Satellite Records and Statistical Models Predict a Greening Future for India’s Mangroves

India’s mangrove forests, sprawling across nearly 4,740 square kilometers of coastline and accounting for roughly 3 percent of the world’s total mangrove extent, have long been monitored through retrospective satellite snapshots. A new study published in Discover Oceans changes that paradigm by doing something deceptively simple yet rarely attempted in mangrove science: forecasting. Researchers led by Mohammed Suhail S., Laxmi Kant Sharma, and Kariya Ishita Bhaveshkumar of the Central University of Rajasthan built a statistical forecasting framework that projects the trajectory of mangrove vegetation health across India’s entire coastline through 2030, and their models suggest the country’s mangroves are on a slow but measurable path toward improvement.

The team harnessed more than two decades of satellite observations from NASA’s MODIS instruments, computing three complementary vegetation indices monthly from 2001 to 2025: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Modified Soil Adjusted Vegetation Index (MSAVI). Each index captures a different facet of canopy condition. NDVI, the workhorse of vegetation remote sensing, responds strongly to chlorophyll content but can saturate in dense canopies. EVI incorporates blue-band reflectance and improved atmospheric correction, making it better suited to the high-biomass tropical forests where NDVI loses sensitivity. MSAVI, designed to suppress the signal of bare soil, proves invaluable in the intertidal zones where tides repeatedly expose mudflats between the trees.

Processing this volume of data required cloud computing. The researchers used Google Earth Engine to aggregate quality-filtered MODIS scenes into monthly composites, then clipped every raster to the mangrove extent defined by the Global Mangrove Watch Version 3.0 dataset, a product built from L-band radar data from the ALOS PALSAR satellite fused with Landsat optical imagery, achieving an overall classification accuracy of 94 percent. In total, 720 monthly raster layers, covering 240 months across three indices, were processed and then collapsed into national monthly time series by averaging all mangrove-masked pixels across India’s twelve mangrove-bearing states and union territories.

The forecasting engine at the heart of the study is the Seasonal Autoregressive Integrated Moving Average model, or SARIMA. Unlike simpler trend analyses, SARIMA explicitly models both the slowly evolving trend of a time series and its recurring seasonal cycles, making it well matched to ecosystems whose phenology is locked to monsoon rhythms. Before fitting any model, the team tested each vegetation index series for stationarity using the Augmented Dickey-Fuller and KPSS tests. Both tests rejected their respective null hypotheses for all three indices, pointing to seasonal non-stationarity, so the researchers applied one non-seasonal and one seasonal difference to stabilize each series before model selection.

Seasonal decomposition using the STL method revealed just how rhythmically these forests breathe. Seasonal strength coefficients ranged from 0.813 for MSAVI to 0.855 for EVI, far above the 0.3 threshold commonly used to flag strong seasonality. Intriguingly, all three indices peaked during the dry season and bottomed out during the wet season, a counterintuitive pattern the authors attribute to the unique physiology of mangroves, which are adapted to tidal and salinity stress rather than limited by freshwater availability. The trend components told a quieter story: relatively flat through the 2000s, then climbing steadily from around 2010 onward, consistent with the documented 1.2 percent annual growth in India’s mangrove area driven by conservation policies and restoration programs.

Automated model selection using R’s auto.arima function, guided by the Akaike Information Criterion, produced a distinct model for each index. NDVI required a first-order autoregressive structure, EVI relied on a fourth-order moving average with no autoregressive term, and MSAVI demanded a third-order autoregressive specification, likely reflecting the complex interplay of tidal cycles, soil moisture, and vegetation phenology that soil-adjusted indices register. Residual diagnostics were clean across the board: Ljung-Box tests found no remaining autocorrelation in any model, although formal normality tests revealed slight heavy-tailedness, a familiar feature of vegetation index series where post-monsoon greening surges occasionally exceed what a Gaussian distribution would predict.

The critical test came from data the models had never seen. The final five years of observations, from 2021 to 2025, were withheld as an out-of-sample validation set. Against this holdout, the SARIMA models achieved mean absolute percentage errors between 2.5 and 4.9 percent, well inside the 10 percent threshold conventionally considered high-accuracy forecasting. NDVI emerged as the most dependable index, with a MAPE of just 2.50 percent and the only statistically significant improvement over a seasonal-naive benchmark in Diebold-Mariano comparisons. EVI and MSAVI performed respectably in absolute terms but were statistically indistinguishable from simpler benchmarks, and their prediction intervals covered fewer observations than intended, signaling that forecast uncertainty for those indices was underestimated.

With the models validated, the team refit each specification to the full 2001 to 2025 record and generated operational forecasts for 2026 through 2030. All three indices are projected to continue their gentle ascent: NDVI is expected to average about 6.4 percent higher than the 2001 to 2020 training-period mean, while EVI and MSAVI are projected to rise by roughly 9.8 and 10.7 percent respectively. The larger relative gains in MSAVI hint at new vegetation colonizing tidally exposed substrates, where a soil-adjusted index is most responsive, while EVI’s rise is consistent with canopy structural recovery in previously degraded high-biomass zones. NDVI’s more modest climb likely reflects saturation in already dense, mature stands rather than an absence of underlying progress. Crucially, all projections remain within the bounds of historical variability, suggesting stable to improving conditions under current human and climatic pressures.

The authors are careful to frame these results as a statistical baseline rather than a guarantee. Because the analysis aggregated all mangrove pixels nationally, the forecasts cannot resolve state-level or site-specific trajectories, and the study itself notes that localized studies have documented declines, such as falling NDVI in Bhitarkanika and widespread EVI-based browning across much of the Sundarbans. The prediction intervals capture only parameter and stochastic uncertainty, not structural risks like major cyclones or land-use change. Moreover, vegetation indices are optical proxies for canopy greenness, not direct measures of biomass, carbon stocks, or physiological health, and MODIS’s 500-meter pixels can blur the narrow fringing mangrove belts of Gujarat and Kerala with surrounding water and aquaculture ponds.

Even with those caveats, the framework offers conservation managers something they have lacked: a quantitative, forward-looking early warning system. Deviations from forecast trajectories could flag ecosystem stress before it becomes visible on the ground, while the strong, predictable seasonality identified in the data points to optimal windows for restoration planting, roughly November through January, when mangroves show peak physiological resilience. Because the entire pipeline runs on open satellite data, Google Earth Engine, and open-source R and QGIS tools, the approach is readily transferable to mangrove regions across the tropical and subtropical world, offering a scalable template for predictive ecology at a moment when coastlines everywhere face mounting climate pressure.

Subject of Research: SARIMA time-series forecasting of satellite-derived mangrove vegetation indices across India

Article Title: SARIMA-based forecasting of mangrove vegetation indices across India

Article References: Suhail S., M., Sharma, L. K., & Bhaveshkumar, K. I. (2026). SARIMA-based forecasting of mangrove vegetation indices across India. Discover Oceans, 3(1), Article 64. https://doi.org/10.1007/s44289-026-00179-5

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00179-5

Keywords: mangroves, SARIMA, time series forecasting, remote sensing, vegetation indices, NDVI, EVI, MSAVI, MODIS, Google Earth Engine, India, coastal conservation