Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data

This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.

Quantification of Sand Dune Movements in the South Western Part of Egypt, Using Remotely Sensed Data and GIS

Sand dune movement is a hazardous phenomenon in Egypt and creates major threat on the existing land use and land cover as well as developmental plans. This paper studied the sand dune morphology and quantified the rate of sand dune movements and direction in a newly developed project in the southwest of Egypt. Two dates of satellite imageries were used to trace the Barchan dunes at various sites with different morphological properties to estimate the annual movement rate based on point to point geo-correlations. 149 dunes of the common sand dunes in the area of study including barchans and transverse dunes were studied to accurately determine their rate of movements, which ranged from 1.3 to 19.3 my-1. The direction of sand dune movements was mainly to the south and slightly southeast with range from 265 to 295 degrees. The quantification of sand dune movement and direction has, indeed, enabled to determine the major threat on the exiting land use and land cover as well as the newly developmental projects.

Assessment of Urban Heat Island Using Remotely Sensed Imagery over Greater Cairo, Egypt

The Urban Heat Island (UHI) results in significant and sometimes dramatic increases in air temperature differences between the urban environment and its surrounding areas. The heat island structure may extend from the ground to the top of roofs and canopy levels above ground. The Urban Heat Island effect is a leading factor in a long list of human health problems which are expected to increase with the rapid growth of urban populations and projected future climate change. Urban heat island studies can be conducted through either direct measurements of air temperature using automobile transects and weather station networks or through measuring surface temperature using airborne or satellite remote sensing. In this study, UHI was investigated over the Greater Cairo during both summer and winter seasons over two different dates. Landsat 7 ETM+ data were used and the mono-window algorithm was applied in the study. Results emphasis that both types of heat islands exist in the study area i.e. the surface and the atmospheric heat islands. Temperature differences ranged between 0.5°C to 3.5°C and these are much related to the existing land use/covers. It could be concluded that expansion of urban areas in Greater Cairo has led to increased thermal radiation of land surface on the highly populated areas.

A Comprehensive Evaluation of PAN-Sharpening Algorithms Coupled with Resampling Methods for Image Synthesis of Very High Resolution Remotely Sensed Satellite Data

The merging of a panchromatic (PAN) image with a multispectral satellite image (MSI) to increase the spatial resolution of the MSI, while simultaneously preserving its spectral information is classically referred as PAN-sharpening. We employed a recent dataset derived from very high resolution of WorldView-2 satellite (PAN and MSI) for two test sites (one over an urban area and the other over Antarctica), to comprehensively evaluate the performance of six existing PAN-sharpening algorithms. The algorithms under consideration were the Gram-Schmidt (GS), Ehlers fusion (EF), modified hue-intensity-saturation (Mod-HIS), high pass filtering (HPF), the Brovey transform (BT), and wavelet-based principal component analysis (W-PC). Quality assessment of the sharpened images was carried out by using 20 quality indices. We also analyzed the performance of nearest neighbour (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods to test their practicability in the PAN-sharpening process. Our results indicate that the comprehensive performance of PAN-sharpening methods decreased in the following order: GS > W-PC > EF > HPF > Mod-HIS > BT, while resampling methods followed the order: NN > BI > CC.

A Review on Extraction of Lakes from Remotely Sensed Optical Satellite Data with a Special Focus on Cryospheric Lakes

Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.

Soil temperature estimation at different depths,using remotely-sensed data

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

Recent Changes Occurred in the Terminus of the Debriscovered Bilafond Glacier in the Karakoram Himalayas Using Remotely Sensed Images and Digital Elevation Models(1978-2011)

Recent changes occurred in terminus of the debris-covered Bilafond Glacier in the Karakoram Range in the Himalayas, Northern Pakistan was investigated in this research. Landsat MSS, TM and ETM+ images were used for this study. Digital elevation models derived from ASTER GDEM and SRTM were also utilized. Visible, infrared and thermal infrared channels were utilized in order to get accurate glacier change maps. Three methods were tried to map this debris-covered glacier in this research. The glacier has been mapped successfully and the changes in the glacier terminus from 1978 to 2011 have been calculated. Manual, semi-automatic and thermal methods were found to give similar results. It was found that the glacier has undergone serious ablation during this period despite of the fact that many of the larger glaciers in the Hindu Kush and Karakoram mountain regions in the Upper Indus Basin were reported to be expanding. The terminus has been moved back about 600 meters during this period and there was an abrupt change in the glacier terminus during 1990-2002. We propose that debris thickness is not the only factor that influences the glacier ablation but the altitude of the debris-covered glacier as well. Many glaciers in the Karakoram region reported to be expanding were having higher altitudes compared to the study area.

Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.

Interpreting the Shortwave Infrared &Thermal Infrared Regions of Remote Sensed Electromagnetic Spectrum with Application for Mineral-Deposits Exploration

The ASTER (Advanced Space-borne Thermal Emission and Reflection radiometer) data, including all the 3 parts: VNIR (Visible and Near-Infrared), SWIR (Short Wave Infrared), TIR (Thermal Infrared), were applied for extraction of mineral deposits, such as the Ni-Cu deposit in eastern Tianshan, the gypsum in western Tianshan, and the borax in Tibetan. This paper discusses the extraction methodology using the ASTER remote sensing data and reveals the good extraction results. This paper bravely represents the summary of the main achievement for this field by the scientists in other countries and gives a comparison with the works by others. The new achievements, described in this paper, comprise the extraction of anomalies for Ni-Cu deposit, gypsum, and borax.

Analysis of Mountain Rangeland Changes, Using Remotely-Sensed Data, (Case Study: Dehdez Area, Iran)

The land-cover dynamics has been quite conspicuous over the last three decades in Dehdez area, Iran. Therefore, the present study was undertaken in the Dehdez area to assess the trends of rangelands dynamics in the study area during the period 1990-2006. Two clear, cloud-free Landsat and one ASTER images were selected to classify the study area. All images were rectified to UTM zone 39, WGS84 using at least 25 well distributed ground control points and nearest neighbor resampling. Land-use/cover mapping is achieved through interpretation of Landsat TM satellite images of 1990, 1998 and ASTER image of 2006. Fieldwork was carried out to collect data for training and validating land-use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land-use/cover class. In order to create a testing sample set, first of all, a set of testing points was selected randomly. A supervised classification technique with Maximum Likelihood Algorithm was applied based on 48 training samples for the image of 2006, and 42 samples for the images of 1990 and 1998 and the land-use/cover maps were produced. Error matrices were used to assess classification accuracy. The results showed rangeland covers about 30.8%, 36.7% and 45% of the total geographical area of the Dehdez area in 1990, 1998 and 2006, respectively. Overall accuracies of land-use/cover classification for 1990, 1998 and 2006 were 89.37%, 75.24% and 71.14%, respectively. Kappa values obtained were of 78.71%, 55.61% and 51.41% of accuracy for the 1990, 1998 and 2006, respectively. During 16 years span period (1990-2006) about 1738.4 ha, 383.7 ha, 32.8 ha and 890.1 ha of rangelands were converted to forest, agriculture, water and settlement. The total rich rangelands in the area, accounted for 38.5%, 44% and 42.2% in 1990, 1998 and 2006, respectively. The total poor rangeland in the area accounted for 61.5%, 56% and 57.8% in 1990, 1998 and 2006, respectively. Satellite Remote Sensing enabled the generation of a detailed rangeland map and the separation of grazing intensity levels in rangelands could be generated with the relatively little effort in areas that were difficult to access.

A Summary of Change Detection Technology of Remotely-Sensed Image

This paper will describe three aspects of change detection technology of remotely-sensed images. At first, the process of change detection is presented. Then, the author makes a summary of several common change detection methods and a brief review of the advantages and disadvantages of them. At the end of this paper, the applications and difficulty of current change detection techniques are discussed.

Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model,in the upstream Citarum Watershed,West Java,Indonesia

In this research,the integration of remotely sensed data and Cellular Automata-Markov model(CA-Markov)have been used to analyze the dynamics of land use change and its prediction for the next year.Training phase for the CA-Markov model has been created based on the input pair of land use,which is the result of land use mapping using Maximum Likelihood(ML)algorithm.Three-map comparison has been used to evaluate process accuracy assessment of the training phase for the CA-Markov model.Furthermore,the simulation phase for the CAMarkov model can be used to predict land use map for the next year.The analyze of the dynamics of land use change and its prediction during the period 1990 to 2050 can be obtained that the land serves as a water absorbent surfaces such as primary forest,secondary forest and the mixed garden area continued to decline.Meanwhile,on build land area that can lead to reduced surface water absorbing tends to increase from year to year.The results of this research can be used as input for the next research,which aims to determine the impact of land use changes in hydrological conditions against flooding in the research area.

Arctic sea-ice extent: No record minimum in 2023 or recent years

Arctic sea-ice extent reaches its minimum each year in September. On 11 September 2023 the minimum was 4.969 million square kilometers(mill.km^(2)). This was not a record low, which occurred in 2012, when the minimum was 4.175 mill.km^(2), 0.794 mill.km^(2) less than the minimum in 2023. However, the ice extent had decreased by 0.432 mill.km^(2) compared with 2022. Nevertheless, the summer melting in 2023 was remarkably less than expected when considering the strong heat waves in the atmosphere and ocean, with record temperatures set around the world. In general, there is a high correlation between the long-term decrease in sea-ice extent and the increasing CO_(2) in the atmosphere, where the increase of CO_(2) in recent decades explains about 80% of the decrease in sea ice in September, while the remainder is caused by natural variability.

Runoff simulation and hydropower resource prediction of the Kaidu River Basin in the Tianshan Mountains,China

The Tianshan Mountains of Central Asia,highly sensitive to climate change,has been comprehensively assessed for its ecosystem vulnerability across multiple aspects.However,studies on the region's main river systems and hydropower resources remain limited.Thus,examining the impact of climate change on the runoff and gross hydropower potential(GHP)of this region is essential for promoting sustainable development and effective management of water and hydropower resources.This study focused on the Kaidu River Basin that is situated above the Dashankou Hydropower Station on the southern slope of the Tianshan Mountains,China.By utilizing an ensemble of bias-corrected global climate models(GCMs)from Coupled Model Intercomparison Project Phase 6(CMIP6)and the Variable Infiltration Capacity(VIC)model coupled with a glacier module(VIC-Glacier),we examined the variations in future runoff and GHP during 2017-2070 under four shared socio-economic pathway(SSP)scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5)compared to the baseline period(1985-2016).The findings indicated that precipitation and temperature in the Kaidu River Basin exhibit a general upward trend under the four SSP scenarios,with the fastest rate of increase in precipitation under the SSP2-4.5 scenario and the most significant changes in mean,maximum,and minimum temperatures under the SSP5-8.5 scenario,compared to the baseline period(1980-2016).Future runoff in the basin is projected to decrease,with rates of decline under the SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 scenarios being 3.09,3.42,7.04,and 7.20 m^(3)/s per decade,respectively.The trends in GHP are consistent with runoff,with rates of decline in GHP under the SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 scenarios at 507.74,563.33,1158.44,and 1184.52 MW/10a,respectively.Compared to the baseline period(1985-2016),the rates of change in GHP under the SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 scenarios are-20.66%,-20.93%,-18.91%,and-17.49%,respectively.The Kaidu River Basin will face significant challenges in water and hydropower resources in the future,underscoring the need to adjust water resource management and hydropower planning within the basin.

On Forging a Strong Sense of Community for the Chinese Nation and the Continuous Development of the Cohesion of the Chinese Nation

Chinese national cohesion,through hard forging in the long course of history,has strong historical inertia.The theory of"Great Unification"a multi-ethnic state structure under this ideology,the"multi-colorfully integrated"Chinese culture,a constantly evolving and subliming sense of"Huaxia identity"and other historical genes make a far-reaching significance to producing and developing the cohesion of the Chinese nation.The Communist Party of China(CPC)fully appreciates the laws and prevailing trends of history,seizes the historical initiative,and combines them with new historical context,so unremittingly promotes the continuous development of the cohesion of the Chinese nation.On the new journey into the new era,General Secretary Xi Jinping,centering on the epochal theme of"the great rejuvenation of the Chinese nation",put forward the original assertion of"forging a strong sense of community for the Chinese nation",pointing out that"forging a strong sense of community for the Chinese nation"as the main theme to continuously promote the construction of the Chinese national community,which has had a tremendous and far-reaching impact on the continued development of the cohesion of the Chinese nation.Forging a strong sense of community for the Chinese nation and continuously developing the cohesion of the Chinese nation reflect the historical consciousness and the spirit of the historical initiative of the CPC in the new era to unite and lead the people of all ethnic groups in the country,to unite the majestic power of the great rejuvenation of the Chinese nation,and to promote the great rejuvenation of the Chinese nation comprehensively.

Graphene/F_(16)CuPc synaptic transistor for the emulation of multiplexed neurotransmission

We demonstrate a bipolar graphene/F_(16)CuPc synaptic transistor(GFST)with matched p-type and n-type bipolar properties,which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters in graphene and F_(16)CuPc channels,separately.This process facilitates fast-switching plasticity by altering charge carriers in the separated channels.The complementary neural network for image recognition of Fashion-MNIST dataset was constructed using the matched relative amplitude and plasticity properties of the GFST dominated by holes or electrons to improve the weight regulation and recognition accuracy,achieving a pattern recognition accuracy of 83.23%.These results provide new insights to the construction of future neuromorphic systems.

Geographical characteristics of China’s wetlands derived from remotely sensed data

In this paper, we report the first wetland mapping of the entire China using Landsat enhanced thematic mapper plus (ETM+) data. These data were obtained from the Global Land Cover Facility at the University of Maryland spanning from 1999 to 2002. A total of 597 scenes of Landsat images were georeferenced and mosaiced. Manual image interpretation of satellite images was aided with elevation data, soil data, land cover/land use data and Google Earth. The minimum mapping unit is 10 pixel × 10 pixel, equivalent to 9 ha. The aim of our first round of mapping was only targeted at the boundary delineation of any type of wetland except those wetlands that are under agricultural use (i.e., paddy fields), which has already been well mapped by others. Our interpretation results indicate that a total of 359478 km2 of wetlands are of non-agricultural use. Among our preliminarily mapped wetland, 339353 km2 are inland wetland, 2786 km2 are non-agricultural artificial wetland, and 17609 km2 are coastal wetland. Because low-tide is rarely captured in satellite images, an under-estimation of coastal wetland is inevitable. We conducted some statistics based on our mapped wetlands and compared them with those previously obtained from a number of sources including a land cover/land use map made with satellite images during the late 1990s and early 2000s, a marshland map developed in approximately the same period, survey data of coastal wetland in early 1980s, and area data for approximately 400 larger patches of marshland in China compiled in 1996. Because some inconsistencies exist in the guidelines of those different wetland surveys, difference in area is expected. Some further comparison indicates that the wetland distributions derived from the preliminary wetland map are reasonable and more objective than other sources. The mapping process also indicated that the method adopted by us was efficient and cost-effective. We also found that in order to ensure comparability of the wetland maps developed at different times, a set of standard guidelines on the wetland categories to be mapped, and the mapping methods to be used must be well conceived, developed and effectively employed. We carried out some initial geographical analysis on the distribution of wetlands.

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

Long-term highly accurate surface soil moisture data of TP(Tibetan Plateau)are important to the research of Asian monsoon and global atmospheric circulation.However,due to the sparse in-situ networks,the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP.Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E(Advanced Microwave Scanning Radiometer for EOS),we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP.Compared to the in-situ observations,the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated.The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E.The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed.The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP.Based on the new soil moisture product,we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP.From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP,we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.

Improvement of Dark Object Method in Atmospheric Correction of hyperspectral remotely sensed data

The existing methods in atmospheric correction of hyperspectral data usually focus on removing the effects of water vapor and other absorptive gases, while this paper mainly studies the method of re- moving the influence of the aerosol and the water vapor simultaneously. Because the hyperspectral data has a larger number of bands, the conventional dark object method cannot be applied to the at- mospheric correction of the hyperspectral data which can be improved, as described in this paper, by adequately making use of spectral characteristics of the hyperspectral data with an iterative correction during the whole process. The effects of the aerosol and water vapor are eliminated at the same time finally. The improved dark object method is used to do the atomospheric correction of the Hyperion data in Yanzhou, Shandong Province as an example. And the result indicates that it can correct the atmospheric influence of the hyperspectral data quickly and remarkably.

Suitability mapping of global wetland areas and validation with remotely sensed data

With increasing urbanization and agricultural expansion, large tracts of wetlands have been either disturbed or converted to other uses. To protect wetlands, accurate distribution maps are needed. However, because of the dramatic diversity of wetlands and difficulties in field work, wetland mapping on a large spatial scale is very difficult to do. Until recently there were only a few high resolution global wetland distribution datasets developed for wetland protection and restoration. In this paper, we used hydrologic and climatic variables in combination with Compound Topographic Index （CTI） data in modeling the average annual water table depth at 30 arc-second grids over the continental areas of the world except for Antarctica. The water table depth data were modeled without considering influences of anthropogenic activities. We adopted a relationship between poten- tial wetland distribution and water table depth to develop the global wetland suitability distribution dataset. The modeling re- suits showed that the total area of global wetland reached 3.316× 10^7 km^2. Remote-sensing-based validation based on a compi- lation of wetland areas from multiple sources indicates that the overall accuracy of our product is 83.7%. This result can be used as the basis for mapping the actual global wetland distribution. Because the modeling process did not account for the im- pact of anthropogenic water management such as irrigation and reservoir construction over suitable wetland areas, our result represents the upper bound of wetland areas when compared with some other global wetland datasets. Our method requires relatively fewer datasets and has a higher accuracy than a recently developed global wetland dataset.