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. This paper provides an overview of the Dragon 5 project dealing with synergistic monitoring of arctic sea ice by multi-source remote sensing data. For operational ice charting, new methods were developed to classify sea ice types from SAR imagery and CFOSAT SWIM data. Operationally used dual-polarization C-band wide-swath data were complemented by corresponding L-band images, and the benefit of an L- and C-band combination for ice type separation and ice feature detection for winter and summer conditions was assessed. The discrimination ability of sea ice types at small incidence angle provided by CFOSAT SWIM data was investigated and analyzed. The polarimetric backscatter behavior of sea ice in L-, S-, and C-band SAR images was compared to spatially and temporally coincident airborne SAR campaign data. A second topic included implementation and development of sea ice concentration (SIC) estimation and SIC noise reduction algorithms with the Chinese microwave radiometers such as e.g. the HY-2 Microwave Radiometer and the 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. Thirdly, we proposed sea ice thickness retrieval algorithms from SAR and altimeter data (e.g. CryoSat-2, Sentinel-3 and HY-2). Especially, the consistency and intermission bias were compared and assessed by using different altimeters, upward looking sonar (ULS) instruments and Operation IceBridge (OIB) data. A method of merging multiple altimeter data to improve the temporal-resolution of the ice thickness product was proposed. Another effort was to develop robust and automated methods for iceberg detection in sea ice and on the open ocean. The study analyzed and evaluated 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.

High Mountain Asia (HMA), including the Tibetan Plateau and the adjacent mountains, is the region with the largest ice masses in the world. Known as the "Water Tower of Asia", meltwater from snow and glaciers feeds the major of Asia's rivers and is an important freshwater resource, affecting the regional water cycle and ecology. Here, we combine Earth System Observations (ESOs) and a novel eco-hydrological model to deliver a new understanding of the cryosphere and water cycle of key water towers of High Mountain Asia (HMA). We focus on blue (runoff) and green (evapotranspiration) water interactions in HMA, to 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. Our investigationstrategy has three steps, and we have progressed substantially on all three.

In the first step, we aimed to substantially advance our understanding of cryospheric, vegetation and land surface changes from remote sensing observations at benchmark sites. We have generated snow cover and glacier extent, glacier mass balance, glacier flow velocity, soil moisture, snow water equivalent, snow and glacier albedo, snow and glacier radiation balance. We studied snow cover variability and its impact on glaciers in the high mountain ranges of the Tarim Basin using multi-temporal remote sensing data. We carried out snow cover monitoring by using Landsat 8 / OLI clear sky condition data with a spatial resolution of 30m. Due to the complex terrain of the mountainous area, in view of the general overestimation problem of the normalized snow cover index method in the Himalayas, we used the support vector machine (SVM) classification method to select the snow cover training samples of different terrain, shadow and other conditions on a scene-by-scene basis to monitor snow accumulation from 2013 to 2020. Using Sentinel-2 as a reference, our retrievals gave a correlation coefficient above 0.95, and root mean square error about 0.1%. We produced a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) for the period (2011-2018) at 25km spatial resolution by applying the Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods. The data set was evaluated against in-situ measurements at 331 sites worldwide, including 57 sites in China, including all the permanent observatories on the Tibetan Plateau. We retrieved glacier albedo in the Western Nyainqentanglha Mountains (WNM) with MODIS data to characterize its spatiotemporal variability from 2001 to 2020. Glacier albedo experienced large inter-annual fluctuations, with an important decreasing trend of 0.043±2.2×10-4 per decade. A new parameterization of snow albedo was developed by combining WRF estimates of snow depth and age with MODIS retrievals of snow albedo, and led to significant relative reductions in RMSE and increases in correlation coefficients in WRF predictions of air temperature, albedo, sensible heat flux and snow depth.
Two advanced algorithms to retrieve land surface albedo and aerosol amount and properties were developed to improve: a) the characterization of the land surface background to separate the surface and atmospheric signals in aerosol retrievals and b) the separation of direct and diffuse irradiance in the retrieval of land surface albedo. The correlation of snow and ice albedo with aerosol loading was documented by comparing the evolution of Aerosol Optical Depth (AOD) at the ITP Nam Co observatory with glacier albedo in the Western Nyainqentanglha Mountains (WNM) during 2009 – 2018. We optimized the procedure to extract a Digital Elevation Model (DEM) from ZiYuan-3 (ZY-3) Three-Line-Array (TLA) stereo images and estimated the geodetic mass balance of glaciers in two areas of the Nyainqentanglha Mountains (NM) using ZY-3 DEMs and the C-band Shuttle Radar Topography Mission (SRTM) DEM in the periods 2000–2013, 2013–2017 and 2000–2017.
A time series of glacier surface velocity in the Parlung Zangbo Basin, southeast Tibetan Plateau, was generated applying the normalized image cross-correlation method to Sentinel 2 (S2) MSI and Landsat-8 (L8) OLI image data from 2013 to 2020.
Enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Tibetan Plateau Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 109 lakes, including 22 lakes for the period 1978 to 2021 and 87 lakes for 2002 to 2021.

Our second step of investigation focuses on generating glacier-specific altitudinal surface mass balance profiles that provide patterns of changes in glacier mass balance at the project study sites. Our approach is based on high-quality digital elevation change and glacier surface velocity datasets along we estimated ice thickness by applying the continuity equation. We derived multidecadal altitudinal mass balance profiles and quantified the equilibrium line altitude and accumulation area ratio for over 5000 glaciers across High Mountain Asia. We applied high resolution Pleiades, Deimos, and UAV datasets to retrieve precise glacier thinning and velocity datasets for the project selected catchments. . These results provide a crucial dataset to validate the eco-hydrological land surface model including its glacier components.

In the third, integrative step, we produce simulations of the land-surface interactions across the cryosphere, hydrosphere and biosphere of the selected study catchments. We used the land-surface model Tethys-Chloris, which describes both vegetation biophysics and cryospheric processes such as snow and ice melt, snow gravitational redistribution and snowpack processes. We have setup the model for five of the study catchments to date, forced with downscaled ERA5 data . We carefully validated the model simulations with the multiple datasets obtained in step one and two. The latent heat flux by snow sublimation and evapotranspiration can account for water losses as high as ice melt.

Part 1, Glacier velocity estimation based on Sentinel-2 observations at the Karakoram.

Twin satellites of Sentinel-2A/B provide 5-day repeat observation to the Earth and are capable of deriving glacier velocity with high-temporal resolution. Here we take the Karakoram as a study site and proposed a data processing procedure of deriving quasi-monthly glacier flow velocity fields by performing offset-tracking technique to 4 years of Sentinel-2 observations. We performed offset-tracking of each acquisition to its next three almost cloud-free acquisitions to increase the number of redundant observations. Flow speed and direction referenced method is taken to remove the wrong value of offset-tracking. Then an iterative SVD method solves the glacier velocity and removes the observation with a large residual. From Oct 2017 to Sep 2021, our results capture plenty of surged glaciers starting and/or ending their surging phases. Several glaciers show speed up one year ago before their surging phase. Rimo south glacier experienced a full surging phase during our study period and last for about two years, the maximum speed exceeded 9m/day. The normal type of glaciers also presented annually speed up and slow down, with acceleration starting in late April or earth May, and ending before September. We estimated RMSE of estimated velocity is about 0.11-0.29 m/day.

Part 2, Sea ice motion detection using Sentinel-1 imagery feature tracking.

Applying feature tracking techniques to Sentinel-1 imagery generates high resolution sea ice motion fields. However, the bad matching vectors still exist after the NNDR (Nearest Neighbor Distance Ratio) test and contaminate the derived motion fields, which need to be identified and filtered out. We propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively.

Part 3, Incidence angle normalization of Sentinel-1 backscatter imagery for Greenland ice sheet.

This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 image for Greenland Ice Sheet. A multiple linear regression model is trained using the ratio between backscatter coefficient differences and incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include geographical position and elevation. The precision evaluation of the ascending and descending images suggests better normalization results than the widely-used cosine-square correction method for HH images and little improvement for the HV images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also employed to evaluate the proposed method and yield similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 dB and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit in applying wide-swath and high temporal resolution Sentinel-1 images for producing backscatter mosaic images for GrIS.