Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images

By Khaled Hazaymeh, Quazi K. Hassan. 2015

64 citations recorded by the source corpus.

Abstract Our aim was to evaluate a spatiotemporal image-fusion model (STI-FM) for enhancing the temporal resolution (i.e., from 16 to 8 days) of Landsat-8 surface reflectance images by utilizing the moderate-resolution imaging spectroradiometer (MODIS) images, and assess its applicability over a heterogeneous agriculture dominant semiarid region in Jordan. Our proposed model had two major components: (i) establishing relationships between two 8-day MODIS composite images acquired at two different times (i.e., time 1 and time 2); and (ii) generating synthetic Landsat-8 surface reflectance images at time 2 as a function of Landsat-8 images available at time 1 and the relationship constructed in the first component. We evaluated the synthetic images with the actual Landsat-8 images and observed strong relations between them. For example: the coefficient of determination ( r 2 ) was in the range: (i) 0.72 to 0.82; (ii) 0.71 to 0.79; and (iii) 0.78 to 0.83; for red, near-infrared (NIR), and shortwave infrared ( SWIR 2.2   μ m ) spectral bands, respectively. In addition, root mean square error (RMSE) and absolute average difference (AAD) values were: (i) in between 0.003 and 0.004, and 0.0002, respectively, for red band; (ii) 0.005 and 0.0003, respectively, for NIR band; and (iii) 0.004 and in between 0.0001 and 0.0002, respectively, for SWIR 2.2   μ m band. The developed method would be useful in understanding the dynamics of environment issues (e.g., agriculture drought and irrigation management), which require both relatively high spatial (i.e., 30 m) and high temporal resolution (i.e., 8 days) images.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/c2d2aff28b6615d8011ff47decf8279bf1169abc

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In Ecology

1. Xiaolin Zhu
2. Jiaqi Tian
3. Fangyi Cai
4. Trecia Kay-Ann Williams
5. Peter M. Atkinson
6. Qunming Wang

In Environmental Engineering

1. Peter M. Atkinson
2. Qunming Wang
3. Fangyi Cai
4. Trecia Kay-Ann Williams
5. Xiaolin Zhu
6. Jiaqi Tian

In Media Technology

1. Xiaolin Zhu
2. Fangyi Cai
3. Trecia Kay-Ann Williams
4. Jiaqi Tian
5. Peter M. Atkinson
6. Qunming Wang

Citations in the corpus

• Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “Weight function-based STARFM [8]; semi-physical fusion approach [19]; ESTARFM [11]; STAARCH [27]; SADFAT [21]; Topographically corrected downscaling [18]; STITFM [23]; STARFM-Sensor difference [54]; mESTARFM [45]; Bilateral Filter method [59]; ET fusion model [53]; STI-FM [64]; operational STARFM [3”
• Spatio-temporal fusion for daily Sentinel-2 images (2018, methodology): “The Fit-FC method is substantially different from the regression-based spatio-temporal fusion method 489 developed in Hazaymeh and Hassan (2015), which classifies the entire ratio image between two coarse images 490 into three clusters (i.e., negligible, negative and positive changes) and build a…”