Modification and validation of a new method to improve the accuracy of MODIS-derived dew point temperature over mainland China

MODIS atmospheric profile products (MOD07_L2 and MYD07_L2) have been widely used for near-surface dew point temperature ($T_d$) estimation. However, their accuracy over large scale has seldom been evaluated. In this study, we validated these two products comprehensively against 2153 stations over ma...

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Bibliographic Details
Main Authors: Wenbin Zhu, Mei Cao
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Digital Earth
Subjects:
Online Access:http://dx.doi.org/10.1080/17538947.2023.2251941
Description
Summary:MODIS atmospheric profile products (MOD07_L2 and MYD07_L2) have been widely used for near-surface dew point temperature ($T_d$) estimation. However, their accuracy over large scale has seldom been evaluated. In this study, we validated these two products comprehensively against 2153 stations over mainland China. MOD07_L2 was suggested by our study because it achieved higher accuracy in either of two frequently-used methods. To be specific, the root-mean-square error (RMSE) achieved by MOD07_L2 and MYD07_L2 was 5.82 and 7.42 °C, respectively. On this basis, a recent ground-based correction method was modified to further improve their accuracy. Our focus is to investigate whether this ground-based approach is applicable to large-scale remote sensing applications. The results show that this new method showed great potential for $T_d$ estimation independently from ground observations. Through the introduction of MODIS land surface products, the RMSE it achieved for MOD07_L2 and MYD07_L2 was 5.23 and 5.59 °C, respectively. Further analysis shows that it was particularly useful in capturing the annual average $T_d$ patterns. The R2, RMSE, and bias of annual average daily mean $T_d$ estimates were 0.95, 1.84 °C, and 0.53 °C, and those achieved for annual average instantaneous $T_d$ estimates were 0.94, 2.09 °C, and 0.75 °C, respectively.
ISSN:1753-8947
1753-8955