We introduce a method to calculate evapotranspiration (ET) for individual plots in agricultural fields using the TSEB (Two-Source Energy Balance) model for high resolution thermal data from a UAS (unmanned aerial system). The model was developed for satellite remote sensing which has coarser spatial and temporal resolution. With the emergence of UAS remote sensing, this model needs to be adapted to be applied to the significantly higher resolution imagery. The average resolution of our thermal dataset is about 5 cm, which means we have multiple temperature measurements for a single plant, as opposed to satellite imagery which often views entire fields. The image resolution also means that soil contributes to overall temperature for certain pixels as well. A new algorithm is developed to classify pixels into 3 categories: soil, plant and mixture of soil and plant. Temperature distributions of plants are established and with other inputs like solar radiation, wind speed, plant height, we estimate ET distributions. Distributions of ET are acquired for the targeted plots in multiple images, and are evaluated versus stomatal conductance measurements from a steady state porometer.
Precise and functional phenotyping is a limiting factor for crop genetic improvement. However, because of its ease of application, imagery-based phenomics represents the next breakthrough for improving the rates of genetic gains in field crops. Currently, crop breeders lack the know-how and computational tools to include such traits in breeding pipelines. A fully automatic user-friendly data management together with a more powerful and accurate interpretation of results should increase the use of field high throughput phenotyping platforms (HTPPs) and, therefore, increasing the efficiency of crop genetic improvement to meet the needs of future generations. The aim of this study is to generate a methodology to high throughput phenotyping based on temporal multispectral imagery (MSI) collected from Unmanned Aerial Systems (UAS) in soybean crops. In this context, ‘Triple S’ (Statistical computing of Segmented Soybean multispectral imagery) is developed as an open-source software tool to statistically analyze the pixel values of soybean end-member and to compute canopy cover area, number and length of soybean rows from georeferenced multispectral images. During the growing season of 2017, a soybean experiment was carried out at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by Parrot Sequoia Multispectral sensor on board senseFly eBee. The results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis of crop extension and affording a constant operational improvement and proactive management.
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