Conference Agenda

At 10:47 on January 19, 2019(Beijing time), the Xuelong ship, which carried out the 35th Antarctic survey mission, sailed in the dense ice area of the Amundsen Sea. Due to the influence of dense fog, Xuelong collided with the iceberg at 69 ° 59.9 ′ south latitude and 94 ° 04.2 ′ west longitude. A SAR image covering the same area was acquired by Sentinel-1 with HH polarization and pixel resolution of 40m at 10:41 on the same day. The research objective is to evaluate the potential of Sentinel-1SAR images to identify icebergs, especially whether the icebergs that hit the Xuelong can be detected to issue early warnings in advance. In addition to the Sentinel-1 data, the research data also includes the location information of the Xuelong provided by the ship's automatic identification system. First the Xuelong ship is detected according to AIS data. Then area of interest around the ship in the image is cropped. CFAR algorithm fit for iceberg detection is select to automatically identify the iceberg. Finally by the visual interpretation, the result is to determine whether the iceberg hitting Xuelong ship can be detected using Sentinel-1 data and the smallest size iceberg.

Nowadays, conventional Delay-Doppler processors (DDP) are continuously operating with data from satellites such as Sentinel-3 and CryoSat-2, providing surface waveforms with along-track resolution around 300m. Such a resolution is the maximum achievable with DDP methods, which process the echoes received within a burst in a coherent way but then combine the echoes from multiple burst incoherently. However, new Fully-Focused SAR (FF-SAR) techniques are expected to improve along-track resolution down to around 1m by coherently combining all the echoes received during the whole observation time. Such a step ahead in resolution opens a new set of possibilities to study the impact of along-track resolution in the evaluation of some sea state parameters.

Indeed, parameters such as the Significant Wave Height (SWH) when retrieved with DDP analytical retrackers often present residual bias that are related to how the illuminated scatters recombine within the 300m DDP footprint into the final waveform. In this sense, usage of FF-SAR processors offers the opportunity to study the SWH dependence on waveforms computed with along-track resolutions much smaller than 300m. Still, current radar altimeters operate at close-burst, which is a non-optimal mode for FF-SAR processing and therefore the presence of along-track replicas affects the measurements.

In this contribution we therefore present a comparison analysis of sea state variables retrieved by evaluating the Fully-Focused SAR processor with different along-track resolutions and multi-looking parameters. We have processed a set of Sentinel-3 passes over the Yellow Sea with a local Fully-Focused SAR processor implemented in time domain. The retracker considered is based on an analytical retracker developed specifically for DDP and adapted to operate with FF-SAR.

Sea ice thickness is one of the important environmental parameters describing the material balance of sea ice and controlling regional heat exchange, which has an extremely important impact on global climate change, resource development and shipping in polar regions. In the past three decades, with the development of sea ice remote sensing technology, satellite altimeters can achieve sea ice thickness observation on a global scale, and a variety of satellite sea ice thickness data products have been publicly released. These products usually provide complete coverage of the polar region for one month, which is limiting for polar sea ice monitoring and sea ice forecasting applications. The recently launched CryoSat-2 and Sentinel-3A, both carrying SAR mode sensors operating in Ku-band, are able to invert sea ice thickness more accurately. Due to the different orbital configurations, the two satellites have a low spatial overlap, making the fusion of sea ice thickness data possible. In this study, we propose to use the spatial complementarity of CryoSat-2 and Sentinel-3A to realize a half-month sea ice thickness fusion product in the Arctic region. The results are also compared and analyzed with Operation IceBridge (OIB) airborne measurement data and Beaufort Gyre Explorer Project (BGEP) elevation sonar actual measurement data to verify the accuracy of this fusion product.

This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated CYGNSS Level 1B (version 2.1) observables and the wind speed from European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data in coastal regions. An ANN model with five hidden layers and 200 neurons in each layer has been constructed and applied to the validation set for wind speed retrieval. The proposed ANN model achieves good wind speed retrieval performance in coastal regions with a bias of −0.03 m/s and a RMSE of 1.58 m/s, corresponding to an improvement of 24.4% compared to the CYGNSS Level 2 (version 2.1) wind speed product. The ANN based retrievals are also compared to the ground truth measurements from the National Data Buoy Center (NDBC) buoys, which shows a bias of −0.44 m/s and a RMSE of 1.86 m/s. Moreover, the sensitivities of the wind speed retrieval performance to different input parameters have been analyzed. Among others, the geolocation of the specular point and the swell height can provide significant contribution to the wind speed retrieval, which can provide useful reference for more generic GNSS-R wind speed retrieval algorithms in coastal regions.

Data preparation is critical to the development of deep learning methods and can mean the difference between an inaccurate or highly accurate model. In standard deep learning image segmentation applications, data preparation normally takes the form of standardising input image sizes, colour and contrast adjustments, and augmentation such as randomised rotation and image cropping to vary the training process and improve accuracy while also reducing overfitting to training datasets.

Multi- and hyper- spectral satellite earth observation data offer many more channels than the consumer cameras used to train the majority of deep learning approaches. Standard pre-processing techniques, such as atmospheric correction, can be applied to earth observation data but, without consideration to their effects, they may remove or alter information that could prove valuable in a deep learning context. Theoretically, a deep learning neural network should be able to make use of all available waveband channels without alteration to achieve best accuracy.

The process of atmospheric correction uses dedicated wavebands and fitting procedures to separate the contributions of water, atmosphere and adjacent land prior to the interpretation of biogeochemical water quality from satellite imagery. The atmospheric correction combines known physics of the interaction of light with the various media and spectral optimisation approaches to remove up to around 90% of the at-sensor signal. This raises two questions. The first is whether atmospheric correction may remove features that deep learning networks can exploit to distinguish between objects. Reciprocally, it is worth exploring whether atmospheric correction over water could benefit deep learning networks by contributing to the level of harmonization in the input data, as the variable atmospheric influence is (in the ideal situation) removed.

To evaluate the need for atmospheric correction when applying deep learning for segmentation of objects in water, we prepared a training dataset processed to three levels of atmospheric correction. These are: (1) no correction (i.e., top of atmosphere), (2) correction for Rayleigh scattering to produce bottom-of-Rayleigh reflectance and (3) atmospheric correction using the POLYMER atmospheric correction tool. The dataset consists of coastal and inland waterbodies assembled from 150 Sentinel-2 MSI observations divided into 55,000 image tiles of varying sizes covering multiple sites across North America, South Africa and China and extensively labelled for the presence of phytoplankton blooms, land, water and cloud to be compatible with Deep Learning methods. This dataset was used to train a variation of Mask-RCNN to segment algal blooms.

We find that a priori atmospheric correction provides significant accuracy gains. Using all 13 MSI wavebands provides maximum accuracy, while subsets of 5 and 3 wavebands can be used with an accuracy loss of less than 10%, which suggests transferability of the method to sensors with alternative spectral capabilities may be possible.

Tropical cyclone (TC)-induced coastal flooding is a dangerous natural hazard to coastal areas. It is the combined influence of storm surge-caused ocean water inundation and heavy rain-caused river water flooding. TC-induced coastal flooding causes massive loss of life and property in coastal areas, where most of the world's population and mega-cities are densely distributed.

Accurate mapping of coastal flooding is important in several aspects: (1) it can assist the management to accurately evaluate the loss and make more suitable disaster relief plans; (2) it can help the scientists to better understand the flooding mechanisms and develop more precise forecasting models; and (3) the flooding extent information can be converted into the flooding depth information.

Synthetic aperture radar (SAR) is a suitable remote sensing means for coastal flooding mapping, since it can provide a day-and-night, all-weather sensing ability and high-resolution images of the flooded scene. In addition, SAR is sensitive to surface flooding, and sometimes it can extract flooding under vegetation. The traditional methods for coastal flooding mapping in SAR images are mainly based on image processing techniques by using backscattering, statistical, and polarimetric information. The traditional methods use pre-defined features and/or rules based on human observation and design. Due to the limitations of human-designed features and rules, it is difficult for them to provide robust performance under various influences, including speckle noise, SAR imaging system parameters, temporal mis-registration, meteorological conditions, and environmental differences.

The rapidly-developed deep learning technology in the big data era provides a promising solution for robust coastal flooding mapping in SAR imagery. In the deep learning paradigm, the deep convolutional neural networks (DCNN) are suitable for image information mining. In the DCNN models, the features for robust image pattern recognition and classification are mined from a large volume of input data, instead of being pre-defined with human's observation of a limited amount of data. These data-driven models with the help of domain knowledge have the advantages of providing robust features for pattern recognition and classification under various influences. In our previous work, we proposed an improved DCNN model which provides robust performance for coastal flooding mapping from bi-temporal and dual-polarization SAR images, and verified this model is useful for geospatial and temporal analysis of TC-induced coastal flooding.

Currently, there exist some DCNN-based models for coastal flooding mapping in SAR images. Most of them are based on supervised DCNN models. The supervised DCNN models need a large volume of ground-truth labels matching the observation data. The ground-truth labels are generated by human experts with the help of multiple data sources and scene information. This process is very time-consuming, costly, and subject to human bias. In addition, the trained supervised model should be fine-tuned for new conditions, if the new conditions are not covered by the data set used in the training. This may affect the generalization abilities of the DCNN model. Therefore, in this study, we propose to explore the DCNN models in another direction, that is, the models without human supervision, for coastal flooding mapping in SAR images.

The proposed design is based on deep convolutional autoencoder (DCAE). The DCAE models can fuse the backscattering, polarimetric, spatial contextual, and temporal information of SAR images together, and mine the most salient features in an unsupervised way from the high-dimensional input image data. In this study, we exploit this ability of the DCAE models, and then perform unsupervised clustering on the DCAE-generated features for coastal flooding mapping. We find the following interesting characteristics of the proposed framework: (1) The framework is robust under local interference, such as speckle noise and temporal mis-registration, since the convolutions in the DCAE can incorporate the spatial contextual information. (2) The framework is robust under meteorological influence, since it deals with the mapping problem case by case. The features for flooding mapping are generated from the data under the same meteorological condition. (3) The framework can not only extract flooded areas, but also classify the scene. Since the flooded areas are linked to the scene, we can get richer information from the flooded areas. For example, the flooded areas can be categorized as flooded areas along water areas, around built-up areas, or over open areas. (4) The framework can not only find surface flooding, but also have the potential to find flooding under vegetation at the same time. Under certain circumstances, the flooding under vegetation can have a distinct pattern in SAR images, since the flooded areas cause the increase of double-bounce scattering. This pattern can be extracted in the proposed framework along with the pattern from surface flooding. Therefore, we find the proposed framework is promising for robust coastal flooding mapping in SAR imagery, and it can get a more comprehensive understanding of the coastal flooding.

We perform some experiments with the Sentinel-1 SAR data during the passages of 2017 Harvey and 2019 Hagibis. The validation data are generated from human labeling with the help of history information and high-resolution optical data, if available. The preliminary results verified the aforementioned characteristics of the proposed framework. The unsupervised framework is suitable for natural disaster impact, such as coastal flooding, information mining.

Ocean monitoring is of paramount importance for maritime surveillance, climate change, environmental purposes and for the blue economy. When considering the microwave range of the electromagnetic spectrum, ocean shows specific scattering characteristics. In this domain, the Synthetic Aperture Radar (SAR) is becoming a well-established tool for maritime applications, including wind speed retrieval, target detection and pollution monitoring.

In this study, the spectral signatures of low-backscattering sea areas in Sentinel-1 SAR imagery, namely the autocorrelation function evaluated along the azimuth direction (AACF), are analyzed. Typically, low-backscattering sea areas are associated to oil spills due to the damping effects they have on the wind-driven short sea waves that result, in the gray-tones intensity SAR images, in patches darker than the surrounding sea. This study aims at analyzing the sensitivity of the AACF over different low-backscattering areas. A dataset composed of six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode under low-to-moderate wind conditions over low-backscattering sea areas of known origin, is considered.

Experimental results show that the AACF is sensitive to low-backscattering sea areas of different nature, i. e., oil spills, algal blooms, etc. The largest departure, with respect to the AACF evaluated over sea surface, is observed over oil slicks. The outcomes of this sensitivity analysis can support the development of improved sea oil spill detection algorithm.

As a final milestone of the ESA-MOST Dragon-4 project, the papers published on peer-reviewed international journals must be mentioned, as follows:

V. Corcione, F. Nunziata, M. Portabella, G. Grieco, X. Yang, and M. Migliaccio, "SAR Azimuth Cut-off to Estimate Wind Speed under High Wind Regimes," Chinese Journal of Geodesy and Geoinformation Science, vol. 4. no.1, pp. 30-37, 2021.

V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. 3331-3340, 2018.

V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. 3331-3340, 2018.

G. Benassai, D. Di Luccio, V. Corcione, F. Nunziata and M. Migliaccio, “Marine Spatial Planning using high resolution SAR measurements,” IEEE Journal of Oceanic Engineering, vol. 43, no. 3, pp. 586-594, 2018.

Abstract: As the nontrivial instrument to probe at high spatial resolution under extreme wind conditions, C-band synthetic aperture radar (SAR) is able to collect high resolution measurements covering with tropical cyclones (TCs) to support the modeling of TC wind speed. The tangential wind profile model is one of the effective and widely used methods to reconstruct the radial wind speed of TCs. In this research, a tangential wind profile model (TWP) in the form of Gaussian function is proposed based on the retrieval wind speed of cross-polarized SAR data. The proposed model is to solve the common defects existing in the present models, such as the unsmooth transition of the wind speed profile in the high wind speed area (near the eyewall) and the poor performance of wind estimation in the TC eye area. In order to respectively estimate the wind speed inside and outside eyewall, the model function is designed as piecewise one with the maximum tangential wind as the threshold. Then, the model parameters can be determined by fitting the model functions to the azimuthal-mean wind speed derived from SAR observations. For model validation, we choose hurricane Arthur (2014) as a study case and compare the TWP model with the widely used single-modified Rankine vortex (SMRV) model, the results show that as the distance from the TC center increases, the tangential wind speed reconstructed by the TWP model changes relatively smoothly in the high wind speed area (near the eyewall). Besides, compared to linear fitting of SMRV in the aera inside eyewall, nonlinear estimating result of TWP performs better. Moreover, both the results of TWP and SMRV model have been validated with collocated Stepped-Frequency Microwave Radiometer (SFMR) measurement. For TWP model, the root means square error (RMSE), bias, and correlation coefficient are respectively 1.01 m/s, 0.58 m/s, and 0.99, while the same statistics are only 2.22 m/s, 1.50 m/s, and 0.96 for the SMRV model, respectively. Finally, the value distribution ranges of parameters are discussed by utilizing flight-level observations collected from Atlantic and eastern Pacific storms in the period 1977–2001, and the results of discussion can be the references for determining value of parameters in model application.

Keywords: C-band synthetic aperture radar (SAR), tropical cyclone (TC), tangential wind speed, parametric model.

Acknowledgments: This work was supported by the ESA-MOST Dragon 4 cooperation project ID 32235 “Microwave satellite measurements for coastal area and extreme weather monitoring”.

Emulsion oil slick is one of the most common types of oil spills in the marine environment. Oil slicks can be observed as “dark” patches on SAR images because: a) oil slicks effectively damp capillary and short gravity sea waves responsible for backscattering energy; b) the relative permittivity of the contaminated area decreases significantly due to the smaller oil permittivity. In this paper, to take both effects into consideration, the backscattering from the oil-covered sea surface is predicted using the Advanced Integral Equation Method (AIEM) model which is augmented with: a) the composite reflection coefficient of the two-layer medium that consists of adding an oil layer on top of the sea surface, where the seawater volume fraction in the oil emulsion modifies the dielectric properties; b) the sea spectrum model combined with the hydrodynamic model of local balance (MLB) which is employed to describe the damping of the small-scale roughness by an oil film. An accurate description of geometrical and dielectric properties for oil-covered sea surface is provided according to a layered medium and used to predict the radar backscattering according to oil’s thickness and oil/water mixing for different scenarios.

According to the model's predictions, the sensitivity of oil-covered sea surface backscattering to oil thickness and water content of emulsion increases as the increasing radar frequency with a reduced L-band sensitivity. The backscattering signals exhibit a nonlinear behavior with respect to oil thickness because oil films affect the backscattering in a twofold way: through the damping of the small-scale roughness as well as the composite reflection coefficient resulting from the three-layer medium. The incidence angle has a relatively minor impact on deviating the contaminated sea's backscattering, and the high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface. Numerical simulations are compared with the multi-frequency SAR observations and results show that it is possible to estimate the oil thickness at reasonably good accuracy.

This work was supported by the ESA-MOST Dragon 4 cooperation project ID 32235 “Microwave satellite measurements for coastal area and extreme weather monitoring”.

Snow falls frequently over the Tibetan Plateau, and is a vital component of the widespread cryosphere which has vital feedback to climate change. Snowfall and the subsequent evolution of the snowpack have a large effect on surface energy balance and water cycle. Albedo, the main determinant of net radiation flux, is a major driver of land surface processes. However, the current widely used Noah land surface model does not describe snow albedo correctly, although it keeps snow-related variables into account. In the improved albedo scheme, albedo was parameterized as functions of snow depth and age which was developed using remote sensing retrievals of albedo. Numerical experiments were conducted to model a severe snow event in March 2017. The performance of WRF coupled with Noah applying the improved albedo scheme was compared with that of applying the default albedo scheme and with that of WRF coupled with CLM applying CLM’s complex albedo scheme. First, the improved albedo scheme largely reduces the WRF coupled with Noah albedo overestimation in the southeastern Tibetan Plateau, remarkably reducing the large cold bias estimates by 0.7 ℃ air temperature RMSE. Second, the improved albedo scheme gives the highest correlationship between the satellite-derived and the model estimated albedo, contributing to achieve the SWE spatial pattern, heavy snow belt and maximum SWE estimates in eastern Tibetan Plateau. Remarkable underestimation of albedo in WRF coupled with CLM contributes to regional maximum SWE underestimation and failure in heavy snow belt estimates.

Over a decade of OMI observations provide insight into the rapidly changing air quality levels in China. Global documentation of key atmospheric pollutant gases such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and formaldehyde (HCHO) allow the study of anthropogenic and natural emissions on different spatial scales.

Based on bottom-up emissions inventories, Chinese SO₂ emissions were the world’s largest, particularly over the North China Plain. SO₂ sources are related to major coal-fired power plants and industrial activities such as oil and gas refining, and metal smelting. North China Plain is also characterized by a strong seasonal footprint of fire emissions, a significant contributor to poor air quality, due to biomass burning from agricultural activities. The highest NO_x emissions are observed over the world’s most populated (increased traffic), highly urbanized and industrialized regions.

Despite the growth of the economy in the rapidly developing China over the past two decades, a substantial overall decrease in the SO₂ and NO_x emissions has been observed with different patterns between the species. Changes are observed also in the human-induced fire emissions over the North China Plain. We identity the origin of these emissions and we investigate the spatial and temporal variability of the source types. Such variability is strongly related to differences in economic and technological activity, and regional environmental policies.

Government efforts to restrain emissions from power plants and industrial sectors (e.g. installation of de-sulfurization devices) have resulted in decreasing SO₂ and NO_x emissions since approximately 2007 and 2011, respectively. We use the SO₂/NO_x ratio to locate and characterize the emissions sources since, to some extent, it reflects the level of the regional modernization and helps us identify the source sector. For instance, the megacity of Shanghai and the areas around it are highly populated with cleaner power plants compared to other regions, therefore a relatively low SO₂/NO_x ratio is observed. We use OMI HCHO observed columns to infer biogenic emissions and fire emissions as an additional human-induced emission source with high impact on the air quality of China. We compare our satellite-derived findings to the MEIC bottom-up anthropogenic emission inventory combined with biogenic emissions from the REAS inventory.

The radiances measured at top of atmosphere are the light back-scattered from surface and atmosphere, which is of the same order over bright surface such as desert and urban, resulting in the failure of current retrieval algorithm over land. The images from orbit sensor with high spatial resolution provide us very fine quality picture of the cloud and surface, it is very clear to discriminate cloud and its shadows projected on the surface from cloud-free pixels. Over non-transparent cloud’s shadow area, it is assumed that only diffused light are reflected, while over a nearby bright surface, both the solar direct beam and the diffused lights are reflected back to the atmosphere, and then transmitted to the space detector. The contrast of pixels over the shadow and nearby bright surface could be used to derive the aerosol optical properties, this method was first proposed by Duan & Lu in 1999 and simulations are given in 2002. As the release of the high quality data of Landsat 8, this method are revisited, cases studies for different aerosol loading and types, surface albedos show the feasibilities of this algorithm over surface where no dark-pixels exist.

China is suffering from severe air pollution in winter with high load of particulate matters (PM_2.5) and in summer with high ozone (O₃) concentration. A great part of PM_2.5 and O₃ are produced in the polluted atmosphere from their precursors, and nitrogen dioxides (NO_x) are important precursors of both PM_2.5 and O₃. The delivery of the Clean Air Action Plan in 2013 has led to substantial reduction of sulfur dioxide (SO₂) emission and remarkable decrease of PM_2.5 concentrations. NO_x emission control is not as effective as that of SO₂, and nitrate (produced from NO_x) is playing an increasingly important role in PM_2.5 during haze. At the same time, surface O₃ concentrations over China has increased through the years. This study is aimed at constraining the emission of NOx from China in July 2019, and exploring the influence on surface ozone simulation.

Sulfur dioxide (SO2) from volcanic eruption has a significant impact on global climate and aviation. Satellite remote sensing technology provides an unprecedented advantage for continuous, large spatial and short-revisit monitoring for atmospheric SO2. GF-5 Environmental Trace Gas Monitoring Instrument (EMI) with high spatial resolution of 7.5 x 13 km is the China’s first instrument of hyper spectral measurements with wavelength range from 240 nm to 710 nm, and makes daily global observations of key atmospheric constituents, including ozone, nitrogen dioxide, sulfur dioxide. In this study, GF-5/EMI SO2 columns were retrieved by using GF-5 EMI UV-2 band observations and DOAS algorithm, and then the retrieved GF-5/EMI SO2 columns were compared with similar AURA/OMI and S5P/TROPOMI SO2 columns to evaluate the ability of GF-5/EMI on global SO2 monitoring. Results show that, GF-5/EMI can obtain the daily distribution of SO2 from volcanic eruption. The accuracy of GF-5/EMI SO2 columns can meet the needs of application of global volcano monitoring.

We have evaluated TanSat carbon dioxide total column measurements against the ground-based measurements from the Total Carbon Column Observing Network (TCCON) instruments. In the evaluation we determined station-specific biases and precisions. Also the trends and seasonal cycle amplitudes were compared to see how well the longer-term changes can be followed by the TanSat measurements.
High latitude measurements were of special interest in our studies. We determined if the measurements were made above a snow-covered surface using NSIDC's IMS Daily Northern Hemisphere Snow and Ice Analysis data. The availability of high latitude measurements over snow and in general were calculated. The retrieval errors for snow and no-snow cases were compared to assess the errors related to snow-covered scenes.
We utilized the AirCore measurements performed at the Arctic Space Centre of Finnish Meteorological Institute to assess the prior profiles used in the TanSat carbon dioxide retrieval. The AirCore profiles can extend up to 30 kilometers altitude with superior vertical resolution in comparison to remote sensing methods.
We found that the TanSat carbon dioxide total columns agree well with the TCCON measurements. The biases were between -2.9 - 1.6 ppm with precision (1-sigma) varying from 0.5 to 2.5 ppm, depending on the station. There were vast amount of TanSat measurements made over snow surfaces but the average retrieval uncertainty over the snow cover was 3.11 ppm when it was only 1.37 ppm over snow-free land. In general, the prior profiles agreed well with AirCore measurements with some deviations in the upper atmosphere during spring.

Since the successful launch of ESA’s Earth Explorer mission Aeolus in August 2018, atmospheric wind profiles from the ground to the lower stratosphere are being acquired on a global scale, deploying the first-ever satellite-borne wind lidar system ALADIN (Atmospheric LAser Doppler INstrument). ALADIN provides one component of the wind vector along the instrument’s line-of-sight (LOS) with a vertical resolution of 0.25 km to 2 km depending on altitude. The wind accuracy is better than 1 m/s, while the random error ranges from 3 to 6 m/s. The near-real-time wind observations contribute to improving the accuracy of numerical weather prediction and advance the understanding of tropical dynamics and processes relevant to climate variability.

Already several years before the launch of the Earth Explorer mission, an airborne prototype of the Aeolus payload – the ALADIN Airborne Demonstrator (A2D) – was developed at the DLR (German Aerospace Center). Like the direct detection Doppler wind lidar on-board Aeolus, the A2D is composed of a frequency-stabilized ultra-violet laser, a Cassegrain telescope and a dual-channel receiver to measure LOS wind speeds by analyzing both molecular and particulate backscatter signals. Thanks to the complementary design of the A2D receiver, broad vertical and horizontal coverage across the troposphere is achieved. In addition to the A2D, DLR’s research aircraft carries a well-established coherent Doppler wind lidar (2-µm DWL). It is equipped with a double-wedge scanner which allows for the determination of the wind vector with accuracy of better than 0.1 m/s and precision of better than 1 m/s. Hence, both wind lidars represent key instruments for the calibration/validation activities during the Aeolus mission.

After the launch of Aeolus, the A2D and 2-µm DWL were deployed during three airborne validation campaigns between November 2018 and September 2019. 20 coordinated flights along the satellite swath were conducted in Central Europe and the North Atlantic region, yielding a large amount of wind data from the troposphere under various atmospheric conditions in terms of cloud cover and dynamics. The high accuracy of the 2-µm DWL allowed to precisely assess the Aeolus systematic and random errors, and thus enabled a comprehensive evaluation of the satellite’s wind data product quality. Due to the high degree of commonality of the A2D with the satellite instrument, the comparative wind results delivered valuable information on potential error sources as well as on the optimization of the Aeolus wind retrieval and related quality-control algorithms. Beyond the airborne campaigns, the A2D has been serving as testbed to explore new measurement strategies and algorithm modifications which cannot be readily implemented in the Aeolus operation modes and processors, respectively.

The poster presentation will provide an overview of DLR’s airborne validation campaigns and show selected highlights both from an instrument and a meteorological point of view.

Humans have transformed many open waters into agricultural/urban land or aquaculture to meet the growing demand for food and living space of the growing population in recent decades. These changes have been putting cumulative impacts on territorial hydrological cycle, the environment and ecology. Though the information on the distribution and changes of water cover types is essential to understand those changes, its timely and accurate acquisition and updating is difficult to be accomplished, mainly due to the lack of effective and practical classification method at various scales.

From water cover perspective, we firstly suggest a comprehensive water cover classification system in terms of water sources, water body shapes and water usage, including rivers, reservoirs, lakes, agricultural ponds, seasonal wetlands and rice fields. Based on this classification system, an automatic classification method based on shape features and flooding frequency that can be carried out on a large scale was developed correspondingly. This method is also based on the time series Sentinel-1 and Sentinel-2 images. Using this method, we develop China Water Cover Map (CWCM) with an unprecedented spatial resolution of 10 meters in the year 2020 based on the Google Earth Engine (GEE) . It has an overall accuracy of 0.86 and kappa coefficient of 0.83. The CWCM is the first effort on water cover mapping for whole China at 10-m spatial resolution, which can provide complete information on water cover types. The CWCM indicates that China’s water cover area is estimated to be 334,550 , of which 47.5% is accounted by artificial water covers (rice fields and agricultural ponds). From the perspective of the geographical distribution of water cover, China’s water cover is mainly distributed in Tibet, Heilongjiang, Xinjiang and Qinghai provinces. The shape-based and inundation frequency-based automatic method highlights the potentials of fast water cover type mapping at various scales using the existing water datasets.

General. The albedo of snow and ice is characterized by a large spatial and temporal variability and determines to a large extent the energy and mass balance of the snowpack and glaciers. Spatial variability in snow and ice accumulation and melt leads to mass flow. The dynamic of the atmospheric boundary layer is determined by the energy balance, particularly net radiation, at the land-atmosphere interface. Accurate retrieval of snow and ice albedo using radiometric measurements by space-borne imaging radiometers requires addressing multiple radiative interactions in the land-atmosphere system. Retrieval of atmospheric properties over land requires accurate knowledge of the background reflectance, thus leading to simultaneous retrieval of land surface reflectance and of atmospheric constituents, particularly aerosols. Multiple effects of the complex terrain characteristic of High Mountain Asia need to be taken into account for accurate retrieval of land surface albedo, particularly of the snowpack and glaciers. First, irradiance at a location is largely affected by radiance reflected by the surrounding terrain. Second, the spatial heterogeneity of the latter needs also to be taken into account. Third, the anisotropy of land surface reflectance, particularly of snow and ice has to be characterized and applied both in the estimation of terrain effects and in the retrieval of the target reflectance.

All these aspects are being addressed in an integrated effort to improve the accuracy of albedo retrievals in High Mountain Asia and to understand how and to what extent albedo drives atmospheric boundary layer and land surface processes.

Aerosols + irradiance. Two advanced algorithms to retrieve land surface albedo and aerosol amount and properties were integrated to improve iteratively two strongly related aspects for simultaneous retrievals: a) the characterization of the land surface background to separate the surface and atmospheric signals in aerosol retrievals and b) the accurate separation and estimation of direct and diffuse irradiance in the retrieval of land surface albedo. The correlation of snow and ice albedo with aerosol loading, due to the deposition of fine particles on the surface, was further documented by comparing the evolution of Aerosol Optical Depth (AOD), measured at the ITP Nam Co observatory with glacier albedo in the Western Nyainqentanglha Mountains (WNM) during 2009–2018. It appears that initially AOD was rather low and albedo rather high, both relatively stable. After 2013 a rapid increase in AOD and rapid decrease in albedo was observed.

Terrain contribution to irradiance. Terrain has a two-fold impact on reflectance retrieved from radiometric measurements by a space-borne imaging radiometer: 1) multiple scattering of down-welling radiance on the surrounding terrain modifies the irradiance onto a specific facet; 2) the exposure of the observed facet modifies the inherent anisotropy of the surface. Both effects were evaluated and taken into account in an advanced retrieval algorithm. The evaluation of these radiation-terrain interactions cannot be separated from the accurate description of radiative transfer in the atmosphere. This was documented by analyzing the difference between the reflectance at l = 0.64 mm corrected for terrain effects using either the MODIS data products on atmospheric conditions or the hourly ERA5 and the CALIPSO aerosol extinction coefficients

Anisotropy correction of snow and ice albedo. We calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets on Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model to retrieve albedo from Landsat 5/Thematic Mapper (L5/TM), Landsat 8/Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world.

Parameterization of albedo and PBL processes. We evaluated whether an advanced hydro-meteorological model, i.e. WRF, correctly accounts for the variability in snow and ice albedo and for the response of the latter to atmospheric forcing. In-situ and satellite observations on snow and ice albedo were combined to develop an improved parameterization of ice and snow albedo in relation with snow age and depth. Numerical experiments applying alternate parameterizations and atmospheric forcing were evaluated against in-situ observations of snow water equivalent. The experiments demonstrated the sensitivity of atmospheric processes to ice and snow albedo in the Qinghai-Tibet Plateau and the need to apply realistic parameterizations taking into account ice and snow properties.

Glacier mass balance and flow. The potential of the high spatial resolution ZiYuan-3 (ZY-3) Three-Line-Array (TLA) stereo images to retrieve glacier mass balance has not been sufficiently explored. We optimized the procedure to extract a Digital Elevation Model (DEM) from ZY-3 TLA stereo images and estimated the geodetic mass balance of representative glaciers in two areas of the Nyainqentanglha Mountains (NM) using ZY-3 DEMs and the C-band Shuttle Radar Topography Mission (SRTM) DEM in three periods, i.e., 2000–2013, 2013–2017 and 2000–2017.

We investigated the glacier surface velocity in the Parlung Zangbo Basin (PZB) by applying the normalized image cross-correlation method to Sentinel 2 (S2) MSI and Landsat-8 (L8) OLI image data acquired from 2013 to 2020. We mapped time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu and Azha) in the PZB. We explored the driving factors of surface velocity and of its spatial and temporal variability.

Net shortwave radiation is an important forcing of glacier energy balance, contributes more than half of the net radiation and is directly determined by glacier surface albedo. Models of So far, in the glacier-energy balance usually estimate albedo by mapping the snow and free ice components. Snow albedo is estimated by parameterizing snow decay with age, while ice albedo is usually set constant when the snow has melt. This albedo parameterization is likely to be inaccurate, especially where a mixture of snow, ice and debris occurs. Previous studies have shown that glacier surface albedo can be accurately retrieved from remote sensing data, especially the Sentinel-2/MultiSpectral Instrument (S2/MSI) radiometric data of high spatial resolution (10 or 20 m) and high radiometric performance. In this study, we used albedo retrieved from S2 / MSI data to evaluate albedo estimates by a glacier energy balance model and then to improve the albedo parameterization and the model performance. A distributed model named Tethys-Chloris (T&C) was applied to test this approach in the Parlung No.4 glacier in the southeast Tibet Plateau. The results show that T&C performs better when ingesting S2 / MSI albedo retrievals, especially in the spatial patterns of glacier albedo, energy and mass balance.

Glaciers are key components of the mountain water towers of Asia and are vital for downstream domestic, agricultural and industrial uses. The highest rate of glacier mass loss in this region occurs in the Southeastern Tibetan Plateau, where it has accelerated in the past four decades. This has been attributed to a warming climate, but the influence of decadal changes in precipitation and its interplay with recent warming remains unclear. Furthermore, Tibetan glaciers’ changing role in catchment hydrology remains largely unknown. Here, we reconstruct catchment runoff and glacier mass changes since 1975 at Parlung No. 4 glacier, using a fully distributed glacio-hydrological model (TOKPAPI-ETH), to shed light on the drivers of mass losses for the monsoonal, spring-accumulation glaciers of this region. Parlung No.4 Glacier is considered as a benchmark glacier in this region, since its meteorology, surface energy fluxes and mass-balance have been examined since 2006. It is a maritime glacier with a spring (April-May) accumulation regime , which is followed by a period of simultaneous accumulation and ablation during the Indian Summer Monsoon (typically June-September). We force the model with climate reanalysis (ERA5-Land, CMFD) and Zayu’s national meteorological station data bias-corrected to a local weather station. TOPKAPI-ETH is calibrated and validated with automatic weather station data, discharge measurements, geodetic mass balance, stake measurements and snow cover data from MODIS. Our modelling shows that increases in annual mean temperatures (0.23°C dec-1) in the region have simultaneously increased melt and decreased accumulation. An increase of total annual precipitation in the late 1990s initially counteracted the detrimental effect of this warming, though was eventually overcome and dominated by the changing phase of monsoon precipitation, reducing monsoon accumulation, decreasing surface albedo and further intensifying ice-melt and catchment runoff since the start of the 21st Century. Our findings also highlight an augmented spring precipitation during the last two decades which was increasingly important in buffering glacier mass loss by simultaneously providing mass to the glacier and protecting it from melting in the early monsoon period. While spring accumulation was relatively insensitive to recent warming during our study period, its potential to halt more rapid mass losses depends on the complex interplay of climate warming and monsoon dynamics in the near future.

Monitoring glacier surface velocity is important to understand the response of mountain glaciers to environmental forcing under climate change. The variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted wide attention. This study investigated in detail the spatial pattern and the temporal variability of surface velocity of glaciers in the PZB. Thanks to the Sentinel-2 MSI instrument, observations of the same region every 5 to10 days are feasible. In this study, we monitored the glacier surface velocity by using the normalized cross-correlation method. We used the Sentinel-2A/B data form October 2016 to December 2019 and Landsat-8 data from June, 2013 to June 2020 to generate velocity maps. Based on the satellite images acquired from 2013 to 2020, we present a map of the annual and of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. The results show that the glacier centerline velocity increased slightly from 2017 to 2020. Results of the L8 / OLI and S2 / MSI datasets were compared to evaluate their accuracy, and assess the reliability of cross correlation for ice flow monitoring. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Besides, there was a clear winter-spring glacier velocity speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual variability of surface velocity was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The interannual and seasonal glacier velocity result would contribute to modelling glacier dynamics in the future.

Ice cliffs play a key role in the mass balance of debris-covered glaciers, dramatically enhancing melt, but their importance has only been assessed for select glaciers due to a lack of datasets on their distribution and evolution. This is partly attributable to the lack of robust automatic methods that can replace the subjective and time-consuming manual delineation approaches used to date. Existing methods have used digital elevation models or multispectral images, but the varying slope and mixed spectral signal of these dynamic features makes the transferability of these approaches particularly challenging. We developed two automated and objective new approaches, based on the Spectral Curvature (SC) and Linear Spectral Unmixing (LSU) of multispectral images, to map these features at glacier to regional scales. These methods are i) robust and transferable to sites with varying lithologic, glaciological and climatic settings, and encompassing different periods of the melt season; and ii) applicable to atmospherically corrected Pléiades (2 m resolution) and Sentinel-2 (10 m resolution) images. We find that the SC method works best for the high spatial resolution, four band Pléaides images, while a modification of the LSU using the scaling factor of the unmixing method is best for the Sentinel-2 products with their coarser spatial resolution but greater number of bands. Comparison of the results with other existing methods shows that our approaches are more accurate and robust than any other existing automated or semi-automated approaches. We then use the LSU-based method to measure the distribution of ice cliffs across selected areas of High Mountain Asia and assess the main drivers of ice cliff density using a statistical analysis. This approach also outlines a high number of small, sometimes shallow-sloping and thinly debris-covered ice patches that differ from our traditional understanding of ice cliffs as isolated, prominent features, and which may have a non-negligible impact on the mass balance of debris-covered glaciers. Overall, our results give insights into the regional distribution of ice cliffs across High Mountain Asia, provide a database for the assessment of their role on the mass balance of glaciers regionally, and pave the way for representation of these features in debris-covered glacier melt models.