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The Gulf of Mannar (GoM) includes the GoM Marine National Park and Biosphere Reserve and is globally recognised as a unique marine system with diverse habitat and rich biodiversity. The present study investigated a massive dinoflagellate bloom caused by the green Noctiluca scintillans during September–October 2022 in GoM. Multi-temporal remote sensing satellite imagery and the Normalized Difference Chlorophyll Index (NDCI) were assessed utilizing data from Sentinel-2 and Sentinel-3 satellites. These satellites offer high spatial and spectral resolutions, complemented by effective atmospheric and sunglint correction achieved through the Sen2Cor and C2RCC processors. This Remote Sensing approach presents a straightforward and practical method for the accurate and timely identification and mapping of green N. scintillans. The total coverage of bloom was calculated to be 1825 and 5433 km2 during the bloom period of 22nd and 27th September 2022, respectively. The highest NDCI value of 0.8 was observed on 27th September 2022 at 42 km away from the shoreline of Ervadi, Puthumadam and Vembar coastal belt. This study highlights the advance monitoring methods of N. scintillans blooms and their spatio-temporal variability, also advantages of Sentinel-3 and Sentinel-2 satellites in algal bloom monitoring in complex coastal waters. [ABSTRACT FROM AUTHOR]

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources. [ABSTRACT FROM AUTHOR]

Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang's Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed 'internal fragmentation' and the NDCI demonstrated 'pixel overflow' during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a 'complementary' effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas. Highlights: Two new remote sensing indexes for coal-covered area extraction were proposed in this paper. Complementary advantages were found between NDBCI and NDCI in the extraction. Accuracy assessment was performed based on manually deciphered high-precision raster data. [ABSTRACT FROM AUTHOR]

Coastal pollutants, from harmful algal blooms, sewage and industrial discharges, pose severe risks to marine ecosystems and public health. Recently, Promenade Beach in Puducherry, Southeast-India, experienced reddish-brown water discoloration, suspected to result from either algal blooms or suspended matter. This study monitored the spatial extent and characteristics of the discoloration using Sentinel-2 satellite images from September to November 2023, with field observations and laboratory analyses. Analyses included measurements of chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and the Normalized Difference Chlorophyll Index (NDCI) to differentiate between algal blooms and other pollutants. The satellite data indicated extents of discoloration, with high TSM concentrations (>45 g/m3) and negative NDCI values suggesting absence of algal blooms. No mortality of aquatic organisms was observed during this discoloration, indicating no deleterious impact on aquatic life. This approach highlights the importance of combining satellite technology with field data for effective coastal pollution monitoring, essential for protecting marine ecosystems. • Sentinel 2 data for monitoring water quality and coastal pollution • C2RCC Atmospheric correction was used to estimate Chl-a and TSM. • Discoloration due to suspended matter and not algal blooms [ABSTRACT FROM AUTHOR]

As the frequency of cyanobacterial harmful algal blooms (CyanoHABs) become more common in recreational lakes and water supply reservoirs, demand for rapid detection and temporal monitoring will be imminent for effective management. The goal of this study was to demonstrate a novel and potentially operational cross-satellite based protocol for synoptic monitoring of rapidly evolving and increasingly common CyanoHABs in inland waters. The analysis involved a novel way to cross-calibrate a chlorophyll- a (Chl- a ) detection model for the Landsat-8 OLI sensor from the relationship between the normalized difference chlorophyll index and the floating algal index derived from Sentinel-2A on a coinciding overpass date during the summer CyanoHAB bloom in Utah Lake. This aided in the construction of a time-series phenology of the Utah Lake CyanoHAB event. Spatio-temporal cyanobacterial density maps from both Sentinel-2A and Landsat-8 sensors revealed that the bloom started in the first week of July 2016 (July 3rd, mean cell count: 9163 cells/mL), reached peak in mid-July (July 15th, mean cell count: 108176 cells/mL), and reduced in August (August 24th, mean cell count: 9145 cells/mL). Analysis of physical and meteorological factors suggested a complex interaction between landscape processes (high surface runoff), climatic conditions (high temperature, high rainfall followed by negligible rainfall, stable wind), and water quality (low water level, high Chl- a ) which created a supportive environment for triggering these blooms in Utah Lake. This cross satellite–based monitoring methods can be a great tool for regular monitoring and will reduce the budget cost for monitoring and predicting CyanoHABs in large lakes. [ABSTRACT FROM AUTHOR]

This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11_L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10_L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10_L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10_L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement. [ABSTRACT FROM AUTHOR]