nasa-gesdisc/es-publications-researchareas huggingface dataset:
A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning (2023)

Agriculture drought is a decrease in soil moisture during a growing season. In this study, a comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) was developed to monitor the agriculture drought in semi-arid environments and assess its effectiveness in rainfed agriculture regions in (A) Jordan and (B) Syria. First, remote sensed-based drought-condition spectral indices [i.e., Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI), Precipitation Condition Index (PCI), Soil Moisture Condition Index (SMCI), and Vegetation Health Index (VHI)] were calculated using data from Moderate Resolution Imaging Spectroradiometer satellite (MODIS) [Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and evapotranspiration (ET)]; the Global Precipitation Measurement (GPMs); the Soil Moisture Active Passive (SMAPs); and Sentinel-1A. Second, Random Forest (RF) was used to estimate and determine the relative importance of these indices based on Standardized Precipitation Index (SPI) values to select the three spectral indices that have the most monthly short-term relative importance in identifying the agriculture drought for semi-arid environments, which were PCI, TCI, and VCI. Third, these indices were integrated to identify the drought severity based on specific thresholds that compare the pixel-specific value with the study area average value. For instance, a severe drought condition is identified if all three indices indicate a drought condition, a moderate drought or mild drought conditions are identified if any two or any one of the indices indicate drought conditions, respectively. Lastly, a none drought condition is identified if none of the indices indicate a drought condition. Finally, the SPI sets for 1 and 3-months (SPI-1 and SPI-3) were used to evaluate the performance of the CADCI. The results showed the CADCI has a high agreement with SPI-1 classes in the study areas, with overall accuracy and Kappa-values of 85% and 0.80, for study area A and 83% and 0.76 for study area B, respectively. Consequently, the CADCI shows its ability to monitor agricultural drought in semi-arid environments. Perhaps, it could be applicable for larger areas due to the spatial resolution of the input dataset.

Research areas: Droughts
Variations in the aerosol index and its relationship with meteorological parameters over Pakistan using remote sensing (2023)

Particulate pollution has become a major issue in developing countries including Pakistan. Aerosols are causing severe impacts on climate and human health. To understand the effects of aerosols on the environment and human health, we must first understand their optical and physical properties. In this paper, we used ozone monitoring instrument (OMI) retrieved ultraviolet aerosol index (UVAI) to analyze spatial and temporal distribution, annual and seasonal trends of absorbing aerosols, and their relationship with meteorological parameters (e.g., temperature, relative humidity, and wind speed) over Pakistan from October 2004 to December 2021. Significant spatiotemporal changes in UVAI values were found with high values in southern and central regions and low values in northern regions of Pakistan. The mean UVAI over Pakistan showed an increasing trend of 2.89% year1. Seasonally, UVAI increases at the rate of 3.97% winter1, 3.24% autumn1, 0.81% summer1, and 0.71% spring1. A strong positive correlation of UVAI with precipitation and temperature (~ 0.6) is observed in the central and southern regions of Pakistan. A negative and positive correlation of 0.3223 and 0.4284 of UVAI with CO2 emissions and primary industry is observed in Pakistan, respectively. We also found potential sources of aerosols over major cities of Pakistan using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model. It determines that the dominant aerosols over Karachi are natural aerosols like sea salt and dust particles and anthropogenic aerosols are dominant over Lahore. Moreover, the natural and anthropogenic factors influencing absorbing aerosols are also discussed herein. Considering the outcomes of this study different methods would be used to reduce the concentration of particulate pollution like afforestation, efficient fuel energy consumption, promotion of public transport networks, etc.

Research areas: Air Quality
Influence of stubble burning on air quality of Northern India: a case study of Indo-Gangetic plains of India (2023)

Stubble burning is an emerging environmental issue in Northern India, which has severe implications for the air quality of the region. Although stubble burning occurs twice during a year, first during AprilMay and again in OctoberNovember due to paddy burning, the effects are severe during OctoberNovember months. This is exacerbated by the role of meteorological parameters and presence of inversion conditions in the atmosphere. The deterioration in the atmospheric quality can be attributed to the emissions from stubble burning which can be perceived from the changes observed in land use land cover (LULC) pattern, fire events, and sources of aerosol and gaseous pollutants. In addition, wind speed and wind direction also play a role in changing the concentration of pollutants and particulate matter over a specified area. The present study has been carried out for the states of Punjab, Haryana, Delhi, and western Uttar Pradesh to study the influence of stubble burning on the aerosol load of this region of Indo-Gangetic Plains (IGP). In this study, the aerosol level, smoke plume characteristics, long-range transport of pollutants, and affected areas during OctoberNovember from year 2016 to 2020 were examined over the Indo-Gangetic Plains (Northern India) region by the satellite observations. By MODIS-FIRMS (Moderate Resolution Imaging Spectroradiometer-Fire Information for Resource Management System) observations, it was revealed that there was an increase in stubble burning events with the highest number of events being observed during the year 2016 and then a decrease in the number of events in subsequent years from 2017 to 2020. MODIS observations revealed a strong AOD gradient from west to east. The prevailing north-westerly winds assist the spread of smoke plumes over Northern India during the peak burning season of October to November. The findings of this study might be used to expand on the atmospheric processes that occur over northern India during the post-monsoon season. The pollutant, smoke plume features, and impacted regions of biomass-burning aerosols in this region are critical for weather and climate research, especially given the rising trend in agricultural burning over the previous two decades.

Research areas: Environmental Impacts, Air Quality
Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area (2023)

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.

Research areas: Floods
Assessment of aerosols over five cities of Angola based on MERRA2 reanalysis data (2022)

The use of Aerosol Optical Depth (AOD) has been proven as an alternative to the traditional ground-level monitoring of air quality in many countries across the world. Therefore, this study based on MERRA-2 data aims: (i) to characterize the spatiotemporal and component variations of aerosols in the atmosphere over the capital cities (Luanda, Sumbe, Benguela, Huambo and Lubango) of the five most densely populated provinces of Angola from 2010 to 2020; (ii) to analyze the relationship between the monthly mean variation of the total AOD and the meteorological parameters (precipitation, temperature, wind speed, and relative humidity) in those five cities; and (iii) to assess the influence of emissions from the Nyamuragira volcano, located in the Democratic Republic of Congo, on the air quality at the five cities. The most significant contribution to the total AOD was derived from organic carbon, in all the cities, whereby the highest values (0.190.23) were in Luanda. Ranges of sulphates across the coastal cities were higher when compared to the interior cities caused by the emissions inventory data. The value of AOD in all the cities increased and reached the peak during the dry season. The HYSPLIT model showed that air masses from Nyamuragira at various heights in November 2011 reached Luanda and Sumbe, and CALIPSO could confirm the existence of volcanic aerosols in this same period. This study allowed to conclude that the variability of AOD loading depends on seasons and regions, thus providing a little more information about the matter.

Research areas: Air Quality, Volcanic Eruptions
Remote sensing techniques for monitoring algal blooms in the area between Jeddah and Rabigh on the Red Sea Coast (2023)

The Red Sea is known to host and facilitate the growth and reproduction of several species of phytoplankton and algae. The degree of density and distribution of these organisms differ with region. However, very few studies have been conducted on algal blooms in this region. Hence, this study aimed to identify the effectiveness of remote sensing techniques in monitoring and controlling algal blooms in the area extending between the cities of Jeddah and Rabigh on the Red Sea coast. A series of satellite images were used to study algae from January 2020 to March 2021. All study data were subjected to processing and analysis using spectral indices [chlorophyll a (Chl-a); Concentration and Sea Surface Temperature (SST); Normalized Difference Turbidity Index (NDTI); Surface Scum Index (SSI); Surface Algal Bloom Index (SABI); Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)], and the classification, overlay, and buffer methods. We found that the rate of chlorophyll a (Chl-a) increased in February 2021 and decreased in May 2020, with the algal bloom dwindling with increasing distance from the shores of the Red Sea. Furthermore, despite the varying effectiveness of the spectral indices in estimating the distribution and density of algae in the Red Sea, Landsat SSI was the most effective. One of the primary results of the study was that algal blooms more frequently occur in the winter (February). The study produced a digital map of algal blooms in the study area. Algal blooms exhibited medium inverse correlations with temperature and specific humidity, and strong direct correlation with wind speed. Valley estuaries, close to the beaches, were found to be the most suitable area for the concentration and growth of algae. The study's findings emphasize the need for close monitoring and controlling changes in the components of the marine environment using remote sensing techniques, in order to track the changes that marine plants (algae) are exposed to in the Red Sea.

Research areas: Water Quality, Ecosystems
Urban air quality in Xinjiang and snow chemistry of Urumqi Glacier No. 1 during COVID-19s restrictions (2022)

The unprecedented COVID-19 outbreak impacted the world in many aspects. Air pollutants were largely reduced in cities worldwide in 2020. Using samples from two snow pits dug separately in 2019 and 2020 in Urumqi Glacier No. 1 (UG1) in the Xinjiang Uygur Autonomous Region (Xinjiang), China, we measured water-stable isotopes, soluble ions, and black and organic carbon (BC and OC). Both carbon types show no significant variations in the snow-pit profiles dated from 2018 through 2020. The deposition of anthropogenically induced soluble ions (K+, Cl, SO42, and NO3) in the snow decreased to 2040% of their respective concentrations between 2019 and 2020; however, they increased 2- to fourfold from 2018 to 2019. We studied the daily concentrations of SO2 (20192020), NO2 (20152020), CO (20192020), and PM2.5 (20192020) measured in the sixteen major cities and towns across Xinjiang. The variabilities in these air pollutants were supposed to illustrate the air quality in the urban area and represent the change in the source area. The NO2 decreased in response to mobility restrictions imposed by local governments, while SO2, CO, and PM2.5 did not consistently correspond. This difference indicates that the restriction measures primarily affected traffic. The increases in chemical species in the snow from 2018 to 2019 and the subsequent decreases from 2019 to 2020 were consistent with the variations in SO2 and NO2 measured in urban air and estimated by MERRA-2 model. Therefore, the pandemic could possibly have an impact on snow chemistry of the Tien-Shan glaciers via reduced traffic and industrial intensity; more evidence would be obtained from ice cores, tree rings, and other archives in the future.

Research areas: Air Quality
Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots (2022)

More timely and accurate air quality forecasting could contribute to better public health protection and air pollution prevention. Particulates are a significant indicator for measuring the degree of air pollution. This paper reports on research to model an early warning tool for coarse particulates when assessing the impact of the 12 satellite-derived and ground-based meteorological pollutants out of 30 pollutants considered using hourly Australian data from January 2018December 2020. A one-dimensional convolutional neural network (CNN) was integrated with a one-directional fully gated recurrent unit (GRU) to forecast consecutive hours' air quality. The CNN model acts as a spatial feature extractor, whereas the new generation GRU makes it computationally efficient. The resultant hybrid CNN-GRU is then comprehensively benchmarked outperforming an ensemble of six other deep learning models. The proposed model's efficacy is indicated at the four most air polluted Australian postcodes in the testing phase. A detailed error analysis with visual and statistical metrics for air quality forecasting ascertains the proposed model's countermeasure to reduce harm and loss. The practical tool is immensely beneficial and can be widely deployed to the regions of public health concern where air pollution is a significant hazard.

Research areas: Air Quality
Contrasting climate controls on the hydrology of the mountainous Cauca River and its associated sedimentary basin: Implications for interpreting the sedimentary record (2021)

In coupled source-to-sink systems, spatial differences in hydrology and geomorphology influence how processes in the source are recorded in the sink. The catchment of the tropical Cauca River (source) in the northern (Colombian) Andes, and its associated Mojana lowland sedimentary basin (sink), are a model system in which to explore the relationship between climate processes that control runoff and erosion in the upper catchment, and sediment deposition in the lower basin that occurs through avulsion events and seasonal wetland flooding. This study employed historical climate and hydrologic data from the Cauca River watershed and its associated sedimentary basin, along with topographic data and satellite imagery. We discovered that the Cauca River catchment and its associated Mojana Basin are governed by different patterns of intra- and inter-annual climate variability. Biannual passage of the Intertropical Convergence Zone (ITCZ) over the watershed, annual incursion of the Choco Jet, and sub-decadal variability of the El Nino Southern Oscillation (ENSO) all modulate runoff and sediment fluxes that ultimately become fluvial deposits (e.g., crevasse splays, channel and floodplain sediments). Fluctuations in wetland water levels and area, preserved as lacustrine and marsh deposits, are influenced primarily by annual variations in rainfall that are controlled by the yearly northernmost extent of the ITCZ. Long-term erosion in the Cauca River catchment is controlled largely by active tectonism in the northern Central Cordillera. Avulsion events on the Cauca River in 2010 and 2011 occurred as a consequence of levee failure during a period of high runoff associated with the negative phase of the El Nino Southern Oscillation (ENSO) along with higher cross-floodplain than down-valley slopes in the basin.

Research areas: Land Surface/Agriculture Indicators, Atmospheric/Ocean Indicators
Understanding Tropical Convection Through Triple Oxygen Isotopes of Precipitation From the Maritime Continent (2021)

Monthly precipitation samples from Singapore were collected between 2013 and 2019 for stable isotope analysis to further our understanding of the drivers of tropical precipitation isotopes, in particular, 17O-excess. 18O ranges from 11.34 to 2.34, with a low correlation to rainfall (r = 0.31, p = 0.014), suggesting a weak amount effect. d-excess is relatively consistent and has an average value of 10.89  3.45. Compared to high-latitude regions, 17O-excess in our samples generally falls in a narrower range from 2 to 47 per meg with an average of 21  11 per meg. Moreover, 17O-excess shows strong periodic variability; spectral analysis reveals 3-month, 6-month, and 2.7-year periodicities, likely corresponding to intraseasonal oscillations, monsoons, and the El NinoSouthern Oscillation (ENSO), respectively. In contrast, d-excess shows no clear periodicities. Although spectral analysis only identifies 6-month periodicity in the 18O time series, 18O tracks the Nino3.4 sea surface temperature variability; the average 18O value (5.2) is higher during El Nino years than ENSO neutral years (7.6). Therefore, regional convection associated with monsoons and ENSO has different impacts on 18O, d-excess, and 17O-excess. 17O-excess and d-excess are anticorrelated and do not relate to the relative humidity in moisture source regions. Extremely low humidity and drought conditions in moisture source regions would be required to account for high 17O-excess. Processes during transport and precipitation likely modify these two parameters, especially 17O-excess, which no longer record humidity conditions of moisture source regions. Our findings will be useful for further modeling studies to identify physical processes during convection that alter d-excess and 17O-excess.

Research areas: Atmospheric/Ocean Indicators
Trends and Variability of PM2.5 at Different Time Scales over Delhi: Long-term Analysis 2007-2021 (2022)

ABSTRACT The present study investigated the long-term inter-annual, seasonal, and monthly trend analysis and variability of PM2.5 at different time scales over the national capital, Delhi, India using high-resolution surface observations from six stations during 2007-2021. The non-parametric Mann-Kendall and Theil-Sen slope estimator were used to study the temporal variations. The long-term PM2.5 concentration showed an overall small but statistically significant decreasing trend with an average decrease of -1.35 (95 % CI: -2.3, -0.47) g m-3 year-1. Seasonal trends revealed a significant decreasing value of -3.05 g m-3 year-1 (p < 0.1) for summer, insignificant declining trend of -1.95 g m-3 year-1 for monsoon and no significant trend was found for the post-monsoon and winter season. Except for December and January, all months displayed a decreasing trend for PM2.5 concentration. These findings indicate that particle pollution over the city is declining at a very slow rate. A rising trend was found for relative humidity and surface pressure, whereas a declining trend for wind speed and PBLH noted. No trend was observed for temperature and rainfall. The Pearson linear correlation between PM2.5 and meteorological variables was studied using monthly mean data. Rainfall, air temperature, PBLH, and wind speed showed a negative correlation with PM2.5, whereas surface pressure had a positive correlation and relative humidity displayed an inverted U-shape relationship. The average concentration of PM2.5 in the study period of 15 years remained 125  86 g m-3 (ranging between 20 to 985 g m-3) and during winter, summer, monsoon, and post-monsoon seasons it was 174  75, 101  48, 66  50, and 192  93 g m-3 respectively. The monthly average PM2.5 concentration observed minimum in August and maximum in November. Satellite data of fire events showed that the crop residue burning over the Punjab region had a significant contribution to the peak PM2.5 levels in Delhi during the crop burning period. Government agencies need more strict action plans, especially during winter, to comply with air quality standards.

Research areas: Air Quality, Environmental Impacts
Impact of COVID -19 pandemic lockdown on distribution of inorganic pollutants in selected cities of Nigeria (2020)

The COVID-19 global pandemic has necessitated some drastic measures to curb its spread. Several countries around the world instituted partial or total lockdown as part of the control measures for the pandemic. This presented a unique opportunity to study air pollution under reduced human activities. In this study, we investigated the impact of the lockdown on air pollution in three highly populated and industrious cities in Nigeria. Compared with historical mean values, NO2 levels increased marginally by 0.3% and 12% in Lagos and Kaduna respectively. However, the city of Port Harcourt saw a decrease of 1.1% and 215.5% in NO2 and SO2 levels respectively. Elevated levels of O3 were observed during the period of lockdown. Our result suggests that there are other sources of air pollution apart from transportation and industrial sources. Our findings showed that the COVID-19-induced lockdown was responsible for a decrease in NO2 levels in two of the locations studied. These results presents an opportunity for country wide policies to mitigate the impact of air pollution on the health of citizens.

Research areas: Air Quality