Spatiotemporal soil canopy full-profile sensing reveals light–water–yield coupling in cotton, facilitating precise resource management in staggered-sowing fields

Coordinating the efficient use of multiple natural resources is critical for ensuring sustainable crop production, particularly under increasingly variable climate conditions. The light interception rate (LIR) and soil water consumption (SWC) are fundamental to increasing cotton productivity, yet studies on their coupled dynamics across the soil–canopy continuum remain scarce. We established a spatial-grid, multisensor network that can be used to continuously monitor the LIR and SWC along with full soil-canopy profiles (-110–110 cm) in a staggered‑sowing cotton experiment in China. Geostatistics and nonlinear models were employed to quantify the spatiotemporal changes in LIR and SWC and their links to biomass and yield. Sowing date substantially affected SWC patterns, which exhibited clear seasonal and vertical stratification characteristics. Compared with the early-sown treatments, the late-sown treatments consistently demonstrated lower consumption of shallow water (10–40 cm) but higher consumption of deep water (70–110 cm). Early sowing advanced the upper-canopy LIR peaks by 10–12 days, whereas late sowing sustained high interception (>90 %) for a longer period during peak flowering. SWC at each position along the soil profile was significantly positively correlated with the canopy LIR, highlighting the spatial coordination between above- and belowground resource capture. In normal years, the SWC at shallow positions was positively correlated with seed cotton yield, with correlations turning negative below 40 cm and being greatest at 90 cm in cotton rows, indicating an optimal zone (10–40 cm) for water extraction that supports productivity. However, this relationship may be disturbed by extreme weather. This study revealed critical spatiotemporal interactions across the soil profile and crop canopy, offering a new technical paradigm for spatiotemporally coupled soil–crop monitoring and providing a functional basis for optimizing cotton production in data-driven agricultural systems to guide precision management, yield optimization, and climate-adaptive decision-making in future smart agriculture.