A Deep Learning Approach to Estimate Gross Primary Productivity in Ethiopia

Understanding the impact of climate change on ecosystems is essential for effective environmental planning and resource management, particularly in sensitive tropical regions. This study explored the impact of climate change on Gross Primary Productivity (GPP), a key indicator of the carbon cycle, in a tropical region with high biodiversity and climate sensitivity in Ethiopia. By leveraging deep learning long short-term memory (LSTM) networks, we captured the complex nonlinear interactions between climate variables and vegetation productivity. We integrated historical climate data, satellite-derived GPP estimates, and projected the Coupled Model Intercomparison Project (CMIP6)-based Shared Socioeconomic Pathways (SSPs) and carbon dioxide (CO2) emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) under six Earth System Models (ESMs). The growth of GPP per decade was expected to increase by 12.34 ± 8.01, 36.71 ± 15.79, 65.48 ± 21.61, and 89.79 ± 27.28 percent (%) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively, by 2100, derived from the Ensemble Mean (MEM) of ESMs. The spatial distribution of GPP in the study area revealed higher values in the southwestern mid-latitude (7-11 degree) and lower values in the northeast high-latitude (11-15 degree) zones. The approach and findings of this study would respond to the anticipated climate change, putting into practice an integrated sustainable management plan to create a green economy in Ethiopia and contribute to the net-zero emissions and climate resilient mission of global sustainable development goals (SDGs).