Remote sensing provides an effective tool for timely oil pollution response. In this paper, the spectral signature in the optical and infrared domains of oil slicks observed in shallow coastal waters of the Arabian Gulf was investigated with MODIS, MERIS, and Landsat data. Images of the Floating Algae Index (FAI) and estimates of sea currents from hydrodynamic models supported the multi-sensor oil tracking technique. Scenes with and without sunglint were studied as the spectral signature of oil slicks in the optical domain depends upon the viewing geometry and the solar angle in addition to the type of oil and its thickness. Depending on the combination of those factors, oil slicks may exhibit dark or bright contrasts with respect to oil-free waters. Three oil spills events were thoroughly analyzed, namely, those detected on May 26 2000 by Landsat 7 ETM + and MODIS/Terra, on October 21 2007 by MERIS and MODIS, and on August 17 2013 by Landsat 8 and MODIS/Aqua. The oil slick with bright contrast observed by Landsat 7 ETM + on May 26 2000 showed lower temperature than oil-free areas. The spectral Rayleigh-corrected reflectance (R_rc) signature of oil-covered areas indicated higher variability due to differences in oil fractions while the R_rc spectra of the oil-free area were persistent. Combined with RGB composites, FAI images showed potentials in differentiating oil slicks from algal blooms. Ocean circulation and wind data were used to track oil slicks and forecast their potential landfall. The developed oil spill maps were in agreement with official records. The synergistic use of satellite observations and hydrodynamic modeling is recommended for establishing an early warning and decision support system for oil pollution response.

A red tide event that occurred in August 2008 in the Arabian Gulf was monitored and assessed using satellite observations and numerical models. Satellite observations revealed the bloom extent and evolution from August 2008 to August 2009. Flow patterns of the bloom patch were confirmed by results from a HYCOM model. HYCOM data and satellite-derived sea surface temperature data further suggested that the bloom could have been initiated offshore and advected onshore by bottom Ekman layer. Analysis indicated that nutrient sources supporting the bloom included upwelling, Trichodesmium, and dust deposition while other potential sources of nutrient supply should also be considered. In order to monitor and detect red tide effectively and provide insights into its initiation and maintenance mechanisms, the integration of multiple platforms is required. The case study presented here demonstrated the benefit of combing satellite observations and numerical models for studying red tide outbreaks and dynamics.

Mangrove forests are among the most productive ecosystems on Earth and mainly grow at tropical and subtropical latitudes. They provide many important ecological and societal functions. However, rapid spatiotemporal variations in mangroves have been observed worldwide, especially in the coastal zones of developing areas, and the integrity of mangroves has been significantly affected by anthropogenic activities in recent decades. The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution over the past 30 years in Guangdong Province. This goal was achieved by classifying multi-temporal Landsat images using a decision tree method based on Classification and Regression Tree (CART) algorithm. The driving forces resulting in these spatiotemporal variations of mangroves were then discussed. Our analysis revealed that the classification method used in this study yielded good accuracy, with an overall accuracy and kappa coefficient of higher than 90% and 0.8, respectively. In Guangdong province, the mangrove forests covered areas of 9305, 9556, 6793, and 9700 ha in 1985, 1995, 2005, and 2015, respectively, with remarkable inter-annual changes. Mangrove forests are mainly located in Western Guangdong, and few are located in Eastern Guangdong. The distribution of mangrove patches became more fragmented from 1985 to 2005 and less fragmented from 2005 to 2015, and the distribution pattern in 2015 showed stronger connectivity than that in 1985. Natural factors, such as temperature, sea level rise, extreme weather events, and the length of the coastline, have macroscopic effects on the distribution of mangrove forests. Anthropogenic activities, such as deforestation, urbanization, and aquaculture development, have negative effects on the distribution of mangroves. On the other hand, the establishment of nature reserves has positive effects on the distribution of mangroves. The findings of this study provide a reference for the management and protection of mangroves, which is of great practical significance.

Total suspended solid (TSS) and chlorophyll‐a (Chl‐a) are critical indicators of water quality. Moderate‐resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite provides a critical tool to map TSS and Chl‐a on a daily basis. However, robust algorithms are required to retrieve TSS and Chl‐a from MODIS/Aqua images in the turbid Pearl River Estuary (PRE). Here, a new artificial neural network algorithm was developed to estimate TSS and Chl‐a in the PRE using a large amount of in situ measurements spanning more than a decade. Cross‐validation showed that the ANN‐based algorithm presented good performance with R²s of 0.73 and 0.66 for TSS and Chl‐a, respectively. The ANN‐based algorithm outperformed existing empirical and semi‐analytical algorithms. By implementing the proposed algorithm to MODIS/Aqua images, TSS and Chl‐a from August 2002 to July 2020 over the PRE were obtained. The results show that TSS decreased from northwest to southeast in all seasons while peaked in winter and summer. Chl‐a peaked in summer and the high Chl‐a patch transferred from the upper reach to the lower reach of the PRE with the increase of river discharge. TSS decreased at an annual rate of ∼0.13 mg L⁻¹ while Chl‐a increased at an annual rate of ∼0.05 mg m⁻³ in the last 18 years. Our findings suggest that machine learning provides a practical approach to estimate TSS and Chl‐a in turbid estuaries via MODIS/Aqua images and the approach developed in this study can be applied to other turbid waters with similar optical properties.

Two oil spill events were investigated using multisensor satellite images in the Al Khafji and Al Fujairah regions. Oil slicks were characterized with Red-Green-Blue (RGB) and floating algae index (FAI) images. Oil slicks near Al Khafji were detected on April 19, 2014 by Landsat 8 and covered around 29.04 km². Sequential VIIRS and MODIS/Aqua images collected on the same day observed the same slicks, which indicated different appearances in the RGB images. Another event was recorded near Al Fujairah on May 11, 2014 by both Landsat 8 and Aqua. The total area coverage of oil slicks was 114.6 km². The estimated slick trajectories from GNOME driven by ocean circulation data from the Regional Ocean Model System (ROMS) and meteorological data were assessed with the movement patterns of satellite-detected oil slicks. The average absolute percentage error of velocity of slick movement between satellite observation and GNOME simulation was 33% for both events. The directions of slick movement showed an agreement between satellite observation and model simulation in 5 out of 7 trajectories' cases. This implies that the integration of multisensor satellite measurements and spill trajectory modeling is very helpful to forecast and predict the fate and transport of oil spills. Résumé. Deux déversements d'hydrocarbures ont été étudiés en utilisant des images satellitaires multicapteurs dans les régions d'Al Khafji et d'Al Fujairah. Les nappes de pétrole ont été caractérisées avec des images rouge-vert-bleu (RVB) « red-green-blue (RGB) » et avec l'indice d'algues flottantes « floating algae index » (FAI). Les nappes de pétrole près d'Al Khafji ont été détectées le 19 avril 2014 par Landsat 8 et elles couvraient environ 29,04 km². Les images séquentielles VIIRS et MODIS/Aqua acquises le même jour ont observé les mêmes nappes, indiquées par différents aspects dans les images RVB. Un autre évènement a été détecté près d'Al Fujairah le 11 mai 2014 à la fois par Landsat 8 et Aqua. La superficie totale de ces nappes était de 114,6 km². Les trajectoires des déversements d'hydrocarbure estimées à partir de GNOME, en utilisant les données de la circulation océanique du Regional Ocean Model System (ROMS) et les données météorologiques, ont été évaluées en utilisant les déplacements des nappes de pétrole détectées par satellite. Le pourcentage d'erreur absolue moyenne de la vitesse de mouvement de la nappe entre l'observation par satellite et la simulation de GNOME était de 33% pour les 2 évènements. Les directions de mouvements des nappes ont montré un accord entre l'observation par satellite et la simulation du modèle dans 5 des 7 cas de trajectoires. Ceci implique que l'intégration de mesures satellitaires multicapteurs et la modélisation des trajectoires des déversements sont très utiles pour prévoir et prédire le devenir et le transport des déversements de pétrole.