• ISSN 1008-505X
  • CN 11-3996/S
马龙飞, 胡乃月, 李伟, 秦伟龙, 黄收兵, 王志敏, 李斐, 于康. 利用无人机多光谱数据监测玉米对不同灌溉模式的响应差异[J]. 植物营养与肥料学报, 2022, 28(4): 743-753. DOI: 10.11674/zwyf.2021392
引用本文: 马龙飞, 胡乃月, 李伟, 秦伟龙, 黄收兵, 王志敏, 李斐, 于康. 利用无人机多光谱数据监测玉米对不同灌溉模式的响应差异[J]. 植物营养与肥料学报, 2022, 28(4): 743-753. DOI: 10.11674/zwyf.2021392
MA Long-fei, HU Nai-yue, LI Wei, QIN Wei-long, HUANG Shou-bing, WANG Zhi-min, LI Fei, YU Kang. Using multispectral drone data to monitor maize’s response to various irrigation modes[J]. Journal of Plant Nutrition and Fertilizers, 2022, 28(4): 743-753. DOI: 10.11674/zwyf.2021392
Citation: MA Long-fei, HU Nai-yue, LI Wei, QIN Wei-long, HUANG Shou-bing, WANG Zhi-min, LI Fei, YU Kang. Using multispectral drone data to monitor maize’s response to various irrigation modes[J]. Journal of Plant Nutrition and Fertilizers, 2022, 28(4): 743-753. DOI: 10.11674/zwyf.2021392

利用无人机多光谱数据监测玉米对不同灌溉模式的响应差异

Using multispectral drone data to monitor maize’s response to various irrigation modes

  • 摘要:
    目的 作物水分状况的实时监测对于节水灌溉、缓解我国水资源紧缺具有重要意义。本研究旨在探寻利用无人机多光谱影像数据实时监测玉米干旱胁迫状况的可行性,比较无人机数据和田间实测农学指标对作物干旱胁迫的敏感程度。
    方法 大田试验在河北吴桥进行,采用两个玉米品种‘富民985’和‘郑单958’,设置畦灌、滴灌和雨养3种模式。分别在大喇叭口期、抽雄期、开花期和灌浆期取玉米最新展开叶测定色素含量和比叶面积(SLA),同时利用无人机搭载多光谱相机采集近地遥感数据,并提取归一化植被指数(NDVI)、绿光归一化植被指数(GNDVI)、归一化红边指数(NDRE)、叶面叶绿素指数(LCI)和优化土壤调节植被指数(OSAVI)等5种植被指数。
    结果 与叶片色素含量和SLA相比,植被指数更早在各处理间表现出差异。播种后70天(抽雄期) NDRE和LCI在各处理之间具有显著性差异,NDVI、GNDVI和OSAVI仅在灌溉和雨养模式之间出现显著性差异;同一时期各处理的色素含量差异不显著,比叶面积差异也不显著;播种后90天(灌浆期)各处理间的色素含量出现显著性差异。此外,相关性分析表明,植被指数与色素含量的相关性随着生育期发生变化。播种后80天(开花期) NDRE、LCI两个植被指数和色素含量的相关性优于NDVI、GNDVI和OSAVI指数;播种后90天(灌浆期) 5种植被指数和色素含量之间的相关性较强。
    结论 利用无人机在播种后70天监测的植被指数(NDRE)对玉米干旱的监测优于部分实测农学指标,在后期其测定的叶片色素值(Ca+Cb)/Car与玉米的衰老相关密切,因而对玉米干旱胁迫的监测早且较准确。但最佳光谱指标及其用于干旱监测的最佳时期仍需在更多品种及不同环境下做进一步验证。

     

    Abstract:
    Objectives Monitoring crop moisture conditions in real-time is critical for adopting water-saving irrigation and reducing China’s present water scarcity. This study explores the feasibility of using drone multi-spectral image data for real-time monitoring of maize drought stress and compares the sensitivity of drone data and field-measured agronomic indicators to crop drought stress.
    Methods Two maize cultivars, ‘Fumin 985’ and ‘Zhengdan 958’, were used as test materials in the field experiment. Border irrigation, drip irrigation, and rainfed irrigation were employed as treatments. The pigment content and specific leaf area (SLA) of the most recently unfolded maize leaves were determined at 60, 70, 76, 84, 90, and 95 days after sowing. Similarly, a UAV equipped with multi-spectral cameras was used to collect near-ground remote sensing data to extract five vegetation indexes: the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), leaf chlorophyll index (LCI), and optimum soil adjust vegetation index (OSAVI).
    Results The changes in vegetation indexes were earlier than in the leaf pigment content and the leaf area index. At 70 days after sowing (tasseling period), there was a significant difference in the NDRE and LCI of the three treatments. NDVI, GNDVI, and OSAVI indexes differed between the irrigation and rain-fed modes. No difference was observed in the treatments' pigment content and specific leaf area index at the same stages. At 90 days following sowing, there was a considerable difference in pigment concentration among the treatments (filling period). Furthermore, correlation analysis revealed that the relationship between vegetation indexes and pigment content varied depending on the growth stage. The correlations between pigment content and two vegetation indices (NDRE and LCI) were higher for 80 days after sowing (flowering phase) than for NDVI, GNDVI, and OSAVI; for 90 days after sowing (filling period), the correlation between the five vegetation indices and pigment content was high.
    Conclusions According to this study, the utilization of UAV multi-spectral data to monitor maize drought proved better than some measured agronomic indicators. However, further research should be conducted on the best spectral indicators and periods for drought monitoring in various situations and environments.

     

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