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Journal of Applied Remote Sensing

Estimating the land-surface temperature of pixels covered by clouds in MODIS products
Author(s): Wenping Yu; Mingguo Ma; Xufeng Wang; Junlei Tan
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Paper Abstract

This study implements the “neighboring-pixel” (NP) theoretical method, which uses spatially and temporally NPs to reconstruct cloud-contaminated pixels in daily Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) products. The 2012 MODIS LSTs of the Heihe River Basin (HRB) region in China are used as an example, and the ground-measured LSTs obtained at 17 sites from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project are used to validate the reconstruction results. The results show a bias of 0.25 K and RMSE of 4.122 K during the day and a bias of −0.1263  K and RMSE of 2.901  K at night. The error analysis reveals an uncertainty in the estimation of the cloud-contaminated pixels that can be attributed to errors in the estimation of parameters and net solar radiation retrieval and inaccuracies inherent in the NP scheme. The analysis results reveal that the time-gap effect is the main cause of uncertainty in the nighttime reconstruction, whereas the large extreme cases for the daytime reconstruction are generally caused by strong convection systems that usually occur with heavy precipitation in the cloud-contaminated pixels. Despite the uncertainty, the proposed approach is promising for the improvement of MODIS LST application in practice.

Paper Details

Date Published: 6 November 2014
PDF: 14 pages
J. Appl. Remote Sens. 8(1) 083525 doi: 10.1117/1.JRS.8.083525
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Wenping Yu, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Univ. of Chinese Academy of Science (China)
Mingguo Ma, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xufeng Wang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Junlei Tan, Cold and Arid Regions Environmental and Engineering Research Institute (China)


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