Abstract
This study aims to evaluate quantitatively the land surface temperature (LST) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) MOD11A1 and MYD11A1 Collection 5 products for daily land air surface temperature (Ta) estimation over a mountainous region in northern Vietnam. The main objective is to estimate maximum and minimum Ta (Ta-max and Ta-min) using both TERRA and AQUA MODIS LST products (daytime and nighttime) and auxiliary data, solving the discontinuity problem of ground measurements. There exist no studies about Vietnam that have integrated both TERRA and AQUA LST of daytime and nighttime for Ta estimation (using four MODIS LST datasets). In addition, to find out which variables are the most effective to describe the differences between LST and Ta, we have tested several popular methods, such as: the Pearson correlation coefficient, stepwise, Bayesian information criterion (BIC), adjusted R-squared and the principal component analysis (PCA) of 14 variables (including: LST products (four variables), NDVI, elevation, latitude, longitude, day length in hours, Julian day and four variables of the view zenith angle), and then, we applied nine models for Ta-max estimation and nine models for Ta-min estimation. The results showed that the differences between MODIS LST and ground truth temperature derived from 15 climate stations are time and regional topography dependent. The best results for Ta-max and Ta-min estimation were achieved when we combined both LST daytime and nighttime of TERRA and AQUA and data from the topography analysis.
Highlights
Land air surface temperature (Ta, called “air temperature” or “near surface temperature”) data are usually collected as point data from weather station locations, typically at 2 m above the land surface
In order to analyze the relations between Ta and all Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, we calculated the coefficient of determination (r2), root mean square error (RMSE) and mean absolute error (MAE) of each type of MODIS LST (TERRA daytime, TERRA nighttime, AQUA daytime and AQUA nighttime) solely with Ta measured from weather stations
The simple method of multiple linear regression analysis was used, and a high accuracy was achieved with r2 = 0.93, RMSE = 1.43, MAE = 1.08 and r2 = 0.88, RMSE = 2.08, MAE = 1.60, for Ta maximum (Ta-max) and Ta minimum (Ta-min), respectively
Summary
Land air surface temperature (Ta, called “air temperature” or “near surface temperature”) data are usually collected as point data from weather station locations, typically at 2 m above the land surface It is an important parameter in a wide range of fields, such as agriculture, e.g., crop evapotranspiration [1], crop yield prediction [2,3], hydrology [4,5], ecology, environment and climate change [6,7]. In order to obtain Ta information for a region, researchers have proposed various methods of interpolation based on known weather station sites [12,13,14].
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