Fellipe Alcantara de Oliveira Mello

Mangroves are widely recognized as climate-relevant ecosystems, yet the extent to which soils are incorporated into climate mitigation research remains unclear. This study conducted a hierarchical bibliometric analysis (Scopus, 1950–2025) across five progressively restrictive search levels, moving from general mangrove research (Level 1) to studies incorporating climate change (Level 2), mitigation (Level 3), and soil-related processes (Levels 4 and 5). Results show that although 30,084 articles addressed mangrove broadly, only 25 articles (0.08%) explicitly linked mangrove soils to climate change mitigation, with the majority published after the emergence of the blue carbon concept in 2009. Keyword evolution and network analyses indicate a shift from descriptive ecological themes (e.g., distribution and vegetation dynamics) toward carbon-related and soil-associated processes (e.g., blue carbon, carbon sequestration, soil organic carbon), particularly after the late 2000s, accompanied by gradual diversification into Environmental Science, Earth and Planetary Sciences, and chemistry-related domains associated with soil processes and mitigation mechanisms. Despite these conceptual advances, keyword analysis shows that mitigation-related studies (Levels 3 and 5) remain largely focused on terms such as “mangroves” (336 occurrences), “carbon sequestration” (187), “organic carbon” (82), and “carbon storage” (62), with limited representation of mechanistic soil processes (e.g., redox-processes, soil greenhouse gas fluxes, carbon–iron–sulfur coupled dynamic) in climate mitigation frameworks. Expanding this integration represents a key scientific frontier for improving the robustness and scalability of mangrove-based climate mitigation strategies.

There was an error in the original publication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R² ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.