Raw Satellite Data Research Articles

Optimal Global Positioning System/European Geostationary Navigation Overlay Service Positioning Model Using Smartphone

The potential for the use of smartphones in GNSSs (Global Navigation Satellite Systems) positioning has increased in recent years due to the emergence of the ability of Android-based devices used to process raw satellite data. This paper presents the results of a study on the use of SBAS data transmitted by the EGNOS (European Geostationary Navigation Overlay Service) system in GNSS positioning using a Xiaomi Mi8 smartphone. Raw data recorded at a fixed point were used in post-processing calculations in GPS/EGNOS positioning by determining the coordinates for every second of a session of about 5 h and comparing the results to those obtained with a Septentrio AsteRx2 GNSS receiver operating at the same time at a distance of about 3 m. The calculations were performed using the assumptions of the GNSS/SBAS positioning algorithms, which were modified with carrier-phase smoothed code observations and the content of the corrections transmitted by EGNOS.

GaiaData Release 3

Context.Blue (BP) and Red (RP) Photometer low-resolution spectral data are one of the exciting new products inGaiaData Release 3 (GaiaDR3). These data have also been used to derive astrometry and integrated photometry inGaiaEarly Data Release 3 and astrophysical parameters and Solar System object reflectance spectra inGaiaDR3.Aims.In this paper, we give an overview of the processing techniques that allow raw satellite data of multiple transits per source to be converted into combined spectra calibrated to an internal reference system, resulting in low-resolution BP and RP mean spectra. We describe how we overcome challenges due to the complexity of the on-board instruments and to the various observation strategies. Furthermore, we show highlights from our scientific validation of the results. This work covers the internal calibration of BP/RP spectra to a self-consistent mean instrument, while the calibration of the BP/RP spectra to the absolute reference system of physical flux and wavelength is covered by one of the accompanyingGaiaDR3 papers.Methods.We calibrate about 65 billion individual transit spectra onto the same mean BP/RP instrument through a series of calibration steps, including background subtraction, calibration of the CCD geometry, and an iterative procedure for the calibration of CCD efficiency as well as variations of the line-spread function and dispersion across the focal plane and in time. The calibrated transit spectra are then combined for each source in terms of an expansion into continuous basis functions. We discuss the configuration of these basis functions.Results.Time-averaged mean spectra covering the optical to near-infrared wavelength range [330, 1050] nm are published for approximately 220 million objects. Most of these are brighter thanG = 17.65 but some BP/RP spectra are published for sources down toG = 21.43. Their signal-to-noise ratio (S/N) varies significantly over the wavelength range covered, and with magnitude and colour of the observed objects, with sources aroundG = 15 having a S/N above 100 in some wavelength ranges. The top-quality BP/RP spectra are achieved for sources with magnitudes 9 < G < 12, with S/N reaching 1000 in the central part of the RP wavelength range. Scientific validation suggests that the internal calibration was generally successful. However, there is some evidence for imperfect calibrations at the bright endG < 11, where calibrated BP/RP spectra can exhibit systematic flux variations that exceed their estimated flux uncertainties. We also report that, due to long-range noise correlations, BP/RP spectra can exhibit wiggles when sampled in pseudo-wavelength.Conclusions.TheGaiaDR3 data products are the expansion coefficients and corresponding covariance matrices for BP and RP separately. Users are encouraged to work with the data in this format, with full covariance information showing that correlations between coefficients are typically very low. Documentation and instructions on how to access and use BP/RP spectral data from the archive are also provided.

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

AbstractClean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

Satellite data for dam safety monitoring

The use of datasets from satellites to aid the monitoring of dams has been possible for many years. Within the last five years there has been a rapid development in the type and accuracy of the data available, led by an influx of new satellites and improved access to these datasets. This paper gives an overview of the different types of satellite datasets and satellite infrastructure relevant to dam safety that are currently available, explains the complex processes of converting raw satellite data into useable information and summarises the advantages and disadvantages of each data type along with indications of the level of accuracy and cost. The information presented by the authors is based on experience gained undertaking a 3 year, £4 m, UK-government-funded research project to investigate and combine many types of space data to improve the monitoring of dams.

On the use of satellite observations to fill gaps in the Halley station total ozone record

Abstract. Measurements by the Dobson ozone spectrophotometer at the British Antarctic Survey's (BAS) Halley research station form a record of Antarctic total column ozone that dates back to 1956. Due to its location, length, and completeness, the record has been, and continues to be, uniquely important for studies of long-term changes in Antarctic ozone. However, a crack in the ice shelf on which it resides forced the station to abruptly close in February of 2017, leading to a gap of two ozone hole seasons in its historic record. We develop and test a method for filling in the record of Halley total ozone by combining and adjusting overpass data from a range of different satellite instruments. Comparisons to the Dobson suggest that our method reproduces monthly ground-based total ozone values with an average difference of 1.1 ± 6.2 DU for the satellites used to fill in the 2017–2018 gap. We show that our approach more closely reproduces the Dobson measurements than simply using the raw satellite average or data from a single satellite instrument. The method also provides a check on the consistency of the provisional data from the automated Dobson used at Halley after 2018 with earlier manual Dobson data and suggests that there were likely inconsistencies between the two. The filled Halley dataset provides further support that the Antarctic ozone hole is healing, not only during September but also in January.

Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions.

Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added earth observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatiotemporal nature in a real operational scenario. In this work, we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (crop diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency checking for noncompliance to the CAP rules: the smart sampling of on-the-spot checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.

Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, affected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results.

Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

High-Resolution Sea Surface Temperature and Salinity in Coastal Areas Worldwide from Raw Satellite Data

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 °C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

DETECTION AND DELINEATION OF SUBSURFACE COAL MINE FIRE FROM SPACEBORNE THERMAL INFRARED DATA IN JHARIA COALFIELD, DHANBAD, INDIA

Abstract. Coal fire has been found to be a major problem worldwide in coal mining areas. The surrounding areas get hugely affected, and significant amount of reserves are wasted due to the burning of coal. This severely affects the environment condition, which leads to a rise in temperature of the region, which is a major reason for climate change. Greenhouse gases like CO2 SO2 NO CH4 are also emitted from the cracks and fissures. Large masses of the burning of coal also causes land subsidence and collapse. Underground coal fires ignited by natural causes or human error leads to atmospheric pollution, acid rain, land subsidence, and increased coronary and respiratory diseases. They consume a valuable energy resource, destroy floral and faunal habitats, and promote human suffering because of heat, subsidence, and pollution. Jharia Coalfield, Jharkhand, India, is well known for being the storehouse of prime coking coal and for accommodating the maximum number of coal fires among all the coalfields in the country. In this paper, some of the important issues of coal fire mapping from satellite thermal infrared data have been addressed in particular reference to Jharia Coalfield. Namely, these are: retrieval of spectral radiance from raw digital satellite data using scene-specific calibration coefficients of the detectors from metadata, thermal emissivity of surface to obtain kinetic temperature at each ground resolution cell of satellite data, field-based modelling of pixel-integrated temperature for differentiating surface and subsurface fire pixels in Landsat 8 thermal IR data, identification of surface coal fire locations from infrared data and lateral propagation of coal fire.

GPS Technology for Semi-Aquatic Turtle Research

Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

Analyzing the Mediterranean Water Cycle Via Satellite Data Integration

The water cycle components are being retrieved by an increasing number of satellite missions. However, the monitoring of the water cycle by satellite Earth Observations is still a challenge. Data sets suffer from numerous systematic and random errors and they are often not coherent with each other. We focus here on the Mediterranean basin, one of the regions most sensitive to climate change. A satellite-based analysis of the water cycle is undertaken using a collection of available satellite data sets. Our satellite data set combination uses a simple bias correction and weighted average, and provides a better water budget closure results than any raw satellite data set. Our almost purely satellite data set allows to better describe the full water cycle, not only over the continents, but also in the atmosphere and over the ocean. The limitation/possibilities of this satellite multi-component data set are described: (1) although improved, the water cycle is still not closed by satellite data and the satellite community should focus on this issue, (2) our combined data set shows good coherency with the ERA-I reanalysis which is the reference so far, both in terms of seasonal climatology and long-term trends. This means that, even if the water budget is not yet closed by satellite data, our monitoring of the water cycle using satellite observations is improving, even over complex regions such as the Mediterranean basin.

Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor

The assessment of water body dynamics is not only in itself a topic of strong demand, but the presence of water bodies is important information when it comes to the derivation of products such as land surface temperature, leaf area index, or snow/ice cover mapping from satellite data. For the TIMELINE project, which aims to derive such products for a long time series of Advanced Very High Resolution Radiometer (AVHRR) data for Europe, precise water masks are therefore not only an important stand-alone product themselves, they are also an essential interstage information layer, which has to be produced automatically after preprocessing of the raw satellite data. The respective orbit segments from AVHRR are usually more than 2000 km wide and several thousand km long, thus leading to fundamentally different observation geometries, including varying sea surface temperatures, wave patterns, and sediment and algae loads. The water detection algorithm has to be able to manage these conditions based on a limited amount of spectral channels and bandwidths. After reviewing and testing already available methods for water body detection, we concluded that they cannot fully overcome the existing challenges and limitations. Therefore an extended approach was implemented, which takes into account the variations of the reflectance properties of water surfaces on a local to regional scale; the dynamic local threshold determination will train itself automatically by extracting a coarse-scale classification threshold, which is refined successively while analyzing subsets of the orbit segment. The threshold is then interpolated by fitting a minimum curvature surface before additional steps also relying on the brightness temperature are included to reduce possible misclassifications. The classification results have been validated using Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and proven an overall accuracy of 93.4%, with the majority of errors being connected to flawed geolocation accuracy of the AVHRR data. The presented approach enables the derivation of long-term water body time series from AVHRR data and is the basis for applied geoscientific studies on large-scale water body dynamics.

Case study on: Scalability of preprocessing procedure of remote sensing in Hadoop

In the field of remote sensing, the recent increase in image sizes has drawn a significant attention on processing these files in a fault tolerant distributed architecture In this regard, Apache Hadoop architecture becomes an efficient MapReduce model. In the satellite image processing, large scale images put the limitation on the single computer analysis. Whereas, Hadoop Distributed File System (HDFS) gives a remarkable solution to handle these files through its inherent data parallelism technique. This architecture is well suited for structured data, as the structured data can be equally distributed easily and access the relevant data. Images are considered as unstructured matrix data in Hadoop and the whole part of the data is relevant for any processing. It leads to a challenge to maintain the data locality with the equal data distribution. In this paper, we introduce a novel technique, which decrypts the standard format of raw satellite data and localizes the distributed preprocessing step on the equal split of datasets in Hadoop. For this purpose, a suitable modification on the Hadoop interface is proposed. For the case study on the scalability of preprocessing steps, Synthetic Aperture Radar (SAR) and Multispectral (MS), are used in a distributed environment.

THE BENEFITS AND CHALLENGES OF HAVING AN OPEN AND FREE BASIS SATELLITE DATA SHARING PLATFORM IN TURKEY: GEZGİN

Abstract. Turkey is a county that experiences rapid socioeconomic development, which, in turn, leads to high urbanization rates due to migration of people from rural to urban areas, many large-scale development projects (e.g. highways, dams, housing and infrastructure), and environmental problems that adversely affect agriculture, such as soil erosion and deforestation. Furthermore, Turkey lies in a region prone to natural disasters, especially earthquakes, landslides, flooding and forest fires. Successfully overcoming these challenges requires continuous monitoring to enable rapid response as well as the development of effective socioeconomic policies. In this regard, space-based earth observation (EO) systems play a critical role in the rapid acquisiton and extraction of crucial information. The first launch of the first Turkish-designed satellite, RASAT, in 2011 led to the wide-spread exploitation of space-based resources by Turkish institutions through the dissemination of EO data on an open and free basis via the GEZGIN internet portal (http://www.gezgin.gov.tr). The push for data sharing was further instigated by the nationally funded project GEOPORTAL (“Satellite Image Processing and Geoportal Development Project”) and European Union FP7 project EOPOWER (“Earth Observation for Economic Empowerment”), which strove to create conditions for sustainable economic development through the increased use of Earth observation products and services for environmental applications. In this work, the technical challenges involving processing and preparing raw satellite data for dissemination as well as software design of the GEZGIN Portal will be presented.

THE BENEFITS AND CHALLENGES OF HAVING AN OPEN AND FREE BASIS SATELLITE DATA SHARING PLATFORM IN TURKEY: GEZGİN

Turkey is a county that experiences rapid socioeconomic development, which, in turn, leads to high urbanization rates due to migration of people from rural to urban areas, many large-scale development projects (e.g. highways, dams, housing and infrastructure), and environmental problems that adversely affect agriculture, such as soil erosion and deforestation. Furthermore, Turkey lies in a region prone to natural disasters, especially earthquakes, landslides, flooding and forest fires. Successfully overcoming these challenges requires continuous monitoring to enable rapid response as well as the development of effective socioeconomic policies. In this regard, space-based earth observation (EO) systems play a critical role in the rapid acquisiton and extraction of crucial information. The first launch of the first Turkish-designed satellite, RASAT, in 2011 led to the wide-spread exploitation of space-based resources by Turkish institutions through the dissemination of EO data on an open and free basis via the GEZGIN internet portal (http://www.gezgin.gov.tr). The push for data sharing was further instigated by the nationally funded project GEOPORTAL (“Satellite Image Processing and Geoportal Development Project”) and European Union FP7 project EOPOWER (“Earth Observation for Economic Empowerment”), which strove to create conditions for sustainable economic development through the increased use of Earth observation products and services for environmental applications. In this work, the technical challenges involving processing and preparing raw satellite data for dissemination as well as software design of the GEZGIN Portal will be presented.

Urban Sprawl Patterns and Processes in Delhi from 1977 to 2014 Based on Remote Sensing and Spatial Metrics Approaches

Abstract Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson’s chi statistics and Shannon’s entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts—Central and New Delhi. Relative entropy, which scales Shannon’s entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density.

Cross-correlation analysis of CMB with foregrounds for residuals

In this paper, we try to probe whether a clean CMB map obtained from the raw satellite data using a cleaning procedure is sufficiently clean. Specifically we study if there are any foreground residuals still present in the cleaned data using a cross-correlation statistic. Residual contamination is expected to be present, primarily, in the galactic plane due to the high emission from our own galaxy. A foreground mask is applied conventionally to avoid biases in the estimated quantities of interest due to foreground leakage. Here, we map foreground residuals, if present, in the unmasked region i.e., outside a CMB analysis mask. Further locally extended foreground-contaminated regions, found eventually, are studied to understand them better. The few contaminated regions thus identified may be used to slightly extend the available masks to make them more stringent.