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Spatial information on plant-water requirement is the most crucial input for designing an efficient site-specific irrigation system. In quantifying this spatial information, canopy temperature-derived crop water stress maps could provide a potential solution. With the support of modern, advanced, and cost-effective remote sensing platforms like Unmanned Aerial Vehicle (UAV), aircraft, and Satellite, remote sensing data can be systematically collected with varying degrees of efficiency for spatial canopy temperature assessment. However, each platform provides remote sensing data at varying degrees of spectral and spatial resolutions, which can impact the user’s ability to develop canopy temperature-based spatial water stress maps and implement precision irrigation systems. Therefore, the main goals of this study were 1) to assess the feasibility and accuracy of UAV, aircraft, and satellite-based imaging for crop canopy temperature and health mapping; and 2) to compare and contrast the resolution of water-stressed regions identification for precision irrigation technology implementation. Thermal infrared (TIR) and multispectral images were obtained over a four-acre cornfield using a quadcopter (Matrice-100), aircraft (Ceres Imaging), and Satellite (Landsat-8). Spatial maps of canopy temperature and NDVI were developed using these images and analyzed for capacity to capture water requirements and crop health accurately. UAV imagery outperformed the other two platforms in providing detailed imagery and sensing changes in crop health throughout the field. For a sample area of dimension 82 m x 44 m, the UAV imagery provided 683 different types of canopy temperature values. In contrast, aircraft imagery provided 158 different values, followed by satellite imagery which provided only 5-6 variations in canopy temperature to represent the same area. Moderate and low spatial resolution imagery from aircraft (0.9-1.2 m/pixel) and satellite (30 m/pixel) was limited in detecting inter-row variability and outputting the average pixels of the crop canopy and inter-row space. Whereas high-resolution UAV imagery (1.5 cm/pixel – 6 cm/pixel) precisely distinguished inter-row gap from plants and provided crop-only pixels without mixing with background soil. UAV imagery was precise and sensitive in detecting crop variability between two nozzles of an irrigation pivot, while aircraft imagery was less precise and sensitive. Satellite imagery was not able to capture the variations at this small scale. So, overall, UAV and aircraft imagery remains competitive in providing infield crop health variability for site-specific management in agriculture. Satellite imagery is limited in providing infield crop health variability to design site-specific irrigation, especially for small-scale farms.
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