LARGE-SCALE CROP YIELD FORECASTING AT FINE SPATIAL AND TEMPORAL GRANULARITIES IS KEY TO CHARACTERIZING AGRICULTURAL PRODUCTIVITY FOR INDIVIDUAL FARM FIELDS TOWARDS MORE INTELLIGENT AND PRECISE FARM MANAGEMENT. IT HAS NOTABLE IMPLICATIONS FOR PREDICTING FUTURE TRAJECTORIES OF FOOD PRICES, FOOD SECURITY, AND AGRICULTURAL DEVELOPMENT. HOWEVER, REGION-WIDE FORECASTING OF FIELD-LEVEL CROP YIELD IN A TIMELY FASHION REMAINS A LONG-LASTING CHALLENGE, DUE TO THE DIFFICULTY IN OBTAINING APPROPRIATE FIELD-LEVEL DATA AND THE POOR SCALABILITY OF EXISTING FORECASTING MODELS. THE RAPID ADVANCES IN SATELLITE REMOTE SENSING AND RECENT INNOVATIONS IN DEEP LEARNING OPEN UP NEW OPPORTUNITIES TO TACKLE THE CHALLENGE. THIS PROJECT THEREFORE AIMS TO ADVANCE AND BENCHMARK CROP YIELD FORECASTING SYSTEMS AT BOTH FINE SPATIAL AND TEMPORAL RESOLUTIONS WITH CUTTING-EDGE DEEP LEARNING APPROACHES. THE OVERARCHING GOAL OF THE PROJECT IS TO DEVELOP A SCALABLE, REAL-TIME, AND FIELD-LEVEL CROP YIELD FORECASTING FRAMEWORK WITH SATELLITE REMOTE SENSING. WE WILL DEVELOP THIS YIELD FORECASTING FRAMEWORK THROUGH THE FOLLOWING SPECIFIC OBJECTIVES: 1) DEVELOP A HYBRID DEEP LEARNING-BASED IMAGE FUSION MODEL TO GENERATE DENSE SATELLITE IMAGERY AT THE FARM FIELD LEVEL; 2) DEVISE A NETWORK-BASED PHENOLOGICAL MODEL TO ESTIMATE CROP PHENOLOGY IN A TIMELY FASHION; AND 3) DEVELOP AN INNOVATIVE DEEP LEARNING-ENABLED INTEGRATED MODEL TO PROTOTYPE SCALABLE, REAL-TIME, AND FIELD-LEVEL CROP YIELD FORECASTING SYSTEMS. CHARACTERIZED BY THOSE UNIQUE FEATURES, THE YIELD FORECASTING FRAMEWORK CAN PROVIDE SUFFICIENT SPATIAL GRANULARITY TO PREDICT CROP PRODUCTIVITY AT THE FIELD LEVEL, WHICH WILL DRASTICALLY BENEFIT THE PRECISE FARM MANAGEMENT, PARTICULARLY IN SMALL-HOLDER AGRICULTURAL SYSTEMS. THE FRAMEWORK WILL ENABLE THE CROP YIELD FORECASTING IN A REAL-TIME FASHION THROUGHOUT THE GROWING SEASON, WHICH WILL FACILITATE GOVERNMENTS, STAKEHOLDERS, AND FARMERS TO MAKE TIMELY ADAPTIVE MANAGEMENT, ECONOMIC, AND POLITICAL DECISIONS. THE FRAMEWORK WILL ALSO EMPOWER THE PARADIGM SHIFT FROM CONVENTIONAL CHRONOLOGICAL CALENDAR-BASED CROP YIELD MODELING ARCHITECTURES TO MORE SCALABLE PHENOLOGY-BASED ONES, AND THUS HOLDS STRONG POTENTIAL TO BE GENERALIZED OVER WIDE GEOGRAPHICAL REGIONS.