Spectral indices with different spatial resolutions in recognizing soybean phenology

• Published in: PloS one 2024-09, Vol.19 (9), p.e0305610
• Main Authors: da Silva, Airton Andrade, Silva, Francisco Charles Dos Santos, Guimarães, Claudinei Martins, Saleh, Ibrahim A, da Crus Neto, José Francisco, El-Tayeb, Mohamed A, Abdel-Maksoud, Mostafa A, González Aguilera, Jorge, AbdElgawad, Hamada, Zuffo, Alan Mario
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Brazil; Computer and Information Sciences; Crop phenology; Crops; Crops, Agricultural - growth & development; Decision making; Discriminant Analysis; Efficiency; Error analysis; Error correction; Experiments; Flowering; Geospatial data; Global positioning systems; Glycine max - growth & development; GPS; Leaf area; Leaf area index; Management decisions; Mathematical functions; Neural networks; Neural Networks, Computer; Phenology; Physical Sciences; Physiology; Plant Leaves - growth & development; Precipitation; Production management; Research and Analysis Methods; Satellite imagery; Satellites; Sensors; Social Sciences; Software; Soybean; Soybeans; Spatial discrimination; Spatial resolution; Variance analysis; Vegetation; Vegetation index
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0305610

The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.