Sci Total Environ

Temperature gradients in cities can cause inter-neighborhood differences in the timing of pollen release. However, most epidemiological studies examining allergenic pollen utilize daily measurements from a single pollen monitoring station with the implicit assumption that the measured time series of airborne pollen concentrations applies across the study areas, and that the temporal mismatch between concentrations at the counting station and elsewhere in the study area is negligible. This assumption is tested by quantifying temperature using satellite imagery, observing flowering times of oak (Quercus) and mulberry (Morus) trees at multiple sites, and collecting airborne pollen. Epidemiological studies of allergenic pollen are reviewed and temperatures within their study areas are quantified. In this one-year study, peak oak flowering time was well explained by average February nighttime temperature (R|\u202f=\u202f0.94), which varied by 6\u202f\ub0C across Detroit. This relationship was used to predict flowering phenology across the study region. Peak flowering ranged from April 20-May 13 and predicted a substantial portion of relative airborne oak pollen concentrations in Detroit (R|\u202f=\u202f0.46) and at the regional pollen monitoring station (R|\u202f=\u202f0.61). The regional pollen monitoring station was located in a cooler outlying area where peak flowering occurred around May 12 and peak pollen concentrations were measured on May 15. This provides evidence that the timing of pollen release varies substantially within a metropolitan area and challenges the assumption that pollen measurements at a single location are representative of an entire city. Across the epidemiological studies, 50% of study areas were not within 1\u202f\ub0C (equal to a lag or lead of 4\u202fdays in flowering time) of temperatures at the pollen measurement location. Epidemiological studies using a single pollen station as a proxy for pollen concentrations are prone to significant measurement error if the study area is climatically variable.F32 ES026477/ES/NIEHS NIH HHS/United StatesT42 OH008455/OH/NIOSH CDC HHS/United StatesUL1 TR002240/TR/NCATS NIH HHS/United States2020-02-25T00:00:00Z30759561PMC64025947245vault:3161

Improving mean minimum and maximum month-to-month air temperature surfaces using satellite-derived land surface temperature

Month-to-month air temperature (T) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month T mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical T modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum T, but also for maximum T. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum T (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data

Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season