Estimated global nitrogen deposition using NO2 column density

Global nitrogen deposition has increased over the past 100 years. Monitoring and simulation studies of nitrogen deposition have evaluated nitrogen deposition at both the global and regional scale. With the development of remote-sensing instruments, tropospheric NO₂ column density retrieved from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) sensors now provides us with a new opportunity to understand changes in reactive nitrogen in the atmosphere. The concentration of NO₂ in the atmosphere has a significant effect on atmospheric nitrogen deposition. According to the general nitrogen deposition calculation method, we use the principal component regression method to evaluate global nitrogen deposition based on global NO₂ column density and meteorological data. From the accuracy of the simulation, about 70% of the land area of the Earth passed a significance test of regression. In addition, NO₂ column density has a significant influence on regression results over 44% of global land. The simulated results show that global average nitrogen deposition was 0.34 g m^?2 yr^?1 from 1996 to 2009 and is increasing at about 1% per year. Our simulated results show that China, Europe, and the USA are the three hotspots of nitrogen deposition according to previous research findings. In this study, Southern Asia was found to be another hotspot of nitrogen deposition (about 1.58 g m^?2 yr^?1 and maintaining a high growth rate). As nitrogen deposition increases, the number of regions threatened by high nitrogen deposits is also increasing. With N emissions continuing to increase in the future, areas whose ecosystem is affected by high level nitrogen deposition will increase.     

Estimating NO2 dry deposition using satellite data in eastern China

The spatial and temporal characters of ground-level NO₂ concentration over eastern China were retrieved from the monthly averaged tropospheric NO₂ column densities from Global Ozone Monitoring Experiment (GOME, data used in this study are from April 1996 to December 2002) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, data used in this study are from January 2003 to December 2011) measurements. Together with the NO₂ concentration and the dry deposition velocity maps of eastern China, the fluxes of NO₂ dry deposition were estimated for three specific regions. The results indicated that the surface NO₂ concentration in eastern China increased dramatically from 1996 to 2011, and it showed distinct regional and seasonal variational characteristics. The highest concentration occurred in winter while the lowest occurred in summer. There was also variation in the spatial distribution with the peak value of NO₂ concentration appearing in the plains of north China (R1), the Yangtze River delta (R2), and the Pearl River delta (R3). A sharp increase of NO₂ concentration appeared in R1 and R2, while it was invariant or showed an obvious decrease in R3 during the period of 1996–2011. Furthermore, we compared the NO₂ dry deposition fluxes estimated from the ground-level NO₂ concentration and the dry deposition velocity of NO₂ with the mass concentration of NO₂ dry deposition that, measured from the control experiments and by consulting the published literature, showed a significant correlation (P < 0.001) and had a high R value (R = 0.73). The results also indicated that the NO₂ dry deposition fluxes increased over eastern China, with a maximum value of 8.25 kg N ha^?1 yr^?1 from 1996 to 2011 in R3, while the value was characterized by fluxes of less than 2.27 kg N ha^?1 yr^?1 in R2. When comparing the NO₂ dry deposition over different land covers, the values distinctly peaked over artificial surfaces and evergreen forests, with maximum values of 10.07 and 9.49 kg N ha^?1 yr^?1 in R1, 5.05 and 4.94 kg N ha^?1 yr^?1 in R2, and 20.95 and 23.15 kg N ha^?1 yr^?1 in R3. However, the lowest value of NO₂ dry deposition flux appeared over needleleaf forests, with 0.53, 0.24, and 1.29 kg N ha^?1 yr^?1 for R1, R2, and R3, respectively.     

Comparison analysis of the global carbon dioxide concentration column derived from SCIAMACHY,AIRS, and GOSAT with surface station measurements

As a significant greenhouse gas, CO₂ plays an important role in not only the formation of, but also in changes to, the Earth’s climate. A series of remote-sensing detectors have been launched into space to aid the understanding of sources and sinks of CO₂. Although measurements from AIRS (Atmospheric Infrared Sounder), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), and GOSAT (Greenhouse Gases Observing Satellite) have been frequently used to retrieve atmospheric CO₂ concentrations, there are no comprehensive comparison analyses between satellite data and ground data. In this article, the characteristics of the current common observing platforms and their product data are compared and analysed. Correlation coefficient, RMSD, and bias are used to evaluate the CO₂ retrieved from satellite data. The results reveal that: SCIAMACHY has limitations in detecting CO₂ over the ocean; GOSAT has the poorest coverage on a global scale but has a better capability to detect CO₂ over the sea than SCIAMACHY; and AIRS can reflect the distribution and changes of CO₂ very well. The differences in coverage and accuracy indicate the necessity to produce consistent products with improved spatial and temporal features and indicate a future development trend for instruments including higher spectral resolution, higher spatial resolution, wider coverage, shorter revisit periods, and higher signal to noise ratios.     

Determination of the cloud fraction in the SCIAMACHY ground scene using MERIS spectral measurements

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields.     

Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera

The application of adequate nitrogen (N) fertilizers to grass seed crops is important to achieve high seed yield. Application of N will inevitably result in over-fertilization on some fields and, concomitantly, an increased risk of adverse environmental impacts, such as ground- and/or surface-water contamination. This study was designed to estimate the N status of two grass seed crops: red fescue (Festuca rubra L.) and perennial ryegrass (Lolium perenne L.) using images captured with an unmanned aerial vehicle (UAV) mounted multispectral camera. Two types of UAV, a fixed-wing UAV and a multi-rotor UAV, operating at two different heights and mounted with the same multispectral camera, were used in different field experiments at the same location in Denmark in the period from 432 to 861 growing degree-days. Seven vegetation indices, calculated from multispectral images with four bands: red, green, red edge and near infrared (NIR), were evaluated for their relationship to dry matter (DM), N concentration, N uptake and N nutrition index (NNI). The results showed a better prediction of N concentration, N uptake and NNI, than DM using vegetation indices. Furthermore, among all vegetation indices, two red-edge-based indices, normalized difference red edge (NDRE) and red edge chlorophyll index (CI_RE), performed best in estimating N concentration (R² = 0.69–0.88), N uptake (R² = 0.41–0.84) and NNI (R² = 0.47–0.86). In addition, there was no effect from the choice of UAV, and thereby flight height, on the estimation of NNI. The choice of UAV type therefore seems not to influence the possibility of diagnosing N status in grass seed crops. We conclude that it is possible to estimate NNI based on multispectral images from drone-mounted cameras, and the method could guide farmers as to whether they should apply additional N to the field. We also conclude that further research should focus on estimating the quantity of N to apply and on further developing the method to include more grass species.     

Validation of Ozone Monitoring Instrument NO2 measurements using ground based NO2 measurements at Zvenigorod,Russia

We present the results of comparison between Ozone Monitoring Instrument (OMI) data of NO₂ measurements (Collection 3) onboard the NASA EOS-Aura satellite and correlative ground-based twilight measurements at Zvenigorod station in Russia in 2004–2008. Compared quantities are unpolluted column and tropospheric column amounts of NO₂ which are standard products of OMI measurements. The NO₂ columns observed by our ground-based instrument have been interpolated to the time of OMI measurements using a one-dimensional photochemical model. According to our comparison, the OMI unpolluted NO₂ columns underestimate ground-based measurements by (0.084 ± 0.025)?×?10¹⁵ molecules/cm², or (3.2 ± 0.9)%. The correlation coefficient between the OMI and ground-based unpolluted NO₂ columns is 0.92. The tropospheric NO₂ columns derived from OMI measurements are on average by (1.8 ± 0.5)?×?10¹⁵ cm^?2, or approximately 40%, less than those derived from ground-based measurements. The correlation coefficient between these data is about 0.3. Reasons for this discrepancy are discussed.     

Modeling the atmospheric deposition and stormwater washoff of nitrogen compounds

We investigated the suitability of integrating deterministic models to estimate the relative contributions of atmospheric dry and wet deposition onto an urban surface and the subsequent amounts removed by stormwater runoff. The CIT airshed model and the United States Environmental Protection Agency Storm Water Management Model (SWMM) were linked in order to simulate the fate and transport of nitrogen species through the atmosphere and storm drainage system in Los Angeles, California, USA. Coupling CIT and SWMM involved defining and resolving five critical issues: (1) reconciling the different modeling domain sizes, (2) accounting for dry deposition due to plant uptake, (3) estimating the fraction of deposited contaminant available for washoff, (4) defining wet deposition inputs to SWMM, and (5) parameterizing the SWMM washoff algorithm. The CIT–SWMM interface was demonstrated by simulating dry deposition, wet deposition, and stormwater runoff events to represent the time period from November 18, 1987 to December 4, 1987 for a heavily urbanized Los Angeles watershed discharging to Santa Monica Bay. From November 18th to December 3rd the simulated average dry deposition flux of nitrogen was 0.195 kg N/ha-day to the watershed and 0.016 kg N/ha-day to Santa Monica Bay. The simulated rainfall concentrations during the December 4th rainfall event ranged from 3.76 to 8.23 mg/l for nitrate and from 0.067 to 0.220 mg/l for ammonium. The simulated stormwater runoff event mean concentrations from the watershed were 4.86 mg/l and 0.12 mg/l for nitrate and ammonium, respectively. Considering the meteorology during the simulation period, the CIT and SWMM predictions compare well with observations in the Los Angeles area and in other urban areas in the United States.     

Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R²_cal) = 0.82 and predicted root mean square error (RMSE_P) = 0.14) and dry leaves (R²_P = 0.88 and RMSE_P = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season.     

Spatiotemporal variations of tropospheric column nitrogen dioxide over Jing-Jin-Ji during the past decade

Of all anthropogenic pollutants, nitrogen dioxide (NO₂) has the most negative effect on atmospheric chemistry. In this study, measurements of tropospheric column NO₂ obtained from the ozone monitoring instrument (OMI) are used to investigate temporal and spatial dynamics of NO₂. Temporal and spatial distributions of tropospheric NO₂ concentrations obtained from OMI over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2007 to 2016 are presented, and annual changes and trends in the seasonal cycle are shown. Annual amounts of NO₂ are found to firstly increase then decrease, where after reaching a maximum in 2012 they begin a progressive yearly decline. NO₂ shows significant cyclical seasonal characteristics over Jing-Jin-Ji, with maximum values in winter and minimum in summer. In addition, the spatial distribution is unbalanced, and Beijing-Tianjin-Tangshan and Shijiazhuang-Xingtai-Handan are found to be highly polluted areas. The many complex factors affecting variations in NO₂ are analysed in this article, and the impact of meteorological factors and human activities are emphasized. It is considered that temperature and precipitation are natural factors influencing NO₂ concentration but there is a stronger negative relationship between tropospheric column NO₂ and temperature. Optimization of the energy structure is thus considered to be important and a reduction in energy consumption is required to control the concentration of pollutants. Coal combustion is a major anthropogenic factor in increasing NO₂ concentrations, and there is a strong correlation between higher amounts of NO₂ and coal consumption in the Jing-Jin-Ji region.     

Correlation between the spatio-temporal distributions of aerosols,sulphur dioxide,and formaldehyde using MODIS and SCIAMACHY satellite data for China

The spatial and temporal relationship between fine-mode aerosol optical depth (AOD) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric columns of SO₂ and formaldehyde (HCHO) from the Scanning Imaging Absorption Chartography (SCIAMACHY) are used to infer information on the variability of sulphate aerosol, which is a major determinant of fine-mode AOD (AOD_f) in east China. When averaging the satellite data over specific regions of China and for longer time periods (from 2005 to 2011), we find obvious similar trends between AOD_f and SO₂, HCHO columns for southern and eastern China. The results of satellite observations show the high spatial and temporal correlation between AOD_f and SO₂ over the Yangtze River Delta (R2), the Pearl River Delta (R3), and the Chongqing–Chengdu region (R4), with the coefficients of determination (R²) of 0.376, 0.438, and 0.837, respectively. These regions are performing with the huge amount of anthropogenic SO₂ emissions by traffic, industry, and domestic heating. The trend is consistent with fine-mode aerosol and SO₂ appearing in summer because AOD_f is often correlated with enhanced HCHO concentrations, reflecting the importance of secondary aerosol formation over industrialized regions in that season. Over the North China Plain, distinct maximum values of AOD_f and HCHO were observed and the minimum value for SO₂ was observed in summer. The decrease in the mean SO₂ column was 35.5%, which has been attributed to the strict security measures by the government during the 2008 Olympic Games period while there was a decrease of 46% in AOD_f at the same time. As sulphate aerosol occurs seasonally in the North China Plain, the strong correlation between anomalies of AOD_f and SO₂ can be used to estimate and analyse the distribution of sulphate aerosol. Singular value decomposition (SVD) analysis showed the same variation between AOD_f and SO₂ over eastern China and Sichuan Province, and a distinct negative value of SVD variables over comparably industrialized regions in southern and eastern China and Sichuan Province. The results for SO₄ from the Model for Ozone and Related chemical Tracers model for southern and eastern China and the Sichuan regions suggest much stronger sulphate contributions to aerosol formation than for other areas of China. We propose that the SVD variables between AOD_f and SO₂, which can be inferred from the satellite data, can be used as indicators for regional pollution control.     

Estimating the effect of gypsy moth defoliation using MODIS

The area of North American forests affected by gypsy moth defoliation continues to expand despite efforts to slow the spread. With the increased area of infestation, ecological, environmental and economic concerns about gypsy moth disturbance remain significant, necessitating coordinated, repeatable and comprehensive monitoring of the areas affected. In this study, our primary objective was to estimate the magnitude of defoliation using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for a gypsy moth outbreak that occurred in the US central Appalachian Mountains in 2000 and 2001. We focused on determining the appropriate spectral MODIS indices and temporal compositing method to best monitor the effects of gypsy moth defoliation. We tested MODIS-based Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and two versions of the Normalized Difference Infrared Index (NDIIb6 and NDIIb7, using the channels centered on 1640 nm and 2130 nm respectively) for their capacity to map defoliation as estimated by ground observations. In addition, we evaluated three temporal resolutions: daily, 8-day and 16-day data. We validated the results through quantitative comparison to Landsat based defoliation estimates and traditional sketch maps. Our MODIS based defoliation estimates based on NDIIb6 and NDIIb7 closely matched Landsat defoliation estimates derived from field data as well as sketch maps. We conclude that daily MODIS data can be used with confidence to monitor insect defoliation on an annual time scale, at least for larger patches (> 0.63 km²). Eight-day and 16-day MODIS composites may be of lesser use due to the ephemeral character of disturbance by the gypsy moth.     

Estimating the number of clusters using a windowing technique

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem using a windowing technique. Extensive empirical tests that illustrate the efficiency and the accuracy of the propsoed method are presented. The text was submitted by the authors in English. Basilis Boutsinas. Received his diploma in Computer Engineering and Informatics in 1991 from the University of Patras, Greece. He also conducted studies in Electronics Engineering at the Technical Education Institute of Piraeus, Greece, and Pedagogics at the Pedagogical Academy of Lamia, Greece. He received his PhD on Knowledge Representation from the University of Patras in 1997. He has been an assistant professor in the Department of Business Administration at the University of Patras since 2001. His primary research interests include data mining, business intelligence, knowledge representation techniques, nonmonotonic reasoning, and parallel AI. Dimitris K. Tasoulis received his diploma in Mathematics from the University of Patras, Greece, in 2000. He attained his MSc degree in 2004 from the postgraduate course “Mathematics of Computers and Decision Making” from which he was awarded a postgraduate fellowship. Currently, he is a PhD candidate in the same course. His research activities focus on data mining, clustering, neural networks, parallel algorithms, and evolutionary computation. He is coauthor of more than ten publications. Michael N. Vrahatis is with the Department of Mathematics at the University of Patras, Greece. He received the diploma and PhD degree in Mathematics from the University of Patras in 1978 and 1982, respectively. He was a visiting research fellow at the Department of Mathematics, Cornell University (1987–1988) and a visiting professor to the INFN (Istituto Nazionale di Fisica Nucleare), Bologna, Italy (1992, 1994, and 1998); the Department of Computer Science, Katholieke Universiteit Leuven, Belgium (1999); the Department of Ocean Engineering, Design Laboratory, MIT, Cambridge, MA, USA (2000); and the Collaborative Research Center “Computational Intelligence” (SFB 531) at the Department of Computer Science, University of Dortmund, Germany (2001). He was a visiting researcher at CERN (European Organization of Nuclear Research), Geneva, Switzerland (1992) and at INRIA (Institut National de Recherche en Informatique et en Automatique), France (1998, 2003, and 2004). He is the author of more than 250 publications (more than 110 of which are published in international journals) in his research areas, including computational mathematics, optimization, neural networks, evolutionary algorithms, and artificial intelligence. His research publications have received more than 600 citations. He has been a principal investigator of several research grants from the European Union, the Hellenic Ministry of Education and Religious Affairs, and the Hellenic Ministry of Industry, Energy, and Technology. He is among the founders of the “University of Patras Artificial Intelligence Research Center” (UPAIRC), established in 1997, where currently he serves as director. He is the founder of the Computational Intelligence Laboratory (CI Lab), established in 2004 at the Department of Mathematics of University of Patras, where currently he serves as director.     

Forecasting of traffic origin NO and NO2 concentrations by Support Vector Machines and neural networks using Principal Component Analysis

The main aim of this paper is to predict NO and NO₂ concentrations four days in advance comparing two artificial intelligence learning methods, namely, Multi-Layer Perceptron and Support Vector Machines on two kinds of spatial embedding of the temporal time series. Hourly values of NO and NO₂ concentrations, as well as meteorological variables were recorded in a cross-road monitoring station with heavy traffic in Szeged in order to build a model for predicting NO and NO₂ concentrations several hours in advance. The prediction of NO and NO₂ concentrations was performed partly on the basis of their past values, and partly on the basis of temperature, humidity and wind speed data. Since NO can be predicted more accurately, its values were considered primarily when forecasting NO₂. Time series prediction can be interpreted in a way that is suitable for artificial intelligence learning. Two effective learning methods, namely, Multi-Layer Perceptron and Support Vector Regression are used to provide efficient non-linear models for NO and NO₂ times series predictions. Multi-Layer Perceptron is widely used to predict these time series, but Support Vector Regression has not yet been applied for predicting NO and NO₂ concentrations. Grid search is applied to select the best parameters for the learners. To get rid of the curse of dimensionality of the spatial embedding of the time series Principal Component Analysis is taken to reduce the dimension of the embedded data. Three commonly used linear algorithms were considered as references: one-day persistence, average of several-day persistence and linear regression. Based on the good results of the average of several-day persistence, a prediction scheme was introduced, which forms weighted averages instead of simple ones. The optimization of these weights was performed with linear regression in linear case and with the learning methods mentioned in non-linear case. Concerning the NO predictions, the non-linear learning methods give significantly better predictions than the reference linear methods. In the case of NO₂ the improvement of the prediction is considerable; however, it is less notable than for NO.     

A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations

Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l’Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth.     

Remote sensing of the urban heat island effect across biomes in the continental USA

Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the non-urban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales.We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8 °C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 °C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 °C temperature difference in summer and only 1.3 °C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.     

Cloud and cloud shadow masking for Sentinel-2 using multitemporal images in global area

ABSTRACT

Sentinel-2 data provided the opportunity for complementary data to existing missions including Landsat and SPOT. In this study, multitemporal cloud masking (MCM) used to detect cloud and cloud shadow masking for Landsat 8 was improved to detect cloud and cloud shadow for Sentinel-2 data. This improvement takes advantages of the spectral similarity between Landsat 8 and Sentinel-2. To assess the reliability of the new MCM algorithm, several data selected from different environments such as sub-tropical South, tropical, and sub-tropical North were evaluated. Moreover, these data have heterogeneous land cover and variety of cloud types. In visual assessment, the algorithm can detect cloud and cloud shadow accurately. In the statistical assessment, the user’s and producer’s accuracies of sample in sub-tropical environments of cloud masking was 99% and 96%, respectively, and cloud shadow masking was 99% and 98%, respectively. In addition, the user’s and producer’s accuracies of sample in tropical environments of cloud masking was 100% and 95%, respectively, and cloud shadow masking was 100% and 92%, respectively. Compared to Fmask, MCM has higher accuracies in most of the results of cloud and cloud shadow masking in both sub-tropical and tropical environments. The results showed that the improved-MCM algorithm can detect cloud and cloud shadow for Sentinel-2 data accurately in all scenarios and the accuracies are significantly high.