Using Fixed Rank Filtering to Make Spatio-Temporal Interpolation of MODIS Temperature

DUAN Yue; SHU Hong; HU Hongda

doi:10.13203/j.whugis20140495

Volume 41 Issue 8

Aug. 2016

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Geomatics and Information Science of Wuhan University > 2016 > 41(8): 1027-1033. > DOI: 10.13203/j.whugis20140495

DUAN Yue, SHU Hong, HU Hongda. Using Fixed Rank Filtering to Make Spatio-Temporal Interpolation of MODIS Temperature[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8): 1027-1033. DOI: 10.13203/j.whugis20140495

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Using Fixed Rank Filtering to Make Spatio-Temporal Interpolation of MODIS Temperature

DUAN Yue^{1, 2,},
SHU Hong^{1, 2, ,},
HU Hongda¹

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Suzhou Institute of Wuhan University, Suzhou 215123, China

Funds:

The National Natural Science Foundation of China 41171313

Hubei Provincial Natural Science Foundation of China 2014CFB725

Suzhou Science and Technology Program of Applied Basic Research SYG201319

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Author Bio:
DUAN Yue, master, specializes in spatio-temporal statistical analysis. E-mail:yue_duan@126.com
Corresponding author:
SHU Hong, PhD, professor. E-mail:shu_hong@whu.edu.cn
Received Date: April 12, 2015
Published Date: August 04, 2016

Graphical Abstract

Abstract

Abstract

Kriging is widely-used for spatial structure exploration and spatial data interpolation. However, most kriging methods are designed for spatial interpolation and not for spatio-temporal data interpolation. After constructing basic functions for a specific experimental area, we made spatio-temporal predictions for MODIS temperature data with Fixed Rank Filtering (FRF). We compared these prediction results with the interpolation results of Fixed Rank Kriging (FRK) and discuss the differences. Experimental results show that when points are evenly distributed in space, the FRK method obtains higher prediction accuracy with results slightly better than FRF. However considering the temporal relevancy of temperature, when data is missing in a larger area, the FRF method shows a capability to comprehensively exploit spatio-temporal information better than other methods for achieving higher quality interpolation results in cases that lack spatial information.
- fixed rank filtering,
- spatio-temporal,
- fixed rank kriging,
- MODIS temperature product,
- spatio-Temporal Random effects

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References

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