Application Of Satellite Data Research Articles

Satellite data application for renewable energy: a comparative study in Khyber Pakhtunkhwa, Pakistan

Solar energy resources have great potential to contribute towards the long-lasting energy deficiency problems being faced by Pakistan. The aim of this study was to utilize 22 years (1983–2005) satellite-based data sets of direct normal irradiation, global horizontal irradiation, day light hours, cloud amount, and Linke turbidity factor for six districts of Khyber Pakhtunkhwa (Chitral, Peshawar, Dir, Dera Ismail Khan, Buner, and Mansehra), respectively, making use of the multi-criteria analysis (MCA). In case of the monthly averages for direct normal irradiation, except Chitral and Dera Ismail Khan, other districts appeared with similar patterns having two maximum peaks, one between April and June and the other peak between October and November. Comparison of monthly averages for global horizontal irradiation showed one peak for all districts between June and July. It should be noted that Peshawar and Buner districts were identified with significant high irradiation (direct normal irradiation and global horizontal irradiation) all over the year as compared to other districts. Multi-criteria analysis involved, creating suitable criteria for selected variables then classification (based on each variable magnitude), and scores were then assigned to each class. Out of six districts, Peshawar district was identified with maximum total score. It was concluded that based on the methodology applied in this research, Peshawar district appeared as the best location for exploiting in terms of solar power. The government should give its full attention towards solar energy resources to overcome the energy crises and therefore help to improve the economic condition of the country.

Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding

Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

Extreme waves induced by cyclone Nargis at Myanmar coast: numerical modeling versus satellite observations

The deltaic coast of Myanmar was severely hit by tropical cyclone Nargis in May 2008. In the present study, a top-down numerical simulation approach using the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models was conducted to study the meteorological and offshore wave characteristics of cyclone Nargis near the coast of Myanmar. The WRF simulation results agree well with the observed data from the India Meteorological Department. SWAN simulation results were compared with the WaveWatch 3 model by National Oceanic and Atmospheric Administration and validated against available measurement data from satellites. The model results show relatively good agreement, and hindcast with satellites data (significant wave height only) shows a correlation coefficient value of 0.89. The SWAN and satellite comparisons also show better fit for high wave conditions. The resulted maximum significant wave height of 7.3 m by SWAN is considerably higher in energy than the seasonal waves normally prevalent at Myanmar’s deltaic coast. The possibility of high energy waves due to cyclones should be considered during the design and operation of coastal and offshore projects in the area, particularly given the risks that climate change can intensify cyclones in the future. Since Myanmar lacks a dense network of in-situ observational stations, the methodology used in the current study presents the potential application of various numerical techniques and satellite data to estimate extreme wave conditions near the Myanmar coast.

Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

Calibration of the space-borne microwave humidity sounder based on real-time thermal emission from lunar surface

Calibration is a key issue for quantitative application of meteorological satellite data. The complex space environment may cause many uncertainties in data calibration. A highly stable and reliable calibrator in flight is needed. Because the Moon has no atmosphere and no environmental variation, the physical and chemical properties of its surface are stable in the long term. The Moon might be an ideal candidate for in-flight thermal calibration. In advanced satellite-borne microwave remote sensing such as NOAA-18, the deep space view (DSV) of the microwave humidity sounder (MHS) has viewed the Moon many times every year. Using the thermal-physical properties of the lunar regolith derived from the Diviner infrared (IR) brightness temperature (TB) data, we solve the one-dimensional heat conduction equation to obtain the temperature profile of the near side of the lunar regolith medium. The loss tangents of the regolith medium are retrieved from microwave TB data of the Chinese satellite Chang’e-2. The integrated radiative transfer equation is used to simulate the weighted disk-average TB of the lunar surface for the MHS channels at 89, 157, and 183 GHz for the year 2011. The Moon is taken as an extended circular target. The simulated TBs are used to correct the full width at half maximum (FWHM) fitted with the MHS counts. We analyze the influences of the distance between the satellite and the Moon, the lunar phase angle, and the FWHM of the radiometer on the inverted FWHM. The corrected TB data are compared with the simulation. This paper presents a new method for thermal calibration of spaceborne in-flight microwave and millimeter-wave radiometers with the weighted disk-average TB of the lunar surface.

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

The COVID-19 related lockdowns have brought the planet to a standstill. It has severely shrunk the global economy in the year 2020, including India. The blue economy and especially the small-scale fisheries sector in India have dwindled due to disruptions in the fish catch, market, and supply chain. This research presents the applicability of satellite data to monitor the impact of COVID-19 related lockdown on the Indian fisheries sector. Three harbors namely Mangrol, Veraval, and Vankbara situated on the north-western coast of India were selected in this study based on characteristics like harbor’s age, administrative control, and availability of cloud-free satellite images. To analyze the impact of COVID in the fisheries sector, we utilized high-resolution PlanetScope data for monitoring and comparison of “area under fishing boats” during the pre-lockdown, lockdown, and post-lockdown phases. A support vector machine (SVM) classification algorithm was used to identify the area under the boats. The classification results were complemented with socio-economic data and ground-level information for understanding the impact of the pandemic on the three sites. During the peak of the lockdown, it was found that the “area under fishing boats” near the docks and those parked on the land area increased by 483%, 189%, and 826% at Mangrol, Veraval, and Vanakbara harbor, respectively. After phase-I of lockdown, the number of parked vessels decreased, yet those already moved out to the land area were not returned until the south-west monsoon was over. A quarter of the annual production is estimated to be lost at the three harbors due to lockdown. Our last observation (September 2020) result shows that regular fishing activity has already been re-established in all three locations. PlanetScope data with daily revisit time has a higher potential to be used in the future and can help policymakers in making informed decisions vis-à-vis the fishing industry during an emergency situation like COVID-19.

China's Gaofen-3 Satellite System and Its Application and Prospect

The Gaofen-3 (GF-3) satellite is China's first civilian quad-polarized C-band imaging microwave satellite that can provide high-resolution marine and land observations. Also, it is China's first low Earth orbit remote sensing satellite with a design life of eight years. During the design and development of this satellite, many major challenges have been overcome, such as the design of complex radio frequency channels, difficult system integration, high quantitative requirements, and strict component life requirements. In this article, a series of independent technological innovations in the development of GF-3 is presented. Meanwhile, the on-orbit operation of the satellite in the past five years is introduced, as well as the application of satellite data in marine observation, emergency relief, disaster reduction, surveying and mapping, water conservancy, etc. The further advancement and improvements of the GF-3 satellite are described at the end of this article.

Application of Long-Term Satellite Data to Assess the Environmental Situation in the Area of Oil and Gas Enterprises

Application of Long-Term Satellite Data to Assess the Environmental Situation in the Area of Oil and Gas Enterprises

Application of Long-Term Satellite Data to Assess the Environmental Situation in the Area of Oil and Gas Enterprises

Application of Long-Term Satellite Data to Assess the Environmental Situation in the Area of Oil and Gas Enterprises

Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data

Modeling of ambient PM2.5 exposure has improved in recent years with the application of satellite data, but in India, where the ground-based measurements are still inadequate, satellite-based assessment cannot provide temporal continuity. In this work, we analyze MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) reanalysis aerosol products to estimate PM2.5 at an hourly scale over India to fill the temporal sampling gap. MERRA-2 PM2.5 calibrated using reference-grade monitors from the Central Pollution Control Board (CPCB) network show a statistically significant (at 95% CI) correlation (r = 0.9) against coincident in-situ measurements. Analyzing 18-years (2000–2017) data, we report the first detailed account of multi-year ambient PM2.5 diurnal patterns in India. Diurnal amplitude is found to be quite large (>30 μg m−3) in the Indo-Gangetic Basin (IGB) and western arid regions. We find a decrease in ambient PM2.5 over the western dust source region and an increase over the parts of IGB and central India, primarily in the morning and evening hours. The annual trend is strongly governed by the trend during the dry season (October–February), attributed to a combination of the changing emission and meteorology. Our results suggest that the satellite-based exposure estimates that typically represent late morning to early afternoon hours are usually lower than the 24-h average exposure in most parts of India. Therefore, we call for the integration of reanalysis data that provide better temporal resolution with satellite data that provide better spatial resolution to further improve exposure estimates in data void regions like India for air quality studies.

Satellite-Based Crop Monitoring and Yield Estimation—A Review

To sustain food security and crop condition monitoring, yield estimation must improve at local and global scales. The aim of this review was to give a background of satellite-based crop monitoring and crop yield estimation, including the use of crop models. Recently, most advances in remote sensing techniques, aimed at complimenting the traditional crop harvest surveys, have focused on high-production and information-rich areas. However, there is limited research in dynamic landscapes using these techniques at local scales in most Southern African countries. Models such as the Decision Support System Agro-Technology’s (DSSAT) CERES-model, and Agricultural Production Simulator (APSIM) have been used to simulate maize biophysical parameters and yield variability in a changing climate. Despite the successes, there is still need to consider yield prediction using simplified models that decision-makers can use to plan for food support and sales. The application of freely-available satellite data with focus on maize crop as a staple for Southern Africa, highlights some challenges such as heavy reliance on agro-meteorological estimations and regional estimations of crop yield. It also raises questions of predicting across large growing belts without consideration of diverse cropping patterns. Conversely, future opportunities in crop monitoring and yield estimation using remotely sensed-data still shed a light of hope. For instance, employing multi-model configurations or multi-model ensembles is one of the major missing gaps needing consideration by crop modeling research. Other simpler, but versatile opportunities are the use of crop –monitoring applications on smart phones by small holder farmers to provide phenological data to decision makers throughout a growing season.

Assessment of Applicability of Satellite-Derived Ocean Color Data for Studying Variability of Total Suspended Matter in the Surface Layer of the Deep Part of the Black Sea

Purpose. Studies of spatial-temporal variability of total suspended matter are necessary for understanding the biochemical processes which form and support stable functioning of a marine ecosystem. The aim of the work is to assess applicability of satellite data for studying total suspended matter variability in the surface layer of the deep part of the Black Sea. Methods and Results. Application of the regression analysis yielded the linear regression equations that unite the in situ measurements of the total suspended matter concentrations in the surface layer in the northeastern (June, 2005–2015) and western (November, 2016, 2017 and December, 2017) deep sea areas, and the regional satellite products (the particulate backscattering coefficient, the absorption coefficient of colored detrital matter and the chlorophyll a concentration). Based on the measured and calculated data arrays, the maps of the total suspended matter concentrations in the surface layer of the northeastern Black Sea were constructed. The interannual changes in the in situ measured concentrations of the total suspended and lithogenic matters, as well as in the quasi-synchronous satellite regional products (the light absorption coefficient of colored detrital matter at 490 nm and the particulate backscattering coefficient at 555 nm) in June, 2005–2015 were considered. High total suspended matter concentrations were noted in 2012, just when extreme growth of the coccolithophorid population was observed in the Black Sea. The correlation coefficients were used to evaluate whether the relation between the total suspended matter concentration and the individual analyzed parameters was fast. Conclusions. Spatial distributions of the measured and calculated total suspended matter contents showed satisfactory agreement. In course of the whole observation period, difference between the values of the measured and calculated total suspended matter concentrations was on average 6–23 %. Possibility of application of the satellite-derived ocean color data for studying spatial-temporal variability of the total suspended matter content is shown.

Application of satellite data and GIS services for studying air pollutants in Lithuania (case study: Kaunas city)

Satellite remote sensing, with its relatively short history, is going to play a major role in the fields that encompass topics related to place and space. Through this innovation in technology, real-time monitoring and mapping of changing phenomena on the surface of the earth has been possible. The purpose of this study was to investigate and evaluate the Kaunas city air pollutants between 14 and 25 October 2019 using environmental station data and satellite data (Terra, Aqua, OMI, and Sentinel-5P). The data obtained from satellite and the pollutant data gathered from air quality monitoring stations located in different parts of Kaunas were used. The data was downloaded for days mentioned above for the geographical bound of Kaunas city. Each data file covered an area of the size of Lithuania; hence, we should have extracted data for the area of interest, which was Kaunas city. The overall results of this study confirmed the capability of Sentinel-5P data to be used in monitoring the air quality and air pollution over the Kaunas local area. The existence of strong and acceptable correlations between satellite data and in situ measurements was indicative of the ability of satellite images to monitor air pollution, particularly over Kaunas urban areas during the fire incident in the city of Alytus.

Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models

Approximately half of satellite aerosol retrievals are missing that limits the application of satellite data in PM2.5 pollution monitoring. To obtain spatiotemporally continuous PM2.5 distributions, various gap-filling methods have been developed, but have rarely been evaluated. Here, we reviewed and summarized four types of gap-filling strategies, and applied them to a random forest PM2.5 prediction model that incorporated ground observations, chemical transport model (CTM) simulations, and satellite AOD for predicting daily PM2.5 concentrations at a 1-km resolution in 2013 in the Beijing-Tianjin-Hebei region and the Yangtze River Delta. The model out-of-bag predictions were compared with national station measurements and external measurements to assess the performance of different gap-filling methods. We also conducted a by-city cross-validation and characterized the spatial distributions of PM2.5 prediction when the AOD coverage was low. We found that the methods filling in missing data by regression, i.e. multiple imputation and decision tree, performed robustly to characterizing PM2.5 variation at a high spatial resolution and the method filling in missing PM2.5 predictions with decision tree overcame the problem of time-consuming computations. The method using spatiotemporal trends to fill in missing data, i.e. ordinary kriging and generalized additive mixed model, may be overrated in statistical evaluation tests, and predicted artificially oversmoothed PM2.5 spatial distributions. We also revealed that CTM simulations benefited the prediction of PM2.5 spatial distribution in all the models with various gap-filling strategies with higher prediction accuracy in the by-city cross-validation. We noticed that the PM2.5 prediction was not sensitive to the resolution of CTM simulations and even the 12-km resolution CTM simulations benefited the high-resolution PM2.5 prediction.

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

The applicability of optical satellite data to quantify coastal erosion across the Arctic is limited due to frequent cloud cover. Synthetic Aperture Radar (SAR) may provide an alternative. The interpretation of SAR data for coastal erosion monitoring in Arctic regions is, however, challenging due to issues of viewing geometry, ambiguities in scattering behaviour and inconsistencies in acquisition strategies. In order to assess SAR applicability, we have investigated data acquired at three different wavelengths (X-, C-, L-band; TerraSAR-X,Sentinel-1,ALOS PALSAR 1/2). In a first step we developed a pre-processing workflow which considers viewing geometry issues (shoreline orientation, incidence angle relationships with respect to different landcover types). We distinguish between areas with foreshortening along cliffs facing the sensor, radar shadow along cliffs facing away and traditional land-water boundary discrimination. Results are compared to retrievals from Landsat trends. Four regions which feature high erosion rates have been selected. All three wavelengths have been investigated for Kay Point (Canadian Beaufort Sea Coast). C- and L-band have been studied at all sites, including also Herschel Island (Canadian Beaufort Sea Coast), Varandai (Barents Sea Coast, Russia), and Bykovsky Peninsula (Laptev Sea coast, Russia). Erosion rates have been derived for a one-year period (2017-2018) and in case of L-band also over 11 years (2007-2018). Results indicate applicability of all wavelengths, but acquisitions need to be selected with care to deal with potential ambiguities in scattering behaviour. Furthermore, incidence angle dependencies need to be considered for discrimination of the land-water boundary in case of L- and C-band. However, L-band has the lowest sensitivity to wave action and relevant future missions are expected to be of value for coastal erosion monitoring. The utilization of trends derived from Landsat is also promising for efficient long-term trend retrieval. The high spatial resolution of TerraSAR-X staring spot light mode (<1m) also allows the use of radar shadow for cliff-top monitoring in all seasons. Derived retreat rates agree with rates available from other data sources, but the applicability for automatic retrieval is partially limited. The derived rates suggest an increase of erosion at all four sites in recent years, but uncertainties are also high.

Application of satellite data in the quantitative assessment of evapotranspiration in northeastern Poland

We describe a method of calculating one of the basic phenomena influenced by groundwater recharge, namely evapotranspiration ( ET ). The Operational Simplified Surface Energy Balance (SSEBop) algorithm was applied to calculate actual evapotranspiration ( ET a ), being modified to include spatiotemporal changes of substrate humidity and so referred to as mSSSEBop. Calculations were performed within the Szkwa and Rozoga River catchments (NE Poland). Quantitative ET a assessment was based on the analysis of Landsat satellite images, hydrometeorological and hydrogeological data. The results obtained for the original SSEBop algorithm and the modified mSSEBop one were compared with the water balance and data from a MOD16A2 dataset. The calculated water balance gave ET a values close to results using mSSEBop (with differences of 9–54 mm/year). In the case of the original algorithm, differences were in range of 42–218 mm/year. When compared with MOD16A2 data, the differences were within the range of –16.7 to 23.2 mm/8 days, with the mSSEBop algorithm giving on average lower ET a sums (~14%) than MOD16A2 while SSEBop gave results higher than MOD16A2 by ~12%. The studies performed indicate that the method presented, using satellite data, gives a reliable, spatial and temporal ET a assessment for the mid latitudes.

VEGA-SCIENCE SERVICES FOR MONITORING THE IMPACT OF TECHNOGENIC WASTE AND DUMPS SOURCES ON ENVIRONMENT

Abstract. The impact of hazardous waste on people and the environment from agricultural, industrial, military and mining activities form one of the most significant global environmental challenges. These wastes often include heavy metals, acid precipitates (sulfur oxide solutions), hydrocarbons, and other organic chemicals. Environmental impact of such technogenic waste and dumps sources (TWDS) is widely reflected in vegetation cover changes, which can be easily detected by satellite instruments. But application of satellite data for TWDS monitoring requires appropriate information tools. At presented work, the authors conducted the development of the design and implementation of these tools within frames VEGA-Science services (http://sci-vega.ru/eng/) in order to provide analysis of longterm variability of the vegetation cover state caused by the influence of TWDS. The result of the work was included in automated system for remote monitoring of vegetation cover (SRMV) of VEGA-Science system.

The NASA Hydrological Forecast System for Food and Water Security Applications

AbstractMany regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam

Using satellite remote sensing data in combining with other technologies (i.e. sensor, ground-based system) for surface air quality monitoring has been considered as one of promising approaches for urban air quality management in future smart cities. This study assessed the accuracy of latest collection (Collection 6.1 - C6.1) of MODerate resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical depth (AOD) retrieval by Dark Target (DT) algorithm at 3 and 10 km spatial resolutions in urban areas in Vietnam. Collocated Aqua and Terra MODIS C6.1 AOD at 550 nm were inter-compared against AOD from two AERONET stations in Vietnam, estimated throughout the satellite passes (± 1 h). Two investigated AERONET stations are Nghia Do (inland urban) and Bac Lieu (coastal urban) with the studying periods of 2010 - 2016 and 2010 - 2017, respectively. Our findings showed that the MODIS DT 3km were performed worse than the MODIS DT 10 km product in urban areas. Furthermore, all two MODIS AOD DT products showed better performance at inland urban site than coastal urban site. In general, both MODIS products were able to follow the monthly-mean AERONET AODs. The peak of aerosol loading at the investigated urban sites could be properly attributed to the combination of the monsoon effect and biomass burning activities. The mean aerosol loading of rainy season appeared to be lowest in the year, which could be attributed to the fact that the frequent rain washes out the dust in the atmosphere.