Exploring the Relation between NPP-VIIRS Nighttime Lights and Carbon Footprint, Population Growth, and Energy Consumption in the UAE

Due to global warming and its detrimental effect, every country is responsible to join the global effort to reduce carbon emissions. In order to improve the mitigation plan of climate change, accurate es-timates of carbon emissions, population, and electricity consumption are critical. Carbon footprint is significantly linked to the socioeconomic development of the country which can be reflected in the city's infrastructure and urbanization. We may be able to estimate the carbon footprint, population growth, and electricity consumption of a city by observing the nighttime light reflecting its urbanization. This is more challenging in oil-producing countries where urbanization can be more complicated. In this study, we are therefore investigating the possibility of correlating the remotely sensed NPP-VIIRS Nighttime light (NTL) estimation with the aforementioned socioeconomic indicators. Daily NPP-VIIRS NTL were obtained for the period between 2012 to 2021 for the United Arab Emirates (UAE) which is one of the top oil producing countries. The socioeconomic indicators of the UAE, including the population, electricity consumption, and carbon dioxide emissions, have been obtained for the same period. The analysis of the correlation between the NTLs and the population indicates that there is a high correlation of more than 0.9. There is also a very good correlation of 0.7 between NTLs and carbon emissions and electricity consumption. However, these correlations differ from one city to another. For example, Dubai has shown the highest correlation between population and NTLs (R2 > 0.8). However, the correlation was the lowest in Al-Ain, a rural city (R2 < 0.4) with maximum electricity consumption of 1.1E04 GWh. These results demonstrate that NTLs can be considered as a promising proxy for carbon footprint and urbanization in oil-producing regions

Applications of biogeochemical models in different marine environments: a review

Marine biogeochemical models are an effective tool for formulating hypothesis and gaining mechanistic understanding of how an ecosystem functions. This paper presents a comprehensive review of biogeochemical models and explores their applications in different marine ecosystems. It also assesses their performance in reproducing key biogeochemical components, such as chlorophyll-a, nutrients, carbon, and oxygen cycles. The study focuses on four distinct zones: tropical, temperate, polar/subpolar, and high nutrient low chlorophyll (HNLC). Each zone exhibits unique physical and biogeochemical characteristics, which are defined and used to evaluate the models’ performance. While biogeochemical models have demonstrated the ability to simulate various ecosystem components, limitations and assumptions persist. Thus, this review addresses these limitations and discusses the challenges and future developments of biogeochemical models. Key areas for improvement involve incorporating missing components such as viruses, archaea, mixotrophs, refining parameterizations for nitrogen transformations, detritus representation, and considering the interactions of fish and zooplankton within the models

Identifying Algal Bloom &lsquo;Hotspots&rsquo; in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity

Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity

Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity.</jats:p

Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images

Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent advancements in satellite navigation technology, which can utilize multi-spectral bands for retrieving information on water depth. As part of these efforts, this study combined deep learning techniques with satellite observations in order to improve the estimation of satellite-based bathymetry. The objective of this study is to develop a new method for estimating coastal bathymetry using Sentinel-2 images. Sentinel-2 was used here due to its high spatial resolution, which is desirable for bathymetry maps, as well as its visible bands, which are useful for estimating bathymetry. The conventional linear model approach using the satellite-derived bathymetry (SDB) ratio (green to blue) was applied, and a new four-band ratio using the four visible bands of Sentienl-2 was proposed. In addition, three atmospheric correction models, Sen2Cor, ALOCITE, and C2RCC, were evaluated, and Sen2Cor was found to be the most effective model. Gradient boosting was also applied in this study to both the conventional band ratio and the proposed FVBR ratio. Compared to the green to blue ratio, the proposed ratio FVBR performed better, with R2 exceeding 0.8 when applied to 12 snapshots between January and December. The gradient boosting method was also found to provide better estimates of bathymetry than linear regression. According to findings of this study, the chlorophyll-a (Chl-a) concentration, sediments, and atmospheric dust do not affect the estimated bathymetry. However, tidal oscillations were found to be a significant factor affecting satellite estimates of bathymetry

Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values

The capability to observe soil moisture frequently and over large regions could significantly enhance our ability to monitor vegetation conditions over time and space. The purpose of this project is to evaluate the effects of soil moisture, temperature, and humidity variations on vegetation conditions in the UAE. Visible and near-infrared channels of MODIS instrument on board of aqua satellite were used in this study. The Normalized Difference Vegetation Index (NDVI) was applied to map the extent of vegetation coverage. It was found in this study that the vegetation areas with NDVI values between 0 and 0.2 have significant correlation with average soil moisture, minimum humidity and maximum temperature and the humidity has the maximum effect on these vegetated areas. However, much lower correlation was found with high NDVI areas