Conference Agenda

Sea ice is a highly sensitive indicator of past and present climate change. The demand for getting comprehensive, continuous, and reliable sea ice information from multi-source satellite data is growing as a result of climate change and its impact on environment, regional weather conditions, and on human activities such as operations in ice-covered ocean regions. The major objectives of the project are to upgrade and develop methodologies to retrieve quantitative sea ice and iceberg information using multiple satellite data provided by the EC Copernicus Earth Observation Program, ESA TPM, and Chinese satellites. During the last year, the Sino-European group has been focusing on the application of microwave sensor for detection sea ice thickness, ice concentration, and iceberg detection.

The partners from NSOAS and FMI developed sea ice concentration (SIC) estimation and SIC noise reduction algorithms with the Chinese microwave radiometers e.g. HY-2 Microwave Radiometer and FY-3 Microwave Radiation Imager. We investigated the brightness temperature signatures of different surface types in various sea ice and weather conditions. The uncertainty and error statistics of the retrieved SIC are determined using validation data from in-situ measurements and high-resolution SAR satellite data.

The work of sea ice thickness estimation by altimeter was carried out at FIO and NSOAS. We developed a method for calculating sea ice freeboard from HY-2B data. Two echo waveform retracking techniques were assessed for their performances in measuring lead and sea ice elevations from HY-2B data. These derived HY-2B sea ice freeboard and thickness data were compared with those from CryoSat-2 acquisitions. Recently the effect of uncertainties in the input parameters and related sensitivities of the algorithms when retrieving ice-thickness from HY-2 is being analyzed.

Another joint effort by AWI/UiT, FIO, FMI, and SCMU is in preparation, namely dealing with the detection of icebergs in sea ice and on the open ocean. The plan is to develop robust and automated methods for iceberg detection. The research will analyze and evaluate the capability of the proposed methods using different radar frequencies, and in dependence of spatial resolution, incident angle, and the surface conditions around the icebergs. This study will be presented in more detail at the Symposium.

High Mountain Asia cryosphere and water resources remain still largely unconstrained, despite very substantial advances in modelling and remote sensing. In this project, we leverage the opportunity offered d by ESA and NRSCC to exploit new remotely sensed datasets to advance our understanding of blue and green water interactions in high elevation catchments in the first truly inter-comparative study across HMA. I will provide an update of the main project objectives, methods and modelling approaches, and present preliminary results in both modelling and remote sensing.

Our main overall aim is to understand how green water processes affect the availability of blue water from glaciers, snow and precipitation across High Mountain Asia. To achieve this goal, we are working with our Chinese partners in a very close collaboration and have designed three main objectives. The first objective is to advance our understanding of cryospheric, vegetation and land surface changes from remote sensing observations at benchmark sites; the second objective is to generate glacier-specific altitudinal surface mass balance profiles to investigate patterns of changes and validate the glacier component of the land surface model; and the third one ist o quantify the water cycle of select HMA water towers using a novel hyper-resolution land surface model to examine variability, seasonality and long term changes, with emphasis on the feedback between blue and green water.

Use of a hyper-resolution ecohydrological model, fed by Earth System Observations, is very novel. We will bridge the modelling gap between snow and glaciers, which generate the runoff that ultimately feeds major rivers, and downstream water cycle components such as vegetation, which buffer, delay or amplify that runoff. We will focus on blue (runoff) and green (evapotranspiration) water interactions in HMA, which are often examined separately. We will integrate water supply changes due to a vanishing cryosphere with the effect of vegetation to dampen or amplify those changes, especially in periods of droughts. The new model will be applied to 10 benchmark catchments representative of the climatic differences of HMA. Its application and validation will be based on remote sensing data: high-resolution satellite data of land-cover, surface albedo, snow, vegetation phenology, surface water, glacier velocities, surface lowering and mass balance will guide model developments and support model calibration and validation in a systematic manner to ensure comparability across case studies. The 10 glacierized sites span a variety of climates, glacier conditions and mass balance regimes. For each catchment, field measurements of glacier melt, mass balance, runoff and meteorological variables are also available and will complement the remote sensing data.

To present, we have advanced on all three main objectives. We have developed a method to derive snow cover and snow line elevation from satellite images and applied to the entire HMA, providing both a novel understanding of snow cover patterns across this very broad scale and validation data for the modelling at the 10 catchments. We have devised a new method to retrieve glacier albedo from remote sensing that has been applied also to the entire HMA region, and is being refined now to understand catchment-scale patterns and variability. For the second objective, we have developed a new method to retrieve altitudinally resolved surface mass balance from satellite derived elevation differences, have applied it to all glaciers in HMA and are now refining it to provide the validation datasets for the 10 catchments. I will present results from its application to the Langtang benchmark catchment in Nepal. Towards objective 3, we have setup the new land surface model in the same Langtang catchment, for the two data-rich years of 2017-2019, and are analysing the first simulations now. The model setup entailed a number of major challenges, from the spatial redistribution of the meteorological forcing from station data to the characterisation of the parameters controlling the vegetation response. It is the first time that a physically-based model that calculates all energy and mass fluxes in a distributed manner is applied to a HMA catchment: the energy balance calculations require knowledge of wind, radiation fluxes, relative humidity fields in addition to the temperature and precipitation forcing commonly used in more empirical glacio-hydrological models, and parameters need to be defined in space and time. I’ll present the application of the model to the Langtang catchment, its validation with remote sensing data and highlight the advantages that this new modelling perspective offers over more traditional modelling results.

Finally, I’ll present the project next steps and the planning towards the achievement of our goals. At the end of the project, our multidisciplinary team of European and Chinese scientists will: i) provide an advanced characterisation of the main glacier and hydrological processes from remote sensing observations in the high elevation catchments of HMA; ii) resolve the altitudinal surface mass balance for all study glaciers and determine patterns and drivers of surface mass balance; iii) use a novel hyper-resolution earth-surface model to simulate the complexity of the high mountain water budget, understand blue-green water fluxes and quantify changes in past and future streamflow.

Backscatter coefficient (σ₀) of SAR is a key feature to map the freezing and thawing status of Greenland Ice Sheet. However, backscatter coefficient not only depends on the status of the observed object but also on the incidence angle of the electromagnetic wave that illuminate the object. For SAR image with a narrow swath width, incidence angle difference near range and far range does not affect the backscatter coefficient much for ice sheet observation, however Sentinel-1 employs IW (Interferometric Wide) and EW (Extra Wide) mode to observe the Greenland Ice Sheet, which means incidence angle difference roughly at 17° and 27°。The traditional method of normalizing backscatter coefficient to a reference angle is by presuming that the observed object behave like a Lamber-tian surface reflection model, which means uniform scattering in the hemisphere, that is Backscatter coefficient (σ₀) is proportional to the square of cosine value of incidence angle. However, snow and ice do not exactly follow the Lamber-tian surface reflection model, as front scattering dominate dry snow zone when surface scattering dominate the bare ice zone on the ice sheet.

Here we present a method by using linear regression to backscatter coefficient (in dB) difference and incidence angle to normalize backscatter coefficient to a reference incidence angle. We employ the Sentinel-1 images that observed the same area within 24 hours, and presume that the scattering feature does not change during such short period. We take the overlap area of ascending and descending area. Considering that the surface features of Greenland Ice Sheet vary when snow metamorphose to ice, which is highly correlated to season and altitude. Therefore, we calculate the regression coefficient for every season (MAM, JJA, SON, DJF) and every 200 meters. We test both Sentinel-1 IW and EW data and find that our method present a lower RMSE for HH images and similar RMSE for HV images by comparing to the traditional Lamber-tian method. Winter images only show RMSE at 0.7dB indicate that our proposed method is potential in normalize backscatter coefficient of Sentinel-1 IW and EW images to derive the freezing and thawing status of the Greenland Ice Sheet.

By employing our proposed method to Sentinel-1 image that observed Greenland Ice Sheet in ascending track and EW mode, we generate the Sentinel-1 mosaic for Greenland Ice Sheet during 2016 to 2020 at every 12 (without S1B) or 6 (with S1A) days.

Glaciers are sensitive indicators of climate change and affect regional and global water circulation. High Mountain Asia (HMA) has the largest volume of glacier ice in mid-latitude regions and is considered as the water tower of Asia. In this project, we are monitoring contemporary glacier changes in HMA using recently available satellite data with the focus on Sentinel-1 and 2, CryoSat-2 and ICESat-2 data but also high-resolution stereo data. We plan to develop new methods to monitor glaciers focusing on changes in area, thickness, velocity and accumulation area ratio (AAR), and reveal the most recent trends in HMA. The methods will be developed, validated and calibrated by multi-temporal very high-resolution stereo satellite data such as TerraSAR-X, Pleiades, ZY3, GF7 and glaciological field measurements. We focus first on selected benchmark sites located in different climatic regions. In the next step it will then be tested with which accuracy the methods can be applied to whole HMA. The benchmark sites include Ile Alatau (northern Tien Shan), Muztag Ata (eastern Pamir), Poiqu and Langtang basin (central Himalaya), Western Nyaiqentanglha (south-central Tibetan Plateau) and the Bomi region (south-eastern Tibetan Mountains). Preliminary results show that highest mass loss is found in the Tien Shan, central and south-east Himalaya in eastern Pamir. Even in regions where glaciers have been previously in balance with climate mass loss now prevails. Overall, this project will provide comprehensive information about heterogenous glacier characteristics and changes which will be of high value calibrate and validate the glacier component of glacio-hydrological models.