Accuracy assessment of global ocean tide models in the South China Sea using satellite altimeter and tide gauge data

In this study,to meet the need for accurate tidal prediction,the accuracy of global ocean tide models was assessed in the South China Sea(0°–26°N,99°–121°E).Seven tide models,namely,DTU10,EOT11 a,FES2014,GOT4.8,HAMTIDE12,OSU12 and TPXO8,were considered.The accuracy of eight major tidal constituents(i.e.,Q1,O1,P1,K1,N2,M2,S2 and K2)were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry(TOPEX and Jason series)and tide gauge observations.The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean(depth>200 m)and 1.18–5.63 cm in shallow water area(depth<200 m).Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait,which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data.In coastal regions,an accuracy performance was investigated using tidal results from 37 tide gauge stations.The root sum square values were in the range of 9.35–19.11 cm,with the FES2014 model exhibiting slightly superior performance.

Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome,Italy

Google Earth(GE)has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects,due to the free and easily accessed satellite imagery provided with global coverage.Nevertheless,the uses of this service raises several research questions on the quality and uncertainty of spatial data(e.g.positional accuracy,precision,consistency),with implications for potential uses like data collection and validation.This paper aims to analyze the horizontal accuracy of very high resolution(VHR)GE images in the city of Rome(Italy)for the years 2007,2011,and 2013.The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points.The validation process includes the comparison of histograms,graph plots,tests of normality,azimuthal direction errors,and the calculation of standard statistical parameters.The results show that GE VHR imageries of Rome have an overall positional accuracy close to 1 m,sufficient for deriving ground truth samples,measurements,and large-scale planimetric maps.

Coseismic surface deformation of the 2014 Napa earthquake mapped by Sentinel-1A SAR and accuracy assessment with COSMO-SkyMed and GPS data as cross validation

The new land observation satellite Sentinel-1A was launched on 25 April 2014 with a C-band synthetic aperture radar(SAR)sensor,which has the significant enhancements in terms of revisit period and high resolution.The Mw 6.1 Napa,California earthquake occurring on 24 August 2014,almost 4 months after the launch,is the first moderate earthquake imaged by the Sentinel-1A.This provides an opportunity to map the coseismic deformation of the event and evaluate the potential of Sentinel-1A SAR for earthquake study.Two techniques including the interferometric SAR(InSAR)and pixel offset-tracking(PO)are,respectively,employed to map the surface deformation along the radar line of sight(LOS),azimuth and slant-range directions.The cross comparison between Sentinel-1A InSAR LOS deformation and GPS observations indicates good agreement with an accuracy of∼2.6 mm.We further estimate the earthquake source model with the external COSMO-SkyMed InSAR and GPS data as constraints,and forward calculate the surface deformation as cross validation with the Sentinel-1A observations.The comparison between the observed and modeled deformation shows that the Sentinel-1A measurement accuracy can achieve 1.6 cm for InSAR technique along LOS direction,and 6.3 and 6.7 cm for PO along azimuth and range directions,respectively.

Land use and cover change and influencing factor analysis in the Shiyang River Basin,China

Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Change Detection of Land Use and Land Cover over a Period of 20 Years in Papua New Guinea

People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.

Monitoring Land Cover Change Using Remote Sensing (RS) and Geographical Information System (GIS): A Case of Golden Pride and Geita Gold Mines, Tanzania

This study monitored land cover change in the mining sites of Golden Pride Gold Mine (GPGM) and Geita Gold Mine (GGM), Tanzania. The satellite data for land cover classification for the years 1997, 2010 and 2017 were obtained from the United States Geologic Survey Departments (USGS) online database and were analyzed using Arc GIS 10 software. Supervised classification composed of seven classes namely forest, bushland, agriculture, water, bare soil, urban area and grassland, was designed for this study, in order to classify Landsat images into thematic maps. In addition, future land cover changes for the year 2027 were simulated using a Cellular Automata (CA)-Markov model after validating the model using the Land Cover for the year 2017. The results from the LULC analysis showed that forest was the most dominant land cover type in 1997 at GPGM and GGM covering 510 ha (52.1%) and 9833 ha (49.7%) respectively. In 2017, the forest area decreased and the bushland replaced forest to be the most dominant land cover type covering 219 ha (22.4%) for GPGM and 8878 ha (44.9%) for GGM. Based on the CA-Markov model, a predicted land cover map for 2027 was dominated by forest covering 340 ha (34.7%) and 8639 ha (43.7%) for GPGM and GGM respectively. An overall accuracy and kappa coefficient for GPGM were 74.7% and 70.2% respectively and for GGM were 71.4% and 66.1% respectively. Thus, land cover changes resulting from mining activities involve reduction of forest land hence endangers biodiversity. GIS and remote sensing technologies are potential to detect the trend of changes and predict future land cover. The findings are crucial as it provides basis for land use planning and intensifies monitoring programs in the mining areas of Tanzania.

The urgent need to develop a new grassland map in China:based on the consistency and accuracy of ten land cover products

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years.Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets(GLC2000, GlobCover, CCI-LC,MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10^(4)to 332.46×10^(4)km^(2), with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of eastcentral Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products,with an overall accuracy(OA) reaching 77.51% and a kappa coefficient of 0.51;CLCD is slightly less accurate than MCD12Q1(OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China’s grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

Reliability and consistency assessment of land cover products atmacro and local scales in typical cities

Urban areas have higher heterogeneity compared to natural areas,it is crucial to assessfine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios.In this study,five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products:GLC_FCS30-2020,FROM-GLC30-2017,Globeland30-2020,and CLCD-2019.We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency.Given the limitations of traditional accuracy assessments at the macro level,we added a local area evaluation to further examine the classification details in these products.The macro results indicated that four land cover products within urban areas have a similar overall accuracy,surpassing 76%,but there was a low consistency among them,ranging from 42.21%to 61.13%.The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city,however,the four products exhibited varying degrees of misclassifications and omissions.These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities sincefine-resolution land cover products may fail to capture complex urban details.

Preliminary Pointing Bias Calibration of ZY3-03 Laser Altimeter

ZiYuan3-03(ZY3-03)satellite was launched on July 25,2020,equipped with China’s second-generation laser altimeter for earth observation.In order to preliminarily evaluate the in-orbit performance of the ZY3-03 laser altimeter,the pointing bias calibration based on terrain matching method was adopted.Three tracks of laser data were employed for the ZY3-03 laser altimeter calibration test.Three groups of pointing parameters were obtained respectively,and the mean value of pointing is considered as the optimal calibration result.After calibration,ZY3-03 laser pointing accuracy is greatly improved by the method,and its pointing accuracy is approximately 12.7 arcsec.The first-track laser data on the Black Sea surface is used to evaluate the relative elevation accuracy of ZY3-03 laser altimeter after pointing bias calibration,which is improved from 0.33 m to 0.19 m after calibration.Meanwhile,the absolute elevation accuracy of ZY3-03 laser altimeter after pointing bias calibration is evaluated by the Ground Control Points(GCPs)measured by RTK(Real-Time Kinematic),which is better than 0.5 m in the flat terrain.

Collaborative validation of GlobeLand30:Methodology and practices

30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmental change studies,and many other societal benefit areas.In the past few years,increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products.However,most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries(areas),and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented.In order to promote a comprehensive and collaborative validation of 30-m GLC data products,the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017,to examine and explore its major problems,including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities.With the joint effort of experts and users from 30 GEO member countries or participating organizations,a technical specification for 30-m GLC validation was developed based on the findings and experiences.An on-line validation tool,GLCVal,was developed by integrating land cover validation procedures with the service computing technologies.About 20 countries(regions)have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Global land cover(LC)maps have been widely employed as the base layer for a number of applications including climate change,food security,water quality,biodiversity,change detection,and environmental planning.Due to the importance of LC,there is a pressing need to increase the temporal and spatial resolution of global LC maps.A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery,which has been developed by the National Geomatics Center of China(NGCC).Although overall accuracy is greater than 80%,the NGCC would like help in assessing the accuracy of the product in different regions of the world.To assist in this process,this study compares the GlobeLand30 product with existing public and online datasets,that is,CORINE,Urban Atlas(UA),OpenStreetMap,and ATKIS for Germany in order to assess overall and per class agreement.The results of the analysis reveal high agreement of up to 92%between these datasets and GlobeLand30 but that large disagreements for certain classes are evident,in particular wetlands.However,overall,GlobeLand30 is shown to be a useful product for characterizing LC in Germany,and paves the way for further regional and national validation efforts.

How do GPM and TRMM precipitation products perform in alpine regions?A case study in northwestern China's Qilian Mountains

Satellite technologies provide valuable areal precipitation datasets in alpine mountains.However,coarse resolution still limits the use of satellite precipitation datasets in hydrological and meteorological research.We evaluated different time scales and precipitation magnitudes of Tropical Rainfall Measurement Mission 3B43 V7(TRMM)and Global Precipitation Measurement(GPM)products for alpine regions using ground precipitation datasets from January 2015 to June 2019 obtained from 25 national meteorological stations and 11 sets of T-200B weighing precipitation gauges in the Qilian Mountains.The results indicated that GPM outperformed TRMM at all temporal scales at an elevation<3500 m with a higher probability of detection(POD),false alarm ratio(FAR),and frequency bias index(FBI)and performed best at 3000 m;TRMM performed better than GPM at an elevation>3500 m,with the best performance at 4000 m.GPM and TRMM had the best estimation accuracy in areas with monthly precipitation of 30 mm and 40 mm,respectively.Both TRMM and GPM products underestimated mid to large daily precipitation and overestimated light daily precipitation averaging<2 mm/d.This research not only emphasizes the superiority of GPM/TRMM in different regions but also indicates the limitations of precipitation algorithms.

Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing,crowdsourced,and Auxiliary Datasets

Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.

A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion people.Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been studied.This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area.The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor.To this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment methods.It was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific community.Finally,several recommendations were proposed to address the identified issues.

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales.However,the rapid growth of urban areas exerts effects on climate,biodiversity,hydrology,and natural ecosystems worldwide.Therefore,regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local,regional,and potentially global scales.This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data,including Moderate Resolution Imaging Spectroradiometer(MODIS)data of 2013,population density of 2012,the Defense Meteorological Satellite Program’s Operational Linescan System(DMSP-OLS)nighttime lights of 2012,and constructed Impervious Surface Area(ISA)data of 2010.The Eurasian urban map was created using the threshold method for these data,combined with references of fine resolution Landsat and Google Earth imagery.The resultant map was compared with nine global urban maps and was validated using random sampling method.Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%.This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.

Validation of GIS layers in the EU:getting adapted to available reference data

An optimal validation of a thematic map would ideally require in-situ observations of a large sample of units specifically conceived for the map under validation.This is often not possible due to budget limitations.The alternative can be using photo-interpretation of high or very high resolution images instead of in-situ observations or using available data sets that do not fully comply with the ideal characteristics:unit size,reference date or sampling plan.This paper illustrates some examples of use of available data in the European Union.For land cover maps,the best existing data set is probably Land Use/Cover Areaframe Survey(LUCAS)that has been conducted by Eurostat on four occasions since 2001.Because LUCAS is based on systematic sampling,advantages and limitations of systematic sampling are discussed.A fine-scale population density map is presented as an example of a situation in which reference data on a statistical sample cannot be collected.