Climate-Resilient Agriculture: Systems, Science and Solutions for a Warming World

Climate-smart agriculture requires information systems that can detect crop and environmental change early enough, at spatial scales fine enough, and with sufficient reliability to alter management or policy decisions. Remote sensing now provides dense optical, radar, thermal and structural observations from satellites and uncrewed aerial vehicles, while geospatial artificial intelligence (GeoAI) can transform these observations into crop maps, phenological indicators, stress diagnostics, yield estimates and risk forecasts. This critical narrative review evaluates whether that technical progress has translated into dependable decision support for the three linked objectives of climate-smart agriculture: productivity, adaptation and, where feasible, mitigation. Literature published principally from 2015 to 15 June 2026 was identified through multidisciplinary scholarly sources and supplementary citation searching, with earlier seminal work retained when needed to establish methodological or operational context. Evidence was appraised for sensor suitability, reference-data quality, spatial and temporal validation, model transferability, uncertainty characterisation, decision relevance and implementation conditions. The strongest evidence supports crop-area and crop-type mapping, seasonal condition monitoring, phenology characterisation and several forms of yield and water-stress assessment, especially when multi-temporal optical data are complemented by synthetic aperture radar or targeted thermal and hyperspectral observations. Yet high mapping accuracy does not by itself establish climate-smart impact. Performance frequently deteriorates under spatial transfer, small and heterogeneous fields, cloud-related data gaps, shifting crop calendars and weak ground reference data. Deep learning and multimodal fusion improve representational capacity, but their advantage is contingent on training diversity, validation design and computational access. Operational systems such as GEOGLAM demonstrate that Earth observation becomes most useful when algorithmic outputs are combined with agronomic knowledge, field reports, institutional interpretation and explicit uncertainty. The central challenge is therefore no longer simply producing more accurate maps; it is building auditable, transferable and inclusive sensing-to-decision chains that connect observations to timely actions and measurable climate-smart outcomes.