Remote sensing-based analysis of yield and water-fertilizer use efficiency in winter wheat management

Abstract

Winter wheat is one of the world’s most important food crops, and effective water and fertilizer management is crucial for optimizing its yield and water-fertilizer use efficiency. Unmanned aerial vehicle remote sensing provides a reliable tool for accurately monitoring winter wheat growth and dynamically adjusting water and fertilizer strategies to enhance yield. In this study, a water and fertilizer experiment was conducted in Xinxiang County, Henan Province, a region with a warm temperate continental monsoon climate, characterized by hot, humid summers and cold, dry winters. Various water (W1: 0 mm, W2: 50 mm, W3: 100 mm, W4: 150 mm) and nitrogen (N1: 0 kg/ha, N2: 90 kg/ha, N3: 210 kg/ha, N4: 330 kg/ha) treatments were applied. Subsequently, the effects of different water and fertilizer treatments on winter wheat yield and water-fertilizer use efficiency were evaluated, and the response patterns between winter wheat spectral features (normalized difference vegetation index, NDVI) and texture features (Contrast) and yield and water-fertilizer use efficiency were analyzed. The main findings are: (1) Winter wheat yield increased with higher irrigation and nitrogen levels but plateaued when irrigation reached 120 mm and nitrogen application was 225 kg/ha, beyond which further increases showed no significant improvement; (2) Water-fertilizer use efficiency decreased with increasing irrigation and nitrogen levels but improved with synergistic water-fertilizer interactions. The N3W3 treatment achieved high yield while maintaining superior water-fertilizer use efficiency (irrigation water use efficiency: 1.28 kg/m³, agronomic nitrogen efficiency: 13.33 kg/kg, and fertilizer benefit: 5961.30 RMB/ha), making it the most effective management strategy; (3) NDVI exhibited saturation under high-density conditions, limiting its sensitivity to subtle differences in winter wheat. Conversely, Contrast provided complementary insights into canopy structure, revealing variations in uniformity and resource efficiency under excessive water and nitrogen inputs. Integrating NDVI with Contrast enabled a more accurate assessment of yield and water-fertilizer use efficiency, offering actionable insights for optimizing water-fertilizer management strategies.