I seek to improve our understanding of crop carbon and water exchange from field to global scales. I answer my research questions with models that capture the spatio-temporal dynamics of agricultural production (area × yield) and evapotranspiration. The models are: (i) implemented in a Geographic Information System; (ii) based on principles of radiation-use efficiency and machine-learning; and (iii) driven by Earth observation, climate reanalysis, and other geospatial information. The process reduces uncertainties in crop models and improves agricultural monitoring systems, as well as motivates my teaching and capacity-building activities. The long-term impact of my work is increased agricultural production, enhanced resilience of crops to drought and other climate-related shocks, and reduced CO₂ emissions caused by agricultural expansion into natural ecosystems. Such outcomes help achieve Sustainable Development Goal SDG-2 (“Zero Hunger”), SDG-6 (“Clean Water and Sanitation”), SDG-13 (“Climate Action”), and SDG-15 (“Life on Land”).

Expertise

• Crop yield and water use
• Land surface processes
• Operational and emerging remote sensing technology
• Integrated (process-based + machine learning) modeling