Statistical estimation of high-resolution surface air temperature from MODIS over the Yangtze River Delta, China
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High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance, urban climate change, and so on. This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) to estimate air temperature at a high resolution over the Yangtze River Delta region, China. It is found that daytime LST is highly correlated with maximum air temperature, and the linear regression coefficients vary with the type of land surface. The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models. The estimated air temperature shows a clear spatial structure of urban heat islands. Spatial patterns of LST and air temperature differences are detected, indicating maximum differences over urban and forest regions during summer. Validations are performed with independent data samples, demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C, and the uncertainty is about 3.1°C, if using all valid LST data. The error is reduced by 0.4°C (15%) if using best-quality LST with errors of less than 1 K. The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.
Key wordsremote sensing surface air temperature land surface temperature land cover type Moderate Resolution Imaging Spectroradiometer (MODIS)
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The authors are grateful to the Monsoon Asia Integrated Regional Study (MAIRS) project and Giovanni system at NASA GES DISC (Goddard Earth Sciences Data and Information Servies Center) for their data exploration of MODIS high resolution land surface temperatures and other land products.
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