A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future.     

Synergetic retrieval of terrestrial AOD from MODIS images of twin satellites Terra and Aqua

Aerosol optical depth (AOD) is one of the most important indicators of atmospheric pollution. It can be retrieved from satellite imagery using several established methods, such as the dark dense vegetation method and the deep blue algorithm. All of these methods require estimation of surface reflectance prior to retrieval, and are applicable to a certain pre-designated type of surface cover. Such limitations can be overcome by using a synergetic method of retrieval proposed in this study. This innovative method is based on the fact that the ratio K of surface reflectance at different angles/geometries is independent of wavelength as reported by Flowerdew and Haigh (1995). An atmospheric radiative transfer model was then established and resolved with the assistance of the ratio K obtained from two Moderate Resolution Imaging Spectroradiometer (MODIS) spectral bands acquired from the twin satellites of Terra and Aqua whose overpass is separated by three hours. This synergetic method of retrieval was tested with 20 pairs of MODIS images. The retrieved AOD was validated against the ground observed AOD at the Taihu station of the AErosol RObotic NETwork (AERONET). It is found that they are correlated with the observations at a coefficient of 0.828 at 0.47 μm and 0.921 at 0.66 μm wavelengths. The retrieved AOD has a mean relative error of 25.47% at 0.47 μm and 24.3% at 0.66 μm. Of the 20 samples, 15 and 17 fall within two standard error of the line based observed AOD data on the ground at the 0.47 μm and 0.66 μm, respectively. These results indicate that this synergetic method can be used to reliably retrieve AOD from the twin satellites MODIS images, namely Terra and Aqua. It is not necessary to determine surface reflectance first.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

Validation of space-born Moderate Resolution Imaging Spectroradiometer remote sensors aerosol products using application of ground-based Multi-wavelength Radiometer

The measurements of aerosol optical properties were carried out during April 2006 to March 2011 over Mohal (31.9°N, 77.12°E) in the northwestern Indian Himalaya, using the application of ground-based Multi-wavelength Radiometer (MWR) and space-born Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensors. The average (±standard deviation) values of aerosol optical depth (AOD) at 500 nm, Ångström exponent and turbidity coefficient during the entire measurement period were 0.25 ± 0.09, 1.15 ± 0.42 and 0.12 ± 0.06 respectively. About 86% AOD values retrieved from MODIS remote sensor were found within an uncertainty limit (Δτ_pλ = ±0.05 ± 0.15τ_pλ). In general, the MWR derived AOD values were higher than that of MODIS retrieval with absolute difference ∼0.02. During the entire period of measurement space-born MODIS remote sensor and ground-based MWR observation showed good correspondence with significant correlation coefficient ∼0.78 and root mean square difference ∼0.06. For daily observations the relative difference between these two estimates stood less than 9%. However, satellite-based and ground-based observation showed good correspondence, but further efforts still needed to eliminate systematic errors in the existing MODIS algorithm.     

Seasonal variability of atmospheric aerosol over the North Indian region during 2005–2009

The Indo-Gangetic basin (IGB) extends 2000 km in length along NW–SE and has 400 km width, in the north the basin is bounded by towering Himalaya. High aerosol optical depth (AOD) is observed over the IGB throughout the year. The Himalaya restricts the transport of aerosols across Tibet and China. We have used ground based Kanpur and Gandhi College Aerosol Robotic Network (AERONET) stations and Multiangle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra level-3 AOD products for the years 2005–2009 to study the variability of aerosol over the Indo-Gangetic (IG) plains. An increase in both satellite-derived as well as ground observed aerosol loading during 2005–2009 has been found over major cities located in the IG plains. The correlation coefficients between AERONET and MISR data are found to be 0.70, 0.36 0.82, in contrast the correlation coefficients between AERONET and MODIS 0.49, 0.68, and 0.43, respectively during summer, winter and monsoon seasons. The AOD estimation using MISR is found to be close to AERONET data during summer and monsoon seasons, in contrast MODIS estimation is better during winter season.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity,India

Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM_2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM_2.5 across the city. The highest percentage reduction was observed in BC_ff (black carbon attributable to fossil fuel combustion), followed by total BC and PM_2.5. No decrease in BC_bb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM_2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BC_ff were reduced, while they remained almost the same for PM_2.5 and BC_bb. Analysis of PCB data reveal the highest reduction in PM_2.5 in an industrial cluster area. The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed.     

Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology provides a new means of snow depth detection. Multi-satellite and multi-Signal-to-Noise Ratio (SNR) provide more data for daily high-precision snow depth retrieval, but also face the problem of data fusion and effective utilization. Therefore, this study proposes a robust estimation algorithm based on multi-satellite and multi-SNR fusion applied to the observations of a GNSS station in Alaska. This study uses four solutions (S_avg, S_med, S_{RE_avg} and S_{RE_med}) to carry out multi-system fusion snow depth inversion and precision comparison research. The S_avg has more obvious disadvantages, which is not suitable for snow depth assessment. The S_{RE_avg} and S_{RE_med} have better snow depth retrieval effects in the snowy periods. The correlation coefficient (R), root mean square error (RMSE) and mean error (ME) of the calculated snow depth using the robust estimation algorithm with respect to the nearest in-situ measurements reached 0.759, 3.7 cm and ?1.4 cm, respectively. Compared with the S_med, the R is increased by 2.0 %, the RMSE corresponds to an improvement of 2.6 %. Moreover, the ME of the snow depth retrievals, as an indicator of the measurement bias, has significantly decreased by 6.7 %. The result also shows that the snow depth inversion by the robust estimation algorithm is more consistent with the in-situ measurements, further extending and advancing the optimal algorithm for snow depth retrieval.     

Monitoring spatio-temporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data

Clouds are important elements in climatic processes and interactions between aerosols and clouds are therefore a hot topic for scientific research. Aerosols show both spatial and temporal variations, which can lead to variations in the microphysics of clouds. In this research, we have examined the spatial and temporal variations in aerosol particles over Pakistan and the impact of these variations on various optical properties of clouds, using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra satellite. We used the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for trajectory analysis to reveal the origins of air masses, with the aim of understanding these spatial and temporal variabilities in aerosol concentrations. We also documented seasonal variations in patterns of aerosol optical depth (AOD) over Pakistan, for which the highest values occur during the monsoon season (June–August). We then analyzed the relationships between AOD and four other cloud parameters, namely water vapour (WV), cloud fraction (CF), cloud top temperature (CTT) and cloud top pressure (CTP). Regional correlation maps and time series plots for aerosol (AOD) and cloud parameters were produced to provide a better understanding of aerosol–cloud interaction. The analyses showed strong positive correlations between AOD and WV for all of the eight cities investigated. The correlation between AOD and CF was positive for those cities where the air masses were predominantly humid, but negative for those cities where the air masses were relatively dry and carried a low aerosol abundance. These correlations were clearly dependent on the meteorological conditions for all of the eight cities investigated. Because of the observed AOD–CF relationship, the co-variation of AOD with CTP and CTT may be attributable to large-scale meteorological variations: AOD showed a positive correlation with CTP and CTT in northern regions of Pakistan and a negative correlation in southern regions.     

Is there an abnormal enhancement of atmospheric aerosol before the 2008 Wenchuan earthquake?

There are extensive reports of ionospheric disturbances before the great 2008 Wenchuan earthquake, which are possibly explained by seismogenic electric field hypotheses linked with the aerosols injected in atmosphere. This paper attempts to investigate the possible change of atmospheric aerosol optical depth (AOD) associated with this earthquake by using MODIS data from both Terra and Aqua satellites. The result shows a clear enhancement of AOD along the Longmenshan faults 7 days before the quake, which is 1 day and 4 days earlier than the reported negative and positive ionospheric disturbances, respectively, and is 1 day earlier than or quasi-synchronism with other reported atmospheric anomalies including air temperature, outgoing longwave radiation and relative humidity. Particularly, the spatial distribution of AOD enhancement is very local and it is correlated well with the active faults and surface ruptures. We suggest that this unique enhancement could be associated with the Lithosphere–Atmosphere–Ionosphere coupling process during the preparation of the Wenchuan earthquake.     

Satellite and ground detection of very dense smoke clouds produced on the islands of the Paraná river delta that affected a large region in Central Argentina

Intense fires were produced on the Paraná river delta islands, Argentina, during most part of 2008, by a combination of an exceptionally dry period and the farmers’ use of a fire land-cleaning technique. In April 2008, those fires significantly affected the nearby regions and their inhabitants, from Rosario city to Buenos Aires mega-city. In this work we present satellite as well as ground Aerosol Optical Depth (AOD) at 550 nm data obtained during the propagation of pollution clouds to the central zone of Argentina. The highest value (1.18) was registered at Buenos Aires by atmospheric remote sensing, using the satellite instrument MODIS/Terra on April 18th 2008 at 10:35 local time (= UT − 3 h). On the same day, ground air quality detectors also measured in this city the highest Total Suspended Particle (TSP) value of the month, 2.02 mg/m³. The AOD(550) daily variation at Rosario Astronomical Observatory, which is located near the Paraná riverside, was derived by combining solar ultraviolet erythemal irradiance data (measured with a YES biometre) with model calculations. On April 25th 2008, from 12:00 to 15:30 local time, a rather high and constant AOD(550) value was registered, with a mean value of (0.90 ± 0.21). Cities located on the side of the Rosario–Buenos Aires highway (San Nicolás, Baradero and San Pedro) were also affected, showing a mean AOD(550) between the Rosario and Buenos Aires values. The particulate matter was collected with gridded samplers placed on the Paraná river islands as well as at the Rosario Observatory. They were analysed with a Scanning Electron Microscope (SEM) and mainly showed a biological origin. Even if normally large particles travel small distances from the source, organic aerosol in the range of 40–100 μm and complex asymmetric structures were registered several kilometres away from the aerosol sources on the islands. Another event of intense UV index attenuation (98.6%) occurred on September 18th 2008, due to very dense smoke clouds that extended over the Rosario area for several hours. The clouds were driven away from the fires by East–northeast and East–southeast winds. The minimum value of this index measured around noon allows to derive a maximum AOD(550)_max = (3.65 ± 0.90) at 12:45 local time. Soot clouds extended over the Paraná river, transporting Burned Biomass Debris (BBD) that deposited on Rosario. In particular, burned leaves and small branches with dimensions of 1–20 cm were collected. The mean (BBD) particles deposited on the ground from 7:00 to 19:00 local time were (0.92 ± 0.20) BBD/(m² h).     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM₁₀ and PM_2.5 exceeded 400?μg/m³ and 150?μg/m³, respectively, the ratio PM_2.5/PM₁₀ being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550?nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532?nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001?km^?1?×?sr^?1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5?km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6?km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China.     

Infrared spectrometry of Venus from “Venera-15” and “Venera-16”

Fourier spectrometers for the investigation of infrared spectra of Venus were installed on the recent Soviet orbiters “Venera-15” and “Venera-16”. Many spectra with reliable absolute calibration were obtained in the 280–1500 cm^?1 region with a spectral resolution of 5 cm^?1 (ground based processing) and about 7 cm^?1 (preoprocessed on board) and a spatial resolution of about 100 km at the Venusian cloud top level. Bands of CO₂, H₂O, H₂SO₄ and SO₂ are identified. The 15 μm-CO₂- fundamental band was used for retrieval of altitude dependent temperature profiles. There are significant differences in the cloud structure above 60 km for distinct regions of Venus, demonstrated by differences in the spectra.     

HRTS results from spacelab 2

The HRTS instrument flew on the Spacelab 2 mission from 29 July - 6 August 1985. HRTS consisted of a 30 cm Gregorian telescope, a slit spectrograph covering the 1190–1680 Å region with 0.05 Å spectral resolution, a broadband (90 Å FWHM) spectroheliograph tuned to 1550 Å, and an H-alpha filter system. The spectrograph slit was 920 arc sec, approximately 1 R₀, in length. Sub arc second spatial resolution along the slit is possible, but because of jitter in the Spacelab Instrument Pointing System (IPS) good exposures actually achieved 1–2 arc sec resolution. We describe the scientific results from HRTS.     

Multi precursors analysis associated with the powerful Ecuador (MW = 7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data

After DEMETER satellite mission (2004–2010), the launch of the Swarm satellites (Alpha (A), Bravo (B) and Charlie (C)) has created a new opportunity in the study of earthquake ionospheric precursors. Nowadays, there is no doubt that multi precursors analysis is a necessary phase to better understand the LAIC (Lithosphere Atmosphere Ionosphere Coupling) mechanism before large earthquakes. In this study, using absolute scalar magnetometer, vector field magnetometer and electric field instrument on board Swarm satellites, GPS (Global Positioning System) measurements, MODIS-Aqua satellite and ECMWF (European Centre for Medium-Range Weather Forecasts) data, the variations of the electron density and temperature, magnetic field, TEC (Total Electron Content), LST (Land Surface Temperature), AOD (Aerosol Optical Depth) and SKT (SKin Temperature) have been surveyed to find the potential seismic anomalies around the strong Ecuador (M_w = 7.8) earthquake of 16 April 2016. The four solar and geomagnetic indices: F10.7, D_st, K_p and a_p were investigated to distinguish whether the preliminary detected anomalies might be associated with the solar-geomagnetic activities instead of the seismo-ionospheric anomalies. The Swarm satellites (A, B and C) data analysis indicate the anomalies in time series of electron density variations on 7, 11 and 12 days before the event; the unusual variations in time series of electron temperature on 8 days preceding the earthquake; the analysis of the magnetic field scalar and vectors data show the considerable anomalies 52, 48, 23, 16, 11, 9 and 7 days before the main shock. A striking anomaly is detected in TEC variations on 1 day before earthquake at 9:00 UTC. The analysis of MODIS-Aqua night-time images shows that LST increase unusually on 11 days prior to main shock. In addition, the AOD variations obtained from MODIS measurements reach the maximum value on 10 days before the earthquake. The SKT around epicentral region presents anomalous higher value about 40 days before the earthquake. It should be noted that the different lead times of the observed anomalies could be acknowledged based on a reasonable LAIC earthquake mechanism. Our results emphasize that the Swarm satellites measurements play an undeniable role in progress the studies of the ionospheric precursors.     

A simplified data assimilation method for reconstructing time-series MODIS NDVI data

The Normalized Difference Vegetation Index (NDVI) is an important vegetation index, widely applied in research on global environmental and climatic change. However, noise induced by cloud contamination and atmospheric variability impedes the analysis and application of NDVI data. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. We extracted 16-Day L3 Global 1 km SIN Grid NDVI data sets for western China from MODIS vegetation index (VI) products (MOD13A2) for the period 2003–2006. NDVI data in the first three years (2003–2005) were used to generate the background field of NDVI based on a simple three-point smoothing technique, which captures annual features of vegetation change. NDVI data for 2006 were used to test our method. For every time step, the quality assurance (QA) flags of the MODIS VI products were adopted to empirically determine the weight between the background field and NDVI observations. Ultimately, more reliable NDVI data can be produced. The results indicate that the newly developed method is robust and effective in reconstructing high-quality MODIS NDVI time-series.     

Estimation of vertically integrated water vapor in Hungary using MODIS imagery

Information about the amount and spatial structure of atmospheric water vapor is essential in understanding meteorology and the Earth environment. Space-borne remote sensing offers a relatively inexpensive method to estimate atmospheric water vapor in the form of integrated water vapor (IWV). The research activity reported in the present paper is based on the data acquired by the HRPT/MODIS (High Resolution Picture Transmission, MODerate resolution Imaging Spectroradiometer) receiving station established in Budapest (Hungary) by the Space Research Group of the Eötvös Loránd University. Integrated water vapor is estimated by the remotely sensed data of the MODIS instrument with different methods and also by the operational numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF). Radiosonde data are used to evaluate the accuracy of the different IWV fields though it has been pointed out that the in situ data also suffers from uncertainties. It was found that both the MODIS and the ECMWF based fields are of good accuracy. The satellite data represent finer scale spatial structures while the ECMWF data have a relatively poor spatial resolution. The high quality IWV fields have proved to be useful for radiative transfer studies such as the atmospheric correction of other satellite data from times different than the overpass times of satellites Terra/Aqua and the forecast times of the model data. For this purpose the temporal variability of IWV is scrutinized both using ECMWF and MODIS data. Taking advantage of Terra and Aqua overpasses, the mean rate of change of IWV estimated by the near infrared method was found to be 0.47 ± 0.45 kg m⁻² h⁻¹, while it was 0.13 ± 0.65 kg m⁻² h⁻¹ based on the infrared method. The numerical weather prediction model’s analysis data estimated −0.01 ± 0.13 kg m⁻² h⁻¹ for the mean growth rate, while using forecast data it was 0.24 ± 0.18 kg m⁻² h⁻¹. MODIS data should be used when available for the estimation of the IWV in other studies. If no satellite data are available, or available data are only from one overpass, ECMWF based IWV can be used. In this case the analysis fields (or the satellite field) should be used for temporal extrapolation but the rate of change should be calculated from the forecast data due to its higher temporal resolution.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Characteristics of recent dust storms over the Indian region using real time multi-satellite observations from the direct broadcast receiving system at IMD

In this study, observations from microwave satellites, visible and infrared instruments have been analyzed to detect dust storm over north and north–west part of India during 18–23 March 2012. This study investigated the approach to utilize the multi satellite data of Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua satellite and the Advanced Microwave Sounding Unit (AMSU) on-board NOAA satellite to study the characteristics of dust storms from real time direct broadcast (DB) receiving system installed at three places of India Meteorological Department (IMD). The dust storm detection is based on the infrared brightness temperature (BT) difference between channels at 11 and 12 μm and polarized BT difference between two channels of 89 and 23.8 GHz. It is found that the significant differences between the BT of channel 89 and 23.8 can be used as a discriminator of identifying dust storm. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index (AI) and AMSU-A 23 GHz channel BT from NOAA satellite over the north and north-west part of India have also been analyzed. The result indicated the characteristic behavior between BT and AI during the different phases of the dust storm. Finally, the occurrence of dust outbreaks has also been validated with sky radiometer of IMD, which confirms the presence of a dust storm over the Indian region. Further, the findings of the study and its approaches apply to the other dust storm cases which occurred during the months of April and June 2012. The integrated approach suggested the potential to use high resolution data of microwave as well as thermal–infrared using multi-satellite observations from real time direct broadcast system for the detection of severe, moderate or weak dust storms very well. The approach is found to be promising for operational application.