Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Abstract

In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called “AggieAir”, developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon”) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m2/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m2/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from −5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times.

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…Thus, the thermal image can be unreliable especially when the internal temperature of the camera is changing rapidly, such as during camera warmup period or during the flight when a gust of cool wind results in cooling of the camera. To overcome this challenge, the user may need to provide sufficient startup time before operation (preferably 30-60 min) [102,[143][144][145], shield the camera to minimize the change in the internal temperature of the camera [142], calibrate the camera [146][147][148][149][150][151][152][153], and perform frequent flat-field corrections.…”

Mapping Surface Temperatures on a Debris-Covered Glacier With an Unmanned Aerial Vehicle

“…Such a sensor bias is likely to be the case for our study (relatively short flight legs, but air temperatures of 0-8 • C at Kyangjin AWS). In our case, the potential biasing influence of the atmospheric column between the sensor and the ground on the measured temperature (Li et al, 2013;Torres-Rua, 2017) is likely limited due to the dry conditions, thin air and low flight height.…”

“…This is a reasonable assumption, but admittedly there are uncertainties regarding its usability. For instance, atmospheric variability, such as air temperature and water vapor content, may affect the bias over time (Torres-Rua, 2017). It was not taken into account because of a lack of accurate data of the atmospheric column over the glacier that is required for such a correction (e.g., Perry and Moran, 1994;Li et al, 2013;Torres-Rua, 2017).…”

“…For instance, atmospheric variability, such as air temperature and water vapor content, may affect the bias over time (Torres-Rua, 2017). It was not taken into account because of a lack of accurate data of the atmospheric column over the glacier that is required for such a correction (e.g., Perry and Moran, 1994;Li et al, 2013;Torres-Rua, 2017). The potential effect on the measurements is likely limited, however, due to the relatively shallow column of about 90 m and the generally dry air (Figure 3).…”

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

“…The resulting thermal mosaic consisted of brightness temperature in degrees Celsius. Moreover, a vicarious calibration for atmospheric correction of microbolometer temperature sensors proposed by Torres-Rua [65] was applied to the thermal images.…”