The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha⁻¹ (average = 87.67 Mg ha⁻¹). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.

The temporal stability and spatial dependence of pixel-specific red-NIR soil line coefficients were studied using analyses of geostatistics and covariance. We used time series of MODIS 8-day composite reflectance data from 2000 to 2019 of bare soil surfaces near Kabul, Afghanistan. The goal was to explore the feasibility of reducing the soil background signal in red-NIR vegetation indices (VIs). The results were that 1) Red-NIR pixel-specific soil line coefficients can be obtained using multi-temporal remote sensing imagery; 2) pixel-specific Red-NIR soil line coefficients derived from multi-temporal remote sensing imagery exhibit clear spatial dependency related to the soil line coefficients of soils sampled in the field, and 3) Red-NIR pixel-specific soil line coefficients were stable for years, but showed instability for periods longer than 16 years. These results imply that A) some changes in soil surface characteristics can be deduced using long-term observations, B) pixel-specific Red-NIR soil lines at any location can be estimated by kriging interpolation. Consequently, soil line coefficients derived from multi-temporal satellite observations can be used to derive information about vegetation, such as the germination stages pixel-by-pixel or above-ground biomass in sparsely vegetated lands. The derived values should be valid over wide areas, minimizing soil background effects.