Assessing the Reliability of the MODIS LST Product to Detect Temporal Variability

Xu, S.; Wang, D.; Liang, S.; Liu, Y.; Jia, A.

doi:10.1109/LGRS.2023.3312384

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Assessing the Reliability of the MODIS LST Product to Detect Temporal Variability

2023
By Xu, S. ; Wang, D. ; Liang, S. ; ...
Source: IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 2504505

[PDF-897.04 KB]

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Journal Title:

IEEE Geosci. Remote Sens. Lett., 20(9), 2504505
Personal Author:

Xu, S. ; Wang, D. ; Liang, S. ; Liu, Y. ; Jia, A.

Xu, S. ; Wang, D. ; Liang, S. ; Liu, Y. ; Jia, A. Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies) Less -
Description:

Land surface temperature (LST) data acquired from satellites are used extensively in studying climate variability. Many researchers have used moderate resolution imaging spectroradiometer (MODIS) LST to detect the temperature trend, however, its reliability has not been fully investigated. Using in situ data acquired from 67 stations worldwide, this study examined the reliability of the detected temperature trends and investigated the associated influencing factors. The high-quality (HQ) MODIS data have a root mean square error (RMSE) of 2.44 and 3.70 K at nighttime and daytime, respectively. However, its trend detection had an RMSE of 0.81 and 0.98 K/decade at nighttime and daytime, respectively. Clear-sky bias, quality control, LST estimation uncertainties, trend magnitude, and length of time were factors that influenced the detected trends. Filling cloud-covered areas in MODIS data may effectively reduce biases in trend detection.
Keywords:

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Remote Sensing Temperature In-situ Measurements Land Surface Temperature (LST) Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite Observations
Source:

IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 2504505
DOI:

https://doi.org/10.1109/LGRS.2023.3312384
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PDF
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Journal Article
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Grant no. NA19NES4320002
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urn:sha256:a82eb3bbac3d55ffafa56a202ab4dd2d3dfd17917a4a7d2676dd39d286dbb21c
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Assessing the Reliability of the MODIS LST Product to Detect Temporal Variability

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