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The editorial discusses the significant transformations in coastal and estuarine ecological environments due to hydrodynamic variability. It emphasizes the importance of understanding hydrodynamic processes through advanced methodologies like remote sensing and numerical modeling to unravel ecological changes. The editorial presents six papers that highlight research on ecological environment changes in response to hydrodynamic variability, covering topics such as dissolved oxygen patterns, boundary current transitions, hypoxia/anoxia evolution, geochemical behavior in sediments, remote sensing estimation of nitrogen concentration, and coastal water darkening. These studies underscore the need for advanced technologies and long-term data to manage and protect vulnerable coastal ecosystems in the face of climate change. [Extracted from the article]

Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology. [ABSTRACT FROM AUTHOR]

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region. [ABSTRACT FROM AUTHOR]

Groundwater is a vital natural resource that is essential for the survival of humans and ecosystems alike. However, the depletion of the global water table has become a significant cause for concern. Hence, it is imperative that groundwater monitoring is managed in a systematic and sustainable way to ensure its long-term availability. Geospatial mapping is considered one of the most crucial tools in the field of subsurface water studies because it aids in finding, observing, and monitoring water levels in underground reservoirs. The purpose of this study is to classify the potential areas for groundwater use for agricultural development in the metropolitan district of Lahore in Punjab, Pakistan. To delineate groundwater potential areas, ten parameters are used including groundwater depth, geology, lineament density, slope, soil type, rainfall, drainage density, Topographic Wetness Index, land use/land cover and roughness. The weighted overlay technique is used to integrate the selected ten parameters for the delineation of groundwater potential zones. The contribution and influence of each parameter on groundwater recharge are considered, and potential groundwater recharge areas are classified into five classes ranging from very low to very high. Finally, the efficiency of the modeled groundwater potential zones is validated with the in-situ groundwater depth data from 40 wells distributed in the study area. The classification of recharge areas into distinct categories provides a useful framework for decision-making, enabling policymakers and stakeholders to prioritize areas for conservation and management based on their potential for groundwater recharge. [ABSTRACT FROM AUTHOR]

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties. [ABSTRACT FROM AUTHOR]

A substantial drop in atmospheric visibility or visual range (VR) is documented in several parts of the world. We examined the long-term spatiotemporal variability in ground-based VR data for ten airports in Pakistan from 1980 to 2020. Average VR time series analysis was performed using nonparametric tests of Mann–Kendall (MK), Modified Mann–Kendall (MMK), Sen's Slope (SS), and Sequential Mann–Kendall (SMK), followed by spatiotemporal mapping. Bad VR (<5 km) conditions prevailed over more than 60% of the study period. Noticeably, VR was better during 1991–2000 due to the high average annual rainfall (905 mm) during the 1990s. Plain areas such as Faisalabad experienced the greatest number of bad VR days with hourly (92.85%), daily (95.35%), monthly (90.97%), and seasonal (90.97%) measurements, followed by Sialkot and Multan. The VR of lowlands in central, southern, and eastern Punjab and northern, central, and eastern Sindh showed decreasing trends. In contrast, the VR in coastal areas of south and southwestern Sindh, in the western highlands, and parts of Khyber Pakhtunkhwa (KPK) province, have indicated positive trends. The results of this study will support policymakers in catering to the issue of declining VR in the region by supporting and developing strategies to limit the factor associated with VR. [ABSTRACT FROM AUTHOR]

Oil spill is an environmental challenthat influences various aspects of coastal zones including humans, health, economy, and coastal ecosystems. The source of the oil spill can be both natural and anthropogenic. Oil spill response is dependent on the prompt detection of spillain open waters and at the coast. This study uses Sentinel-1 (S-1) C-band Synthetic Aperture Radar (SAR) data to detect oil spill that transpired at Karachi's coastline from 23 to 26 October 2018. The oil spill detection was conducted using the K-means clustering algorithm of S-1 SAR data that involves reduction of speckle. Meteorological data consisting of wind speed and direction from S-1 Level-2 Ocean product (L2 OCN) based on the European Centre for Medium-RanWeather Forecasts (ECMWF) was also utilized. Initially, two long streaks of bunker oil covering an approximate area of 9.45 km2 was detected on 23 October 2018. Later oil spill spread over an area of ∼188.30 km2 (∼20 times more) until 26 October 2018 along the Karachi shoreline. Spilled oil covered ∼20 times more of its first spread area at an alarming rate of ∼37.63 km2 per day due to wind and wave action. Oil spill damaged the local ecology along the western coast of Karachi. Such damages can be avoided by installing an oil spill monitoring system using SAR and ground data which will support both environment and economy. [ABSTRACT FROM AUTHOR]

This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February–May 2020) compared to the pre-COVID-19 period (February–May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019. [ABSTRACT FROM AUTHOR]

This study analyzed the land deformation associated with Mw 6.3 earthquake along Pasni coast, Pakistan. Post-earthquake widespread surface displacements were found using Sentinel-1 data. Pre, Co and Post-seismic images were used to investigate the deformation trends. Before the earthquake, 89.65% of Pasni land mass showed uplifting from 0.0 to 3.0 cm at 1.00 mm/day while 3.0 cm subsidence was noted in 86.36% of the land mass after the earthquake at 2.5 mm/day. However, two weeks after the earthquake, 72.9% Pasni land mass showed uplifting at an unprecedented rate of 3.3 mm/day. The maximum deformation along the Line Of Sight (LOS) direction in co-seismic time was about -4.0 cm. Azimuthal interferogram showed more complex displacement pattern with both negative and positive displacements between ±5.0 cm. Pasni is already facing many problems due to increased sea water intrusion under prevailing climatic changes and land deformation due to strong earthquakes. [ABSTRACT FROM AUTHOR]