Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Authors

Linglin Zeng, Wuhan UniversityFollow
Brian D. Wardlow, University of Nebraska - LincolnFollow
Tsegaye Tadesse, University of Nebraska-LincolnFollow
Jie Shan, Purdue UniversityFollow
Michael Hayes, University of Nebraska - LincolnFollow
Deren Li, Wuhan University & Collaborative Innovation Center for Geospatial TechnologyFollow
Daxiang Xiang, Changjiang River Water Resources CommissionFollow

Date of this Version

2015

Citation

Remote Sens. 2015, 7, 951-970

Comments

© 2015 by the authors

Open access

doi:10.3390/rs70100951

Abstract

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.