Abstract: Accurate assessment and coordinated governance of agricultural carbon footprints are critical to achieving China's dual-carbon goals and sustainable agricultural development. Taking cotton, maize, and wheat in Xinjiang as the study objects, this paper constructs a multidimensional framework that integrates life-cycle assessment (LCA), spatial autocorrelation analysis, spatiotemporal hotspot detection, and the LMDI decomposition model, using life-cycle data during 2004-2020. The results indicate that: (1) The total carbon footprints of the three crops increased over time, with average annual growth rates of cotton (7.6%) > maize (7.0%) > wheat (4.8%). (2) Spatially, the total carbon footprint exhibits a "high in the west, low in the east" gradient, and in most prefectures, the ranking is cotton > maize > wheat. Global Moran's I reveals an overall random spatial pattern, except for wheat in 2012, which shows significant negative spatial autocorrelation. Hotspot analysis shows that the Kashgar region is a common hotspot for the three major crops. Cotton hotspots are stably distributed in Kashgar and Aksu. The high-value areas of maize have shifted from the southwest to the central-north and east. The high-value areas of wheat also show an eastward trend. Kashgar, Ili, and Changji continue to be high hotspot areas. (3) LMDI results show that production structure optimization (AI) exerts a significant mitigation effect (cumulative contribution rates: cotton -255.5%, maize -256.9%, wheat -533.5%), whereas rising carbon intensity (CI) and economic development (EI) are the primary drivers of emission increases. These findings provide a theoretical basis for innovating mitigation pathways in arid agricultural regions and offer decision support for achieving regional dual-carbon targets and green agricultural development.