Unmanned aerial vehicles (UAVs) open new opportunities in precision agriculture and phenotyping because of their flexibility and low cost. In this study, the potential of UAV imagery was evaluated to quantify lodging percentage and lodging severity of barley using structure from motion (SfM) techniques. Traditionally, lodging quantification is based on time-consuming manual field observations. Our UAV-based approach makes use of a quantitative threshold to determine lodging percentage in a first step. The derived lodging estimates showed a very high correlation to reference data (R2 = 0.96, root mean square error (RMSE) = 7.66%) when applied to breeding trials, which could also be confirmed under realistic farming conditions. As a second step, an approach was developed that allows the assessment of lodging severity, information that is important to estimate yield impairment, which also takes the intensity of lodging events into account. Both parameters were tested on three ground sample distances. The lowest spatial resolution acquired from the highest flight altitude (100 m) still led to high accuracy, which increases the practicability of the method for large areas. Our new lodging assessment procedure can be used for insurance applications, precision farming, and selecting for genetic lines with greater lodging resistance in breeding research.

Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.

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<p><strong>Abstract.</strong> Unmanned Aerial Vehicles (UAVs) are increasingly used, and open new opportunities, in agriculture and phenotyping because of the flexible data acquisition. In this study the potential of ultra-high spatially resolved UAV image data was investigated to quantify lodging percentage, lodging development and lodging severity of barley using Structure from Motion techniques. The term lodging is defined as the permanent displacement of a plant from the upright position. Traditionally lodging quantification is based on observations that need, and vary with observers in the field. An objective threshold approach was proposed in this study to improve the accuracy in lodging determination. Across breeding trials, manual reference measurements and UAV based lodging percentage showed a very high correlation (R²&amp;thinsp;=&amp;thinsp;0.96). In addition, the multi-temporal lodging percentage development was used to estimate the recovery rate and to determine the influence of different lodging events. Based on the parameter lodging percentage an approach was developed that allowed the assessment of lodging severity, an information that is important to estimate the yield impairment. Lodging severity can be used for insurance applications, precision farming and breeder research. This trait, together with differentiated recovery are novel traits next to lodging severity that will aid the selection for genetic lines.</p>