Marino Stefano
Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, Via De Sanctis, I-86100, Campobasso, Italy.
Heliyon. 2023 Aug 30;9(9):e19507. doi: 10.1016/j.heliyon.2023.e19507. eCollection 2023 Sep.
The study investigates the suitability of time series Sentinel-2 NDVI-derived maps for the subfield detection of a sunflower crop cultivated in an organic farming system. The aim was to understand the spatio-temporal behaviour of subfield areas identified by the K-means algorithm from NDVI maps obtained from satellite images and the ground yield data variability to increase the efficiency of delimiting management zones in an organic farming system. Experiments were conducted on a surface of 29 ha. NDVI time series derived from Sentinel-2 images and k-means algorithm for rapidly delineating the sunflower subfield areas were used. The crop achene yields in the whole field ranged from 1.3 to 3.77 t ha with a significant within-field spatial variability. The cluster analysis of hand-sampled data showed three subfields with achene yield mean values of 3.54 t ha (cluster 1), 2.98 t ha (cluster 2), and 2.07 t ha (Cluster 3). In the cluster analysis of NDVI data, the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability. The best period for identifying subfield areas starts from the inflorescences development stage to the development of the fruit stage. Analyzing the NDVI subfield areas and yield data, it was found that cluster 1 covers an area of 42.4% of the total surface and 50% of the total achene yield; cluster 2 covers 35% of both surface and yield. Instead, the surface of cluster 3 covers 22.2% of the total surface with 15% of achene yield. K-means algorithm derived from Sentinel-2 NDVI images delineates the sunflower subfield areas. Sentinel-2 images and k-means algorithms can improve an efficient assessment of subfield areas in sunflower crops. Identifying subfield areas can lead to site-specific long-term agronomic actions for improving the sustainable intensification of agriculture in the organic farming system.
本研究调查了基于哨兵 - 2 归一化植被指数（NDVI）得出的时间序列地图用于有机农业系统中向日葵作物亚田块检测的适用性。目的是了解通过 K 均值算法从卫星图像获得的 NDVI 地图所识别的亚田块区域的时空行为以及地面产量数据的变异性，以提高有机农业系统中划定管理区的效率。实验在 29 公顷的土地上进行。使用了从哨兵 - 2 图像得出的 NDVI 时间序列和用于快速划定向日葵亚田块区域的 K 均值算法。整个田块的作物瘦果产量在 1.3 至 3.77 吨/公顷之间，田块内存在显著的空间变异性。对手工采样数据的聚类分析显示有三个亚田块，其瘦果产量平均值分别为 3.54 吨/公顷（聚类 1）、2.98 吨/公顷（聚类 2）和 2.07 吨/公顷（聚类 3）。在 NDVI 数据的聚类分析中，K 均值算法较早地划定了亚田块作物的时空产量变异性。识别亚田块区域的最佳时期是从花序发育阶段到果实发育阶段。通过分析 NDVI 亚田块区域和产量数据发现，聚类 1 覆盖总面积的 42.4%和总瘦果产量的 50%；聚类 2 覆盖面积和产量的 35%。相反，聚类 3 的面积覆盖总面积的 22.2%，瘦果产量占 15%。从哨兵 - 2 NDVI 图像得出的 K 均值算法划定了向日葵亚田块区域。哨兵 - 2 图像和 K 均值算法可以改进对向日葵作物亚田块区域的有效评估。识别亚田块区域可以导致针对特定地点的长期农艺措施，以促进有机农业系统中农业的可持续集约化发展。
