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

Abstract

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 μm ) 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 μm 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.

Estimation of Groundwater Use Pattern and Distribution in the Coastal Mekong Delta, Vietnam via Socio-Economical Survey and Groundwater Modelling

“…The land surface temperature (LST) is the temperature of the Earth's surface as derived from remotely sensed thermal infrared data [8]. The Landsat 8 LST was computed by fusing images of MODIS LST and Landsat 8 brightness temperature (Tb), provided by Hazaymeh and Hassan (2015) [9]. In Tra Vinh Province, most of abstraction well located in household area of near household area which was used for the agriculture demand of some annual crops or in some sand dunes along the coastal line.…”

A Hybrid Color Mapping Approach to Fusing MODIS and Landsat Images for Forward Prediction

“…Moreover, since there are multiple steps in the prediction process, it is hard to grasp which step contributes the most to the prediction performance. A relatively simple and more efficient algorithm, the SpatioTemporal Image-Fusion Model (STI-FM) [10] applies clustering to the images first, and, for each cluster, performs a separate prediction. In addition to the above algorithms, some alternative fusion ideas [11][12][13][14][15][16] were proposed and evaluated.…”

An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

“…Another enhanced version of STARFM (ESTARFM) was proposed to improve the fusion accuracy in heterogeneous regions [5]. A disadvantage of STAARCH and ESTARFM is that the two methods both require two pairs of images as input [6], which may limit their use in days that are frequently cloudy (e.g., rainy season) [7]. Several algorithms using only one image pair as input have been developed [8] and shown to outperform STARFM in detecting land-cover changes [9,10].…”