Climate change is impacting polar regions at an accelerated rate, causing rapid changes in land cover and biodiversity. One case is the “greening up” of the Western Antarctic Peninsula (WAP), the result of receding glacial fronts exposing substrate for plants and soil development, together with higher temperatures and potential increases in cloud-free conditions conducive to plant growth. The 2019/2020 austral summer was the warmest on record for the WAP, yet the controls on land surface temperature (LST) here are not well understood. We investigated the relationships between land cover type, solar radiation, and LST for several vegetated coastal outcrops (0.3 to 0.5 ha) distributed from 64 to 65°S along the WAP. Remote sensing data was collected in February/March 2020 using a consumer-grade unmanned aerial vehicle (UAV), additionally equipped with near-IR and thermal-IR sensors. Digital surface models produced from the UAV imagery were used to calculate surface solar radiation. NDVI was used to identify four land cover classes: healthy vegetation, unhealthy vegetation, loose substrate, bedrock. Thermal-IR data provided sub-decimeter LST mapping. LST ranges varied depending on atmospheric conditions. A site surveyed under cloud-free conditions and air temperature of 6.6°C showed a 37.2°C range in LST, while a nearby site surveyed the next day under overcast conditions and air temperature of 2°C showed a 10.4°C range in LST. Vegetation at these two sites reached maximum temperatures of 27°C and 11.6°C, respectively. We found little within-site difference in either mean or range of LST among the land cover classes. Using linear regression, solar radiation explained less than 50% of the observed LST. Healthy vegetation showed the strongest relationship between solar radiation and LST. It was determined that LST in the WAP was strongly affected by factors other than solar radiation, implying latent heat effects. As the abundance of healthy vegetation increases in these areas, LST may show a stronger relationship with solar radiation, thus effecting local feedbacks to warming. This study presents the first application of UAV-derived thermal-IR data for the analysis of LST controls in Antarctica, highlighting the capability of UAV as a data collection platform for use in remote and relatively data-poor environments.

Cloud water interception (CWI) is not captured by conventional rain gauges and not well characterized, but could have ecohydrological significance in systems such as tropical montane cloud forests. Quantifying CWI is necessary to assess the impacts of climate and land cover changes in places such as Hawai‘i. CWI can be estimated from wind speed, cloud liquid water content (LWC), and vegetation characteristics with an empirical model. Cloud microphysics sensors measure LWC accurately, but are expensive and often designed only for use on aircraft. LWC can be estimated by fog gauges, but poorly constrained catch efficiency and spurious rain catch can cause large errors. Visibility is related to LWC, but the relationship is non-linear and depends on the (usually unknown) drop size distribution. This study is part of a project aimed at mapping CWI across the Hawaiian Islands. Earlier analyses found disagreement between LWC estimated from fog gauge and visibility observations at the project field sites. In this study, we experimented with a novel in situ observation platform and cross disciplinary collaboration to compare cloud microphysics observations with those commonly used in cloud forest studies. Field missions took place from April to July 2018 at the summit of Mt. Ka‘ala (1,200 m) on O‘ahu Island. We built a pickup truck-mounted mobile weather station that can be assembled in the field, with weather-sensitive processing modules inside the cab. A total of 10 instruments were deployed: Phase Doppler Interferometer, Cloud Droplet Probe, fog gauge, visibility sensor, rain gauge, wind monitor, camera, water isotope sampler, UAV atmospheric sensor, and Aerosol Spectrometer. A nearby long-term station provides climate and canopy water balance data. Analyses found a strong relationship between visibility and LWC in dense fog. The fog gauge showed weak correlations due to coarse resolution and false rain catch, but had a reasonable catch efficiency. The start of fog catch lagged compared to the nearby station possibly due to screen surface wetting. Concurrent with other analyses, one goal is to calibrate the fog gauge and visibility sensor for long-term LWC monitoring. The mobile platform was effective for short-term deployment of airborne sensors. We hope to repeat the experiment in the future on O‘ahu and other islands.