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Site-specific management zone of field scale based on remote sensing image in a black soil area

  • role： First author第一作者
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

  • Email：huanjunliu@yeah.net
  • Introduction：刘焕军(1981—　)，男，副教授，研究方向为地物高光谱遥感。E-mail：huanjunliu@yeah.net
LIU Huanjun,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

QIU Zhengchao,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

MENG Linghua,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

FU Qiang,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

  • Email：jbwneau@163.com
  • Introduction：姜佰文(1971—    )，男，教授，研究方向为植物营养与施肥技术。E-mail：jbwneau@163.com
JIANG Baiwen*,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

YAN Yan,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

XU Mengyuan

resumen

Although the precision management-partition method, which is based on grid sampling and spatial interpolation, is highly precise, It has poor timeliness and high costs. Thus, this study proposes a precise management-partition method based on remote sensing images. Hongxing farm fields in the northeast agricultural reclamation area are used as the research objects. High-resolution remote sensing images of bare soil and grid sampling data in the field are utilized as data sources. This study focuses onsite-specific management zone in atypical black soil area. Management is based on the significant correlation between the spectral reflectance characteristics of bare soil and the main physical and chemical properties of black soil. In addition, the zoning result is evaluated using the soil’s physical and chemical properties and physical physiological parameters of crops. The object-oriented segmentation and spatial statistical analysis methods are utilized in this study. The following conclusions are drawn: (1) The spatial variation of soil nutrients is significantly distinct in the typical black soil field. (2) The site-specific management zone, which is based on a bare soil image and highly accurate object-oriented segmentation, enhances the differences of soil nutrients, the normalized difference vegetation indexes between zones, and the consistency in zones. (3) The two images obtained on May 20th and April 1stare processed into single image and double image partitions. The ratios of inter zone to in zone variable coefficients is 1.42, 1.39, and 7.63, which prove that the result of the site-specific management zone that is based on double images is better than those based on a single image. (4) The precision management-partition method that is based on the bare soil image and segmentation technique has strong timeliness, low cost, and good accuracy. Results of the study provide the basis for field variable fertilization, development of intelligent precision agriculture, and sustainable agricultural development.

palabra clave

bare soil;remote sensing images;precise-management zoning;object oriented;precision agriculture

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