Conference Agenda

All over the world, and more precisely in China, freshwater is an increasingly scarce resource suffering from rapidly changing environments due to human activities Study location Poyang and Dongting Lake are China’s first and second largest freshwater lake in the middle reaches of Yangtze River catchment. Its lakes and associated wetlands deliver important ecosystem functions such as freshwater supply, water purification, flood and climate regulation, and biodiversity’s sanctuaries. The development of comprehensive water resource management and nature conservation strategies requires detailed mapping and monitoring of inland waters. The generation of such information requires either large human resources for conventional ground surveying or expensive data. In addition to costly methods, the monitoring of large wetlands such as the Poyang and Dongting lakes study site with a water surface of up to 3.500 km² is difficult due to its inaccessibility during annual flood period.

Remote sensing offers a mature and comprehensive tool to solve this task with large area coverage at very low costs. Until today, in addition to satellite data with lower temporal resolution such as Envisat ASAR, for SAR sensors, or HJ1A and Landsat for the optical High resolution satellite, as well as daily middle resolution MODIS satellite data and the MERIS sensors onboard of Envisat, were frequently selected source for capturing lake dynamics. Satellite data from the new European Sentinel and Sentinel2 fleet has been available since 2014 and provides high-resolution information. In this paper we present the application of Sentinel-1and Sentinel 2 time series data for spatio-temporal high-resolution wetland and lakes mapping. New is the level of detail that can be achieved with Sentinel data. Potential and limitations are analyzed. Water surface obtained from the Sentinel were also compared with water bodies database such the GRW of Pekel as well as the ones generated by the UCLA lakes group.

Wetlands have two distinctive features, which are spatiotemporal dynamics and spatial heterogeneity. The dynamic characteristic of the water makes its cover fraction show seasonal changes. At the same time, the spectrum of wetland vegetation has a high similarity with agricultural land vegetation, causing accurately distinguish wetlands difficultly by remote sensing technology.

The amount of cloud in the mid-latitude region is relatively abundant, and a single sensor cannot provide the time density required for accurate identification of wetlands. Multi-sensor technology has been applied well in monitoring urban landscapes and invasive species mapping. Compared with previous remote sensing data, Sentinel-1/2 and GF-1 data have an advantage of high temporal and spatial resolution, but their application in wetland classification is worthy of the further exploration.

The landscape types of wetland ecosystems not only have large heterogeneity in space, but also have strong dynamic characteristics with changes in water bodies. This brings many challenges to accurately map and monitor wetlands. The normalized difference vegetation index (NDVI) is effective in monitoring multi-temporal vegetation phenological changes. There are many studies using time series remote sensing data which has a significant advantage of high observed density to monitor highly dynamic systems.

Dongting Lake is located in the northern part of Hubei Province, the middle reaches of the Yangtze River Basin. It is the second largest freshwater lake in China. As one of the two remaining natural rivers in the middle and lower reaches of the Yangtze River, Dongting Lake plays an extremely important role in regulating runoff and protecting biodiversity.

In this paper, the Dongting Lake wetlands, which contains three important international wetlands is considered as study area. The data source is the Sentinel-1/2 and GF-1 time series data acquired in 2017, the Sentinel-2 and GF-1 data construct the NDVI time series, and the Sentinel-1 data constructs the backscatter coefficient time series. we fuse the Sentinel-2 and GF-1 data to improve temporal and spatial resolution. In order to avoid the errors caused by a single classifier, the study area is classified using support vector machine and random forest classifier. Results showed the following: (1) The accuracy of the three types of data using the RF classifier is not much different from that of the SVM classifier. Prove that in the aspect of time series wetland classification, the impact of the data source is more significant than which classifier to choose. (2) The overall classification accuracy of Sentinel-2, GF-1 and Sentinel-1 time series are 93.93%, 84.30% and 48.04%, the accuracy of seasonal marshes that symbolizes the dynamic characteristics of wetlands is 92.23%, 79.43% and 48.32%. The classification method based on optical remote sensing time series data can fill the needs of wetland dynamic mapping, while the wetland mapping making use of SAR time series data is not available. (3) The classification accuracy of Sentinel-2+GF-1 fusion time series is 94.09%, and the commission rate of each class is significantly lower than that of the three sensors. The time series mapping based on multi-source data fusion has a wonderful robustness in the aspect of wetland classification.

In inland and coastal waters, organic carbon undergoes seasonal and spatial variations in relation to river run-off and biological processes. Particulate organic carbon, while being a smaller fraction of the total organic carbon with respect to dissolved organic carbon, plays an important role in the local and regional carbon dynamics. In fact, it is the variation of POC that can be used to examine carbon sequestration and well as carbon sink. Particulate matter strong influences the optical, chemical and biological conditions of most inland and coastal aquatic ecosystems.

There are numerous challenges to identifying particulate carbon, and most importantly specifying particulate carbon classes. These latter are dominated largely by productive particulate carbon from phytoplankton and detrital particulate organic carbon. These two pools play very different roles in the aquatic carbon dynamics, with high temporal and spatial variability within a single waterbody in relation to local sources and seasonal changes. However, many studies examine particulate carbon dynamics with a single algorithm, which leads to poor estimation where multiple numerous sources and sinks are present, typical for inland and coastal water environments.

The BioGeoLakes team has been working on an absorption-based approach was used to determine surface particulate organic carbon based on the specification of local POC absorption characteristics of dominant POC sources; phytoplankton or detritus based. This specification was made using a new particulate organic matter index, which was tested across a range of modelled and real lake/coastal conditions. Based on remote sensing reflectance in four wavebands, the model provided a good separation of organic particulate types and a good estimate of organic particulate concentrations in shallow lakes in the Yangtze River valley and estuary. These study lakes include Taihu, Chaohu and Poyang while work in the coming year will include a large number of smaller lakes in the Valley using the OLCI/Sentinel-3 satellite data. The approach shows a good potential to quantify particulate carbon dynamics in ecosystems where multiple organic carbon sources are present

As the second largest freshwater lake of Yunnan Province,Erhai Lake is an important drinking water source in Dali. In recent decades. With the rapid development of economy, the impact of human activities on Erhai is becoming increasingly prominent. Water quality deterioration and eutrophication led to the occurrence of cyanobacterial blooms and affected the normal ecological function of lakes.

Based on multi-source remote sensing data, this paper studies the spatiotemporal distribution of cyanobacteria in Erhai lake from 1999 to 2016 and analyzes the effects of both meteorological and human activites on the cyanobacteria.

By combining the results of random forest classification with the results of DMSP(Defense Meteorological Satellite Program-the Operational Linescan System) nightlight remote sensing images, the method decision tree was used to extract the new building land in Erhai Basin. Remote sensing monitoring of cyanobacteria in Erhai sea, extracted by Landsat TM, ETM + and Sentinel 2A, shows that from 1999 to 2016, there was an increasing trend in the scale and duration of water blooms.

Based on the analysis of the correlation between the scale, duration, frequency and first occurrence time of water and the new building rate, it is found that since 2003, the algal bloom frequency of Erhai has been positively correlated with the growth rate of the building, especially in the double porch and Taoyuan Town.

For short-term effect factor researches, we obtained the data of cyanobacterial blooms in Erhai Lake in 2013 and analyzed the effects of temperature, sunshine, precipitation, air pressure and wind, we summarized the meteorological characteristics when blooms breaking-out, that is the blooms often occurred when the sun shines again after rain, the strong sunshine and higher daily temperature variation were the main factor of blooms, low wind speed and lower air pressure were in favour of the formation of blooms.

Biodiversity stakes within Yangtze watershed are very important at national level but also international ones. These very rich ecosystems, being key wintering areas for many waterfowl of SE Asia, are suffering from rapidly changing environments due to human activities. It’s crucial to understand what the key factors and their effects are. As data within large spatial and temporal scales are difficult to get, remote sensing and spatial analysis technology turns out to be a useful tool to access information. Coupling environmental information obtained from remote sensing and biological and ecological data could help understand what factors are driving observed phenomena. Studies are on progress within the Yangtze River basin to understand the link between the lakes water surfaces and the date of arrival and departure of some bird species during migration.

Being the eldest of the Copernicus program, the Sentinel -1 mission has been imaging the Earth since April 2014 using a C-band Synthetic Aperture Radar. The high temporal and spatial resolutions (12 days at the equator, 10x10m pixel spacing in full resolution GRD) allow to create dense and accurate time series. Standing water has a distinguishable signature using radar wavelengths, which are very rarely disturbed by cloud coverage. This makes the use of radar imagery and thus Sentinel-1 more than relevant for the study of water body dynamics. However data quality comes with the price of high data volume. As these data flows are exponentially increasing, the use of “cloud computing” becomes more and more appealing. It allows to process information without downloading teraoctets of data. Google EarthEngine is an interface proposing such solution. The extensive catalog of available data allows advanced processing over large amount of data without being limited by local computational capacities.

The general aim is to use the backscatter GRD Sentinel-1 images to extract water surfaces of lakes of interests in the Yangtze River basin for years 2015, 2016, 2017 and 2018, and then put them in relation with ecological data. The first intermediate objective was to export monthly aggregated water surfaces for above years and for the months of January, February, July, November and December. A total of 513 images, distributed among 227 dates were processed. The water class was created using a simple k-mean algorithm and corrected with a DEM, considering standing water presence possible only on slopes inferior to 5°. In a second time, each Sentinel-1 GRD image is processed independently and the water extents are extracted. This captures faster dynamics, but classifications are more difficult to conduct due to noisier signal compared to a multi date composition. The final time series is composed of 340 images in 114 dates, from October to December for the years 2016, 2017 and 2018. The use of a k-mean algorithm makes the pre-processing step almost inexistent and keeps the processing time within minutes; the most time consuming step is the download of the final binary rasters. These time series of water extraction, put in relation with bird species presence, will allow to study if water levels in studied lakes have an impact on the arrival and departure dates of the flocks.

Radar signal is by itself a very powerful tool for water classification and the Sentinel-1 mission makes access to such products very easy. Even though confusion exist between water and some smooth surfaces such as roads, airports or desert areas during classification, the amount of data available is enormous and the use of cloud infrastructures like Google EarthEngine makes computing significantly faster and easier, allowing to study large areas for a long period of time. This allows to generate the water dynamics for more than 80 lakes of the Yangtze watershed, from the very large Poyang Lake to smaller ones such as Wuchang, Shengjin or Baitu Lakes. This study provides the first analysis of the water dynamics of these keys lakes in terms of biodiversity with such a time frequency.

In this paper, snow depth is derived using GPS Interferometric Reflectometry (GNSS-IR) and a method is presented to derive snow water equivalent (SWE) by using refracted GPS signals (GPS refractometry) from an antenna buried underneath the snow pack. The GPS monitoring system is installed at the narrow Grimsel mountain pass located in the Swiss Alps and is surrounded by high mountains. The GNSS-IR retrieved snow depth shows a certain correlation to the reference snow depth. The terrain influences thereby the precision of the retrieved snow depth seriously. GPS refractometry is able to correct the influence of the snow pack above the buried antenna. The systematic and stochastic snow induced effects in the GPS residuals are significantly reduced by estimating the SWE above the antenna. The method is thus able to estimate the SWE. Results of refractometric determination of the SWE show a very high correspondence within less than 5% with the results of conventional SWE determinations. This has be shown over three consecutive winter seasons.

Soil moisture plays an important role in water cycle study. Modern remote sensing technique has demonstrated that L-band is very sensitive to soil moisture variation. With the design and implementation of the Global Navigation Satellite System (GNSS) which working on L-band as well, remote sensing using navigation signal of opportunity gained wide interests. With two decades’ development, two technique based on signal reflection have been proposed including GNSS-R (GNSS-Reflectometry) and GNSS-IR (GNSS-Interferometric Reflectometry). More recently, some researchers tried to utilize the penetrating signal to measure soil moisture (Franziska Koch et al., 2016) and snow water equivalent (Franziska Koch et al., 2014 and Ladina Steiner et al,. 2018). For the soil moisture measurement, the investigation of the penetrating signal leads to better understanding of the sensing depth of the reflected signal, which is related to estimating the Root-Zone soil moisture and Field Capability.

We are going to study how different soil moisture affect the signal attenuation in the soil and the penetration depth of the signal under different soil moisture condition. We hope to predict the reflection caused by the soil based on the above analysis. Finally, we want to analyze the sensing depth of the GNSS signal which is defined as the maximum depth from where the signal reflected off can be received under certain receiving sensitivity. A long term experiment is carried out along with this study. Two identical antennas are used with one placed in the air and the other is placed at the bottom of a big plastic bag filled with soil of different thickness. At the same time, three FDR soil moisture probes are evenly buried at vertical direction with one probe always stay at the bottom of the bag. The increment of soil thickness is about 2 cm with its initial depth being 2 cm. Different navigation system will be investigated such as GALILEO and BEIDOU. Particularly, the BEIDOU System contains different kinds of satellite orbits including GEO, IGSO, and MEO. The GEO satellite can give quasi-static measurements, while the IGSO and MEO can give dynamic measurements.

The response of glaciers to climate in the high elevation Tibetan Plateau (TP) is highly variable in space and time and strongly influenced by the monsoon, which affects both mass and energy fluxes. The contribution of mixed-phase precipitation events are rarely quantified in melt and mass balance models. Here we use a distributed energy balance model with new schemes for precipitation discrimination and albedo evolution, to understand the effect of dynamic modelling of monsoon precipitation on the summer mass balance of a glacier in the southeast TP. The main effect of modelling mixed-phase precipitation events in a dynamic way is to accumulate more high elevation snow and maintain higher albedo for most of the season. We show that it is challenging to reproduce this effect with traditional approaches based on simple discrimination of solid/liquid precipitation. The glacier-wide mass balance is found to be 1.01 m w.e. (~72%) more negative over one ablation season. This is due to the fact that a static threshold for rain-snow events reduces total snow accumulation and promotes earlier retreat of the snowline altitude during the pre-monsoon season which heightens the dominance of net shortwave energy fluxes for most of the summer.

This paper presents a new water and energy budget-based glacier mass balance model. Enthalpy, rather than temperature, is used in the energy balance equations to simplify the computation of the energy transfers through the water phase change and the movement of liquid water in the snow. A new parameterization for albedo estimation and state-of-the-art parameterization schemes for rainfall/snowfall type identification and surface turbulent heat flux calculations are implemented in the model. This model was driven with meteorological data and evaluated using mass balance and turbulent flux data collected during a field experiment implemented in the ablation zone of the Parlung No. 4 Glacier on the Southeast Tibetan Plateau during 2009 and 2015–2016. The evaluation shows that the model can reproduce the observed glacier ablation depth, surface albedo, surface temperature, sensible heat flux, and latent heat flux with high accuracy. Comparing with a traditional energy budget-based glacier mass balance model, this enthalpy-based model shows a superior capacity in simulation accuracy. Therefore, this model can reasonably simulate the energy budget and mass balance of glacier melting in this region and be used as a component of land surface models and hydrological models.

The effect of monsoon on the mass balance of glaciers in High Mountain Asia, especially of those that are partly or fully covered by debris, has not yet been fully understood. Due to its insulating effect, debris strongly alters energy fluxes reaching the ice, and thus affects the rates and timing of melt. Monsoon conditions, dominated by persistent clouds, lower temperature ranges, high atmospheric water content, lower incoming shortwave radiation and higher receipts of incoming longwave radiation, can result in very distinct surface fluxes and mass balance of glaciers. The energy balance further changes under the presence of water within the debris, which controls conductive and latent heat fluxes, while another flux is added to the balance by rainfall. These effects have rarely been quantified, and to date only for single glaciers. In this study, we investigate how monsoon events influence the summer surface energy balance of debris-covered glaciers along the climatic gradient of High Mountain Asia, where monsoon dominates in the Eastern regions and progressively looses influence when moving westwards towards the Karakoram, where westerlies influence is predominant.

We use for this energy balance models (EB) developed to simulate melt of ice under debris, or debris energy balance (DEB) models, and Automatic Weather Stations (AWS) data. Most DEB models have been developed and tested for glaciers in temperate and arid climates, where the influence of water within the debris plays a less important role and many neglect or treat only simplistically the water content of the debris. We thus also evaluate the transferability of DEB models to monsoonal environments, and test distinct schemes to account for water content in the debris. We validate results against in-situ measurements, and describe how these events influence the summer surface energy balance of debris-covered glaciers. This work is fundamental to the development and optimization of more simplified approaches, such as the Debris-Enhanced Temperature Index (DETI) model, to distributed glacier melt modelling and as a result, to catchment-scale glacio-hydrological modelling.

Heihe River Basin (HRB), regarded as the second largest endorheic river basin in China, originates from the alpine region, flows through the Hexi Corridor, and ends at desert hinterland. The unique and various climatic and landscape types make the basin an ideal testbed for multi-disciplinary research, including hydrology, climatology, geography, ecology and so on. Over the past decades, extensive research was conducted over the basin, and fruitful new findings were obtained consequently. Based on these scientific bases, we have carried out two large-scale remote sensing experiments [1, 2] and Integrated research on the eco-hydrological process of the Heihe River Basin [3]. The main scientific contribution of the Heihe remote sensing experiments and integrated research can be summarized into: 1) a comprehensive watershed observing system was established [4] and a multi-scale dataset for understanding watershed ecohydrological processes was obtained [5], 2) a comprehensive modeling platform was designed and implemented for integrated hydrological simulation [6], and 3) a multivariate land data assimilation system was established [7]. These key progresses have been well documented and reported. Nevertheless, several new points have been observed in recent years, including: 1) developing an integrated watershed system model and closing hydrological cycle at watershed scale, 2) improving data assimilation algorithm and data assimilation system, and 3) developing key water cycle elements estimating algorithms and products. Here we focus on summarizing these recent progresses.

New modeling strategy and model platform for hydrological simulation were proposed. We proposed a new modeling framework to incorporate emerging knowledge into integrated models through data exchange interfaces to comprehensively understand complex watershed systems and to support integrated river basin management [6, 8]. The model is expected to represent the coevolution of the water-land-air-plant-human nexus in a watershed and provide capability of decision-making support and to provide an overarching framework for linking natural and social sciences. Based on the framework of the watershed system model, we analyzed the hydrological cycle in the Heihe River Basin [9]. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012.

An updated data assimilation scheme was proposed and parallelized assimilation system was implemented. A soil moisture assimilation scheme that jointly assimilated the brightness temperature of Advanced Microwave Scanning Radiometer-Earth Observing System and Land Surface Temperature products of Moderate Resolution Imaging Spectroradiometer [10] was proposed recently, which could correct model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter. In addition, we developed a physically based hydrological data assimilation system using the gridded and parallelized Soil and Water Assessment Tool distributed hydrological model [11]. The system integrated remotely sensed and ground-based observational data with the Parallel Data Assimilation Framework. The system could accurately characterize watershed hydrological states and fluxes. As to the application of data assimilation to hydrological flux, significant progress has been obtained as well. For instance. Pan et al. [12] assimilated the two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM and Fengyun-2D: FY-2D) into the weather research and forecasting model under framework of the 4D-Var data assimilation method in Heihe River Basin. The improved precipitation forecasting has been observed.

Key retrieval algorithms for hydrological elements have been witnessed progress. For instance, Li et al. [13] estimated continuous daily evapotranspiration at a 90-m spatial resolution using the Surface Energy Balance System (SEBS) by fusing high-temporal resolution Moderate Resolution Imaging Spectroradiometer and high spatial-resolution Advanced Space-borne Thermal Emission Reflectance Radiometer images. Ma et al. [14] proposed a probabilistic inversion algorithm for soil moisture estimation based on Bayes’ theorem and the Markov Chain Monte Carlo technique. They not only obtained highly accurate soil moisture estimation, but also quantified the uncertainties in the inversion algorithm.

Overall, ecohydrological research over HRB in terms of the hydrological observation, modeling and data assimilation has been witnessed huge progress. The Chinese Academy of Sciences is performing the Second Tibetan Plateau Scientific Expedition (STEP) Program. The Qilian mountain and other endorheic river basins are the key expedition regions. The scientific findings and practical experiences of HRB should and could provide very useful prior knowledge for the program. Simultaneously, the observing system design scheme, modeling idea and data assimilation systems can be extensively examined, extended and widely applied in a more generic scope.

The main objective of this project is to improve the estimate of water balance under natural and human pressure on the Heihe basin in China and in some Italian river basin by using MOST, ESA and NASA multi-source satellite data coupled with distributed hydrological models.

In this year presentation, following the scheduled activities, results will be presented for the Chinese Heihe basin and for the Italian Capitanata irrigation district.

For both case studies, the FEST-EWB hydrological model will be used in a synergic way with satellite data. In particular, its algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET) that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to the land surface temperature (LST) as retrieved from operational remote sensing data (SENTINEL3, MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale. Vegetation information (LAI, NDVI, fractional cover) and albedo are obtained from satellite data (SENTINEL2, MODIS, LANDSAT) and used as input parameters to the hydrological model.

For the Heihe river basin, FEST-EWB model is run at spatial resolution of 0.05° and temporal resolution of 1 hour. Results are provided in terms of hourly evapotranspiration, soil moisture and land surface temperature maps for the 2012.

Evapotranspiration estimates are then compared at local scale with two eddy covariance data, showing good agreement, and at basin scale with the estimates from the Chinese ETMonitor, and also global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM, reporting a general agreement but with irregularities, due to the different models hypotheses and algorithms.

For the Capitanatairrigation district, the model is run at 30 m of spatial resolution using the SENTINEL2 and LANDSAT images for vegetation input data and LANDSAT data for land surface temperature hydrological model calibration and data assimilation. Good estimates are obtained at basin scale in terms of RET, but also at local scale in terms of evapotranspiration and soil moisture against eddy covariance stations.

The district is an intensive cultivation area, mainly devoted to wheat, tomatoes and fresh vegetables cultivation. Hence, distributed irrigation quantity maps are also estimated from the combined use of satellite data and hydrological modelling.

Hydrological model is a simplification of a real-world system that aids in understanding, predicting, and managing regional water resources. However, it required different kinds of data for model localization and simulation. Microwave remote sensing products could provide space-time continuous data for improvement of hydrological modelling. In this study, two typical monsoon basins, Red River Basin (RRB) with tropical monsoon climate and Luan River Basin(LRB) with temperate monsoon climate, were selected for comparison. By using two long-term driving forcing dataset: The Chinese Meterological Assimulation Driving Dataset for the SWAT model(CMADS) and Global Land Data Assimilation Systems (GLDAS) , as well as GPM IMERG V5B and TRMM precipitation products, we simulated the soil moisture,runoff and crop yields in both basins by using Soil Water Assessment Tool (SWAT) model. The simulated runoff by SWAT model is expected to be fit well with observations; The simulated soil moisture can be validated by local measurements and improved SMOS& SMAP soil moisture products (Stefan et al., 2019). We compared the soil evaporative efficiency by remote sensing and those by SWAT. Then, we simulated crop yields under different irrigation Scenario in the downstream Red River Delta and Luanhe Delta. Our study reveals the usefulness of remote sensing products for water resource simulation in monsoon climate basins.

Observation and modelling of the coupled energy and water balance is the key to understand hydrospheric and cryospheric processes at high elevation. In the Qinghai – Tibet Plateau (QTP) in – situ observations of liquid and solid precipitation are very sparse and studies on the mass balance of glaciers and the water balance of catchments are hampered by this gap. We are exploring the potential of using model forecasts of precipitation at high spatial resolution to replace or complement in-situ observations. A first experiment on applying WRF to model an extensive snowfall event on the entire QTP was completed and the results are very encouraging. In this study in – situ observations of air temperature, snow depth and snow water equivalent were used to evaluate model performance and particularly alternate model configurations. Our experiments did show that the WRF configurations with advanced land surface physics schemes captured better the spatial distribution of the snow event, but overestimated the intensity and extent of SD and SWE. Next, we focused on areas at lower elevation to carry out experiments with a coupled energy and water balance model of a catchment using again model output on precipitation.

A second set of experiments with WRF targeted the evaluation of model precipitation and other at-surface fields, e.g. air temperature and wind speed, for individual glaciers. This approach can potentially overcome a major challenge in energy and mass balance of glaciers, i.e. the lack of spatially distributed forcing data at high spatial resolution.

The energy and mass balance of glaciers was also analysed using a suite of earth observation data. The trend in glacier thickness at very high spatial resolution was determined for several glaciers using multi – temporal DEM-s generated with ZY – 3 stereo-image data. This study determined changes in glacier surface elevation separately for the accumulation and ablation zone. For the same glaciers, accurate surface velocity fields were retrieved by staking L-TM, L8-OLI and S2-MSI images.

Quantitative information on water losses is important to understanding the global terrestrial water cycle and land – atmosphere interactions. However, land surface water loss (evapotranspiraiton, ET) estiamted by land surface models usually neglects the sub-grid heterogeneity of landatmosphere parameters, and it will cause aggregation biases in spatially-averaged ET estimates, considering the nonlinear dependences of ET on the heterogeneous land-atmosphere parameters. One frequently adopted strategy clusters the heterogeneous surface within a model grid into several tiles, assumed to be homogeneous, usually based on high-resolution land cover data. While the differences in bulk-averaged parameters between different tiles are considered, the heterogeneity within each tile is neglected. To evaluatethe aggregation bias, a numerical analysis was conducted to compare the aggregation bias was calculated by comparing ET estimates based on bulk-averaged SSM and LAI with the one obtained by aggregation of the flux estimates based on the Probability Distribution Function (PDF), which complies with energy conservation. Four types of PDF were used to simulate different scenarios on the heterogeneity (within a tile) of SSM and LAI, i.e., from water scarcity to wet, and from sparse to dense vegetation covered surfaces.Overall, the numerical experiments indicate that impacts on tile ET related to LAI are smaller than the ones related to SSM. Different meteorological conditions combined with the nonlinear dependence of ET on SSM/LAI may lead to large changes in the aggregation bias, even from underestimates to overestimates or conversely. In climate conditions with larger atmospheric water demand, enhancing evaporation, underestimation is more likely, and vice versa. Neglecting the actual spatial variability of both SSM and LAI within tiles can lead to both large relative error (> 20%) and absolute error (> 1 mm/day) in the estimated ET in semi-arid areas. A negative bias is expected at low ET / ET0 and a positive bias is expected at large ET / ET0, regardless of climate conditions (i.e., ET0). The relation between aggregation bias and meteorology found in this study has the potential to identify or even as a starting point to correct the possible serious underestimations and overestimations in applications.

Meanwhile, to achieve a better water loss products, the ETMonitor algorithm was further improved following the former study of last year, to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature - vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d-1, which showed better accuracy than the former ET products, especially for the forest sites. The improved algorithm was further applied based on ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product, and ET products in the northeastern Thailand from 2001 to 2015 was achieved and analyzed.

This project aims at developing new algorithms and finding new synergies between different remote sensing products in order to better monitor water resources in the Red River basin and in the Luan River basin, namely by combining water level, soil moisture (SM) and runoff products. Over the Red River, water balance and water management can prove quite challenging, for a number of different reasons: it has a complex topography, with a high drop of 2574 m, and since it is a transboundary river, there is a lack of information on reservoir management. The Luan River is characterized by steep hills and deep ravines at its upper reaches, leading to it overflowing during the rainy season. On the contrary, the water flow is much reduced in winter, with the river being icebound for some months. All these features amount to difficulties in getting the information on time for flood or drought early warning systems. Nevertheless, by using remote sensing data, the aim is to tackle these difficulties and obtain a better estimation of water resources, leading to a better management.

In this respect, SM products can be a powerful asset. Currently the spatial resolution of satellite SM products is quite coarse, ranging from 36 km for SMAP (Soil Moisture Active Passive) to 40 km for SMOS (Soil Moisture and Ocean Salinity). However, in some cases, higher resolutions are required. In this respect, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) is an algorithm that downscales the SMOS and/or SMAP SM data by using MODIS (Moderate Resolution Imaging Spectroradiometer) or Sentinel-3 land surface temperature (LST) and vegetation cover data, along with a self-calibrated evaporation model. The algorithm estimates the SM variability at a 1km resolution within a low resolution pixel by relying on the self-calibrated evaporation model. More specifically, it derives a term, called soil evaporative efficiency (SEE), defined as the ratio of actual to potential evaporation, from LST and vegetation cover data. By taking into account the instantaneous spatial link between SEE and SM, it then distributes the high resolution SM around the low resolution observed mean values. Previous results obtained over the Red River basin and derived from SMOS needed further investigation due to Radio Frequency Interference (RFI) detected over the area. Since then, work has been undergone to filter the RFI from the SMOS-derived 1 km SM products. For this study, 1 km SM products have been produced over the entire Red River basin and the Luan River basin for the 2015-2018 time period, derived from both SMOS and SMAP. Preliminary results show to be promising, with an improvement with respect to the SMOS-derived products, thanks to the RFI filtering. SMAP-derived SM products have also shown promising results over the area.

By combining these enhanced 1 km soil moisture products over the Red River basin and the Luan River with water level products, the hydrological model estimations can be further improved (Li et al. 2019).

Land surface temperature (LST) plays an important role in the processes controlling energy and water exchanges at the surface-atmosphere boundary. It has been widely used in studies such as hydrology, ecology, meteorology, and climatology. Satellite remote sensing makes it possible to retrieve LST at relatively dense and regular spatial sampling intervals over large areas. Over the past three decades, satellite thermal infrared (TIR) remote sensing has become one of the most important approaches to estimate LST.

However, a major shortcomingof satellite TIR remote sensing for LST estimation is its extremely low tolerance to clouds. Clouds not only reduce the spatial coverage of the TIR LST but also decreases the actual temporal resolution. The evidence has been reported for current satellite TIR LST products over different areas in previous studies. Therefore, the performance of current satellite TIR LST product is greatly limited in many applications, especially for those requiring LST with both high temporal resolution and dense spatial coverage. In contrast, passive microwave (MW) remote sensing is insensitive to clouds: thus, it is an important independent source for LST estimation complementing the available TIR LST. Merging TIR and MW observations is able to overcome shortcomings of single-source remote sensing to derive such a LST, in which how to efficiently improve the spatial resolution of MW LST to the same level as the TIR LST is a crucial link. However, in current merging methods, models adopted for downscaling MW LST fails to quantify the effect of temporal variation of LST. Thus, the accuracy and the image quality of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas.

In this context, this study proposes a practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: the annual temperature cycle component (ATC), diurnal temperature cycle component (ΔDTC) as prescribed by solar geometry and weather temperature component (WTC) driven by weather change. For each component, a dedicated algorithm was applied to improve its spatial resolution or optimize its accuracy according to its thermal properties. The merged LST can be obtained by combining the improved components together,

The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over northeast China: the resulting merged LST have a standard deviation of error (STD) of 1.29-2.71 K compared to the 1-km MODIS LST (MYD11A1) and successfully fill missing pixels due to clouds. Validated against in-situLST from three ground sites with diverse land cover types, the merged LST have a root mean square of error (RMSE) of 1.20-2.75 K, which is comparable to MODIS LST; besides, no obvious differences in the accuracy of the merged LST were found between daytime and nighttime, or under clear sky and unclear sky conditions.

The generated all-weather LST was also compared with the 1-km AATSR LST from the European GlobTemperature project. Good agreement between these two products was also found: the mean bias error (MBE) was from -0.04 to 0.14 K and the STD was from1.02 to 1.61 K.

Compared to a 1-km all-weather LST from a previous method, the merged LST derived from this study performs better in both accuracy and image quality, indicating the proposed method has an improved capability to generate 1-km all-weather LST data. The method was further applied to generate the daily all-weather LST during 2003-2017 for the Tibetan Plateau and its surrounding areas. This dataset is now being utilized in the modelling of water cycle over the Tibetan Plateau.

In this study, a time series ground-based active and passive microwave experiment for snow is presented. The experiment is carried out in 2017-2018 and 2018-2019 winter in Xinjiang, China. In the experiment of 2017-2018 winter, ground based SAR and microwave radiometers are used to measure the multiple frequency and multiple polarization backscattering coefficient and brightness temperature of snow covered soil. In 2018-2019 winter experiment, precise phase measurement is emphasized in the SAR observations to study the phase change due to snow accumulation and co-polar phase difference of terrestrial snow. Different microwave scattering and emission models of snow are used to study the measurement results, and the microwave signature of snow are studied by model simulations. The application of backscattering coefficient, brightness temperature, phase change and co-polar phase difference in snow water equivalent retrieval will be discussed.

Mountain glaciers can directly reflect local climate change and play a crucial role in the terrestrial water cycle and food security of local people. Nyainqentanglha Mountains (NM) have about 9600 km² glaciers, which account for 18.47% of the Tibetan Plateau (TP) and are the major water resources of rivers, lakes and human activities as well. The field observation is difficult to implement because of the high altitude and risk, therefore, many different experimental remote sensing techniques have been applied to estimate the glacier mass balance by several authors. Although the spaceborne optical photogrammetry is one of the promising ways to capture the glacier mass balance, the High Resolution TLA stereo images have been used less frequently. And the glacier mass balance patterns in the EM need to be further explored.

In this study, we used Zi Yuan-3 (ZY-3) Three-Line-Array (TLA) stereo images to extract the glacier mass balance in two study sites during 2000–2017. One is located in the western of the NM (WNM), a moderately complex terrain. The other one lies in the eastern end of the NM in the southeastern TP (ENM), where the topography is more complex than in the WNM. The glaciers in the WNM and ENM are of a subcontinental and maritime type, which provides an opportunity to compare and analyze the glacier mass balances of different glacier types during a decade.

The results showed that the glaciers in the WNM and ENM experienced mass loss in the 2000-2017, and the glacier thinning rates in the ablation regions were apparently larger than in the accumulation regions. In the WNM, the mean glacier elevation change and mass balance were -0.31 m a^-1 and -0.26±0.18 m w.e. a^-1, while the glaciers in the ENM obviously melted faster than the WNM, and these two values became -0.92 m a^-1 and -0.78±0.12 m w.e. a^-1, respectively. In the WNM and ENM, the glacier mass balances in the ablation zones were -0.57±0.18 m w.e. a^-1 and -1.02±0.12 m w.e. a^-1, while both values in the accumulation zones were 0.16±0.02 m w.e. a^-1 and -0.08±0.12 m w.e. a^-1.

Multi-source remote sensing data, from visible to thermal infrared are used for forcing, calibration, validation and data assimilation of/into basin scale hydrological models.

FEST-EWB model is run for the whole Heihe River basin at spatial resolution of 0.05° and temporal resolution of 1 hour. Results are provided in terms of hourly evapotranspiration, soil moisture and land surface temperature maps for the 2012.

FEST-EWB model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET) that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to LST as retrieved from operational remote sensing data (MODIS, LANDSAT) which is used for the calibration of soil and vegetation parameters at pixel scale.

Evapotranspiration estimates are then compared at local scale with two eddy covariance data showing an overall agreement between the estimated and measured data, as certified by numerous statistical indexes.

Then, at basin scale the modelled Evapotranspiration has been compared with a number of global products: the Chinese ETMonitor, and with global reanalysis products MOD16 ET, MERRA2, ERA-INTERIM, GLDAS-2 and GLEAM. At basin scale, the agreement between the model and the ET data is consistent but presents some irregularities, as a consequence of each ET product’s own foundational hypotheses and algorithms.

The objective of this study is the evaluation of the potential of two Land Surface Temperature downscaling algorithms with respect to high resolution LST from LANDSAT 7 ETM+ and resampled MODIS LST (MOD11A1).

Four different LST sources have been compared over the Heihe river basin, an endorheic basin in China, characterized by a wide variety of ecosystems, from desert oases to irrigated croplands and wooded mountain ridges.

The first two LST sources are measurements provided by the ETM+ instruments (aboard satellite Landsat-7) at 30m spatial resolution every 16 days, and MODIS (aboard Terra at the lower resolution of 1000m. The other two sources are products of downscaling algorithms. The STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm merges ETM+ and MODIS images to obtain LST data with the spatial resolution of the former and the temporal frequency of the latter, involving neighbouring pixels in the process. On the other hand, the DisTrad (Disaggregation of radiometric Temperature) algorithm, establishes a link between LST and NDVI, in order to revert to the former anytime vegetation data is the only data available.

Globally, resampled MODIS and DisTrad perform better, not quite reaching the accuracy of ETM+ but all the same yielding an accurate approximation. On the other hand, STARFM struggles with the variety of land cover types, offering an acceptable performance in the desertic area, which is the most uniform of all. Categorizing the pixels according to their land cover type, it is found that vegetated areas, especially croplands, are the most difficult to interpret for the LST sources, with low performance in the early summer during the peak of the maize season. Furthermore, grouping pixels by their lighting condition (whether they are in light or in shadow) does not offer major results: data quality for shadowed pixels is quite worse than for lighted pixels, but the number of the formers is so low that the impact on the overall result is close to negligible.

Overall, MODIS and DisTrad are the best candidates to compensate for the low temporal frequency of ETM+ without losing too much accuracy. Furthermore, DisTrad application requires more input data and computing effort than re-sampling MODIS.

Land Surface Temperature (LST) is an indicator for the exchange of energy in the process of atmosphere-ground interaction. It is an important parameter indicator for global resource and environmental dynamic analysis. Sentinel-3A satellite was jointly developed by theEuropean Space Agency (ESA) and the European Meteorological Satellite Organization (EUMETSAT),and was successfully launched in February 2016. One of its main payloads is the Sea and Land Surface Temperature Radiometer (SLSTR), which has three channels (i.e. S7-S9) in the thermal infrared range with a 1000 m resolution in the nadir view mode. The central wavelengths of S8 and S9 are 10.85 μm and 12 μm, respectively. Thus, images of these two channels can satisfy the requirement of LST estimation.

The objective of this study is (i) to explore the applicability of the classical split window algorithms(SWA)for estimating LST from the SLSTR data acquired by S8 and S9 channels and (ii) to analyze the possible sources of error. Nine SWAswidely accepted by the scientific communities were selected as the candidate algorithms, including PR1984, BL-WD, VI1991, UL1994, WA2014, ULW 1994, SR2000, BL 1995, and GA2008. These SWAs were also used to develop the algorithm for the Chinese GLASS LST product.

The aforementioned SWAs were trained globally based on simulation datasets from the forward radiative transfer simulation. The SeeBor atmospheric profile database was used as the basis in the forward simulation. For each profile, 10 LST and near-surface air temperature differences were defined, i.e. from -16 K to 20 K in increments of 4 K; spectral emissivities of 48 materials were used; the view zenith angles were defined as 0 to 55°in increments of 5°. MODTRAN 5 model was employed to conduct the forward simulation. For each trained SWA, the NDVI threshold method was used to determine the land surface emissivity of each pixel. The European Mesoscale Weather Forecasting Center (ECMWF) data were used to determine the atmospheric water vapor content and near-surface air temperature of each pixel.

Results show that all the selected SWAs have accuracies better than 2 K in training. Ground measured LST at four ground sites of HiWATER with good spatial representativeness were used to validate the estimated LST from the actual SLSTR data during November 2016 to December 2017. Validation show that the accuracies of the nine SWAs are approximately 2-4 K, better than the official SLSTR LST product. The estimated LST is affected by many factors, such as the land surface emissivity, air humidity, land surface type, air temperature, and atmospheric water vapor content. The study would be beneficial for improving the SLSTR LST product.

The Lancang-Mekong river, known as the Lancang river in China, the Mekong reiver outside China. The Lancang-Mekong Basin is a trans-boundary river with an area of 795,000 km², including territorial parts of six countries: namely China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. With a total length of over 4350 km, the Lancang-Mekong River is the longest river in Southeast Asia. It originates from the glacier melting of Qinghai Tibet plateau at the elevation of 5200 m, and eventually flows into the South China Sea at Mekong Delta in Vietnam. More than 72 million people benefit from this river. Consequently, the Lancang-Mekong basin has an outstanding practical significance for the ecological and economy development of alongshore area. However, the current land use/cover in the Lancang-Mekong river basin is in a very critical situation. Large patches of primary and secondary forests have been destroyed in Laos, Myanmar and Cambodia. Crop rotation is replaced by single cropping of rubber, cashew, sugar cane, and eucalyptus etc. Social and economic transformation, urbanization and interregional cooperation brought by increasing human activities also play an important role in land use/cover change in the Lancang-Mekong river basin. The land use/cover change have influenced climate, precipitation, the energy balance, carbon budget, and hydrological cycle in the basin.

Remote sensing has long been recognized as an effective tool for broad-scale(such as global scale, regional scale and basin scale) land use /cover mapping. At present, eight land cover thematic datasets( such as USGS with 1km, UMD with 1km, BU with 1km, GLC2000 with 1km, Globcover 2005 with 300m, GlobCover 2009 with 300m, GlobCover 2010 with 250m, Globeland30 with 30m) at a global scale have been developed with resolution ranging from 30m to 1km. In recently years, remote sensing scientists are interested in land use /cover change, climate variation, and urbanization in the Lancang-Mekong river basin. Remote-sensing technology has the potential to monitor the environmental changes in basin scale. However, the Lancang-Mekong Basin is very large, and covers numerous climate zones and eco-regions, and needs seven MODIS tiles, or over 50 Landsat frames to cover the complete north–south-trending basin. Furthermore, the almost persistent cloud cover over the Lancang-Mekong Basin for large parts of the year, and optical remote sensing images are unavailabe in part of regions. The landscapes have complex spectral and textural characterization in the Lancang-Mekong river Basin. Because of these factors, there is reported about high resolution(≤10m) land use/cover products in the Lancang-Mekong river Basin in recent years. As new satellites and sensors become avaiable. the Sentinel-2A/B are optical satellites, which respectively launched in 2015 and 2017. The Sentinel-2 has multi-spectral data with 13 bands in the visible, near infrared and shortwave infared with respectively spatial resolution of 10m, 20m, and 60m. Multi-source remotely sensed images with high temporal, spatial and spectral resolution in the Lancang-Mekong river basin have obtianed by the ESA Sentinels satellites in recently years.

Lots of research has shown that the land use /cover information play a role in climate variation, ecology and environment destroy, and naural hazards. The land use/ cover is fundamental information for natural resource management, environmental change studies, urban planning to sustainable developemnt, and many other societal benefits in the Lancang-Mekong river basin. Bacause of region area large, complex topography, cloudy and rainy environment in the Lancang-Mekong river basin, so far, the high resolution land use/cover products have not presented. The relation research has became urgent. More specifically, the whole research may include the following some parts. (1) employing Sentinel Application Platform (SNAP) toolboxes to pre-process the data sets over the Lancang-Mekong river basin (2) Developing object-oriented random forest (OORF) Classifcation algorithm for mapping Land use/cover in basin scale . (3) Mapping high resolution land use/cover in the basin using the presented alogrithms in the basin.

Glaciers in the Tibetan Plateau have significantly influenced the local ecology and economy as a water resource. Southeast Tibetan Plateau is a typical region of debris-free and debris-covered glaciers in China. Automatic glacier mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Therefore, the knowledge of the changes of debris-free and debris-covered glaciers in the southeastern Tibetan Plateau is still limited. This study investigated spatial patterns and area changes at decadal scales of debris-free and debris-covered glaciers in the southeastern Tibetan Plateau by utilizing a machine-learning algorithm based on multi-temporal satellite images. Specifically, Random Forest method was applied based on Landsat and ASTER GDEM V2 data for 3 target years over 20 years (1995, 2005, and 2015). Glacier area changes were analyzed in terms of glacier characteristics (size, elevation and debris coverage) over the period of 1995 – 2015. The results demonstrated that this region has experienced a significant deglaciation of 29.86% (2842.08 km²) over a period of 20 years. The glacier size greatly influenced the change of glacier area and the shift of glacier retreat to higher elevations was notable. The melt rate of absolute area of the debris-free glaciers was faster than that of debris-covered glaciers and glaciers with varying supraglacial debris coverage responded differently. Meteorological data suggested that increasing temperature since 1995 probably represented the primary controlling factor for glacier variations in this region.

Unlike most glaciers experience mass lost in recent decades, the Karakoram and its surroundings seem rather stable. The phenomenon is claimed as ‘Karakoram anomaly’ and/or ‘Karakoram-Pamir anomaly’. Given its remote, the field observations for glacier mass balance and flow rates are hard and archive data are rare. Remote sensing technique plays an essential role in alpine glaciers observations. Previous studies yielded a positive glacier mass balance for the Karakoram and later extended to the Pamir Plateau between 2000 and ~2012, however the laser altimetry observation at almost the same period (2003 - 2009) found slight glacier mass loss but claimed that the anomaly centred at the West Kunlun.

To make a more accurate observation and to understand the ‘Karakoram anomaly’, we applied Differential SAR Interferometry (D-InSAR) technique to a set of X-band bistatic TerraSAR-SAR-X/TanDEM-X (TSX/TDX) images observed at ~2013 by respecting to SRTM DEM observed in 2000 to derive glacier mass balance. The topographic residual phase of D-InSAR is unwrapped and then transferred to height changes. By presuming density of 850 Kg/m³, the volume changes of glaciers are converted to glacier mass balance. We compare quasi-simultaneously observed C-band and X-band SRTM (both in February of 2000) to evaluate and to remove the penetration depth differences at different elevation bins. The possible seasonal variation in terms of glacier mass balance was evaluated at an adjacent site by using TSX/TDX images observed in different months in one year. The standard deviation of differential processing between SRTM and TSX/TDX is about 6.27m, which is more accurate than the previous study using SPOT DEM and SRTM. Besides, it noticed that TSX/TDX make more efficient observations at accumulating area than optical observations. The result found that both east and west part of the Karakoram presents almost zero glacier mass balances, which were −0.020 ± 0.064 m w.e. yr⁻¹ and −0.101 ± 0.058 m w.e. yr⁻¹, respectively. Most negative glacier mass balance was contributed by the southern slope of the Karakoram while the northern slope was rather stable. At the most northeastern part of the Karakoram, where are very close to the edge of Tarim basin, the glaciers presented thickening in also most every elevation levels. The glacier mass balance at the Karakoram presented a decreasing gradient from the edge of the Tarim basin to the southwest of Karakoram. The mass balance for 2000 to ~2013 is almost identical comparing to 1974 to 2000, which implies that the stable environment at the Karakoram despite the global warming trend.

The re-analysis monthly GHCN_CAMS Gridded 2m Temperature data found an increasing start from 1995 to 2000 for about 1 degree and kept stable after then. Monthly precipitation Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) suggest rather stable annual and seasonal precipitation from 1980 to 2000, followed by an increasing trend. The precipitation increased from ~260mm/yr before 2000 to ~350mm/yr by 2010. The meteorological data suggested that a warming and wetting trend after 2000 for the Karakoram, which possibly explains the ‘Karakoram anomaly’ was induced by increasing precipitation rather than a cooling trend of temperature.

Thermokarst, a process that characterizes landforms caused by thawing of ice-rich permafrost, is a key indicator of permafrost degradation. Surface dynamics of thermokarst processes on Qinghai-Tibet Plateau (QTP) of China, is still poorly quantified or understood. It is also challenging to detect and measure surface subsidence due to loss of subsurface ice over a large area. The Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique has the potential to detect local or regional thermokarst-induced surface subsidence with the advantage of full resolution and millimeter to centimeter accuracy by less affected related to the temporal or geometric decorrelations. Previous studies based on multi-baseline time series analysis have separated the seasonal and thermokarst-induced surface subsidence only using SAR images acquired during thaw seasons. To fully usage of the SAR images, we introduce frost heave processes during early freeze season and subsequent stable stage when the layer is completely frozen.

Applying our improved PSInSAR method to 17 L-band ALOS-1 PALSAR images over Eboling Mountain where 22 thermal erosion gullies are well developed, we found a mean gradual subsidence trend of 1.3 cm/year, with a maximum of 5 cm/year near the thermal erosion gullies. It is equivalent to an ice volume loss of 1.4810⁴ m³/year over the entire thermokarst landform in the study area. We also found that the ground surface nearby the thermal erosion gullies is more likely to undergo subsidence. It indicates that the thermal erosion gullies could affect the permafrost processes at its surroundings. This study promises a potential of using PSInSAR to identify thermokarst landforms, map and quantify permafrost thaw subsidence, and assess its impacts over large areas such as the QTP.

Several decades of glacier recession has led to the formation of large populations of glacial lakes in most glacierised high mountain regions of the world. In many of these regions, enhanced glacier retreat and mass loss has been observed from glaciers which remain in contact with a glacial lake over a prolonged time period. Such negative glacier-lake interactions may heavily influence the long term ice mass loss budget of high mountain regions, and heighten the threat posed by glacial lake outburst floods (GLOFs). Glacier-lake interactions remain scarcely studied in the Himalaya, thus our aim of this work is to quantify the impact of lake expansion on glacier mass loss and glacier retreat across the main Himalayan arc. We generated geodetic mass balance estimates for two time periods (1970s-2000 and 2000-2016) over several regions of the main Himalayan arc, and observed 39-51% greater ice mass loss from lake-terminating glaciers when compared to land-terminating glaciers. Lake-terminating glaciers contributed ~19% of the total ice mass loss across the region, across both time periods, despite comprising only 9% of the glacier population. The mapping of glacier terminus positions over coincident periods shows land-terminating glacier retreat rates of 6.6-12.4 m a^-1, and 16.5-26 m a^-1 for lake-terminating glaciers. Over the later time period, land-terminating glacier length reduced by 8.5%, whereas lake-terminating glacier length reduced by 19.2%. Our results emphasise the role of lake expansion in glacier evolution across the Himalaya, and the requirement to consider glacier-lake interactions in future assessments of glacier recession in the region.

There are more than 5000 glacial lakes > 0.003 km² in the Third Pole region including the Pamir-Hindu Kush-Karakoram-Himalaya and the Tibetan Plateau. Around 30 lakes were identified as being potentially the most dangerous across the Tibetan Plateau. This assessment was based on four core determinates of GLOF hazard, including lake size, watershed area, dam steepness, and topographic potential for ice/rock avalanching. This study found that the potentially most dangerous lakes are located in the central Himalaya, the region where past glacial lake outburst floods (GLOFs) have occurred most frequently. Despite this fact, in-situ measurements relating to lake bathymetries, dam stability, and mother glacier dynamics are still very limited. We selected the most dangerous lakes in the central Himalaya (mainly in Poiqu basin), and mapped lake extents, glacier outlines, their frontal positions and ice flow from optical remote sensing data, and calculated glacier surface elevation change from digital terrain models between 1970s and 2018. Measurements of bathymetries, ground temperature, moisture and heat fluxes at different depths in the lake dam and outwash areas are ongoing. The stability of moraine dams, eventual failure and possible GLOF impacts will be modelled, and observations will be extended over long timescales. The glacial-lake change analysis presented in this study can significantly improve our knowledge of past lake evolution in the central Himalayas and the future GLOF threat.

Meltwater from rock glaciers could provide a relevant contribution to water supply especially in dry regions. Moreover, rock glaciers could have serious hazard potentials when located at or above steep slopes or when damming lakes. Existing investigations about rock glaciers in High Mountain Asia indicate that the landforms are abundant, but information is rare for the Tibetan Plateau and the northern slopes of the Himalaya.

We compiled a rock glacier inventory for the Poiqu basin (~28°17´N, 85°58´E) – central Himalaya/Tibet. The mapping was mainly based on optical Pleaides imagery with 0.5m resolution. Rock glaciers were identified based on their characteristic shape and their surface structure. In addition, we generated a Pleiades DEM and used it for a) creating a hillshade to support rock glacier identification and b) to derive their topographical parameters. Additional information on the occurrence and activity of the rock glaciers was provided by the InSAR technique using ALOS-1 data.

The results of the inventory reveal 370 rock glaciers covering an area of about 21.2 km². The largest one has an area of 0.5 km² and three have an area of more than 0.3 km². The rock glaciers are located between ~3715 m and ~5850 m with a mean altitude of ~5075 m a.s.l.. The mean slope of all rock glaciers is close to 17.4° (min. 6.8°, max. 37.6°). Most of the rock glaciers face towards the Northeast (19%) and West (18.5%). Our study indicates that 147 rock glaciers can be classified as active. We also found rock glaciers damming lakes and rock glaciers located above roads which could threaten the infrastructure in case of instability.

Preliminary results of rock glacier mapping of the same region, which were based on Sentinel 2 images with 10 m resolution and the 8 m High Mountain Asia DEM revealed slightly less rock glaciers (362) in numbers and but indicated a much larger rock glacier area (>40 km²). We conclude, that high resolution data is of utmost importance when creating a rock glacier inventory.

Active rock glaciers (ARGs) and protalus lobes (PTLs) are characteristic periglacial landforms indicating the presence and creeping process of permafrost underground in an alpine environment. However, little information about such landforms has been provided in mountainous western China. In this work, we compiled an inventory including ARGs and PTLs in part of the West Kunlun Shan based on satellite Synthetic Aperture Radar (SAR) interferometry and optical images from Google Earth. Fifteen interferograms generated from ALOS-1 PALSAR images were used for identifying ground movements. Their geomorphic parameters such as aspect, area, altitude, and slope angle, were quantified using the SRTM digital elevation model. Within the 70000 km² study area, we identified 67 ARGs and 22 PTLs. The preliminary results reveal that the mean downslope velocities of the ARGs and PTLs are 79 cm/yr and 25 cm/yr, respectively. The maximum downslope velocity for the ARGs is about 200 cm/yr. The aspects of the landforms vary significantly: 45% of the ARGs are located on northeast-facing slopes while 50% of the PTLs are located on southeast-facing slopes. Our inventory shows the total areas covered by ARGs and PTLs are 14.4 km² and 1.2 km², respectively. The largest ARG has an area of 0.7 km². The ARGs are located between 4100 m and 5600 m and have a mean slope angle of 13°. While the PTLs are in a narrower band, between 4600 m and 5300 m and have a slightly steeper angle of 18°.Compared with the existing rock glacier inventories in High Mountain Asia such as the northern Tien Shan and the Hindu Kush Himalaya, the distribution density of ARGs and PTLs in the western Kunlun Shan is much lower.