Abstract | ||
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MODIS land surface temperature (LST) products have been widely used in numerous applications. Each pixel within the MODIS LST products is acquired at different local solar time even though they are in the same granule. A temporal consistency and spatial comprehensiveness data set will benefit us in the utilization of the LST products in related applications and researches. In this study, a diurnal temperature cycle (DTC) model was employed to normalize the MODIS LSTs to the same local solar time. The MODIS LSTs were derived from the Terra/MODIS and Aqua/MODIS LST products (MOD11_L2 and MYD11_L2, respectively). The results at daytime only are presented because the larger LSTs heterogeneity makes the comparison of LSTs before and after the temporal normalization much clearer. The preliminary results indicate that the spatial variations of the MODIS LSTs caused by different local solar time are removed after the temporal normalization. The temporal normalized LSTs may become more suitable for the analysis of land surface processes. |
Year | DOI | Venue |
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2011 | 10.1109/IGARSS.2011.6048951 | IGARSS |
Keywords | Field | DocType |
remote sensing,modis land surface temperature,lst products,aqua modis,moderate resolution imaging spectroradiometer (modis),dtc model,spatial comprehensiveness,terra modis,land surface temperature,temporal consistency,diurnal temperature cycle model,local solar time,temporal normalization,diurnal temperature cycle,ocean temperature,spatial variation | Land surface temperature,Normalization (statistics),Diurnal temperature variation,Sea surface temperature,Computer science,Remote sensing,Daytime,Pixel,Temporal consistency,Solar time | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 |
ISBN | Citations | PageRank |
978-1-4577-1003-2 | 0 | 0.34 |
References | Authors | |
1 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sibo Duan | 1 | 60 | 13.11 |
Hua Wu | 2 | 0 | 0.68 |
Ning Wang | 3 | 230 | 87.46 |
Xiao-Ming Zhou | 4 | 0 | 0.34 |
Bo-Hui Tang | 5 | 103 | 26.15 |
Zhao-Liang Li | 6 | 416 | 127.21 |