Abstract

The aim of the present investigation was to develop a basic methodological scheme for distinguishing organic, biodynamic and conventional areas of winter wheat cultivation, based on the satellite images from Sentinel-2. The target areas are located in the experimental field of the Institute of Agriculture in Karnobat, Bulgaria. The plant condition was determined on a three-point rating scale according to the average plant height, percentage of projective cover of the aboveground plant parts and intensity of the green color of leaves during the end of tillering to complete ripeness. To determine the homogeneity of the target polygons, we used a small drone DJI Phantom 4, with 1/2.3 CMOS camera and Dronedeploy to compile flight plans for shooting and photogrammetric processing of the images. ArcGIS was used in GIS procedures and analyses. Microsoft Excel, with Solver ADD-IN was used to clusterize the target polygons in order to distinguish the polygons of organic, biodynamic and conventional farming by the multispectral indices (CRI 1, MSAVI, NDVI, NDWI, PSSRa, PSSRc, WDRVI). All selected indices were calculated in GIS environment. Ground observations showed that there were differences in the condition of plants in different cultivation systems during the selected observation phases. The obtained results reveal that the differences in dynamics and development during the growth and development stages of the wheat in the organic, biodynamic and conventional fields can be traced by spectral characteristics from satellite images and they can be used to distinguish them.

摘要

本调查的目的是根据 Sentinel-2 的卫星图像，制定一种基本的方法学方案，用于区分冬小麦种植的有机、生物动力和常规区域。目标区域位于保加利亚卡尔诺巴特农业研究所的试验田。根据分蘖末期至完全成熟期间的平均株高、地上植物部分的投射覆盖百分比和叶子绿色的强度，以三分评定量表确定植物状况。为了确定目标多边形的均匀性，我们使用了一架带有 1/2.3 CMOS 相机和 Dronedeploy 的小型无人机 DJI Phantom 4 来编制飞行计划，用于拍摄和摄影测量处理图像。ArcGIS 用于 GIS 程序和分析。使用带有 Solver ADD-IN 的 Microsoft Excel 对目标多边形进行聚类，以便通过多光谱指数（CRI 1、MSAVI、NDVI、NDWI、PSSRa、PSSRc、WDRVI）区分有机农业、生物动力农业和常规农业的多边形。所有选定的指标均在 GIS 环境中计算。地面观测表明，在选定的观测阶段，不同栽培系统的植物状况存在差异。所得结果表明，小麦在有机田、生物动力田和常规田中生长发育阶段的动力学和发育差异可以通过卫星图像的光谱特征进行追踪，并可以用于区分。多光谱指数（CRI 1、MSAVI、NDVI、NDWI、PSSRa、PSSRc、WDRVI）的生物动力和传统农业。所有选定的指标均在 GIS 环境中计算。地面观测表明，在选定的观测阶段，不同栽培系统的植物状况存在差异。所得结果表明，小麦在有机田、生物动力田和常规田中生长发育阶段的动力学和发育差异可以通过卫星图像的光谱特征进行追踪，并可以用于区分。多光谱指数（CRI 1、MSAVI、NDVI、NDWI、PSSRa、PSSRc、WDRVI）的生物动力和传统农业。所有选定的指标均在 GIS 环境中计算。地面观测表明，在选定的观测阶段，不同栽培系统的植物状况存在差异。所得结果表明，小麦在有机田、生物动力田和常规田中生长发育阶段的动力学和发育差异可以通过卫星图像的光谱特征进行追踪，并可以用于区分。地面观测表明，在选定的观测阶段，不同栽培系统的植物状况存在差异。所得结果表明，小麦在有机田、生物动力田和常规田中生长发育阶段的动力学和发育差异可以通过卫星图像的光谱特征进行追踪，并可以用于区分。地面观测表明，在选定的观测阶段，不同栽培系统的植物状况存在差异。所得结果表明，小麦在有机田、生物动力田和常规田中生长发育阶段的动力学和发育差异可以通过卫星图像的光谱特征进行追踪，并可以用于区分。