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.     

基于克里金空间插值的MODIS积雪覆盖率产品去云方法

由于云与积雪在可见光和远红外波段都具有相似的光谱特征,使得光学遥感监测积雪受到天气的严重干扰,如何消除亚像元尺度上MODIS积雪覆盖率(Snow Cover Fraction,SCF)产品中云的干扰成为了一个亟待解决的难题。通过分析亚像元尺度上SCF分布的空间变异性,提出了一种基于克里金空间插值的MODIS SCF产品去云方法,分别利用普通克里金(Ordinary Kriging,OK)和以海拔为协变量的普通协克里金(Ordinary Co\|Kriging,OCK)进行去云实验。11个不同日期的实验结果表明:OK和OCK方法在MODIS SCF产品去云中均能达到较高的精度,特别是在云覆盖率低于20%的情况下,此时OCK的精度要好于OK;而当云覆盖率大于20%时,OK的精度略高于OCK,但两者的精度都明显低于云覆盖率低于20%的情况,而且平滑效应都比较明显。     

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.     

Analysis of the carbon dioxide concentration in the lowest atmospheric layers and the factors affecting China based on satellite observations

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas contributing to global climate change. SCIAMACHY on board ENVISAT (launched in 2002) is the first satellite instrument to monitor the changes in CO₂ concentration in the lowest atmospheric layers. The temporal and spatial distribution of CO₂ (2003–2009) concentration based on SCIAMACHY over China is presented and discussed. It shows an annual increase and a seasonal cycle. The CO₂ annual growth rate was about 1.8 ppm year^?1, with the highest value being in spring and the lowest in autumn. The CO₂ concentration variation is determined by many complex factors. In this article, we analyse the important factors affecting CO₂ variations, with special emphasis on terrestrial ecosystems and energy consumption. Terrestrial ecosystems are an important sink in the global carbon cycle. The relationship between CO₂ concentration and Moderate Resolution Imaging Spectroradiometer (MODIS) net primary production (NPP) in 2008 is analysed. CO₂ concentration is inversely proportional to NPP both in regions with high-density vegetation and in deserts. The Yunnan province has the highest NPP value and the lowest CO₂ concentration, whereas the Takla Makan Desert has the lowest NPP value and the highest CO₂ concentration. Energy consumption is the main emission source of atmospheric CO₂. CO₂ emissions from energy consumption show a steady increase in China since 1980. China's CO₂ concentration variation shows a high correlation with energy consumption (coefficient of determination (R ²) > 0.8). The regions with high energy consumption are major industrial regions such as Shandong, Guangdong, Jiangsu, Zhejiang, Hebei, and Henan.     

The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data

Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperformed OLS at all sampling densities; however, they produced similar results at low sampling densities, suggesting that GWR may not produce significantly better results than OLS in remote-sensing operational applications where only a small number of field data are collected. (ii) The performance of GWR was better than those of IDW, OK and COK at most sampling densities. Among the spatial interpolation techniques we examined, IDW was the best to estimate the canopy height at most densities, while COK outperformed OK only marginally and produced larger canopy height estimation errors than both IDW and GWR. (iii) GWR had the advantage of generating canopy height estimation maps with more accurate estimates than OLS, and it preserved patterns of geographic features better than IDW, OK or COK.     

Spatial methane emission modelling from wetlands using geospatial tools

Wetland ecosystems have acquired importance among the scientific community because of their role in biogeochemical cycling and as source and sink of greenhouse gas emissions particularly methane (CH₄) in addition to the ecosystem services that they provide. To estimate the CH₄ emission from wetlands in spatial domain, models incorporating the geospatial tools are required. Accordingly, main focus of this study is to demonstrate the utility of geospatial techniques in assessing the spatial CH₄ emission variability from four different regions of Uttar Pradesh (UP), India, namely, Western, Central, Bundelkhand, and Eastern regions deploying Semi-Automated Empirical CH₄ emission Model (SEMEModel) using Moderate Resolution Imaging Spectro-radiometer data of 2010–2012. SEMEModel is a three-tier model which determines the CH₄ emissions in spatial domain as a function of remote sensing (RS) and Geographic Information System (GIS) derived wetland components including wetland area and corresponding temperature factors coupled with point CH₄ emission coefficients developed via field measurements. Results of the study have shown that eastern region of UP exhibited maximum estimated/modelled CH₄ emissions (43.10 Gg yr^?1) as compared to other regions due to more area being under wetlands whereas central region was found to be the least contributor (0.266 Gg yr^?1) due to the fact that it has minimum wetland area (0.40%) among all the regions. It was observed that estimated/modelled CH₄ emissions depicted an increase by 4.96 orders of magnitude in 2010–2011 and 4.04 orders of magnitude in 2011–2012 when estimated by applying literature-based global CH₄ emission coefficients for UP in place of CH₄ flux values derived in field. It signifies that the upscaling of CH₄ flux values using literature-based CH₄ flux values of one region to another region may not reflect actual values. Therefore, this study not only helps to improve accuracy of CH₄ emission estimates from wetlands but also credibly adjudges that integration of CH₄ flux field measurements with modern tools of RS and GIS will immensely assist to reduce the uncertainties in CH₄ emission predictions done over larger regions.     

Estimation of methane emission from West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV

Wetlands are one of the most important ecosystems in the world and at the same time they are presumed to be a source of methane gas, which is one of the most important greenhouse gases. The West Siberian wetlands is the largest in the world and remote sensing techniques can play an important role for monitoring the wetland.High spatial resolution satellite data are effective for monitoring land cover type changes, but can't cover a wide area because of a narrow swath width. On the other hand, global scale data are indispensable in covering a large area, but are too coarse to get the detailed information due to the low spatial resolution. It is necessary to devise a method for the fusion of the data with different spatial resolutions for monitoring the scale-differed phenomena.In this paper, firstly, a SPOT HRV image near Plotnikovo mire was used to map four wetland ecosystems (birch forest, conifer forest, forested bog and open bog) supplemented by field observation. Then, spectral mixture analysis was performed between NOAA AVHRR and SPOT HRV data acquired on the same day.Secondly, field observations were scaled up with these different spatial resolution satellite data. Each of the wetland ecosystem coverage fraction at the sub-pixel level was provided by spectral mixture analysis. Field observation shows that the mean rate of CH₄ emission from forested bog and open bog averaged 21.1 and 233.1 (mg CH₄/m²/day), respectively. The methane emission from the area was estimated by multiplying these average methane emission rates and the fraction coverage in each AVHRR pixel.Finally, the total methane emission over AVHRR coverage was estimated to be 9.46 (10⁹ g CH₄/day) and the mean methane emission over AVHRR coverage was calculated as 59.3 (mg CH₄/m²/day). We could conclude that this mean value is within the probabilistic variability as compared with the airborne measurement results.     

Mapping methane emissions from a marine geological seep source using imaging spectrometry

Methane (CH₄) is an extremely important greenhouse gas that has increased significantly in pre and post-industrial times. Due to CH₄'s strong absorptions in the shortwave infrared (SWIR), the potential exists to use imaging spectrometers, such as the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), to map CH₄ emissions. Here, we present research evaluating the ability of AVIRIS to map CH₄ emitted by one of the largest marine geologic CH₄ sources in the world, the Coal Oil Point seep field in the Santa Barbara Channel, California. To develop algorithms for detecting CH₄ and to establish detection limits, initial analysis focused on simulated radiance spectra, calculated using Modtran 5.2 radiative transfer code that was parameterized to match scene conditions for a 6 August 2007 AVIRIS flight over the area. Model simulations included a range of surface albedos, variable column water vapor, and CH₄ concentrations ranging from 1.7 ppm (background) up to the equivalent of 2500 ppm in the lower 20 m of the boundary layer. A multistep CH₄ detection algorithm was developed using Modtran simulations. First, surface albedo was estimated from the radiance at 2139 nm. Next, albedo-specific radiance for background CH₄ was used to calculate spectral residuals for CH₄ above background. A CH₄ index, C, calculated as the average residual between 2248 and 2298 nm, showed high sensitivity to CH₄, with minimal sensitivity to water vapor or surface albedo. Detection limits in simulations depended on CH₄ concentrations and albedo, ranging from 990 ppm for a 0.5% albedo surface, to as low as 18 ppm for albedos higher than 22%. Application of this approach to the AVIRIS data demonstrated considerable potential for mapping CH₄. Due to specular reflectance off of the ocean surface, albedo in the scene varied significantly, from less than 0.5% to over 30%. Strong CH₄ anomalies were observed in the data acquired over the seep field, which produced large C values with spectral residuals consistent with CH₄ and estimated radiance spectra that matched measurements. All strong anomalies were located in close vicinity to and downwind from known CH₄ sources. However, contrary to simulated data, C was overly sensitive to albedo, restricting high confidence anomalies only to the brightest surfaces, and showing high frequency spatial variation throughout the AVIRIS image. CH₄ concentration was overestimated by C, potentially due to a spectral trend in sea surface reflectance and/or the impact of diffuse light on dark surfaces (< 1%) leading to the over-expression of CH₄ absorptions.     

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH₄)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH₄ and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion. By using PCA, the correlation of CO and CH₄ data is carried out and by identifying the areas of high correlation (along the principal component axis) the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions. Wireless Sensor Network is deployed and simulations are carried with heterogeneous sensors (Carbon Monoxide and Methane sensors) in NS-2 Mannasim framework. The rise in the value of CO even when CH₄ is below the toxic level may become hazardous to the people around. Thus our proposed methodology will detect the combined presence of both the gases (CH₄ and CO) and provide an early warning in order to avoid any human losses or toxic effects.     

Global patterns in daytime cloud properties derived from GOME backscatter UV-VIS measurements

In this paper, we present an overview of the cloud property data set derived from 8 years of reflected solar ultraviolet-visible (UV-VIS) measurements taken by the global ozone monitoring experiment (GOME) instrument from April 1996 to June 2003. We consider four such properties: cloud amount, cloud-top pressure, cloud optical thickness and cloud type. Cloud amounts are generated from GOME broadband polarization data using data fusion techniques, while cloud-top height (pressure) and cloud-top albedo are retrieved from GOME backscatter measurements in the oxygen (O₂) A-band via neural network inversion of simulated reflectances. Cloud optical thickness is derived as an additional parameter from the cloud-top albedo and radiative transfer model simulations, and cloud type is determined from the cloud-top pressure and optical thickness. We analyse global and seasonal patterns for these properties, looking at monthly means, standard deviations and the 8-year average values. We compare GOME results with the longer-period multisatellite international satellite cloud climatology project (ISCCP) D-series cloud climatology. The overall good agreement demonstrates that GOME provides accurate and complementary cloud information. Differences in cloud amount, cloud-top height and optical thickness values are due primarily to contrasting measurement strategies (GOME measures daytime-only UV-VIS backscatter, ISCCP is based on several day and night infrared satellite observations). We look forward to the extension of this UV-VIS cloud parameter series with the advent of more recent backscatter atmospheric composition instruments such as the scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY) on-board the environmental satellite (ENVISAT) and the GOME-2 series on the MetOp platforms.     

Study of the impact of wildfire emissions,through MOPITT total CO column,at different spatial scales

The estimation of total carbon monoxide (CO) column has been identified as essential to improve our understanding of its role in the global climate system. The Earth Observing System (EOS) Science Steering Committee and the World Meteorological Organization (WMO) has suggested that a satellite-borne CO sensor, which would operate for extended periods, would be useful for that task. Measurements of Pollution in the Troposphere (MOPITT), on board the Terra spacecraft, is a correlation radiometer for estimating CO vertical profiles and total CO column in the lower atmosphere, through the thermal radiance received in the 4.7 μm spectral region. One of the main sources of CO in the atmosphere is the fires and global biomass-burning emissions that are produced when combustion is not complete, especially in the smouldering phase. This article presents a methodology based on a Fourier technique and spatial analysis in order to estimate the total CO column contribution of wildfires at three different spatial scales. First, in a seasonal study, a Mediterranean country (Spain) is selected, and the main regions affected by fire during four years in the summer season are analysed. Second, in order to estimate CO emissions at a local scale, a large fire (in Spain) and a cluster of fires (in North China) are selected. Third, for a global study at large scale and for comparing with CO and carbon dioxide (CO₂) data from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), locations in North China, equatorial Africa, and Amazonia are selected. Results obtained show that MOPITT data are suitable to assess and to discriminate CO emissions at local spatial scales. Finally, a qualitative agreement between CO behaviour obtained by MOPITT and CO and CO₂ obtained by SCIAMACHY is found.     

A systematic investigation of geostatistical image fusion for the improvement of the spectral fidelity and spatial detail in Landsat MS imagery

This is the first systematic investigation into the assumptions of image fusion using regression Kriging (RK) – a geostatistical method – illustrated with Landsat MS (multispectral) and SPOT (Satellite Pour l’Observation de la Terre) panchromatic images. The efficiency of different linear regression and Kriging methods in the fusion process is examined by visual and quantitative indicators. Results indicate a trade-off between spectral fidelity and spatial detail preservation for the GLS (generalized least squares regression) and OLS (ordinary least squares regression) methods in the RK process: OLS methods preserve more spatial detail, while GLS methods retain more spectral information from the MS images but at a greater computational cost. Under either OK (ordinary Kriging) or UK (universal Kriging) with either OLS or GLS, the spherical variogram improves spatial details from the panchromatic image, while the exponential variogram maintains more spectral information from the MS image. Overall, RK-based fusion methods outperform conventional fusion approaches from both the spectral and spatial point of view.     

Spatial distribution of the surface rupture zone associated with the 2001 Ms 8.1 Central Kunlun earthquake,northern Tibet,revealed by satellite remote sensing data

The Ms 8.1 Central Kunlun earthquake occurred on 14 November 2001 in northern Tibet, China. Landsat Enhanced Thematic Mapper (ETM) and Thematic Mapper (TM), Système Probatoire de l'Observation de la Terre (SPOT) High Resolution Visible (HRV) panchromatic, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) images before and after the earthquake are compared to detect the spatial distribution of the surface rupture zone. A surface rupture zone at least 400 km long produced by this event can be accurately identified from satellite sensor images. It is the longest surface rupture zone among the coseismic rupture zones ever reported worldwide. In spatial terms, the surface rupture zone consists of two segments. The eastern segment extends about 350 km striking N75°-85°W (between 91°07' E and 94°55' E), and the western segment bends south-west extending more than 70 km to west of Sun Lake with a strike of N50°E-N70°W (west of 91°07' E). Analyses of satellite sensor images are well consistent with ground data obtained from the field survey. High spatial resolution satellite remote sensing techniques, therefore, provide a rapid and powerful tool to detect the co-seismic surface rupture in the remote and high mountainous Tibet area.     

Compression of AVHRR images by wavelet packets

AVHRR is an imager flying on polar meteorological satellites that, due to its spatial resolution (about 1 km at Nadir view), produces a huge amount of data. A method based on the wavelet packet transform is devised to compress AVHRR images. The method is driven by the compression error (application dependent), so that different types of images have the same quality. The best basis wavelet packet is chosen by L₁ norm criterion that was found to be the most suited for the problem at hand. Ability of the compression method to preserve most fine structures of the images even at the highest resolution is demonstrated based on some examples. Comparison with wavelet algorithms is performed.     

The effects of sand dust storms on greenhouse gases

In Asia, sand dust storms (SDSs) occur nearly every year, especially in northern China. However, there is little research about the relationship between SDSs and greenhouse gases (GHGs). In this article, we selected four SDSs that occurred in Asia in the spring of 2009 and 2010. We monitored the areas covered by these SDSs using Moderate Resolution Imaging Spectroradiometer (MODIS) data, then we used Greenhouse Gases Observing Satellite (GOSAT) data to check how the SDSs affected the concentrations of CO₂ and CH₄. We then compared the concentrations of CO₂ and CH₄ on SDS days with the monthly mean values of the months in which SDSs occurred. We also compared the concentrations of CO₂ and CH₄ on SDS days with the values before and after the SDSs. After analysis, we found that SDSs had increased the concentrations of CO₂ and CH₄ in the atmosphere. When the SDSs occurred, the concentrations of CO₂ and CH₄ increased and reached peak values on the last or penultimate days of the storms and then decreased to their normal values. Atmospheric flow is the main reason for the increase in concentration of CO₂, and the lack of the free radical (OH) during SDSs and the presence of CH₄ sources in southeast China are the main reasons for the increase in concentration of CH₄. We also found that in arid and semi-arid areas, SDSs had little effect on the concentration of these two GHGs.     

Real-time continuous feature extraction in large size satellite images

Remotely sensed imagery is being increasingly used for the development of the earth observation satellites to investigate human activities, to monitor environmental changes and to update existing geospatial data. The ordinary pictures are difficult to process automatically by computers but can be easily interpreted by humans. The most significant step is how to get anticipated information from the images and how to convert these images into useful data for further studies. The key objective is to satisfy an algorithm claiming to be efficient in large size image processing includ enhanced processing efficiency, finding correlation among data, and extracting continuous features. To achieve these objectives in the setting mentioned above, we propose a real-time approach for continuous feature extraction and detection in remote sensory earth observatory satellite images to find rivers, roads, and main highways. Deep analysis is made on the ENVISAT satellite missions datasets and based on this analysis the algorithm is proposed using statistical measurements, RepTree machine learning classifier, and Euclidean distance. The system is developed using Hadoop ecosystem to improve the efficiency of the system. The designed system consists of various steps including collection, filtration, load balancing, processing, merging, and interpretation. The system is implemented on Apache Hadoop system using MapReduce programming with higher efficiency results in a massive volume of satellite ASAR/ ENVISAT mission datasets.     

Monitoring 2.5 μm particulate matter within urbanized regions using satellite-derived aerosol optical thickness,a study in Hong Kong

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM_2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R ²?=?0.78 and R ²?=?0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM_2.5 concentrations. When a whole year (2007) of AOT images was compared with PM_2.5 concentrations recorded at five ground stations, correlations of R ²?=?0.31, R ²?=?0.10 and R ²?=?0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM_2.5 concentration were also observed for individual stations (R ²?=?0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM_2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities.     

A methodological approach for spatial downscaling of TRMM precipitation data in North China

Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25° to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25°, 0.50°, 0.75°, 1.00°, and 1.25°). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25°; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R² = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R² = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).     

Assessment of NO2 satellite observations for en-route aircraft emissions detection

In this work we examine the possibility of using satellite remote sensors for the detection of air traffic emissions produced during the en-route segment of flight in the Upper Troposphere/Lower Stratosphere region (8000-12,000 m). NO₂ has been considered as the tracer of aircraft plumes with highest possibility of being successfully detected from space. An analysis of the technical potential of the current orbital sensors capable of measuring NO₂ in the proximity of the tropopause has been conducted. In order to estimate an upper bound for the NO₂ column related to aircraft emissions, the Canary Islands Corridor has been selected for conducting a simple emission calculation exercise based on real air traffic and operational data, assuming an ideal atmospheric scenario. The results obtained in this approximation have been compared to the actual information retrieved from space sensors. An in-depth inspection of the NO₂ column data for two particular areas (Canary Islands Corridor and North Atlantic Flight Corridor) obtained in recent years by SCIAMACHY and OMI has also been carried out.The general conclusions of this viability study are not optimistic. The estimated maximum NO₂ column value attributable to aircraft emissions at cruise altitudes were lower than the detection limits associated with SCIAMACHY and OMI for NO₂ column measurements. As a result, detecting and quantifying the actual NO₂ levels in aircraft corridors by space remote sensing is a very challenging task.