Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: An integrated use of AMSR series, MODIS, and Landsat

REMOTE SENSING OF ENVIRONMENT

卷:199

Article

Mizuochi, Hiroki¹  Hiyama, Tetsuya²  Ohta, Takeshi³  Fujioka, Yuichiro⁴  Kambatuku, Jack R.⁵  Iijima, Morio⁶  Nasahara, Kenlo N.¹ 

[1] Univ Tsukuba, Fac Life & Environm Sci, 1-1-1 Tennoudai, Tsukuba, Ibaraki 3058572, Japan

[2] Nagoya Univ, Inst Space Earth Environm Res ISEE, Chikusa Ku, Furo Cho, Nagoya, Aichi 4648601, Japan

[3] Nagoya Univ, Grad Sch Bioagr Sci, Chikusa Ku, Furo Cho, Nagoya, Aichi 4648601, Japan

[4] Kyushu Univ, Grad Sch Social & Cultural Studies, Nishi Ku, 744 Motooka, Fukuoka 8190395, Japan

[5] Univ Namibia, Fac Agr & Nat Resources, Oshakati, Namibia

[6] Kindai Univ, Sch Agr, Nara 6318505, Japan

关键词: AMSR-E;    AMSR2;    Data fusion;    Database unmixing (DBUX);    Landsat ETM;    Landsat TM;    MODIS;    Seasonal wetlands;   

DOI  :  10.1016/j.rse.2017.07.026

来源: Elsevier

【 摘 要 】

Broad scale monitoring of inland waters is essential to research on carbon and water cycles, and for application in the monitoring of disasters including floods and droughts on various spatial and temporal scales. Satellite remote sensing using spatiotemporal data fusion (STF) has recently attracted attention as a way of simultaneously describing spatial heterogeneity and tracking the temporal variability of inland waters. However, existing STF approaches have limitations in describing abrupt temporal changes, integrating dissimilar datasets (i.e., fusions between microwave and optical data), and compiling long-term, frequent STF datasets. To overcome these limitations, in this study we developed and evaluated a lookup table (LUT)-based STF, termed database unmixing (DBUX), using multiple types of satellite data (AMSR series, MODIS, and Landsat), and applied it to semi-arid seasonal wetlands in Namibia. The results show that DBUX is: 1) flexible in integrating optical data (MODIS or Landsat) with microwave (AMSR series) and seasonal (day of year) information; 2) able to generate long-term, frequent Landsat-like datasets; and 3) more reliable than an existing approach (spatial and temporal adaptive reflectance fusion model; STARFM) for tracking dynamic temporal variations in seasonal wetlands. Water maps retrieved from the resulting STF dataset for the wetlands had a 30-m spatial resolution and a temporal frequency of 1 or 2 days, and the dataset covered from 2002 to 2015. The time series water maps accurately described both seasonal and interannual changes in the wetlands, and could act as a basis for understanding the hydrological features of the region. Further studies are required to enable application of DBUX in other regions, and for other landscapes with different satellite sensor combinations.

【 授权许可】

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