THIS PROPOSAL DIRECTLY ADDRESSES THE FIRST COMPONENT OF THE NASA LCLUC ANNOUNCEMENT: IDENTIFYING HOTPOT AREAS USING MULTI-SOURCE DATA APPROACHES TO QUANTIFY LAND-COVER AND LAND-USE CHANGES (LCLUCS) INCLUDING THE EXPANSION, INTENSIFICATION, AND ABANDONMENT OF CROPLANDS AND RANGELANDS. WE WILL LEVERAGE MACHINE LEARNING ALGORITHMS TO DEVELOP NOVEL DATA SETS OF LCLUC AND LAND USE TRANSITIONS (LUTS) WITH OUR ULTIMATE GOAL BEING TO INTEGRATE SOCIOECONOMIC AND ENVIRONMENTAL DATASETS TO BETTER UNDERSTAND FARMERS DECISION MAKING UNDER A CHANGING CLIMATE IN A CRITICALLY ENDANGERED GLOBAL BREADBASKET. BELOW WE DETAIL THE KEY OBJECTIVES, METHODS, AND SIGNIFICANCE OF THE WORK. THE PROJECT PERSONNEL, WHICH INCLUDE SOCIAL AND NATURAL SCIENTISTS, HAVE A PROVEN HISTORY OF PRODUCING DEEPLY INTEGRATED WORK ON ENVIRONMENT AND SOCIOECONOMICS. GALFORD, FISHER, SPERA, MACEDO AND RATTIS HAVE A COMBINED EXPERIENCE OF > 4 DECADES WORKING ON BRAZILIAN LAND-USE DEVELOPMENT, INCLUDING GENERATING NOVEL LCLUC DATASETS APPLYING STATISTICAL, EMPIRICAL AND PROCESS-BASED MODELS ANALYZING SOCIOECONOMIC DATA AND SURVEYS AND COLLABORATING WITH RESEARCHERS AND STUDENTS.