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Related Experiment Video

Updated: Jan 15, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

Optimized spectral indices for global vegetation and water mapping using Sentinel-2.

Charalambos Chrysostomou¹, Stelios P Neophytides², Michalis Mavrovouniotis²

¹ERATOSTHENES Centre of Excellence, Limassol, Cyprus. charalambos.chrysostomou@eratosthenes.org.cy.

Scientific Reports

|January 13, 2026

Summary

New satellite indices, Symbolic Regression Vegetation Index (SRVI) and Symbolic Regression Water Index (SRWI), improve vegetation and water mapping. These data-driven indices offer better accuracy across diverse ecosystems and seasons.

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Area of Science:

Earth Observation
Remote Sensing
Geospatial Analysis

Background:

Traditional spectral indices for vegetation and water mapping face limitations like saturation, ecosystem sensitivity issues, and confusion with other surface features.
Developing accurate and robust indices is crucial for global environmental monitoring using satellite data.

Purpose of the Study:

To introduce novel spectral indices, the Symbolic Regression Vegetation Index (SRVI) and the Symbolic Regression Water Index (SRWI), derived using a data-driven symbolic regression framework.
To evaluate the performance of SRVI and SRWI against established indices for vegetation and water mapping across diverse biomes and seasons.

Main Methods:

Utilized a symbolic regression framework on Sentinel-2 Level-2A reflectance data, guided by ESA WorldCover labels.
Evolved index expressions from physically interpretable band combinations (visible, NIR, SWIR).
Assessed index performance using Jeffries-Matusita distance across eleven independent regions and compared with benchmark indices.

Main Results:

SRVI demonstrated improved separability between vegetation and non-vegetation, and higher discrimination among vegetation types compared to benchmarks.
SRWI provided more consistent water delineation, reducing confusion with built-up and shadowed areas, outperforming existing water indices.
Both indices showed generalizability across different regions and seasons.

Conclusions:

Symbolic regression is an effective method for discovering compact, interpretable, and generalizable spectral indices for remote sensing.
SRVI and SRWI offer practical advancements for global vegetation and surface water mapping, addressing limitations of traditional indices.