Ghosh et al. (2026) Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions

Identification

• Journal: Smart Agricultural Technology
• Year: 2026
• Date: 2026-09-08
• Authors: Avijit Ghosh, Bappa Das, Ajay N. Satpute, Md. Ashraful Haque, Amit Kumar Singh, Abhishek Chakroborty, Nagaratna Biradar, Ajay Kumar Gupta, Sandipan Mukherjee
• DOI: 10.1016/j.atech.2026.102556

Research Groups

• ICAR-Indian Grassland and Fodder Research Institute (Jhansi, India)
• ICAR-Central Coastal Agricultural Research Institute (Goa, India)
• ICAR-Indian Agricultural Statistics Research Institute (New Delhi, India)
• Ladakh Regional Centre, Govind Ballabh Pant National Institute of Himalayan Environment (Leh, India)

Short Summary

This study developed a remote sensing-based grassland ecosystem monitoring tool called the Grassland Ecosystem Monitoring Index (GEMI) to predict provisioning, regulating, and supporting services in semi-arid regions. The GEMI showed remarkable association with indicators of these services and performed better than other vegetation indices.

Objective

• To develop a low-cost and spatially explicit tool for grassland ecosystem service assessment and monitoring.
• To evaluate the potential of GEMI as a robust indicator for predicting provisioning, regulating, and supporting services in semi-arid regions.

Study Configuration

• Spatial Scale: Semi-arid region (Amrit Mahal grassland in peninsular India)
• Temporal Scale: July to December 2024

Methodology and Data

• Models used:
  • Advanced Vegetation Index (AVI)
  • Green Leaf Index (GLI)
  • Elevation (ELE)
• Data sources:
  • Landsat8 OLI TIRS images (30 m)
  • Shuttle Radar Topography Mission (SRTM) DEM data (30 m)
  • MODIS MOD17A3HGF products at 500 m resolution
  • Sentinel 5P TROPOMI sensor

Main Results

• The GEMI showed strong associations with pasture biomass productivity (R² = 0.392), soil erosion (R² = 0.287), carbon sequestration (R² = 0.333), carrying capacity (R² = 0.377), microbial diversity (R² = 0.385).
• The GEMI could also explain ~75 % variation for moisture-related ES, >55 % variation for air quality, and >60 % variation for NPP.

Contributions

• This study fills the gap in developing a composite proxy index combining spectral and topographic data to monitor multiple ecosystem services.
• The GEMI can serve as an aggregated indication of the state and/or trends of ES of grassland at broad spatial scales.

Funding

• ICAR (Indian Council of Agricultural Research)
• Govind Ballabh Pant National Institute of Himalayan Environment

Citation

@article{Ghosh2026Development,
  author = {Ghosh, Avijit and Das, Bappa and Satpute, Ajay N. and Haque, Md. Ashraful and Singh, Amit Kumar and Chakroborty, Abhishek and Shukla, Arun Kumar and Biradar, Nagaratna and Gupta, Ajay Kumar and Mukherjee, Sandipan},
  title = {Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions},
  journal = {Smart Agricultural Technology},
  year = {2026},
  doi = {10.1016/j.atech.2026.102556},
  url = {https://doi.org/10.1016/j.atech.2026.102556}
}