Mangrove ecosystems are often described as “carbon powerhouses,” but that phrase only begins to capture their true significance.
Unlike terrestrial forests, where much of the carbon is visible in trunks and leaves, mangroves hide their greatest climate value underground.
Scientific evidence consistently shows that between 50% and 90% of mangrove carbon is stored below ground in soils and sediments.
Globally, the majority of mangrove carbon nearly three quarters is held in soils rather than biomass.
This reality creates a fundamental challenge for carbon markets and monitoring systems: how do we measure what we cannot easily see?
This is where the conversation around Digital Measurement, Reporting, and Verification (dMRV) becomes both promising and controversial.
dMRV is not a single technology, but a system an integration of digital tools such as satellites, drones, sensors, artificial intelligence, and data platforms to improve how carbon projects are monitored and verified.
It aims to transform traditional, manual MRV systems into automated, transparent, and near real-time processes.
Yet, for mangrove carbon projects, the central question remains: can dMRV meaningfully capture below-ground carbon, or is it limited to what lies above the surface?
To answer this, we must first acknowledge a critical truth. Most remote sensing technologies whether LiDAR, drone imagery, or satellite monitoring are fundamentally designed to observe above-ground biomass (AGB).
These tools can estimate tree height, canopy density, and forest cover with increasing precision.
They are extremely effective for tracking deforestation, degradation, and restoration trends.
In fact, modern AI-driven satellite analysis has significantly improved mangrove mapping and monitoring accuracy at scale. However, these tools primarily “see” the canopy, not the carbon-rich sediments below.
This limitation is not a flaw of dMRV itself, but rather a reflection of the physics of observation.
Soil organic carbon (SOC) in mangroves is buried, often several meters deep, accumulated over centuries.
It cannot be directly measured from space or from aerial imagery. Even the most advanced LiDAR systems, which can penetrate vegetation structure, cannot directly quantify carbon stored in saturated soils.
This creates a mismatch between what is easiest to monitor and what matters most.
But concluding that dMRV is not useful for below-ground carbon would be an oversimplification and frankly, a mistake.
The real strength of dMRV lies not in directly measuring every carbon pool, but in integrating multiple data sources to improve overall accuracy, frequency, and transparency.
Traditional MRV systems often rely heavily on periodic field sampling teams visiting sites, extracting soil cores, and conducting laboratory analysis.
While scientifically robust, this approach is expensive, slow, and infrequent. Years can pass between measurements, creating uncertainty and risk in carbon accounting.
dMRV changes this dynamic by enabling continuous monitoring and data integration.
For mangrove projects, this typically means combining remote sensing (for above-ground biomass and land-use change) with field-based soil measurements, ecological models, and statistical relationships between AGB and SOC.
Research has shown that integrating above-ground and below-ground datasets often through machine learning can improve total carbon estimation compared to treating them separately.
In other words, while satellites cannot measure soil carbon directly, they can help infer changes in it when combined with other data.
This is where dMRV becomes particularly powerful. It enables a hybrid monitoring approach:
First, remote sensing provides high-frequency, landscape-scale data on mangrove extent, health, and biomass. This ensures that any loss or gain in vegetation is detected quickly.
Second, ground-based measurements such as soil cores and biomass plots are used to calibrate and validate models. These are not replaced by dMRV but enhanced by it.
Third, digital platforms integrate these datasets, applying models that estimate total carbon stocks, including below-ground pools, based on observed changes above ground and known ecological relationships.
Finally, automated reporting and verification systems improve transparency, reduce human error, and allow auditors to access real-time evidence rather than relying solely on periodic reports.
Through this integration, dMRV does not eliminate the need for soil sampling it makes it more strategic, targeted, and cost-effective.
Still, there are important caveats that must be clearly stated, especially in a public discussion.
First, below-ground carbon remains the largest source of uncertainty in mangrove carbon accounting. Soil carbon varies significantly depending on site conditions, hydrology, species composition, and historical land use.
Models can estimate it, but without sufficient field data, those estimates can be inaccurate.
Second, there is a risk of over-reliance on remote sensing proxies. If carbon projects depend too heavily on above-ground indicators without robust soil validation, they may overestimate carbon credits.
This is a legitimate concern in voluntary carbon markets and one that dMRV alone cannot solve.
Third, the effectiveness of dMRV depends on methodology design and governance, not just technology.
Standards such as those developed by organizations like the Gold Standard require that any dMRV system still complies with rigorous methodologies, including baseline setting, additionality, and verification processes.
Technology can improve data quality, but it does not replace scientific rigor.
So where does this leave us?
The most balanced conclusion is this: dMRV is highly useful for mangrove carbon projects, but not as a standalone solution for below-ground carbon measurement.
Instead, it should be viewed as an enabling system one that enhances traditional methods rather than replacing them.
In practical terms, dMRV delivers three major benefits for below-ground carbon monitoring:
It improves temporal resolution. Changes in mangrove condition can be detected in near real-time, allowing models to update carbon estimates more frequently.
It enhances spatial coverage. Instead of relying on a limited number of field plots, projects can scale insights across entire landscapes.
It increases transparency and trust. By digitizing data flows and verification processes, dMRV reduces the risk of errors, inconsistencies, and even fraud.
At the same time, it must be paired with robust field measurements and scientifically sound models to ensure that below-ground carbon is not misrepresented.
Looking ahead, emerging technologies may further strengthen this integration. Advances in soil sensing, environmental DNA, geophysical methods, and AI-driven modeling could improve our ability to estimate subsurface carbon with greater accuracy.
The future of mangrove MRV will likely be neither purely digital nor purely manual, but a carefully designed combination of both.
For policymakers, investors, and project developers, the message is clear: do not expect dMRV to “solve” the below-ground carbon challenge. Instead, use it to build better systems-systems that are more transparent, more frequent and more scientifically grounded.
Mangroves remind us that the most valuable things are often hidden. The same is true for carbon. Measuring it requires humility, rigor, and the willingness to combine tools rather than rely on any single one.
dMRV, when used wisely, is not a shortcut. It is a step forward.
The views expressed here are those of the writer and do not necessarily represent the views of Sarawak Tribune. The writer can be reached khanwaseem@upm.edu.my
