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

Abstract

Abstract: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.

Mesoscale spatial variability of soil‐water content in an alpine meadow on the northern Tibetan Plateau

“…Point observations are common in SWC studies on the plateau (Yang et al, ), having the advantages of high precision and temporal resolution but the disadvantage of weak spatial representativeness. Remote sensing has been an important method for acquiring data in studies with large areas (Cai, Chen, Wang, & Yang, ; Guo et al, ; Guo et al, ; Huang et al, ; Li, Bing, & Jin, ; Sun et al, ; Tian & Sun, ; Wang et al, ; Wang, Zhang, Qiu, et al, ). It has the advantages of being nondestructive, large scale, and independent of weather but is strongly influenced by land surface roughness and meteorological condition.…”

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

“…As shown in Figures 7-9, land cover contributed little in most of the climates. This seems to contradict other work (e.g., [31,33,65]) that confirmed the noticeable impacts of land cover on Tmax estimation as the specific heat varies significantly from one land cover type to another [31,33,65]. Nevertheless, Benali et al [12] and Janatian et al [32] indicated the independence of land cover when estimating Ta based on remote sensing data.…”

“…At a local scale, many previous studies reported different relationships between Ta and LST and reached divergent or even contradictory conclusions in studies, e.g., if the land cover plays an important role in Ta estimation or not [12,[31][32][33][34] and if nighttime LST can outperform in daily maximum Ta than daytime LST estimation or not [12,[31][32][33][34]. For example, usually, daytime and nighttime LST is expected to be more effective and simply used in estimating daily maximum and minimum Ta (hereafter Tmax and Tmin), respectively [12,[31][32][33][34], as the observation time of daytime and nighttime LST from MODIS is close to the occurrence of the maximum and minimum LST and Ta, respectively. However, Zhang et al [35] and Zeng et al [4] showed that using nighttime LST resulted in higher accuracy Tmax estimations than using daytime LST.…”

Effect of intra-urban temperature variation on tree flowering phenology, airborne pollen, and measurement error in epidemiological studies of allergenic pollen

“…Many phenological studies have predicted plant phenology using LST (e.g., Chen et al 2018;Hanes and Schwartz 2011;Zhang et al 2004), often because LST is readily available from remote sensing imagery whereas air temperature is measured at sparsely-located weather stations. While air and land surface temperature are well correlated (Cai et al, 2017;Mildrexler et al, 2011), there is also a direct mechanistic link between LST and phenology: LST is directly related to energy exchanges between the atmosphere and soil (Quattrochi and Luvall, 1999;Simó et al, 2018) and soil temperature affects root phenology and plant reproductive phenology (Chen et al, 2016;Delpierre et al, 2016;Greer et al, 2006). LST measured by MODIS sensors on the Aqua and Terra satellites was extracted for each site (using coordinates of the site centroid) during each month from 2007 through 2017 and then reprojected to WGS84.…”