Moderate Resolution Imaging Spectroradiometer Satellite Data Research Articles

The Global Spatial and Temporal Distribution of Ice Cloud Optical Thickness Based on MODIS Satellite Data during 2000–2021

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a “W” distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2.

Capturing the spatiotemporal variations in the gross primary productivity in coastal wetlands by integrating eddy covariance, Landsat, and MODIS satellite data: A case study in the Yangtze Estuary, China

Accurate monitoring of the spatiotemporal variations in the gross primary productivity (GPP) in coastal wetlands is essential for blue carbon quantification. However, the currently available moderate-spatial-resolution GPP algorithms and products may contain large uncertainties and may rarely meet the demands of tidal wetland research. In this paper, we examine two statistical methods, namely, simple linear regression and random forest regression, to capture the high spatial resolution GPP variations in the salt marshes in the Yangtze Estuary by integrating multi-source data, including eddy covariance, Landsat, and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite data. The satellite-derived photosynthetically active radiation (PAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) are used individually and in different combinations to drive the two statistical methods and investigate their performances in estimating the GPP. The results show that together with the PAR, the EVI generally has the greatest potential for GPP estimation (R2 > 0.75, RMSE < 6.80 μmol m−2 s−1) based on simple linear regression. However, the NDVI outperforms the EVI in wetlands with little tidal flooding and plant litter. The random forest method improves the performance in terms of upscaling the GPP measurements to large regions, and the combination of the EVI, NDVI, NDWI, and PAR performs the best, indicating that the use of the more advanced algorithm and the incorporation of a remotely sensed index that reflects the tidal influence are beneficial to GPP estimation in coastal salt marshes. Moreover, using Landsat data with a high spatial resolution (30 m) yields a much more accurate GPP estimation than using MODIS data with a 500 m resolution. Our results demonstrate that the use of high spatial resolution data, the proper use of remotely sensed indices, and the incorporation of a good combination of these indices through an advanced algorithm such as a machine learning algorithm are vital for capturing the nearly instantaneous GPP variations in coastal wetlands at large spatial scales.

Variations in aerosols and aerosols–cloud interactions in Bangkok using MODIS satellite data during high PM2.5 concentrations

The relationships between particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5), aerosol optical depth (AOD), and cloud parameters were investigated during high PM2.5 concentrations from 1 January 2016 to 31 December 2021 in Bangkok using satelliteremote sensingdatafrom Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud parameters included: Cloud effective radius (CER), Cloud fraction (CF), Cloud optical depth (COD), Cloud liquid water (CLW), Cloud top pressure (CTP), and Cloud top temperature (CTT). The analysis showed a strong negative correlation between PM2.5 and CF. There was a negative correlation between CF and AOD. In addition, PM2.5 showed a negative relationship with CER. In contrast, PM2.5 showed a positive correlation with CTP and CTT. A negative correlation was detected between COD and PM2.5. During high PM2.5 concentrations, cloud parameters are significantly influenced by PM2.5 concentrations in the atmosphere. The three-month of CER, CF, CLW, COD, CTP, and CTT ranges from 16.34 μm to 37.05 μm, 0.22 to 0.86, 15.67 g/m2 to 169.80 g/m2, 1.14 to 19.06, 261.67 hPa to 991.67 hPa, and −66.26C to –22.17C, respectively. Consequently, Altostratus clouds most appear during high PM2.5 concentrations in Bangkok. The findings may help better understand Bangkok's cloud properties and atmospheric aerosols, providing more information for rainmaking during high PM2.5 concentrations.

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

ABSTRACT The all-wave net radiation (Rn) at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles. Many studies have been conducted to estimate from satellite top-of-atmosphere (TOA) data using various methods, particularly the application of machine learning (ML) and deep learning (DL). However, few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving. Based on extensive in situ measurements distributed at mid-low latitudes, the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) TOA observations, and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) used as a priori knowledge, this study assessed nine models for daily estimation, including six classic ML methods (random forest -RF, adaptive boosting - Adaboost, extreme gradient boosting -XGBoost, multilayer perceptron -MLP, radial basis function neural network -RBF, and support vector machine -SVM) and three DL methods (multilayer perceptron neural network with stacked autoencoders -SAE, deep belief network -DBN and residual neural network -ResNet). The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost, although they all performed poorly in certain conditions such as winter days, rugged terrain, and high elevation. ResNet had the most robust performance across different land cover types, elevations, seasons, and latitude zones, but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency. The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite (GLASS) product.

Analysis of land surface temperature with land use and land cover and elevation from NASA MODIS satellite data: a case study of Bali, Indonesia.

The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth's datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.

Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019

AbstractSatellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250‐m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p &lt; 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17‐year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

Modeling bulk surface resistance and evaluation of evapotranspiration using remote sensing and MATLAB

Abstract In developing countries, computation of actual evapotranspiration (AET) is challenging due to the lack of ground-based flux measurement data. The estimation of AET is crucial for water resources management involving the allocation of water for different land use/land cover (LULC) classes. The study's novelty was mapping pixel-by-pixel spatial variations of bulk surface resistance and evaluating the derived actual evapotranspiration in a sub-humid tropical river basin where flux tower data was lacking for validation. This study aimed to map bulk surface resistance and evaluate the estimated AET by global evapotranspiration data product (MOD16A2). Moderate Resolution Imaging Spectroradiometer (MODIS) data products, including land surface reflectance (LSR), land surface temperature (LST) and leaf area index (LAI) data, were used as input in MATLAB for mapping pixel-wise variations to analyze the seasonal variations in bulk surface resistance (rs) and AET in pre-monsoon and post-monsoon seasons during the years 2019 and 2012. The years 2019 and 2012 were selected because 2019 experienced a relatively wet pre-monsoon and post-monsoon, whereas 2012 experienced the opposite conditions, which proved useful when interpreting variations that are influenced by wetness conditions. Overall, the results indicated significant variability in the rs and AET for different LULC classes. MOD16A2 AET was determined to be slightly higher than the LULC classes' estimated AET. This study's MODIS satellite data products provided information on surface characteristics at a reasonable resolution. This permitted the identification of differences in LULC classes and changes in surface characteristics by season and wetness conditions, which are helpful when estimating AET.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

Ability of Remote Sensing Systems to Detect Bark Beetle Spots in the Southeastern US

Research Highlights: Sentinel-2 Normalized Difference Vegetation Index (NDVI) products show greater potential to detect indications of disturbance by bark beetles in the southeastern US than Moderate Resolution Imaging Spectroradiometer (MODIS), as the high spatiotemporal heterogeneity of the southeastern forest land prevents its deployment at the current resolution. Background and Objectives: Remote sensing technologies have been an essential tool to detect forest disturbances caused by insect pests through spectral trait variation. In the US, coordinated efforts such as ForWarn, led by the US Forest Service and based on MODIS satellite data, are used to monitor biotic and abiotic disturbances. Because of the particular characteristics of the southeastern US landscape, including forest fragmentation and rapid forest turnover due to management, detection and visualization of small bark beetle spots using remote sensing technology developed for more homogeneous landscapes has been challenging. Here, we assess the ability of MODIS and Sentinel-2 time-series vegetation index data products to detect bark beetle spots in the Florida Panhandle. Materials and Methods: We compared ForWarn’s detection ability (lower resolution images) with that of Sentinel-2 (higher resolution images) using bark beetle spots confirmed by aerial surveys and ground checks by the Florida Forest Service. Results: MODIS and Sentinel-2 can detect damage produced by bark beetles in the southeastern US, but MODIS detection via NDVI change exhibits a high degree of false negatives (30%). Sentinel-2 NDVI products show greater potential for identifying indications of disturbance by bark beetles than MODIS change maps, with Sentinel-2 capturing negative changes in NDVI for all spots. Conclusions: Our research shows that for practical bark beetle detection via remote sensing, higher spatial and temporal resolution will be needed.

Monitoring of aerosols and studying its effects on the environment and humans health in Iran.

Natural hazards affect different parts of living organisms. One of these hazards is aerosols. Addressing this issue in areas that suffer from this hazard is of critical importance. Scientific research over the past two decades has shown that aerosol particles are one of the main pollutants from the perspective of public health and health. The purpose of the present study is to monitor and model aerosol temporal and spatial variations in Iran. Aerosols or airborne particles with health effects such as heart, vascular and respiratory diseases are associated. For this purpose, MODIS (or Moderate Resolution Imaging Spectroradiometer) and NOAA (or National Oceanic and Atmospheric Administration) satellite data and BTD (or Brightness Temperature Difference) were used. Given the 20-year study period (2000-2019), the output of much satellite data was divided into four five-year periods to monitor aerosols with high accuracy. The results showed that aerosol values in terms of intensity and frequency of optical depth (AOD) increased over the 20-year time series, and its intensity was higher in the last 5years. According to the results obtained from the NODIS and NOAA satellite data and comparing their outputs, NOAA satellite data were associated with outliers, which was significantly different from the other cohort data. For this reason, the MODIS satellite image output was used to monitor aerosol images. The innovation of the present study is the use of remote sensing science to monitor the effects of aerosols on the environment and human's health. According to the results from MODIS satellite data, the maximum optical depth (AOD) of the aerosols is for July 2003 with a value of 0.63, but according to the NOAA satellite data output, the maximum optical depth (AOD) of the aerosols is for March 2013 with a value of 2.54. As the values of aerosols increase in frequency and intensity and the areas with the highest intensity of aerosols have been identified, it can be overcome by careful planning of their problems. Areas, where the amount and volume of aerosols were higher, should be observed in health protocols. By preventing and maintaining good hygiene, the negative effects of aerosols can be reduced.

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.

Corrigendum to: Risk of acute respiratory infection from crop burning in India: estimating disease burden and economic welfare from satellite and national health survey data for 250 000 persons.

BACKGROUND Respiratory infections are among the leading causes of death and disability globally. Respirable aerosol particles released by agricultural crop-residue burning (ACRB), practised by farmers in all global regions, are potentially harmful to human health. Our objective was to estimate the health and economic costs of ACRB in northern India. METHODS The primary outcome was acute respiratory infection (ARI) from India's fourth District Level Health Survey (DLHS-4). DLHS-4 data were merged with Moderate-Resolution Imaging Spectroradiometer satellite data on fire occurrence. Mutually adjusted generalized linear models were used to generate risk ratios for risk factors of ARI. Overall disease burden due to ACRB was estimated in terms of disability-adjusted life years. RESULTS Seeking medical treatment for ARI in the previous 2 weeks was reported by 5050 (2%) of 252 539 persons. Living in a district with intense ACRB-the top quintile of fires per day-was associated with a 3-fold higher risk of ARI (mutually adjusted risk ratio 2.99, 95% confidence interval 2.77 to 3.23) after adjustment for socio-demographic and household factors. Children under 5 years of age were particularly susceptible (3.65, 3.06 to 4.34 in this subgroup). Additional ARI risk factors included motor-vehicle congestion (1.96, 1.72 to 2.23), open drainage (1.91, 1.73 to 2.11), cooking with biomass (1.73, 1.58 to 1.90) and living in urban areas (1.35, 1.26 to 1.44). Eliminating ACRB would avert 14.9 million disability-adjusted life years lost per year, valued at US$152.9 billion over 5 years. CONCLUSIONS Investments to stop crop burning and offer farmers alternative crop-residue disposal solutions are likely to improve population-level respiratory health and yield major economic returns.

Snowmelt timing impacts on growing season phenology in the northern range of Yellowstone National Park estimated from MODIS satellite data

Trends and geographic patterns of change in vegetation phenology metrics and snowmelt timing from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the Northern Range of Yellowstone National Park over the period 2001 to 2017. The main question posed in this analysis was “Where has the growing season length, amplitude, and integrated greenness cover changed over the past two decades on the Northern Range, particularly in relation to vegetation cover types and the timing of spring snowmelt?” Phenology metric patterns derived from the Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were used to track changes in the growing season length, amplitude, and integrated greenness cover over the past two decades. Trend analysis showed that end of the growing season timing (EOST) and integrated greenness increased significantly over nearly 30% of the Northern Range study area, and NDVI amplitude increased significantly in several large drainages, particularly in shrub-grassland cover types. Significant variation in the start of the growing season (SOST), NDVI amplitude and integrated greenness could be further explained by the timing of spring snow melt. In years with relatively late snowmelt dates (after mid-May), higher plant growth was observed over the ensuing growing season, as captured in the amplitude and integrated greenness metrics, potentially due to elevated snow water inputs that can maintain available soil moisture levels for plant growth into the mid- and late-summer months. The ecological implications of an extended period in November–December when grassland and shrub biomass supplies remain relatively snow-free and readily accessible to grazing ungulates will require further assessments.

Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data

Earth observation satellite technologies allow the status of crops to be assessed at various geographical scales. The most popular vegetation index for analyzing this status is the Normalized Difference Vegetation Index (NDVI). This article studies the relationship between the NDVI and cereal-grain yield in selected regions (corresponding to NUTS2 levels) of four Central European countries: Poland, Germany, the Czech Republic, and Slovakia. The remote sensing data cover the vegetation seasons (from the beginning of March until the end of June) of 2012–2016, and were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor of NASA's Terra satellite. The average NDVI of the data was calculated for both the entire area and the arable-land area of the regions (according to the Corine Land Cover program). Statistical analysis includes correlation and linear regression analyses between cereal grain yield and mean NDVI for entire area and arable land of the regions in each year of the study. They are strongly correlated from the beginning of March until mid-May (most of the correlation coefficients for arable land are between 0.50 and 0.85), the strongest relationship being around the beginning of April in the Czech Republic and Slovakia (most of the correlation coefficients are between 0.80 and 1.00). In most regions of Poland, the relationships are quite strong, but in German regions they are weak and inconsistent. Regression coefficients (slopes) for relationships between the NDVI in the beginning of April and the grain yield of cereals range from 10.8 to 26.2. This means that an increase in the NDVI in early spring by 0.1 unit increases the grain yield of cereals by about 1.1–2.6 t/ha. The obtained results are promising because they prove the possibility of forecasting cereal grain yield at the regional level, three–four months before the harvest, which is important for planning food policy.

A satellite algorithm for estimating relative humidity, based on GOES and MODIS satellite data

ABSTRACTA real-time algorithm is proposed to estimate hourly relative humidity at the surface level. The algorithm was developed with Geostationary Operational Environmental Satellite (GOES)-13 and the Moderate-Resolution Imaging Spectroradiometer (MODIS) data. The methodology is also designed to operate with the newer GOES-16 satellite data. The algorithm uses the brightness temperatures from the water vapour (6.7 µm) channel and albedo from the near-infrared (3.9 µm) channel to model short-term variations. The algorithm also includes three physical parameters extracted from satellites to complement the short-term variations of relative humidity at surface level. The parameters are: precipitable water (PW), land surface temperature (LST), and normalized difference vegetation index (NDVI). Two years (2010–2011) of hourly data from 458 ground stations were used to build the model. Validation was conducted with 2012 (GOES-13) and 2018 (GOES-16) data. Stations are located in the Caribbean islands, including the Peninsula of Florida, and in Mesoamerica countries, including part of Mexico, Central America, Colombia, and Venezuela. A regression model was used to estimate relative humidity, and validation was performed over different rainfall seasons. Results show that the average mean absolute error, root mean squared error, and the coefficient of multiple determination were 7.4, 9.9, and 0.58, respectively.

Risk of acute respiratory infection from crop burning in India: estimating disease burden and economic welfare from satellite and national health survey data for 250 000 persons.

BackgroundRespiratory infections are among the leading causes of death and disability globally. Respirable aerosol particles released by agricultural crop-residue burning (ACRB), practised by farmers in all global regions, are potentially harmful to human health. Our objective was to estimate the health and economic costs of ACRB in northern India.MethodsThe primary outcome was acute respiratory infection (ARI) from India’s fourth District Level Health Survey (DLHS-4). DLHS-4 data were merged with Moderate-Resolution Imaging Spectroradiometer satellite data on fire occurrence. Mutually adjusted generalized linear models were used to generate risk ratios for risk factors of ARI. Overall disease burden due to ACRB was estimated in terms of disability-adjusted life years.ResultsSeeking medical treatment for ARI in the previous 2 weeks was reported by 5050 (2%) of 252 539 persons. Living in a district with intense ACRB—the top quintile of fires per day—was associated with a 3-fold higher risk of ARI (mutually adjusted risk ratio 2.99, 95% confidence interval 2.77 to 3.23) after adjustment for socio-demographic and household factors. Children under 5 years of age were particularly susceptible (3.65, 3.06 to 4.34 in this subgroup). Additional ARI risk factors included motor-vehicle congestion (1.96, 1.72 to 2.23), open drainage (1.91, 1.73 to 2.11), cooking with biomass (1.73, 1.58 to 1.90) and living in urban areas (1.35, 1.26 to 1.44). Eliminating ACRB would avert 14.9 million disability-adjusted life years lost per year, valued at US$152.9 billion over 5 years.ConclusionsInvestments to stop crop burning and offer farmers alternative crop-residue disposal solutions are likely to improve population-level respiratory health and yield major economic returns.

Analysis of Physical and Biogeochemical Control Mechanisms on Summertime Surface Carbonate System Variability in the Western Ross Sea (Antarctica) Using In Situ and Satellite Data

In this study, carbonate system properties were measured in the western Ross Sea (Antarctica) over the 2005–2006 and 2011–2012 austral summers with the aim of analysing their sensitivity to physical and biogeochemical drivers. Daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration maps, obtained prior to and during the samplings, were used to analyse the sea ice evolution throughout the experiment periods. Monthly means and 8-day composite chlorophyll concentration maps from the Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua satellite at 4-km resolution were used to investigate inter-annual and basin scale biological variability. Chlorophyll-a concentrations in surface waters estimated by MODIS satellite data contribute to descriptions of the variability of carbonate system properties in surface waters. Mean values of carbonate system properties were comparable across both investigated years; however, the 2012 data displayed larger variability. Sea ice melting also had a pivotal role in controlling the carbonate system chemistry of the mixed layer both directly through dilution processes and indirectly by favouring the development of phytoplankton blooms. This resulted in high pH and ΩAr, and in low CT, particularly in those areas where high chlorophyll concentration was shown by satellite maps.

Spatial and temporal distribution of the cloud optical depth over China based on MODIS satellite data during 2003–2016

The cloud optical depth (COD) is one of the important parameters used to characterize atmospheric clouds. We analyzed the seasonal variations in the COD over East Asia in 2011 using cloud mode data from the AERONET (Aerosol Robotic Network) ground-based observational network. The applicability of the MODIS (Moderate Resolution Imaging Spectroradiometer) COD product was verified and compared with the AERONET cloud mode dataset. There was a good correlation between the AERONET and the MODIS. The spatial and temporal distribution and trends in the COD over China were then analyzed using MODIS satellite data from 2003 to 2016. The seasonal changes in the AERONET data and the time sequence variation of the satellite data suggest that the seasonal variations in the COD are significant. The result shows that the COD first decreases and then increases with the season in northern China, and reaches the maximum in summer and minimum in winter. However, the spatial distribution change is just the opposite in southern China. The spatial variation trend shows the COD in China decreases first with time and gradually increases after 2014. And the trend of COD in the western and central China is consistent with that in China. While the trend of COD shows a continuously increasing over time in northeast China and the Pearl River Delta.

Phytoplankton size class in the East China Sea derived from MODIS satellite data

Abstract. The distribution and variation of phytoplankton size class (PSC) are key to understanding ocean biogeochemical processes and ecosystems. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many algorithms have been developed to estimate PSC. Here based on a local dataset from the ECS, a regional model was tuned to estimate the PSC from the spectral features of normalized phytoplankton absorption (aph) using a principal component analysis approach. Before applying the refined PSC model to MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite remote sensing reflectance (Rrs) at 412 and 443 nm was critical through modeling them from Rrs between 469 and 555 nm using multiple regression analysis. Satellite-derived PSC results compared well with those derived from pigment composition, which demonstrated the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. Application of the refined PSC model to the reconstructed MODIS data from 2003 to 2016 yielded the seasonal distributions of the PSC in the ECS, suggesting that the PSC distributions were heterogeneous in both temporal and spatial scales. Micro-phytoplankton were dominant in coastal waters throughout the year, especially in the Changjiang estuary. For the middle shelf region, the seasonal shifts from the dominance of micro- and nano-phytoplankton in the winter and spring to the dominance of nano- and pico-phytoplankton in the summer and autumn were observed. Pico-phytoplankton were especially dominant in the Kuroshio region in the spring, summer, and autumn. The seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio. Additionally, human activity and riverine discharge might also influence the PSC distribution in the ECS, especially in the coastal region.

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACTThe impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.