Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Abstract

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

“…The determined emissivity of dense canopy ( veg = 0.988) agreed with frequently cited values, as in [27,29,31,60]. However, the plants must be healthy, as the emissivity decreases significantly in the case of withered crops.…”

“…They measured the emissivity at a certain temperature and integrated it with the spectral response function of the thermal imager [26]. Malbéteau et al [27] estimated emissivity in terms of vegetation cover, expressed by vegetation index values [17,19]. Some studies, however, do not take emissivity into account and therefore provide results that should be viewed as qualitative rather than quantitative [14,28].…”

Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

“…Scientific interests such as phenology, changes in transpiration, plant water status, and carbon fluxes can be studied at unprecedented temporal resolutions (e.g., hourly or daily), potentially creating hypertemporal vegetation products for input into ecosystem models and new insights into ecosystem behavior. For example, Malbéteau et al [57] explored diurnal temperature dynamics of grass and maize canopies in Saudi Arabia with varying water status levels at ultra-high spatial resolution (0.02 m). The use of UAVs allowed for fine temporal characterization of plant-water relations, as leaf skin temperature is strongly coupled with stomatal conductance and related to transpiration rates [58].…”

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

“…TIR data can therefore be used to derive the LST of different surfaces. Hitherto, a range of airborne and satellite sensors were developed to record TIR image data i.e., Landsat TM/ETM+, MODIS, ASTER, and new satellites such as the HyspIRI that are under development Given a large number of influences on LST, airborne platforms [21] and UAV [383,384] are in use for the retrieval of LST. RS platforms and sensors currently providing TIR data differ in the spatial, spectral, and temporal resolution of LST data and are the only way to measure LST from the local to the global scale with high spatial and temporal resolution (see also Figure 11 and Table 7).…”