This study employed Landsat reflectance data from the years 2009 to 2019 to track mangrove forest cover changes. To address the persistent cloud cover issues impacting certain areas, mosaics of three-year windows were used to generate cloud-free mosaics. Analysis was conducted using the Google Earth Engine and a random forest classifier. Additionally, secondary information, such as the shuttle radar topography mission (SRTM) and previously established mangrove land cover maps, were incorporated. The overall classification accuracies and Kappa coefficient for the land cover maps in the years 2008-2010 and 2018-2020 were both determined to be 95%. Furthermore, a decrease of 1480 hectares (\(-2.31\%\)) in mangrove coverage was also observed from 2009 to 2019. Extending from this, an examination of intra- and inter-annual seasonality in mangrove growth phenology was conducted using an NDVI-based time series. Linear and harmonic regression models, as well as TIMESAT metrics, were employed to analyze mangrove forests in three sections of the study region. Together, these findings indicated a correlation between the growth -phenotype of mangrove forests, precipitation anomalies, and the occurrence of severe tropical cyclones over the time series. [PERSON] et al. conducted a global sensitivity analysis for the canopy reflectance and vegetation indices of mangroves. This study focused on a one-dimensional canopy reflectance model to systematically analyze the sensitivity of mangroves to various biophysical and environmental factors. Different scenarios were created, including sparse and dense canopies, to assess the impact of these factors on simulated canopy reflectance spectra and selected Sentinel-2A vegetation indices. This study employed a variance-based method and a density-based method to compare the computed sensitivity indices. The results revealed that the fractional cover and leaf-to-total area ratio of mangrove crowns were among the most influential factors across all examined scenarios. These findings highlight the significant role of these factors in shaping the canopy reflectance and vegetation indices of mangroves. [PERSON] et al. utilized Sentinel-1 and Sentinel-2 satellite data to map the mangrove ecosystem in the Hara protected area in Qeshm, Iran. The analysis was conducted on the Google Earth Engine at a spatial resolution of 10 m. The data acquired using these Sentinel satellites in 2019 were utilized to generate a composite of optical and synthetic aperture radar (SAR) data. To classify the different components, a pixel-based random forest (RF) classifier was employed. This study successfully generated six distinct classes: mangrove, mudflat, deep water, tidal zone, shallow water, and aerial roots. The resulting mangrove ecosystem map exhibited a high accuracy, with an average overall accuracy of 93.23% and a Kappa coefficient of 0.92. These findings demonstrate the reliability and effectiveness for mapping mangrove ecosystems using Sentinel-1 and Sentinel-2 satellite data. [PERSON] et al. utilized the MODIS-derived leaf area index (LAI) and gross primary productivity (GPP) to investigate the fragmentation of mangrove forests in the southern region of Peninsular Malaysia, specifically in Iskandar Malaysia. This study aimed to assess the impact of land cover changes, including urbanization, plantations, and aquaculture activities, on the mangrove forest ecosystem. The results revealed a decline in the mangrove forest area due to land cover changes. However, the areas that remained undisturbed revealed an increase in both the mean LAI and GPP, signifying the mangrove forest's capacity to absorb CO\({}_{2}\) when left undisturbed. Furthermore, areas that experienced mangrove loss but were replaced with oil palm plantations exhibited a decrease in the mean LAI. The fragmented mangrove patches also showed an increase in GPP, potentially due to the smaller patch sizes (<9 ha) and the edge effects, which promote higher levels of productivity in areas exposed to abundant solar radiation along the patch edges.