The stakes of this measurement problem are enormous. Mangroves are among the most carbon-rich forests in the tropics, storing vast quantities of carbon in their trees and, even more importantly, in the waterlogged sediments beneath them. When these forests are cleared or degraded, that carbon can be released rapidly, turning a powerful sink into a source of greenhouse gases. Indonesia holds some of the world’s most extensive mangrove estates, which makes the country a linchpin in global blue carbon accounting, yet ground surveys of mangrove biomass are laborious, expensive, and often dangerous in the mud, tides, and mosquitoes of a tropical estuary. A reliable remote sensing shortcut has long been the holy grail of coastal carbon science.

The logic behind the new approach rests on a decades-old insight from vegetation remote sensing. The NDVI exploits the fact that healthy, chlorophyll-rich leaves absorb red light strongly while reflecting near-infrared light, so the ratio between the two bands serves as a proxy for the amount and vigor of green vegetation. The Sentinel-2 mission, with its twin satellites delivering multispectral imagery at ten-meter resolution every few days, offers an ideal platform for this kind of work: the data are free, frequent, and sharp enough to resolve the patchwork of mangrove stands, aquaculture ponds, and open water that characterizes an Indonesian bay. What the Pangpang Bay study adds is a direct, locally calibrated link between that spectral signal and the actual kilograms of carbon locked in tree trunks, branches, and leaves.

To forge that link, the researchers established thirteen sample plots in the field, measured the trees within them, and converted those structural measurements into aboveground biomass using established mangrove allometric equations, the standard formulas that translate trunk diameter and tree height into woody mass. They then extracted NDVI values from Sentinel-2 imagery corresponding to each plot and fitted an empirical regression between the two. The resulting model, in which estimated biomass equals negative 871.15 plus 4562.73 multiplied by NDVI, produced a coefficient of determination of 0.649 with a p-value below 0.001, meaning the relationship was statistically strong and that the vegetation index accounted for approximately two-thirds of the observed variability in biomass. For a single, freely available spectral index, that level of explanatory power is respectable, particularly in the notoriously heterogeneous environment of a tropical mangrove bay.

With the model in hand, the team turned it loose on the archive. They applied the regression to Sentinel-2 scenes from 2019, 2022, and 2025, producing multitemporal maps of biomass and carbon across the bay and revealing a story that unfolds in space as well as time. The temporal signal was sobering. Between 2019 and 2022, mean NDVI across the mangrove area fell by 35.1 percent, and the model translated that spectral decline into a marked drop in aboveground biomass and carbon stocks. By 2025 the forests had shown only limited signs of recovery, suggesting that whatever disturbance drove the loss, whether conversion, cutting, storm damage, or hydrological change, had left a lasting imprint on the canopy and that regrowth of mature, carbon-dense trees is a slow business measured in decades rather than years.

The spatial patterns were equally telling. Areas dominated by mature Rhizophora and Bruguiera stands, the classic old-growth mangrove genera with their stilted roots and dense, heavy wood, consistently registered higher biomass and carbon stocks than stands dominated by Sonneratia, a faster-growing, softer-wooded pioneer genus that often colonizes newly opened mudflats. This distinction matters for carbon accounting because it means that two patches of mangrove that look superficially similar on a map can differ substantially in the carbon they hold, and that species composition, stand age, and successional stage all leave fingerprints in the satellite record. A bay-wide carbon estimate that ignores these differences risks being badly wrong, which is precisely why a locally calibrated NDVI-to-biomass relationship, rather than a generic global model, is such a valuable asset.

The authors are careful to frame the work as an exploratory local assessment, and the limitations are worth taking seriously. Thirteen field plots is a modest sample for calibrating a regression, and the scatter around the fitted line means that roughly a third of the biomass variability remains unexplained by NDVI alone. Canopy greenness, after all, is not a perfect stand-in for wood volume: a dense stand of young Sonneratia can be as green as a sparse stand of ancient Rhizophora while holding far less carbon. Saturation effects also loom, since NDVI tends to plateau in very dense vegetation, potentially underestimating the most carbon-rich stands. The researchers position the approach accordingly, not as a replacement for field inventories but as a practical, cost-effective screening and monitoring tool for coastal environments where the resources for repeated ground surveys simply do not exist.

That framing points directly at one of the most consequential acronyms in modern climate policy: MRV, or monitoring, reporting, and verification. Carbon markets, national greenhouse gas inventories, and international climate finance mechanisms all demand credible, repeatable, and affordable measurements of carbon stocks, and mangrove ecosystems have historically been among the hardest to deliver. A workflow that combines a modest number of field plots with freely available Sentinel-2 imagery offers exactly that: a method that a provincial forestry office or a conservation NGO in Banyuwangi could operate without a supercomputer or a six-figure budget. The study’s authors explicitly highlight applications for blue carbon assessment, ecosystem restoration planning, and MRV programs in data-limited coastal settings, and the multitemporal demonstration in Pangpang Bay shows the method doing real work, detecting a major biomass decline and a sluggish recovery that would otherwise have gone unquantified.

The broader context makes the findings resonate well beyond one bay in East Java. Indonesia has set ambitious targets for mangrove rehabilitation as part of its low-carbon development strategy, and recent research suggests that conserving and restoring mangroves and peat swamps could mitigate a substantial share of Southeast Asia’s land-use carbon emissions. Yet restoration is expensive, and global analyses of mangrove restoration costs underscore that every hectare planted must be justified and then monitored for decades. Tools like the one demonstrated here provide the accounting backbone for that effort, allowing managers to identify which existing stands are the most carbon-rich and therefore most worth protecting, and to verify whether young restoration sites are actually accumulating biomass over time.

There is also a quiet democratization at work in this study. The Sentinel-2 data are free, the NDVI calculation requires nothing more exotic than arithmetic on two spectral bands, and the calibration demands only a handful of well-executed field plots. In an era when high-end biomass mapping increasingly relies on airborne laser scanning, spaceborne radar, and machine learning pipelines that demand both money and expertise, the demonstration that a humble vegetation index can capture two-thirds of the biomass variability in a tropical mangrove bay is a reminder that sometimes the simplest tool, applied locally and honestly, is the one that gets used. As Pangpang Bay’s mangroves slowly claw back the carbon they lost between 2019 and 2022, the satellites above will be watching, and now, thanks to this study, scientists will know what they are looking at.

Subject of Research: Estimating mangrove aboveground biomass and blue carbon stocks in Pangpang Bay, Indonesia, using Sentinel-2 NDVI and field calibration

Article Title: A local exploratory assessment of mangrove aboveground carbon stocks based on Sentinel-2 NDVI in Pangpang Bay, Indonesia

Article References: Hidayat, E. F., Simarmata, N., Cansery, A., Julian, M. M., & Ansari, K. (2026). A local exploratory assessment of mangrove aboveground carbon stocks based on Sentinel-2 NDVI in Pangpang Bay, Indonesia. Environmental Monitoring and Assessment, 198(11), Article 1153. https://doi.org/10.1007/s10661-026-16008-3

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16008-3

Keywords: mangroves, blue carbon, Sentinel-2, NDVI, aboveground biomass, carbon stocks, remote sensing, Indonesia, Pangpang Bay, coastal monitoring, MRV, ecosystem restoration