Precision irrigation management through thermal and multispectral remote sensing: an integration of sensing systems and analytical techniques - Gadhwal, Manoj (Kansas State University, 2023)
Irrigation water management starts with quantifying irrigation prescriptions based on crop water requirements at a spatial scale. For determining the water requirement of the plants, canopy temperature-dependent crop water stress could provide a potential solution. The use of a small unmanned aerial system (sUAS) with a thermal infrared (TIR) camera has long been established as an effective method of measuring plant canopy temperatures at a spatial scale. However, concerns still exist about the accuracy of these systems in collecting canopy temperatures and estimating crop water stress. The overall goal of this research is to assess high spatial resolution crop water stress in a corn field and evaluate UAS-based thermal imagery’s capacity to provide precise canopy temperature. As a part of this study, the capacity and feasibility of UAV-based imagery to detect infield crop health variability were also evaluated against aircraft and satellite imagery. To analyze the effects of flying altitude and camera view angle on thermal infrared imagery, thermal cameras with different focal lengths (9 mm, 13 mm, and 19 mm) were flown at different altitudes (30 m, 50 m, and 70 m). The orthomosaics generated from images were examined for the accuracy of the corn-canopy temperature sensing, ability to differentiate between hot and cold surfaces, ease of image stitching, geometric accuracy, image quality, and spatial resolution. The results indicated that a canopy temperature map of crops with a temperature error of less than 2° C from the actual canopy temperature can be produced with the combination of appropriate camera focal length, altitude, and image calibration techniques. A narrow-angle thermal camera flying at low altitudes (<50 m) was found to be the least suitable combination for corn canopy temperature sensing. The most appropriate combination for temperature estimation of corn canopies was with a 13 mm focal length camera flying at an altitude of 50 m above ground level. To quantify corn’s crop water stress index (CWSI), images were collected using a thermal camera and a multispectral camera mounted on a Matrice 100 sUAS, over a four acres corn field divided into three irrigation levels (50%, 75%, and 100% irrigation level). Field-specific water stress baselines were developed and used in CWSI quantification to consider the effect of the instant local environment. High-resolution precise crop water stress maps developed from thermal images were capable of inter-row and intra-row detection of corn water stress. The vegetative indices significantly explained variation in crop water stress, with NDRE (Normalized Difference Red Edge index) having the highest R² value of 0.8 and NDVI (Normalized Difference Vegetation Index) having an R² value of 0.7. Field-measured leaf water potential also significantly affected water stress but showed a weaker correlation with R² values of 0.6. Overall results from this study showed that the combination of thermal imaging and NIR imaging could be utilized to determine accurate crop water stress on the spatial scale for irrigation water management and scheduling. A comparative assessment of UAS (Matrice-100), aircraft (Ceres Imaging), and satellite (Landsat-8) imagery to detect infield crop health variability for the implementation of a precision irrigation system was also accomplished. Spatial maps of canopy temperature and NDVI were developed using the images from different imaging platforms and analyzed for capacity to capture water requirements and crop health accurately. UAV imagery outperformed the other two platforms by providing the highest number of pixels and variations in temperatures and NDVI values to represent a given target area. Moderate and low spatial resolution imagery from aircraft (1-1.5 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 and aircraft imagery remains competitive in detecting crop variability between two nozzles of an irrigation pivot. UAV imagery was much more sensitive and precise in detecting minute changes as compared to other platforms. Satellite imagery was limited in capturing the variations at this small scale. In summary, this study provided an appropriate combination of camera focal length and flying altitude to accurately and efficiently estimate canopy temperature and crop water stress in corn. Methods were developed to precisely detect inter and intra-row crop water stress and health variability using low-altitude high-resolution UAV imagery. Detailed insight into the capacity of different remote sensing platforms was provided to detect crop health variability in small-scale farms and implement crop irrigation management based on crop canopy temperatures.
Keywords: Thermal remote sensing; Irrigation management; Crop canopy temperature; Crop water stress; Multispectral remote sensing