Climate-based Pre-screening of Self-sustaining Regreening Opportunities in Drylands: A Case Study for Saudi Arabia

TL;DR

This paper introduces a climate-based pre-screening framework using machine learning and remote sensing to identify sustainable vegetation restoration sites in Saudi Arabia's drylands, reducing costs and effort.

Contribution

It presents a novel, scalable method combining climate data and remote sensing to efficiently locate viable restoration sites without intensive field campaigns.

Findings

Identified 13 priority locations for restoration in Saudi Arabia.

Climate suitability score effectively predicts vegetation persistence.

Restoration efforts could increase vegetation coverage by 2.5 times.

Abstract

Large-scale restoration in drylands is widely promoted to address land degradation and biodiversity loss, yet many efforts rely on long-term irrigation, limiting sustainability in water-scarce regions. A key challenge is identifying locations where native vegetation can persist without intensive management while minimizing costly field campaigns. A scalable pre-screening framework is presented that integrates climate and remote sensing data to enable cost-efficient site selection in arid environments using Saudi Arabia as a case study. A Climate Suitability Score (CSS), derived from machine learning models trained on expert-curated reference sites, captures complex climatic dependencies on vegetation persistence. Using multi-year ERA5-Land data for Saudi Arabia, national-scale prediction maps are generated and combined with vegetation indices to identify areas where climate is favorable, but vegetation remains underdeveloped. Multi-criteria screening reduces candidates to thirteen priority locations. Climatically analogous intact ecosystems provide benchmarks for restoration targets and indicate that an average 2.5 fold increase in vegetation coverage is a realistic target for restoration efforts. Overall, this approach narrows the search space, reduces costs, and supports resilient ecosystem recovery planning in water-limited regions.