Land degradation poses significant challenges to sustainable agriculture and ecosystem resilience in Ghana’s agroecological zones. These regions—critical for crops like cocoa, which underpin national food security and economic stability—are increasingly threatened by unsustainable practices and climatic pressures. Leveraging the potential of remote sensing, this study employs Trends.Earth, a geospatial platform aligned with SDG 15.3, to monitor land degradation.
This methodology integrates multiple remote sensing datasets to evaluate land productivity, land cover changes, and soil organic carbon (SOC). Productivity is calculated using MODIS NDVI data (250 m resolution, 2001–2020) to analyze long-term trends, current states, and relative performance against ecological benchmarks. Land cover dynamics are derived from ESA CCI Land Cover data (300 m resolution, 1992–2020), reclassified into seven standardized categories. SOC estimates are based on SoilGrids (250 m resolution), providing insights into carbon dynamics in the top 30 cm of soil. By combining these indicators through a one-out all-out approach, this multi-source framework provides a detailed spatial and temporal assessment of degradation patterns, highlighting priority areas for intervention.
The key findings reveal that forest zones are severely impacted by deforestation and declining soil health, savannah areas suffer from productivity losses, and transition zones face complex interactions between climatic and anthropogenic pressures. Preliminary analyses focus on areas of cocoa presence, revealing that many of these zones overlap with regions affected by land degradation, which may pose significant risks to agricultural productivity and sustainability.
This study offers actionable insights for targeted interventions, such as implementing climate-smart agriculture and sustainable land management practices. This research underscores the critical role of advanced remote sensing tools in achieving SDG 15.3 and informing adaptive strategies to combat land degradation. It demonstrates how integrating high-resolution geospatial data with policy-oriented analysis can guide sustainable resource management, ensuring resilient ecosystems and livelihoods.