Mangroves provide ecosystem services worth billions of dollars worldwide. Although countries with extensive mangrove areas implemented management and conservation programs since the 1980s, the global area is still decreasing. To recuperate this lost area, both restoration and rehabilitation (R/R) projects have been implemented but with limited success, especially at spatial scales needed to restore functional properties. Monitoring mangroves at different spatial scales in the long term (decades) is critical to detect potential threats and select cost-effective management criteria and performance measures to improve R/R program success. Here, we analyze the origin, development, implementation, and outcomes of a country-level mangrove monitoring system in the Neotropics covering >9000 km² over 15 years. The Mexico’s Mangrove Monitoring System (SMMM) considers a spatiotemporal hierarchical approach as a conceptual framework where remote sensing is a key component. We analyze the role of the SMMM’s remote sensing products as a “hub” of multi- and interdisciplinary ecological and social-ecological studies to develop national priorities and inform local and regional mangrove management decisions. We propose that the SMMM products, outcomes, and lessons learned can be used as a blueprint in other developing countries where cost-effective R/R projects are planned as part of mangrove protection, conservation, and management programs.

Mangrove forests are commonly mapped using spaceborne remote sensing data due to the challenges of field endeavors in such harsh environments. However, these methods usually require a substantial level of manual processing for each image. Hence, conservation practitioners prioritize using cloud computing platforms to obtain accurate canopy classifications of large extensions of mangrove forests. The objective of this study was to analyze the spatial distribution and rate of change (area gain and loss) of the red mangrove (Rhizophora mangle) and other dominant mangrove species, mainly Avicennia germinans and Laguncularia racemosa, between 2015 and 2020 throughout the northwestern coast of Mexico. Bimonthly data of the Combined Mangrove Recognition Index (CMRI) from all available Sentinel-2 data were processed with the Google Earth Engine cloud computing platform. The results indicated an extension of 42865 ha of red mangrove and 139602 ha of other dominant mangrove species in the Gulf of California and the Pacific northwestern coast of Mexico for 2020. The mangrove extension experienced a notable decline of 1817 ha from 2015 to 2020, largely attributed to the expansion of aquaculture ponds and the destructive effects of hurricanes. Considering the two mangrove classes, the overall classification accuracies were 90% and 92% for the 2015 and 2020 maps, respectively. The advantages of the method compared to supervised classifications and traditional vegetation indices are discussed, as are the disadvantages concerning the spatial resolution and the minimum detection area. The work is a national effort to assist in decision-making to prioritize resource allocations for blue carbon, rehabilitation, and climate change mitigation programs.

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals.