Change in observed long-term greening across Switzerlandevidence from a three decades NDVI time-series and its relationship with climate and land cover factors

Environmental changes are significantly modifying terrestrial vegetation dynamics,with serious consequences on Earth system functioning and provision of ecosystem services.Land conditions are an essential element underpinning global sustainability frameworks,such as the Sustainable Development Goals(SDGs),requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces.At the global scale,long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate.However,greening trends at the national scale have received less attention,although countries like Switzerland are prone to important changing climate conditions.Hereby,we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index(NDVI)to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature,precipitation,and land cover to investigate possible responses of changing climatic conditions.Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61%significant pixels across Switzerland.In particular,the seasonal mean NDVI shows an important break for winter,autumn and spring seasons starting from 2010,potentially indicating a critical point of changing land conditions.At biogeographical scale,we observed an apparent clustering(Jura-Plateau;Northern-Southern Alps;Eastern-Western Alps)related to landscape characteristics,while forested land cover classes are more responsive to NDVI changes.Finally,the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation.The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions.This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.

Towards global coverage of gridded parameterization for CLImate GENerator(CLIGEN)

Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations.The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator(CLIGEN)that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations,thereby obtaining near-global availability of combined coverages.This dataset primarily covers countries north of 40°latitude with 0.25°spatial resolution.Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products.Precipitation parameters were statistically downscaled to estimate point-scale values,while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets.Surrogate parameter values were used in some cases,such as with wind parameters.Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations.These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations.Two sensitive parameters,monthly average storm accumulation and maximum 30-minute intensity,were shown have RMSE values of 1.48 mm and 4.67 mm hr^(−1),respectively.Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5%of ground-based parameterizations,effectively improving climate data availability.

DynIceData:a gridded ice-water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone

High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual satellite sensors provide periodic sea ice obser-vations with spatial resolutions of tens of meters,information regarding changes that occur over short time intervals of minutes or hours is limited.In this study,a gridded ice-water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone(MIZ).This dataset-DynIceData-which combines Sentinel-1 Synthetic Aperture Radar(SAR)data with Gaofen-3(GF-3)SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ran-ging from minutes to tens of hours.The areas of the Arctic covered include the Kara Sea,Beaufort Sea,and Greenland Sea during the period from August 2021 to August 2022.Object-oriented segmen-tation and thresholding were used to obtain the ice-water classifi-cation map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs.The time interval between the images in each pair ranged from 1 minute to 68 hours.Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used.The classification was verified as having an overall accuracy of at least 95.58%.The DynIceData dataset consists of 7338 samples,which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmen-tal data,thus potentially improving the accuracy of sea ice forecast-ing using Artificial Intelligence.The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.

The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season

Climate warming rates in the Arctic are far greater than the global average,exerting stronger impacts on permafrost degradation and thermokarst landform development.Thermokarst lakes and ponds(TLPs),which are widely distributed in the Lena Basin in the Russian Arctic,play a vital role in altering local ecosystem.However,the detailed distribution of TLPs in the Lena Basin still remains poorly known.In this study,we built the first 10 m resolution TLP dataset for the Lena Basin in the 2020 thawing season by utilizing 4902 Sentinel-2 images.A robust mapping workflow was developed and implemented in the Google Earth Engine(GEE)platform.The accu-racy assessment demonstrates a satisfactory accuracy(93.63%),and our results exhibit a better consistency with real TLPs than global water body products.A total of 380,477 TLPs(~0.53%of the total surface area of the Lena Basin)were identified,showing an uneven distribution in the five sub-basins.The TLPs were found to be mainly located within plain areas,with an active layer thickness in the range of 80-100 cm.The higher ground ice content and mean annual ground temperature were favorable for TLP development.This dataset will be valuable for investigating the complex interac-tion between TLPs and permafrost.It will also serve as a baseline product for better incorporating thermokarst processes into perma-frostclimate models.

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input,and a case study during a heavy air pollution event

We integrated Enviro-HIRLAM(Environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical weather prediction(NWP)model.The publicly available version of FLEXPART can utilize either ECMWF(European Centre for Medium-range Weather Forecasts)Integrated Forecast System(IFS)forecast or reanalysis NWP data,or NCEP(U.S.National Center for Environmental Prediction)Global Forecast System(GFS)forecast or reanalysis NWP data.The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields.We compared backward trajectories gener-ated with FLEXPART using Enviro-HIRLAM(both with and without aerosol effects)to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs,for a case study of a heavy haze event which occurred in Beijing,China in November 2018.We found that results from FLEXPART were considerably different when using different meteorological inputs.When aerosol effects were included in the NWP,there was a small but noticeable differ-ence in calculated trajectories.Moreover,when looking at potential emission sensitivity instead of simply expressing trajectories as lines,additional information,which may have been missed when looking only at trajectories as lines,can be inferred.

The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine

Due to the coarse scale of soil moisture products retrieved from passive microwave observations(SMPMW),several downscaling methods have been developed to enable regional scale applications.However,it can be challenging for users to access final data products and algorithms,as well as managing different data sources and formats,various data processing methods,and the complexity of the workflows from raw data to information products.Here,the Google Earth Engine(GEE),which as of late offers SMPMW,is used to implement a workflow for retrieving 1 km SM at a depth of 0-5 cm using MODIS optical/thermal measurements,the SM_(PMW)coarse scale product,and a random forest regression.The proposed method was implemented on the African continent to estimate weekly SM maps.The results of this study were evaluated against in-situ measurements of three validation networks.Overall,in comparison to the original SM_(PMW)product,which was limited by a spatial resolution of only 9 km,this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy(an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m^(3)/m^(3)).The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.

A mediation system for continuous spatial queries on a unified schema using Apache Spark

Recent advances in big and streaming data systems have enabled real-time analysis of data generated by Internet of Things(IoT)systems and sensors in various domains.In this context,many applications require integrating data from several heterogeneous sources,either stream or static sources.Frameworks such as Apache Spark are able to integrate and process large datasets from different sources.However,these frameworks are hard to use when the data sources are heterogeneous and numerous.To address this issue,we propose a system based on mediation techniques for integrating stream and static data sources.The integration process of our system consists of three main steps:configuration,query expression and query execution.In the configuration step,an administrator designs a mediated schema and defines mapping between the mediated schema and local data sources.In the query expression step,users express queries using customized SQL grammar on the mediated schema.Finally,our system rewrites the query into an optimized Spark application and submits the application to a Spark cluster.The results are continuously returned to users.Our experiments show that our optimizations can improve query execution time by up to one order of magnitude,making complex streaming and spatial data analysis more accessible.

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube-a case study from the Lake Geneva region(Switzerland)

Accurate,consistent,and high-resolution Land Use&Cover(LUC)information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers,pressures,state,and impacts on land systems.Nevertheless,the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change.Hereafter,we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data,combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region.Findings suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification performances.Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower uncertainty.The produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(i.e.class proportion)of the official national dataset.This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability.However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

GEOSatDB:global civil earth observation satellite semantic database

Satellite remote sensing,characterized by extensive coverage,fre-quent revisits,and continuous monitoring,provides essential data support for addressing global challenges.Over the past six decades,thousands of Earth observation satellites and sensors have been deployed worldwide.These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies.This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential.In this paper,we describe the develop-ment of GEOSatDB,the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model.A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information.The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors.The global Earth observation capabil-ities of 195 countries worldwide have been analyzed in detail,and a concrete use case along with an associated query demonstration is presented.This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observa-tion resources.

Two decades of fire activity over the PEEX domain:a look from space,with contribution from models and ground-based measurements

It has been suggested that forest fires will become more frequent/intense with changing climate,which would increase aerosol/gas emissions into the atmosphere.A better under-standing of the relations between meteorological conditions,fires,and fire emissions will help estimate the climate response via forest fires.In this study,we use ERA5 meteor-ological products,including temperature,precipitation,and soil moisture,to explain the frequency of forest fires and the amount of radiant energy released per time unit by burning vegetation(fire radiative power,FRP).We explore the relation-ships between satellite-retrieved fire products and aerosol properties(aerosol optical depth,AOD),carbon monoxide(CO),formaldehyde(HCHO),and nitrogen dioxide(NO_(2))con-centrations over the PEEX domain,which covers different vegetation zones(e.g.croplands/grasslands,forest,arctic tun-dra)of Pan-Eurasia and China.We analyse the concentrations of black carbon and absorbing organic carbon using groundbased AErosol RObotic NETwork.The analysis covers the months of May to August from 2002 to 2022.We show posi-tive temperature trends in the Northern zone(>65°N)in June and August(1.56°C and 0.64°C,respectively);all statistically significant trends for precipitation and soil moisture are nega-tive.This can explain increased fire activity in Siberia over the recent years(2019-2022).Over the whole PEEX domain,FC and FRP trends remain insignificant or negative;a decrease in AOD may address those negative trends.We show that intrasummer variations exist for cropland/grassland fires,which occur most often in May and August,while Siberian forest fires occur more often in July and August.We show that CO concentration has been gradually decreasing in the last two decades in May and June.CO trends are negative in May,June,and over summer for all regions,in July in Europe,China,the Southern zone(<55°N),and the PEEX domain.HCHO trends are not significant in all regions.NO_(2)trends are positive in May and negative in June in all zones.We calculated total column enhancement ratios for satellite obser-vations influenced by wildfires.A common feature has been recognized with measurements and ratios utilized in SILAM(System for Integrated Modelling of Atmospheric Composition):AOD(or PM):CO and AOD(or PM):HCHO ratios for grass are clearly lower than for shrubs,opposite for AOD:NO_(2).We showed that emission ratios are increasing towards South and are 2-3 times higher for high(>0.5)AOD.Using a 21-year satellite record of the AOD and CO,an 18-year record of NO_(2),and a 16-year record of HCHO,we created background products of those variables over the PEEX domain.In the regions with low anthropogenic activity and conditions where long-range transport is not happening,anomalies in AOD,CO,and HCHO over biomass-burning areas may be assigned directly to the wildfire emissions.

An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea

This paper presents a novel approach for predicting the water quality indicator-Secchi disk depth(Z_(SD)).Z_(SD)indirectly reflects water clarity and serves as a proxy for other quality parameters.This study utilizes Deep Neural Network(DNN)trained on satellite remote sensing and measured data from three sources:two datasets obtained from official agencies in Croatia and Slovenia,and one citizen science data source,all covering the northern coastal region of the Adriatic Sea.The proposed model uses 1D Convolutional Neural Network(CNN)in the spectral dimension to predict Z_(SD).The model’s performance indicates a strong fit to the observed data,proving capability of 1D-CNN to capture changes in water transparency.On the test dataset,the model achieved a high R-squared value of 0.890,a low root mean squared error(RMSE)of 0.023 and mean absolute error(MAE)of 0.014.These results demonstrate that employing a 1D-CNN in the spectral dimension of Sentinel-3 OLCI data is an effective approach for predicting water quality.These findings have significant implications for monitoring Z_(SD)in coastal areas.By integrating diverse data sources and leveraging advanced machine learning algorithms,a more accurate and comprehensive assessment of water quality can be achieved.

Long-term(2013-2022)mapping of winter wheat in the North China Plain using Landsat data:classification with optimal zoning strategy

security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area.Although various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse time-series.Machine learning is an effective method for crop classification using Landsat data.Yet,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area.To address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps.Then,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification features.For each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data.Field sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different years.The derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial level.The results underscore the reliability of the produced annual winter wheat maps.Additional experiments demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research Programme.The PEEX MP has more than 30 models and is directed towards seamless envir-onmental prediction.The main focus area is the Arctic-boreal regions and China.The models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and resolutions.This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical domain.The employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are described.Several virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are introduced.The over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,etc.for population,envir-onment and climate in the PEEX domain.This work was also one of the last projects of Prof.Sergej Zilitinkevich,who passed away on 15 February 2021.Since the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020

ABSTRACT A high-resolution meteorological dataset(≤10 km)over the Tibetan Plateau(TP)is the foundation for investigating and predicting the weather and climate over Asia.The TP Subregional Dynamical Downscaling(TPSDD)dataset is a newly developed high-spatial-temporal resolution gridded dataset for land‒air exchange pro-cesses and lower atmospheric structure studies over the whole TP region,taking the climate characteristics of each TP subregion into consideration.The dataset spans from 1981 to 2020,covering the TP with a temporal resolution of 2 hr and spatial resolution of 10 km.Meteorological elements of the dataset include near-surface land-air exchange parameters,such as downward/upward long-wave/shortwave radiation flux,sensible heat flux,latent heat flux,etc.In addition,the vertical distributions of 3-dimensional wind,temperature,humidity,and pressure from the surface to the lower stratosphere are also included.Independent evaluations were con-ducted to verify the performance of the TPSDD dataset by compar-ing TPSDD/reanalysis with surface and vertical observations through the calculation of statistical parameters.The results demonstrate the accuracy and superiority of this dataset against reanalysis data,which provides great potential for future climate change research.

An elevation change dataset in Greenland ice sheet from 2003 to 2020 using satellite altimetry data

ABSTRACT A decade-long pronounced increase in temperatures in the Arctic resulted in a global warming hotspot over the Greenland ice sheet(GrIS).Associated changes in the cryosphere were the consequence and led to a demand for monitoring glacier changes,which are one of the major parameters to analyze the responses of the GrIS to climate change.Long-term altimetry data(e.g.ICESat,CryoSat-2,and ICESat-2)can provide elevation changes over different periods,and many methods have been developed for altimetry alone to obtain elevation changes.In this work,we provided the long-term elevation change rate data of the GrIS in three different periods using ICESat data(from February 2003 to October 2009),Cryosat-2 data(from August 2010 to October 2018)and ICESat-2 data(from October 2018 to December 2020).Optimal methods were applied to the datasets collected by three different altimeters:crossover analysis for ICESat/ICESat-2 and the surface fit method for Cryosat-2.The data revealed that the elevation change rates of the GrIS were-12.19±3.81 cm/yr,-19.70±3.61 cm/yr and-23.39±3.06 cm/yr in the three different periods,corresponding to volume change rates of-210.20±25.34 km^(3)/yr,-339.11±24.01 km^(3)/yr and-363.33±20.37 km^(3)/yr,respectively.In general,the obtained results agree with the trends discovered by other studies that were also derived from satellite altimetry data.This dataset provides the basic data for research into the impact of climate change over the GrIS.The dataset is available at https://doi.org/10.57760/sciencedb.j00076.00121.

Marginal land in China suitable for bioenergy crops under diverse socioeconomic and climate scenarios from 2020 to 2100

Maximizing the development of renewable energy plays a critical role in mitigating the climate crisis.Marginal land provides space for the development of biomass energy;however,it remains unclear how the amount and spatial distribution of marginal land that is suitable for energy crop development will change in the future.Here,we project energy marginal land changes in China following the shared socioeconomic pathway(SSP)and/or repre-sentative concentration path(RCP).We provide datasets of mar-ginal land,agriculturally suitable land,and potentially suitable for energy crops under historical scenarios and six future scenarios(i.e.SSP1-1.9,SSP1-2.6,SSP4-3.4,SSP2-4.5,SSP4-6.0,and SSP3-7.0)for the period 2020-2100,with a spatial resolution of 5 km.Under the six scenarios,from 2020-2100,the area of suitable marginal land ranged from 1.90-16.28(Jatropha curcas L.)to 37.37-73.97(Panicum virgatum L.)(×10^(4)km^(2)),depending on the choice of energy crops and climate scenario.Based on the growing suitability of eight important bioenergy crops-Ricinus communis L.,Saccharum officinarum L.,Pistacia chinensis Bunge,Panicum virga-tum L.,Jatropha curcas L.,Miscanthus giganteus J.,Manihot esculenta Crantz,and Sorghum bicolor Moench-our dataset can be used to identify suitable locations for specific energy crops.This new syn-thetic dataset could support the development of multiscenariobased solutions related to carbon neutrality,ecosystem services,and energy transition.

Spatial-temporal variations of surface water area during 1986-2018 in Qinghai Province,northwestern China based on Google Earth Engine

Called the“Water Tower”,the water resource in Qinghai Province plays a vital role in the ecological environment of northwestern China.However,significant uncertainty exists regarding its spatialtemporal variations.In this study,the data on the surface water of Qinghai Province from 1986 to 2018 was extracted and a systemic analysis of its spatial-temporal variations and responses to climate change was conducted using the Google Earth Engine(GEE)cloud processing platform.Our findings revealed that the surface water in Qinghai Province was primarily concentrated in the Qaidam Basin,the Hoh Xil Nature Reserve,and Qinghai Lake.The surface water area in Qinghai Province exhibited an overall increasing trend,with the temperature and precipitation being the primary drivers of this expansion.These results provide crucial insights into the variations of surface water variations in Qinghai Province,northwestern China,under the influence of climate change.

A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data

Lake water levels are an important indicator of water balance and water cycles,and are essential for climate and environmental change studies and water resource evaluation.Currently,lake level measurements are scarce or inconsistent throughout the country,and traditional gauge measurements of many lakes are not feasible,so satellite altimetry is a vital alternative to gauge lake levels.However,the accuracy and sam-pling frequency of lake level time series are usually low because of time and space coverage limitations;therefore,it is necessary to utilize multialtimeter data to monitor lake levels and obtain lake level changes over long time series.In this study,we extracted the water level changes in 988 lakes(>10 km^(2))in China between 2002 and 2023 based on ICESat/-2,Cryosat-2,Jason-1/2/3,and Sentinel-3A/3B altimetry data using waveform retracking,lake level extraction,lake level time series construction,the fusion of multi-altimeter lake level time series,and outlier removal.A total of 55%of the lakes in this dataset have been monitored for more than 10 years,and 34%have more than 12 times the annual average water level monitoring.At the same time,in situ data from 21 lakes were used for validation,and the average root mean square error(RMSE)for each of the datasets of ICESat/-2,Cryosat-2,Jason-1/2/3,and Sentinel-3A/3B versus the in situ lake levels are 0.223 m,0.163 m,0.207 m,0.596 m,0.295 m,0.275 m,0.243 m,and 0.317 m,respectively,and the mean RMSE of the fused lake levels reaches 0.332 m.During the monitoring period,the water levels in Chinese lakes generally increased.The overall annual average rate of change at the 20 and 10-year scales was 0.123 m/a and 0.151 m/a,respectively,among which the overall water levels in large lakes increased significantly.The lakes with a faster rate of decline in the water level were primarily small.The water storage in each lake region in China shows an upward trend,with the most significant increase in the Tibetan Plateau region,where the average annual water level change rate has remained above 0.15 m/a over the past two decades.This dataset has high spatiotemporal coverage and accuracy and can support the estimation of changes in lake water storage,analysis of lake level trends,plateau flooding,and the relationship between lake ecosystems and water resources.

Time series analysis of L-band PALSAR-2 images in Istanbul and Kocaeli,Turkey

ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task.Conventional methods are both timeconsuming and expensive,limiting their use for large-scale applica-tions.The synergy of synthetic aperture radar(SAR)and geographic information systems(GIS)offers a complementary approach.This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli,significant industrial and residential areas in Turkey.PALSAR-2 phase and intensity information were analyzed.For phase analysis,14 L-band images from 2014 to 2021 were taken into account.Small baseline subset(SBAS)analysis was performed using 44 pairs,and results of the velocity,coherence and back-scattering values are presented.Coherence of all pairs and their correlations were calculated.Principal Component Analysis(PCA)reduced the dimension of coherence pairs,enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of−58 mm/yr and a mean subsidence of−20 mm/yr.These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations.The results showed that immature land subsidence in the mentioned areas are growing slowly,which can be taken as a serious risk in future.

A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

A high-quality snow depth product is very import for cryospheric science and its related disciplines.Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories:remote sensing snow depth products and reana-lysis snow depth products.However,existing gridded snow depth products have some shortcomings.Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth,while reanalysis snow depth products have coarse spatial resolutions and great uncertainties.To overcome these problems,in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E),Advanced Microwave Scanning Radiometer-2(AMSR2),Global Snow Monitoring for Climate Research(GlobSnow),the Northern Hemisphere Snow Depth(NHSD),ERA-Interim,and Modern-Era Retrospective Analysis for Research and Applications,ver-sion 2(MERRA-2),incorporating geolocation(latitude and longitude),and topographic data(elevation),which were used as input indepen-dent variables.More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods.This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°.Here,we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites,showing an improved precision of our product.The evaluation indices of the fused(best original)dataset yielded a coeffi-cient of determination R2 of 0.81(0.23),Root Mean Squared Error(RMSE)of 7.69(15.86)cm,and Mean Absolute Error(MAE)of 2.74(6.14)cm.Most of the bias(88.31%)between the fused snow depth and in situ observations was in the range of−5 cm to 5 cm.The accuracy assessment of independent snow observation sites-Sodankylä(SOD),Old Aspen(OAS),Old Black Spruce(OBS),and Old Jack Pine(OJP)-showed that the fused snow depth dataset had high precision for snow depths of less than 100 cm with a relatively homogeneous surrounding environment.The results of random point selection and independent in situ site validation show that the accuracy of the fused snow depth product is not significantly improved in deep snow areas and areas with complex terrain.In the altitude range of 100 m to 2000 m,the fused snow depth had a higher precision,with R2 varying from 0.73 to 0.86.The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method.This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change,hydrological and water cycle,water resource manage-ment,ecological environment,snow disaster and hazard prevention.