Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Abstract

The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 • C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 • C compared to those where no drift correction was applied.

Assessing the potential of drone‐based thermal infrared imagery for quantifying river temperature heterogeneity

“…Similarly, the relative strength of TS is likely due to the role of topographic shading from the mountainous Baddoch Burn terrain in controlling streamwise variability in the receipt of solar radiation by (and hence, radiative warming of) the camera. Elapsed time was also identified as an important covariate, in agreement with Mesas‐Carrascosa et al (). This variable integrates both time of day (comprising diurnal air temperature variation) and time passed during the survey (potentially corresponding to heat build‐up due to energy dissipation from internal electronics).…”

“…Not only does this partially account for why drift is present in night‐time flights, it also indicates that even through minimising all external sources of drift, it will still be difficult to separate true diffuse heterogeneity from drift caused by internal sensor warming. Although researchers have published a range of drift compensation methods, these are either experimental hardware‐based techniques (e.g., Olbrycht & Więcek, ; Ribeiro‐Gomes et al, ) or involve modelling or additional image acquisition to remove interimage bias and “normalise” image sequences (e.g., Abolt et al, ; Jensen, McKee, & Chen, ; Mesas‐Carrascosa et al, ), which, when applied over the spatial scales at which diffuse thermal heterogeneity occurs (10 2 –10 4 m), may also have the unwanted effect of removing true longitudinal temperature variability present within the image series. We therefore advocate the development of new processing techniques specific to river environments that are capable of compensating for sUAS‐based TIR drift while preserving true streamwise temperature variability.…”

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.…”

“…Temperature accuracy within a few degrees was achieved by flying over the targets three times (at the start, middle and end of UAS operation) and using three separate calibration equations for each overpass [142]. Additionally, using the redundant information from multiple overlapping images, drift correction models have been proposed, which lowered temperature error by 1 • C as compared to uncorrected orthomosaic [152]. The manufacturer stated accuracies (generally ±5 • C) can be sufficient to access the field variability and to detect "hotspots" of water status.…”

Combining continuous spatial and temporal scales for SGD investigations using UAV-based thermal infrared measurements

“…possess the ability to discriminate between the SGD shares and allow a linkage to the underlying processes, since the investigated temporal scale is continuous and ranges between daily and seasonal cycles (Taniguchi et al, 2003a;Michael et al, 2011). Yet, all of this cannot account for the spatial variability, as the entity and interaction of terrestrial and marine controls lead to a highly variable SGD appearance in terms of discharge type (diffuse vs. focused), temporal discharge behaviour, flow rates, spatial abundance (even over small spatial scales), and mixing (Michael et al, 2003;Taniguchi et al, 2003b;Burnett et al, 2006). U.…”