MODIS-based Daily Lake Ice Extent and Coverage dataset for Tibetan Plateau

The Tibetan Plateau houses numerous lakes,the phenology and duration of lake ice in this region are sensitive to regional and global climate change,and as such are used as key indicators in climate change research,particularly in environment change comparison studies for the Earth three poles.However,due to its harsh natural environment and sparse population,there is a lack of conventional in situ measurement on lake ice phenology.The Moderate Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Snow Index(NDSI)data,which can be traced back 20 years with a 500 m spatial resolution,were used to monitor lake ice for filling the observation gaps.Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm,and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps.Through time series analysis of every single lake with size greater than 3 km2 in size,308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset,including 216 lakes that can be further retrieved with four determinable lake ice parameters:Freeze-up Start(FUS),Freeze-up End(FUE),Break-up Start(BUS),and Break-up End(BUE),and 92 lakes with two parameters,FUS and BUE.Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing.The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations.The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes.The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover.We used 500 m Moderate Resolution Imaging Spectroradiometer(MODIS)datasets during 2001–2010 to examine the snow–vegetation relationships,specifically,(1)the influence of snow melting date on vegetation green-up date and(2)the effects of snow cover duration on vegetation greenness.The results showed that the alpine vegetation responded strongly to snow phenology(i.e.,snow melting date and snow cover duration)over large areas of the Qinghai-Tibetan Plateau.Snow melting date and vegetation green-up date were significantly correlated(p<0.1)in 39.9% of meadow areas(accounting for 26.2% of vegetated areas)and 36.7% of steppe areas(28.1% of vegetated areas).Vegetation growth was influenced by different seasonal snow cover durations(SCDs)in different regions.Generally,the December–February and March–May SCDs played a significantly role in vegetation growth,both positively and negatively,depending on different water source regions.Snow’s positive impact on vegetation was larger than the negative impact.

Long-term records of glacier evolution and associated proglacial lakes on the Tibetan Plateau(1976‒2020)

The glaciers on the Tibetan Plateau(TP)constitute critical sources of water for the proglacial lakes and many rivers found downstream.To better understand the evolution of glaciers and the impact of this on proglacial lakes,seven glaciers corresponding to continenṅtal,sub-continental,and marine climate types that are influenced by wester-lies and the Indian summer monsoon were selected for study.The evolution of the edges of these glaciers and their associated progla-cial lakes were identified based on the visual interpretation of Landsat TM/ETM+/OLI images.A dataset covering the period 1976-2020 that included the glacier and proglacial lake edge vectors was then created.The relative errors in the areas of the individual glaciers were less than 3%,and for the proglacial lakes these errors were in the range 0%-7%.The dataset was used to effectively compare the changes in glaciers and proglacial lakes that have occurred over the past four decades.The most striking changes that were found were the retreat of glaciers and the formation of small proglacial lakes.This dataset could also be used as a proxy to support research on changes in mountain glaciers,particularly their response to climate change and water resources.This response is of great scientific significance and is important in many applications,including assessments of the ecological problems caused by melting glaciers.The dataset can be downloaded from http://doi.org/10.57760/sciencedb.j00076.00131.

Constructing dataset of classified drainage areas based on surface water-supply patterns in High Mountain Asia

The High Mountain Asia(HMA)region,ranging from the Hindu Kush and Tien Shan in thewest totheHimalaya inthe southwith an altitude between 2000 and 8844 m,holds the largest reservoir of glaciers and snow outside Earth Polar Regions.In the last decades,numerous glaciers and lake areas there have undergone tremendous changes with water redistribution.In order to increase understanding of the pattern of distribution of water resources,and their dynamic changes at the basin scale,a watershed classification based on the water replenishment patterns dataset was constructed.The input dataset are from the Randolph Glacier Inventory V.6.0 and the vector data of rivers and streams.Four datasets were thus obtained:Glacier-fed and Runoff-fed Drainage Area(GRDA),Glacier-fed and Runoff-free Drainage Area(GDA),Glacier-free and Runoff-fed Drainage Area(RDA),and the Glacier-free and Runoff-free Drainage Area(NGRDA),and the numbers of these four types of basins are 87,107,32,and 448 separately.The statistical results show GRDA has the largest surface area,accounting for 82.2%of the total basin area in HMA,mainly in the region of the basin with outflow rivers or streams.Dominated by small basins,the GDA area accounts for the smallest area,only 3.86%and the RDA accounts for 5.62%.For NGRDA,most are with small areas,accounting for 8.32%,and mainly distributes in the closed basin of the Qiangtang Plateau.This dataset provides a fundamental classified data source for research on water resources,climate,ecology,and environment in HMA.The published data are available at https://data.4tu.nl/download/uuid:d07d748f-d10b-4308-9626-199ef05cc9af/and http://www.dx.doi.org/10.11922/sciencedb.923.

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

Lake ice phenology(LIP)is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts.Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe,North America,and the Tibetan Plateau,but there is a lack of data for inner Eurasia.In this work,enhanced-resolution passive microwave satellite data(PMW)were used to investigate the Northern Hemisphere 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 753 lakes,including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020.Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF,respectively,and the corresponding values of the RMSE were 11.84 and 10.07 days.The lake ice phenology in this dataset was significantly correlated(P<0.001)with that obtained from Moderate Resolution Imaging Spectroradiometer(MODIS)data-the average correlation coefficient was 0.90 and the average RMSE was 7.87 days.The minimum RMSE was 4.39 days for CIF.The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations.The PMW LIP dataset pro-vides the basic freeze-thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere.The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.

The Digital Belt and Road program in support of regional sustainability

The Belt and Road initiative has a significant focus on infrastructure,trade,and economic development across a vast region,and it also provides significant opportunities for sustainable development.The combined pressure of climate variability,intensified use of resources,and the fragility of ecosystems make it very challenging,however,to achieve future sustainability.To develop the path in a sustainable way,it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach.In this context,the Digital Belt and Road(DBAR)program was initiated as an international venture to share expertise,knowledge,technologies,and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development.In this paper,we identify pressing challenges,present the research priorities and foci of the DBAR program,and propose solutions where big Earth data can make significant contributions.This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national,regional and global levels.

Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data

The near-surface soil freeze–thaw(FT)transition is an important factor affecting land-atmosphere exchanges,hydrology and carbon cycles.Thus,effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research.Several approaches have been developed to detect the soil FT state from satellite observations.The discriminant function algorithm(DFA)uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced(AMSR-E)passive microwave satellite observations.Although it is well validated,it was shown to be insufficiently robust for all land conditions.In this study,we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection.We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference.Considering the distinction between ascending and descending orbits,two different sets of parameters were acquired for each frequency pair.The validation results indicate that the overall discriminant accuracy of the new function can reach 90%.We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product,and a reasonable consistency between them was found.

Daily snow water equivalent product with SMMR,SSM/I and SSMIS from 1980 to 2020 over China

The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE product of 1980-2020 with a linear unmixing method through passive microwave data including SMMR,SSM/I and SSMIS over China after cross-calibration and bias-correction.The unbiased root-mean-square error of snow depth is about 5-7 cm,corresponding to 10-15 mm for SWE,when compared with stations measurements and field snow course data.The spatial patterns and trends of SWE over China present significant regional differences.The overall slope trend presented an insignificant decreasing pattern during 1980-2020 over China;however,there is an obvious fluctuation,i.e.a significant decrease trend during the period 1980-1990,an upward trend from 2005 to 2009,a significant downward trend from 2009 to 2018.The increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per year.Whereas in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per year.In the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.

Observations and geophysical value-added datasets for cold high mountain and polar regions

The Earth’s cold regions,in particular,the Arctic,Antarctic,and High-Mountain Asia(HMA),are dominated by the changing cryosphere and have inherently fragile environ-ments(Guo,2018;Kulmala,2018;Guo et al.,2020;Li et al.,2020;Yao et al.,2022;Group on Earth Observations(GEO),2022).Warming has reshaped the regions where the cryo-sphere is located;it has also been affecting water availability in lowland downstream areas,opening up northern sea routes,and affecting the stability of roads and infrastruc-ture in permafrost rich areas(Pulliainen et al.,2019).Changes in the phase of water and its consequences have thus had a major impact on the environment and the lives of billions of people.

The influence of tree transmissivity variations in winter on satellite snow parameter observations

The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing.Existing correction approaches to brightness temperatures for northern boreal forest regions consider forest transmissivity constant during wintertime.However,due to biophysical protection mechanisms,below freezing air temperatures freeze the water content of northern tree species only gradually.As a consequence,the permittivity of many northern tree species decreases with the decrease of air temperature under sub-zero temperature conditions.This results in a monotonic increase of the tree vegetation transmissivity,as the permittivity contrast to the surrounding air decreases.The influence of this tree temperature-transmissivity relationship on the performance of the frequency difference passive microwave snow retrieval algorithms has not been considered.Using ground-based observations and an analytical model simulation based on Mätzler’s approach(1994),the influence of the temperaturetransmissivity relationship on the snow retrieval algorithms,based on the spectral difference of two microwave channels,is characterized.A simple approximation approach is then developed to successfully characterize this influence(the RMSE between the analytical model simulation and the approximation approach estimation is below 0.3 K).The approximation is applied to spaceborne observations,and demonstrates the capacity to reduce the influence of the forest temperature-transmissivity relationship on passive microwave frequency difference brightness temperature.

Snow depth and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm

An ice mass balance buoy(IMB)monitors the evolution of snow and ice cover on seas,ice caps and lakes through the measurement of various variables.The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array(SIMBA)measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string.The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process.We present an automated algorithm to perform this task.The algorithm is based on heat flux continuation,limit ratio between thermal heat conductivity of snow and ice,and minimum resolution(±0.0625°C)of the temperature sensors.The algorithm results are compared with manual analyses,in situ borehole measurements and numerical model simulation.The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7%of the mean observed values.The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.

Improved alpine grassland mapping in the Tibetan Plateau with MODIS time series: a phenology perspective

The Tibetan Plateau is primarily composed of alpine grasslands.Spatial distributions of alpine grasses,however,are not well documented in this remote,highly uninhabited region.Taking advantage of the frequently observed moderate resolution imaging spectroradiometer(MODIS)images(500-m,8-day)in 2010,this study extracted the phenological metrics of alpine grasses from the normalized difference vegetation index time series.With the Support Vector Machine,a multistep classification approach was developed to delineate alpine meadows,steppes,and desert grasses.The lakes,permanent snow,and barren/desert lands were also classified with a MODIS scene acquired in the peak growing season.With ground data collected in the field and aerial experiments in 2011,the overall accuracy reached 93%when alpine desert grasses and barren lands were not examined.In comparison with the recently published national vegetation map,the alpine grassland map in this study revealed smoother transition between alpine meadows and steppes,less alpine meadows in the southwest,and more barren/deserts in the high-cold Kunlun Mountain in the northeast.These variations better reflected climate control(e.g.precipitation)of different climatic divisions on alpine grasslands.The improved alpine grassland map could provide important base information about this cold region under the pressure of rapidly changing climate.

Sustainable earth observations for the Arctic,the Antarctic and the high-altitude mountain cold regions

The Arctic,the Antarctic as well as the Qinghai-Tibet Plateau(QTP)are cold regions on Earth where snow and ice dominate the local environment and impact the global climate via several strong feedback mechanisms.The observations and information service for those cold regions are vital for the societal benefits,and support from the science to policy making(Qiu et al.2016).Earth cold regions exhibit strong sensitivity to global climate change and are thus some of the most fragile regions in terms of global ecology(Pulliainen et al.2017).Due to these inherent similarities,the Qinghai-Tibet Plateau area has been termed as the‘Third Pole’,which is experiencing profound environmental changes.

Remote sensing experiments for earth system science

The Earth system is an integrated system that can be divided into six main subsystems:geosphere,atmosphere,hydrosphere,cryosphere,biosphere,and anthrosphere.These subsystems are interconnected through the flows of global energy,water,and carbon,which are fundamental constituent cycles within the Earth system.To improve our predictive understanding of the subsystem on our changing planet,there is a need to better represent these subsystems through modelling and observations.The central role of these fundamental cycles has prompted scientific institutions or meteorological centers to develop digital platforms dedicated to the integration,modelling,and assimilation of Earth observation data,e.g.,Big Earth Data Science Engineering(Guo,Li,and Qiu 2020).