Terra Satellite Research Articles

The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000–2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 “high” incidence states and the District of Columbia for 2000–2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8–8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a “u-shaped” association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed “n-shaped” relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.

Comparison and Impact Factor Analysis of Ground PM2.5 Retrieved by Aqua and Himawari-8 Satellite Products in Guangdong, China

Fine particulate matter (particles sizes < 2.5 µm; PM2.5) concentrations can be obtained at high spatial resolutions using satellite remote sensing. Previous studies on PM2.5 concentrations in Guangdong have used the aerosol optical depth (AOD) determined by MODIS sensors on the Terra and Aqua satellites, which both have a transit time of once per day. In this study, based on AOD data obtained from the Aqua and Himawari-8 (H8) satellites and ground-based measurements, ground PM2.5 concentrations were compared using radiation theory and a semi-empirical model. The temporal resolution of PM2.5 concentrations retrieved by the H8 satellite improved from once per day to once per hour when compared with the temporal resolution of those retrieved by the Aqua satellite. The data from both satellites were consistent with ground-based measurements (R ≥ 0.65 and reaching 0.96 for the annual mean), indicating that the retrieved data could characterize the spatial and temporal variations in ground PM2.5 concentrations in Guangdong Province. The two satellites had similar performances for the annual mean, dry season mean, and hourly data; however, H8 performed better than Aqua for the wet season and monthly averages. The performances of both satellites decreased when relative humidity increased and visibility decreased. H8 performed better at high relative humidity, low visibility, and during the PM2.5 pollution process, thus reflecting the advantage of geostationary satellite in capturing PM2.5 pollution distributions. In addition, the data retrieved by both satellites exhibited higher PM2.5 concentrations near the junctions of some cities and regions, indicating a new pollution distribution pattern after the industrial shift that cannot be observed by ground-based measurements. Aerosol scale height is the most important parameter for retrieving PM2.5 concentrations. Thus, improving the computational accuracy of aerosol scale height may improve the effectiveness of satellite-based PM2.5 concentration retrievals.

Spatial-Temporal Analysis of the Relationship Between Aerosol Optical Depth and Seasonal Land and Ocean Temperature Around Shanghai

Few people use remote sensing data to simultaneously retrieve the seasonal changes in aerosol optical depth (AOD) on land and ocean, as well as its relationship with surface temperature. Aerosols, as one of the indicators of air pollution, directly or indirectly affect people's numerous activities. This article uses Terra satellite MODIS data as the data source and conducts AOD inversion work on the land and ocean areas around Shanghai, China based on IDL, 6S model, and dark pixel method. The results show that for land areas, the higher the surface temperature, the lower the AOD value, with summer being the most significant; On the ocean, the higher the ocean surface temperature, the greater the AOD value. This experiment provides a theoretical basis for the relationship between AOD and temperature, and confirms the possibility of retrieving ocean AOD from MODIS data.

Aerosol Optical Depth Spatial and Temporal Variability Using Satellite Data Over Indian Major Cities

Introduction: The study’s main aim is to investigate the long-term variation of Aerosol Optical Depth (AOD). It also aims to show the relationship between meteorological parameters. This study evaluates long-term (2010 to 2021) special and temporal changes over major Indian regions using satellite-based data from NASA’s Terra Satellite.&#x0D; Materials and Methods: This study was carried out during 2010-2021 using MODIS data for long-term analysis. Variation of AOD with meteorological parameters was also performed to show the impact of these parameters (Temperature and Relative Humidity) on AOD by the MERRA-II model.&#x0D; Results: Based on Terra AOD data, in all the studied regions, especially the eastern region (Kolkata), the mean AOD was high (0.9-1.2). In the western region (Mumbai, Ahmedabad, and Surat), the mean AOD was low to moderate during all seasons. Furthermore, the impact of meteorological parameters on AOD shows significant variation in average annual AOD in Kolkata (0.70 ± 0.09), while other regions reported lower than average AOD values during the study period. Mumbai and Surat had average AODs (0.44 ± 0.13), (0.45 ± 0.14), while Ahmedabad and Jaipur reported average AOD of 0.45 ± 0.14 and 0.23 ± 0.11 during the study period, respectively.&#x0D; Conclusion: Generally, AOD values vary from season to season due to aerosol’s optical and microphysical properties being affected by meteorological conditions and surface albedo. This study examined the spatial and temporal distribution of AOD over five major Indian cities.

Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification

Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km2 in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process.

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p &lt; 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p &lt; 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.

Gross primary productivity by remote sensing in the Serra das Confusões National Park, Piauí, Brazil

The increase in anthropogenic CO2 emissions has raised the Earth's surface temperature in an anomalous and unprecedented way. Among the activities that contribute most to these increased emissions is the burning of fossil fuels and the change in land use. Part of these emissions are absorbed by terrestrial ecosystems, potentially productive and important carbon sinks (C) among them, we can mention the seasonally dry tropical forests (SDTF), which are found mainly in the Caatinga biome. Although little explored, the role they play in the global carbon (C) cycle is not negligible. Therefore, this research aimed to evaluate the seasonal behavior of biophysical variables and their effect on gross primary productivity (GPP) in a Conservation Unit in the Caatinga biome. The study area is located in the Conservation Unit (UC) of the Serra das Confusões National Park, located in the southwest region of the state of Piauí. Data for the years 2010–2020 were obtained from the MODIS sensor, which is inserted in the Terra satellite. Data on air temperature, precipitation, relative humidity and global solar radiation were obtained from the National Institute of Meteorology. According to the results obtained, the dynamics of GPP is modulated by the seasonal distribution of precipitation, presenting an average value of 5.53 gC m−2 day−1 in the months of higher precipitation and 1.43 gC m−2 day−1 in the period of lower precipitation. Additionally, precipitation also exerts a strong influence on vegetation status, as observed from the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) the maximum values observed during the rainy season, reflecting directly on the radiative and water components. However, even subjected to adverse environmental conditions, the potential of the area as a sink for C. Thus, in a future scenario of projected climate changes, and considering the vulnerability of these ecosystems, studies that evaluate the seasonal behavior of its structure and functioning is of fundamental importance for the knowledge of its resilience mechanisms and also as a contribution to the implementation of public policies that promote the conservation of the ecosystem.

Experimental evaluation of fire weather conditions during periods of wildfires in Ukraine in 2020

Introduction. Almost every year, the territory of Ukraine is affected by drought of different intensity and duration, what is a favorable condition for wildfires. In the forest areas of the country arose about 106.8 thousand fires with a total area of 139.2 thousand hectares during 1990-2017. An analysis of the long-term dynamics shows that wildfires in Ukraine are a sustainable phenomenon. In 2020, under the severe drought conditions, 209 forest fires occurred, the most significant of them were in the Chernobyl zone and in the Lugansk region. Formulation of the problem. There is still no universally accepted method to describe all drought related processes due to complexity of drought phenomena. Although drought indices and fire weather indices usually have different time scales, their combination can be a basis in the prediction scheme in which the drought index acts as a background indicator, which enhances or decreases the current fire weather index. The purpose of this study is evaluate informativeness of a new fire weather index HDW (Hot-Dry-Windy Index) for the territory of Ukraine, which takes into account the main meteorological parameters necessary to describe the degree of wildfire danger due to weather conditions, so this index may become a component of the drought and fire monitoring system. Data and methods. The HDW index has a physical justification as the atmosphere affects the fire, namely, the combination of three atmospheric parameters characterizes the current conditions for the ignition and distribution of fire: wind, temperature, humidity. For the calculation of the HDW index, the data of GFS global numerical model with grid step 0.25 degrees is used. The calculation of the daily fields of the HDW index was carried out for the territory of Ukraine and for local areas. To localize areas of wildfires, in the study were used daily hotspots data provided by the web-resource Firms (https://firms2.modaps.eosdis.nasa.gov/), which are determined using satellite instruments MODIS (AQUA and Terra Satellites) and VIIRS (S-NPP and NOAA-20 satellites). Research results. Comparison of the calculated HDW fields in Ukraine with the position of hotspots showed that the maximum values of the HDW index are generally concentrated in fire areas. Comparison with the synoptic situation showed that the maximum values of the HDW are located in the zones of advection of warm and dry air in the lower troposphere and zones of strong surface winds, which usually correspond to the periphery of anticyclone or the warm sector of cyclone. The analysis of the time courses of the HDW index showed the typical feature, which expressed in the fact that in all cases there was an increase in the index values in the period before the wildfire, maximum of the HDW is fixed at the time of ignition and after that the index values sharply decreases. Scientific novelty and practical significance. The fields of new HDW index reflects the weather conditions typical for wildfires periods in the territory of Ukraine. The presence of specific features and extreme values in the index time course can be used to track the process of increase of fire danger under changing weather conditions to make short- and medium-range fire danger forecasts with evaluation of fire danger classes.

The Use of Remote Sensing Data for Investigation of Environmental Consequences of Russia-Ukraine War

Abstract The usage of remote sensing data for tracking or monitoring war conflicts is a reality nowadays. The Russian invasion in Ukraine seriously impacted on the environment of the attacked country in all areas: air, soil, water, flora and fauna. The war has created a massive increase in air pollution in some regions of Ukraine, and might have effect in neighbouring countries. The Russian attack has caused many fires at places such as oil depots, industrial complexes, equipment and ammunition stockpiles, the forests, and residential areas. In addition to this, many bridges, hydraulic and other structures located over and near the water bodies were destroyed. These events were analysed in this paper by using publicly available remote sensing data: NOAA-2, Suomi NPP, Aqua and Terra satellites, Sentinel and Landsat satellites, as well as from other sources open to public. We assume that the use of remote sensing data is an excellent tool to monitor effects of military conflicts on environment. The conducted research with proposed method can serve as a good scientific practice that should be implemented for monitoring of the harm to nature caused by wars.

Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires.

The MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series acquired during nearly two decades (2000 to 2020) covering the area burned by the Camp Fire (California) in 2018 is investigated in this study by using the multifractal detrended fluctuation analysis in relation to the recovery process of vegetation after fire. In 2008, the same area was partially burned by two wildfires, the BTU Lightning Complex Fire and the Humboldt Fire. Our results indicate that all vegetation index time series are featured by six- and twelve-month modulating periodicities, with a larger spectral content at longer periods for two-fire-affected sites. Furthermore, two fires cause an increase of the persistence of the NDVI and EVI time series and an increase of the complexity, suggesting that the recovery process of vegetation dynamics of fire-affected sites is characterized by positive feedback mechanisms, driving the growth-generating phenomena, which become even more effective in those sites affected by two fires.

From a Love of Nature to a World of Earth Observations

AbstractThis article is a reflection on my career in Earth remote sensing and its evolution from an early childhood love of nature. I recount the influence of key people along the way to my development as an atmospheric scientist, and ultimately to my role as senior project scientist of NASA's Earth Observing System, one of the largest science program in the world in the 1990s and 2000s. Special focus is on unexpected encounters and opportunities to learn and explore, and ultimately to play a role in the remote sensing of aerosol and cloud properties from the ground, aircraft, and satellites. A major focus of much of my research at NASA Goddard Space Flight Center was on the development and exploitation of the Moderate Resolution Imaging Spectroradiometer that ultimately flew on NASA's Terra and Aqua satellites. As a participant on this science team, I had the opportunity to learn from and collaborate with biological oceanographers, atmospheric scientists, and terrestrial ecologists, and ultimately to publish in each of these areas, far more than my basic academic “home.”

A comparison of carbon monoxide retrievals between the MOPITT satellite and Canadian high-Arctic ground-based NDACC and TCCON FTIR measurements

Abstract. Measurements of Pollution In The Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60∘ N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada, to validate the MOPITT version 8 (V8) retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a radius of 110 km (1∘) from the PEARL Ridge Laboratory and within a 24 h time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR–NIR) channels. Each channel's detector has 4 pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3 %–10 % depending on the pixel. The bias values are negative for the TIR product, with values between −5 % and 0 %. The joint TIR–NIR products show differences of −4 % to 7 %. The drift in MOPITT biases (in units of % yr−1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts vs. TCCON are smaller than those vs. NDACC; however, this scenario is reversed for the MOPITT TIR and joint TIR–NIR products. Overall, this study aims to provide detailed validation for MOPITT version 8 measurements in the Canadian high Arctic.

Integration of multiple earthquakes precursors before large earthquakes: A case study of 25 April 2015 in Nepal

In this work, we analyze magnetic data, Land Surface Temperature (LST), Outgoing Longwave Radiation (OLR), Air Temperature (AT) and Relative Humidity (RH) around the epicenter before and after the large EQs occurred on April 25, 2015 in Nepal. Daytime and nighttime LST values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. We used the magnetic data acquired by Swarm satellites in the top side ionosphere, and daily OLR data was obtained from the United States National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Moreover, AT and RH values were provided from the nearest meteorological ground station to the epicenter located at Katmandu airport. As data analysis, short-term (15 days before and 5 days after the earthquakes) and medium-term (2 months before and 1 month after the earthquakes) evaluations were carried out. For short-term analysis, the results of the magnetic field in the topside ionosphere detected an interesting anomaly on April 12, 2015. Besides, nighttime LST anomaly was observed on April 13, 2015, 14 days before the earthquakes, which was also an anomalous day for AT and RH analysis. Moreover, in the OLR analysis, April 12, 2015 was anomalous day based on the time series of the epicentral pixel that coincides with the magnetic field anomaly. Concerning the medium-term nighttime LST analysis, 13th April was the day, which may be associated with the earthquakes. For the medium-term OLR analysis, April 12, 2015 was found to be related to the seismic activity based on the epicentral pixel. Besides, the RH anomaly on 13 April was the only mutual day with the anomalies of LST and OLR for the medium-term analysis. Based on the short-term and medium-term results, these two days, 12 and April 13, 2015, may be associated with the preparation for the earthquake occurrence. Overall, it is concluded that these methods could be effective and reliable in detecting anomalies that precede upcoming EQs with further investigations.

Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada

Monitoring spatiotemporal changes in climate and vegetation coverage are crucial for various purposes, including water, hazard, and agricultural management. Climate has an impact on vegetation, however, studying their relationship is challenging. We implemented the Least-Squares Wavelet (LSWAVE) software for investigating trend, coherency, and time lag estimation between climate and vegetation time series. We utilized Normalized Difference Vegetation Index (NDVI) time series provided by the Terra satellite and hybrid climate time series. We found that the seasonal cycles of climate and NDVI are coherent with time delay. For the entire Athabasca River Basin (ARB), the most coherent component was the annual cycle with 84% annual coherency between vegetation and temperature and 46% between vegetation and precipitation. The annual cycles of temperature and precipitation led the ones in vegetation by about two and three weeks, respectively. Relatively lower coherency was observed in the mountainous region (upper ARB) and higher coherency in the middle ARB. From the cross-spectrograms, a clear time delay pattern was observed between the annual cycles of climate and vegetation since 2000 but not for other high-frequency seasonal cycles. The results also highlighted the advantages of LSWAVE algorithms over traditional algorithms, such as linear regression and correlation. Furthermore, we analyzed the annual land use and land cover data provided by the Terra and Aqua satellites and discussed their linkage with the climate and NDVI results.

Spatial distribution model of terrestrial radiation imbalance measured by means of orbiting radiometers

A spatial distribution model was applied to net radiation levels in central Colombia obtained from remote sensing data; this model was based on the use of spatial interpolation geostatistical techniques and consists of three essential phases: an evaluation of the quality of the spectral scenes, the application of spatial interpolation techniques and the statistical evaluation of the model. These procedures allowed a more accurate analysis of the variables that influence the spatial patterns of terrestrial radiation imbalance (T.R.I) in specific areas of the country. The input data used in this model are called FlashFlux Time Integrated and Spatially Averaged (TISA) and come from observations of the Terra and Aqua satellites of the CERES (Clouds and the Earths Radiant Energy System) and MODIS (Moderate-Resolution Imaging Spectroradiometer) projects. In summary, the way in which this research was carried out was initially with an evaluation of the image quality, to analyse the potential and statistical characteristics of the FlashFlux TISA data; subsequently, a kriging interpolation method was applied adapting the radiometric data for a correct analysis and the patterns of terrestrial radiation imbalance in the centre of the country were analysed, trying to explore variables that influence these spatial dynamics. Finally, an evaluation of the spatial distribution model of terrestrial radiation imbalance was established, where the virtues, relevance and limitations of the techniques used in it were validated. This research was able to implement statistical methods of spatial interpolation, specifically the Ordinary Kriging technique, allowing the modelling of the recent situation of terrestrial radiation imbalance in central Colombia, identifying some processes and factors that are closely related to this atmospheric phenomenon responsible for the effects of regional warming.

Mapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product.

Comprehensive Validation and Calibration of MODIS PWV over Mainland China

Although ground-based precipitable water vapor (PWV) can be obtained with a high temporal resolution and spatial resolution of tens of kilometers in an urban area using Global Navigation Satellite System (GNSS) observation, it remains fairly sparse in the vast regions over Mainland China. Satellite-derived PWV has a high spatial resolution, thereby enabling the accurate investigation of regional climate change. However, understanding the quality of satellite-derived PWV products is a prerequisite before use, which has become the focus of this study. PWV products, namely, MOD05_L2 and MYD05_L2, over the entirety of Mainland China derived from MODerate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites are validated over the period 2000–2017 using multiple sources, including GNSS, radiosonde, AErosol RObotic NETwork (AERONET), and European Center for Medium-Range Weather Forecasts ERA-Interim (ECMWF). The accuracy of MODIS PWV products is less than 4.3 mm over the entirety of Mainland China; however, it varies in four areas of South China (SC), North China (NC), Northwest China (NWC), and Tibetan Plateau (TP), separately with values ranging from 3 mm to 6 mm. A linear fit model is applied to calibrate the MODIS PWV products, and the accuracies of the corrected PWV from MODIS infrared (IR) and near-infrared (NIR) products have been improved by approximately 7.5% and 50.6%, respectively. The MODIS PWV is compared and calibrated in the four areas, and the improved accuracies vary widely. The root mean square error (RMSE) of IR PWV is approximately less than 4 mm over China except for the SC area with a value of approximately 5.3 mm after calibration, whereas the values of NIR PWV are approximately 2 mm over the entirety of Mainland China, except for the TP area, with a value of approximately 2.6 mm. The MODIS NIR PWV performs better than that of IR PWV data in most areas of Mainland China regardless of whether with calibration. The validation and calibration of MODIS water vapor products over Mainland China also indicate their capability to investigate the seasonal and annual variations as well as long-term trend changes in water vapor in China.

Estimation of surface PM2.5 with MODIS Aerosol optical depth and source identification using trajectory analysis: A case of Hyderabad City, India

Satellite measurements are important for quantifying the ground observations and atmosphere columnar properties like Aerosol Optical Depth (AOD) especially in developing countries like India. In this study Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval’s AOD product has been used having 3 km and 10 km spatial resolution from terra and aqua satellites, The MODIS AOD data and meteorological parameters from May 2017 to May 2019 were used. The Multiple linear regression method is implemented in this study. The study concluded that there is a good agreement in the prediction of PM2.5 at Zoopark location, whereas in other monitoring locations the agreement between AOD and measured PM2.5 is relatively poor. The particulate matter (PM) concentrations are influenced by the local source regions and the long-range transport of pollutant through the wind, whereas the source regions identified based on the Potential Source Contribution Function (PSCF), Concentration-Weighted Trajectory (CWT) and Cluster analysis indicate the dominant source regions. Results indicate that the Central India and East Indian regions are more dominating source regions at Hyderabad location in the winter season. It was found that the lower altitude layer showed the major source of local regions nearby receptor. The cluster analysis indicates that the high intensity from the East Indian regions. This paper not only demonstrates reasonable prediction accuracy but also provides the model uncertainties which lays foundation for further study.

Evaluation of the Bias in the Use of Clear-Sky Compared with All-Sky Observations of Monthly and Annual Daytime Land Surface Temperature

Abstract Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.