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<p>This study pioneers the integration of Sentinel-1 Synthetic Aperture Radar (SAR) data, specifically Ground Range Detected (GRD) and Single Look Complex (SLC) products, with a novel synergistic framework combining Cumulative Sum-based Change Detection (CUSUM-CD), Dual-polarization Radar Vegetation Index (DpRVI), and H-Alpha polarimetric decomposition for cotton phenology monitoring in the Yisiqi farmlands of Xinjiang, China. Unlike conventional SAR-based approaches that rely on single-index analysis or optical-SAR fusion, this method achieves 15–20% higher accuracy in detecting critical phenological transitions (planting, flowering, senescence) through CUSUM-CD’s bootstrapped change point detection, while DpRVI and H-Alpha decomposition jointly reduce false-positive stress identification by 25% compared to backscatter-only methods. The research exclusively utilizes SAR data to capitalize on its all-weather, day-and-night imaging capabilities and sensitivity to vegetation structure and moisture, thereby overcoming the persistent cloud-cover limitations inherent to optical remote sensing in this semi-arid region. These advancements enable precision irrigation scheduling and targeted pest management that reduce water consumption by an estimated 12–18% and minimize agrochemical runoff, supporting sustainable intensification of cotton production in water-scarce environments.</p>

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Unlocking Sentinel-1 GRD/SLC Synergy for Precision Phenology Monitoring in Arid Cotton Fields of Yisiqi, Xinjiang, China

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Mohamed Elhag,
  • Aris Psilovikos,
  • Lifu Zhang,
  • Wei Tian,
  • Junna Yuan,
  • Dinara Talgarbayeva

摘要

This study pioneers the integration of Sentinel-1 Synthetic Aperture Radar (SAR) data, specifically Ground Range Detected (GRD) and Single Look Complex (SLC) products, with a novel synergistic framework combining Cumulative Sum-based Change Detection (CUSUM-CD), Dual-polarization Radar Vegetation Index (DpRVI), and H-Alpha polarimetric decomposition for cotton phenology monitoring in the Yisiqi farmlands of Xinjiang, China. Unlike conventional SAR-based approaches that rely on single-index analysis or optical-SAR fusion, this method achieves 15–20% higher accuracy in detecting critical phenological transitions (planting, flowering, senescence) through CUSUM-CD’s bootstrapped change point detection, while DpRVI and H-Alpha decomposition jointly reduce false-positive stress identification by 25% compared to backscatter-only methods. The research exclusively utilizes SAR data to capitalize on its all-weather, day-and-night imaging capabilities and sensitivity to vegetation structure and moisture, thereby overcoming the persistent cloud-cover limitations inherent to optical remote sensing in this semi-arid region. These advancements enable precision irrigation scheduling and targeted pest management that reduce water consumption by an estimated 12–18% and minimize agrochemical runoff, supporting sustainable intensification of cotton production in water-scarce environments.