The use of small unmanned aircraft systems (sUAS) for applications in the field of precision agriculture has demonstrated the need to produce temporally consistent imagery to allow for quantitative comparisons. In order for these aerial images to be used to identify actual changes on the ground, conversion of raw digital count to reflectance, or to an atmospherically normalized space, needs to be carried out. This paper will describe an experiment that compares the use of reflectance calibration panels, for use with the empirical line method (ELM), against a newly proposed ratio of the target radiance and the downwelling radiance, to predict the reflectance of known targets in the scene. We propose that the use of an on-board downwelling light sensor (DLS) may provide the sUAS remote sensing practitioner with an approach that does not require the expensive and time consuming task of placing known reflectance standards in the scene. Three calibration methods were tested in this study: 2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our study indicates that the traditional 2-Point ELM produces the lowest mean error in band effective reflectance factor, 0.0165. The 1-Point ELM and AARR produce mean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARR approach indicates that the technique has the potential to perform better than the 2-Point ELM method, with a 0.0026 mean error in band effective reflectance factor, indicating that this newly proposed technique may prove to be a viable alternative with suitable on-board sensors.

No abstract

Abstract. Since its launch in 2013, the Thermal Infrared Sensor (TIRS) onboard Landsat 8 has exhibited artifacts in its image data that can be attributed to stray-light. A 3-year effort was initiated to develop a stray-light correction algorithm to support TIRS calibration. A methodology was developed to predict the additional (stray-light) signal on each detector from an estimate of the stray-light source locations in the sensor's out-of-field area. The initial version of the algorithm estimated the magnitude of out-of-field radiance sources through the use of geostationary wide-field thermal band imagers. However, this methodology necessitated a strong effort to cross calibrate the two sensors. Ultimately, a variation of the algorithm was implemented operationally into the United States Geological Survey ground system that utilizes image data from TIRS itself as an estimate of the out-of-field stray-light sources. This paper highlights the intercalibration techniques investigated while developing the stray-light correction algorithm. The impact of differing view-angles, spectral responses, and collection times on at-sensor radiance was considered to assess the feasibility of using data from Geostationary Operational Environmental Satellite geostationary instruments to estimate the out-of-field stray-light radiance incident on the TIRS detectors. Results of the studies presented here illustrate the complexities associated with intercalibration in the thermal and provide justification for the current form of the TIRS stray-light correction algorithm.

Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.

No abstract