The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes

• Published in: Water (Basel) Vol. 12; no. 8; p. 2095
• Main Authors: Mzid, Nada, Cantore, Vito, De Mastro, Giuseppe, Albrizio, Rossella, Sellami, Mohamed Houssemeddine, Todorovic, Mladen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2020
• Subjects: Agricultural production; Agriculture; Analysis; Artificial satellites in remote sensing; Clouds; Conductance; Crops; Data acquisition; Durum wheat; Earth resources technology satellites; enhanced vegetation index; Experimentation; Growth; Image acquisition; Irrigation; Irrigation requirements; Landsat; Leaf area; Leaf area index; Leaves; Methods; Normalized difference vegetative index; Parameter estimation; Physiology; Remote sensing; Resistance; Satellite imagery; Satellites; Sensors; Stomata; Stomatal conductance; Studies; Transpiration; Vegetation; vegetation indices; Water deficit; water deficit index; Water regimes; Wheat; Italy
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w12082095

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter–spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production.