5 December 2017 Reconstructing spatial–temporal continuous MODIS land surface temperature using the DINEOF method
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Abstract
Land surface temperature (LST) is one of the key states of the Earth surface system. Remote sensing has the capability to obtain high-frequency LST observations with global coverage. However, mainly due to cloud cover, there are always gaps in the remotely sensed LST product, which hampers the application of satellite-based LST in data-driven modeling of surface energy and water exchange processes. We explored the suitability of the data interpolating empirical orthogonal functions (DINEOF) method in moderate resolution imaging spectroradiometer LST reconstruction around Ali on the Tibetan Plateau. To validate the reconstruction accuracy, synthetic clouds during both daytime and nighttime are created. With DINEOF reconstruction, the root mean square error and bias under synthetic clouds in daytime are 4.57 and 0.0472    K , respectively, and during the nighttime are 2.30 and 0.0045 K, respectively. The DINEOF method can well recover the spatial pattern of LST. Time-series analysis of LST before and after DINEOF reconstruction from 2002 to 2016 shows that the annual and interannual variabilities of LST can be well reconstructed by the DINEOF method.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Wang Zhou, Wang Zhou, Bin Peng, Bin Peng, Jiancheng Shi, Jiancheng Shi, } "Reconstructing spatial–temporal continuous MODIS land surface temperature using the DINEOF method," Journal of Applied Remote Sensing 11(4), 046016 (5 December 2017). https://doi.org/10.1117/1.JRS.11.046016 . Submission: Received: 29 June 2017; Accepted: 14 November 2017
Received: 29 June 2017; Accepted: 14 November 2017; Published: 5 December 2017
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