New satellite-based index tracks multiple grassland ecosystem services at once

Grasslands cover roughly forty percent of the planet’s land surface, yet scientists have long struggled to keep tabs on the many services they quietly deliver: forage for livestock, storage of soil carbon, regulation of water and air quality, control of erosion, and support for the microbial communities that keep soils alive. Now, a research team working in the semi-arid grasslands of peninsular India has unveiled a single, low-cost index that promises to track nine of these ecosystem service parameters simultaneously from space, potentially transforming how data-poor nations monitor some of the world’s most neglected landscapes.

The tool, called the Grassland Ecosystem Monitoring Index, or GEMI, was developed and evaluated by Avijit Ghosh of ICAR-Indian Grassland and Fodder Research Institute and colleagues, with results published in the journal Smart Agricultural Technology. Rather than relying on the familiar Normalized Difference Vegetation Index, the workhorse of vegetation monitoring that is notorious for saturating in dense canopies and being distorted by dust, haze, and bright soils, GEMI fuses two less celebrated spectral measures with a topographic variable: the Advanced Vegetation Index, the Green Leaf Index, and elevation derived from shuttle radar topography data.

The choice of ingredients is deliberate. AVI blends near-infrared and red reflectance in a cube-root transformation that dampens atmospheric scattering, making it more stable under the dusty, thin-cloud conditions that plague semi-arid rangelands. GLI, built entirely from red, green, and blue bands, excels at separating sparse green canopy from bright soil backgrounds, a persistent headache in landscapes where vegetation cover is patchy and soil noise is high. Because GLI depends only on visible bands, it could even be replicated with low-cost drones or standard digital cameras. Elevation, meanwhile, acts as a proxy for the climatic and hydrological gradients that govern productivity, moisture, and carbon cycling across terrain.

To build and test the index, the team selected the Amrit Mahal Kaval grasslands of Karnataka, a roughly 13,738-hectare semi-arid expanse where mean annual rainfall of 518 millimetres falls far short of the 1,307 millimetres lost to evaporation. Between July and December 2024, spanning the peak monsoon growing season, the researchers sampled 220 stratified random plots, each separated by at least five kilometres and fenced against grazing. They harvested and dried above-ground biomass across four campaigns, analysed soil organic carbon by wet oxidation, measured basal soil respiration through laboratory incubation, and estimated carrying capacity assuming 30 kilograms of green fodder per adult cattle unit per day.

Satellite data filled in the rest. Landsat 8 imagery was composited into cloud-free seasonal medians, Sentinel-5P TROPOMI supplied column-averaged methane and carbon monoxide concentrations, MODIS products yielded net primary productivity, and the Universal Soil Loss Equation provided erosion estimates. The researchers then regressed GEMI against all nine service parameters, from forage yield and soil moisture to methane, carbon monoxide, erosion, soil organic carbon, net primary productivity, microbial respiration, and carrying capacity.

The results were striking. GEMI explained seventy-five percent of the variation in vegetation moisture, the strongest single association, and achieved an R-squared of 0.61 for net primary productivity, 0.52 for methane, and 0.53 for carbon monoxide. For above-ground biomass, soil organic carbon, erosion, microbial respiration, and carrying capacity, the index captured between roughly twenty-nine and thirty-nine percent of the variance, all statistically significant. Crucially, when the team pitted GEMI against NDVI, EVI, SAVI, and even the raw AVI-plus-GLI combination, the composite index outperformed every rival across all nine parameters. Where NDVI could explain only about nine percent of variation in forage biomass, GEMI managed nearly thirty-nine percent.

Robustness testing added weight to the claims. A Monte Carlo uncertainty analysis with ten thousand iterations showed the index converging on a stable mean of about 0.38 with a standard deviation of roughly 0.22, while Sobol global sensitivity analysis ranked GLI as the dominant driver, followed by AVI and then elevation, with interactions between inputs accounting for barely two percent of output variance. The near-additive behaviour means managers can interpret changes in GEMI by looking at individual inputs directly, a practical advantage for operational monitoring. On the ground, forty-four percent of the grassland scored in the good range of 0.4 to 0.8, a third was moderate, seventeen percent fell into the degraded category below 0.1, and only six percent reached the very good class above 0.8, a spatial fingerprint the authors say can pinpoint degradation hotspots for timely restoration.

The mechanistic story behind the numbers is equally telling. Plots with greener canopies showed soil erosion reductions of up to eighty percent, soil organic carbon gains of up to seventy percent, and microbial respiration increases of roughly twenty-five percent compared with sparser sites. Elevation behaved as a genuine topographic constraint: below about 835 metres and above about 920 metres, ecosystem performance declined, so the index credits only the optimal altitudinal band. This inclusion of terrain explains much of GEMI’s edge, because erosion, productivity, and carbon storage depend not just on vegetation greenness but on slope, temperature, and moisture regimes that no spectral index alone can capture.

The authors are candid about limits: GEMI has so far been validated in a single semi-arid grassland during one growing season, and calibration coefficients may need adjustment before extrapolation to regions with different soils, rainfall regimes, or species composition. Multi-year evaluation and independent validation with external field data are the stated next steps. Still, the implications are considerable. For countries where field surveys are unaffordable and ecosystem service monitoring has largely stalled, a freely computable index built from open Landsat, MODIS, and Sentinel data offers a way to track fodder supply, grazing pressure, carbon sequestration, air quality, and soil health in one number, and to do so repeatedly, cheaply, and across entire landscapes.

Subject of Research: Development and evaluation of a remote sensing-based composite index for monitoring multiple grassland ecosystem services in semi-arid regions

Article Title: Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions

Article References: Ghosh, A., Das, B., Satpute, A. N., Haque, M. A., Singh, A. K., Chakroborty, A., Shukla, A. K., Biradar, N., Gupta, A. K., & Mukherjee, S. (2026). Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions. Smart Agricultural Technology, 15, Article 102556. https://doi.org/10.1016/j.atech.2026.102556

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102556

Keywords: grasslands, remote sensing, ecosystem services, GEMI, vegetation indices, soil organic carbon, soil erosion, carrying capacity, semi-arid regions, Landsat 8, Sentinel-5P, net primary productivity