’. Based on the so generated data, we analyse the detectability of looting activity in TerraSAR-X imagery at different resolutions and analyse the influence of polarizations, looking angles, and other SAR acquisition parameters.

Within the experimental area of Wuhan, we are also interested in the threat that the fast urban development of Wuhan poses to cultural heritage sites in and around the city. The fast development of the urban area of Wuhan leads to encroachment of buildings on cultural heritage sites, that, although often protected, are in danger due to the economic pressure with rising property prices. Additionally, the urban development leads to subsidence, which can also threaten the stability of sites. Using long-term SAR interferometry, the subsidence affecting sites of cultural heritage are identified as well as possible endangerment from urban encroachment.

Using a similar approach, threats to cultural heritage assets in the capital city of Rome (Italy) and its surrounding rural landscape are characterised. Sentinel-1 image stacks acquired in 2018-2022 are processed with the SBAS method, and a series of urban sectors affected by ground instability are identified across the wider Province, such as in the area of Fiumicino international airport (representing a relatively young phase of urban development and associated land conversion) and along the Tiber River alluvium, involving monuments and heritage assets. Given the paucity of studies using multi-polarization datasets in InSAR deformation investigations, the performances of the SBAS chain using Sentinel-1 VV and VH cross-polarised channels were also trialled to identify the amount and quality of coherent targets that the method is capable to detect and track using the two polarisations.

So far, the team's work focuses on employing long-term multi-baseline SAR interferometry for continuous surface motion stability analysis on cultural heritage sites, as well as change detection methods. Furthermore, the project has also shown the importance of continuous observation missions, like Sentinel-1, for damage detection and mapping especially in the context of the earthquake in Turkey. The team's experiments on the detectability of looting activities within SAR data will be significant contributions to the field of archaeology and heritage site protection. With further development and collaborations, the SARchaeology project can continue to make significant contributions to the preservation and protection of cultural heritage sites globally.

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The 3nd Year Progress of CEFO project are:

1、 System integration, multi-source LiDAR data acquisition and application

We have designed and integrated a novel airborne system, which integrates commercial waveform LiDAR, thermal, CCD camera and hyperspectral sensors into a common platform system (CAF-LiTCHy). Based on this system, the airborne data of Pu’er research area was collected. And then, the data obtained from each sensor were processed and provided a foundation for further data analysis. Furthermore, we have completed the forest inventory using a combination of point clouds generated by airborne LiDAR, drone overflight and mobile Laser scanning surveys. This method combines growth models with LiDAR point clouds analysis and the Sub-Compartment Database of the public state and the National Forest Inventory maps for the private forests. Overflights with drones and mobile Laser scanning have been used in plots across the country to validate the estimates with R² ranging between 0.7-0.9 for broadleaves and above 0.95 in conifers. Time-series of LiDAR surveys and drone data have also been used to validate growth in time as estimated by the yield models. Some plots have been covered with GeoSLAM and the point clouds have been analysed to produce estimates of DBH and stem profiles.

2、The joint use of Chinese and European satellites and data process of Chinese Terrestrial Ecosystem Carbon Monitoring Satellite

We developed a cloud free remote sensing image composition algorithm that accounts for forest phenology, along with a technology for aggregating multiple land cover products. The resulting process allowed for high-precision mapping of forest cover remote sensing in the Pu'er area, resulting in the development of 30 m resolution 2000/2010/2020 Pu'er forest cover products. Based on the cloud free images, the vegetation coverage of Pu'er City was estimated and the forest cover mapping was conducted using Sentinel-2 and GF-6 Data, field survey data, airborne data and terrain auxiliary data. To achieve measurement of forest height and terrain, the potential of GF-7 LiDAR and stereo image was evaluated. The validation test was conducted in Pu'er City, and encouraging results were obtained. To preliminarily evaluate the parameter estimation ability of waveform LiDAR data for complex forest conditions, we conducted data collection of Pu'er ALS data and screening, preprocessing, and parameter extraction of TECIS waveform data. The preliminary research results showed that when the SNR was greater than 15, in the consistency results between ALS and TECIS data for two-track data, the R² was greater than 0.6 and the RMSE was lower than 3.7 m.

3、 Forest disturbance, stress, diseases, drought and flux monitoring

Our study employed the long-term time series Landsat 8 images spanning the period of 2015 to 2020, and utilized the continuous change detection and classification algorithm to detect forest changes in the Pu'er region. The accuracy of the algorithm was evaluated by means of visual interpretation of high-resolution images and forest inventory data, yielding an overall accuracy of over 88%. Results show that the loss of forest cover is primarily caused by urbanization, cash crop plantations, and regular harvesting of fast-growing plantations. Furthermore, we have proposed methods for assessing forest stress with satellite remote sensing. This work shows a methodology to detect, quantify and better understand forest stress produced by climatic and structural variations using time-series of satellite imagery in the United Kingdom. Time-series analysis of vegetation indexes are detrended to eliminate systematic noise and historical trends, to elaborate models of phenological cycles of the vegetation at pixel level. These data-based models are used to detect differences in new image acquisitions that are compared to climatic and structural variations. Once climatic effects like drought or temperature variations are integrated, the anomalies are used as a proxy for pathogen activity in a forest area. Understanding how species and genotypes respond to drought can inform the transition to more drought tolerant forests in the future. Remote sensing provides tools to non-destructively monitor plant health at multiple spatial scales. Therefore, Sitka spruce clones were exposed to an experimental drought and monitored over eight weeks. The spruce expressed stress pigments and lost water content as the drought progressed. The stress response differed between clones suggesting intraspecific drought tolerance detectable by remote sensing. This work can inform future spruce breeding programmes and contribute to national forest health monitoring. Based on observation data of Puwen Forest flux Tower, the daily net ecosystem carbon exchange (NEE), evapotranspiration (ET) and canopy greenness index (GI) were calculated. We found that the tropical evergreen broad-leaved forest was a carbon sink in February, March and April. GI、photosynthetically active radiation and air temperature in April was the highest in three months, but carbon sink became weaken compared with that in March. Maybe drought in April reduced gross primary productivity more than ecosystem respiration.

4、 Forest gap identification and aboveground biomass calculation based on multi-source LiDAR

The LiDAR biomass index (LBI) was applied to pinus khasys species in Pu’er city. Terrestrial laser scanning data and airborne laser scanning data was collected on the field sample plots and used for accurate estimation of forest aboveground biomass from individual tree level to stand level. For the model that established using the TLS data, R² of 0.61 and RMSE of 27.04 kg was obtained. For the model of ALS data, R² of 0.83 and RMSE of 15.68 kg was obtained. In addition, the CHM was derived from the point cloud data of UAV LiDAR and the fixed threshold method was used to identify forest gaps in CHM. The reference data from visual interpretation of images was used for accuracy assessment of forest gap identification. The overall accuracy of the fixed threshold method was 92%, and the spatial distribution of the gap was aggregation. Forest gap information from UAV LiDAR can be used for the accuracy assessment and validation for the forest gap derived from GF-7 satellite imagery for large area.

As the largest terrestrial ecosystem in China, as well as the sources of many major rivers and key areas of water and soil conservation, grassland plays an irreplaceable role in ensuring national scale ecological security and promoting ecological civilization construction. However, grassland ecosystem in China has been greatly degrading caused by climate change, overgrazing and other human activities. Therefore, monitoring and assessment of grassland degradation have become an extremely urgent work. 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 past 3 years of Dragon 5, joint research results have been achieved in the following four aspects:

(1) Types of grassland identification: Integrating the advantages of Sentinel-1 and Sentinel-2 active-passive synergistic observation, takes the typical grassland of Zhenglan Banner in Inner Mongolia grassland, China as the study area, and innovates the method of grassland types classification by applying the object-oriented techniques, which improves the accuracy and refinement of grassland type classification.

(2) High temporal and spatial estimation of grass yield: Based on the Carnegie–Ames–Stanford approach (CASA) model, integrating the advantages of the high spatial resolution of GaoFen-6 wide-field-of-view data and the high temporal resolution of MODIS NDVI data, we propose a reasonable expression method for the optimal temperature of the model. The applicability of the NPP conversion method to estimation of grass yield in different grassland types is then analyzed in Zhanglan Banner.

(3) Identification of shrub-encroached grassland: In order to explore the application potential of remote sensing technology in the recognition of spatial distribution of shrub-encroached grassland, combing the domestic multi-source remote sensing data GF-2, GF-3 and GF-6 to study the remote sensing technology in identification of shrub-encroached grassland at different scales from the perspective of classification identification and quantitative extraction respectively by using random forest algorithm and scrub cover estimation model.

(4) Global grassland degradation detection and assessment: We quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth and its response to climate change. Furthermore, the driving factors behind these trends were analyzed, especially in hotspots.