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

Bence Budavari,Xiaolin Zhu,Feng Gao,Chiman Kwan

doi:10.3390/rs10040520

Abstract

We present a new, simple, and efficient approach to fusing MODIS and Landsat images. It is well known that MODIS images have high temporal resolution and low spatial resolution, whereas Landsat images are just the opposite. Similar to earlier approaches, our goal is to fuse MODIS and Landsat images to yield high spatial and high temporal resolution images. Our approach consists of two steps. First, a mapping is established between two MODIS images, where one is at an earlier time, t1, and the other one is at the time of prediction, tp. Second, this mapping is applied to map a known Landsat image at t1 to generate a predicted Landsat image at tp. Similar to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SpatioTemporal Image-Fusion Model (STI-FM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) approaches, only one pair of MODIS and Landsat images is needed for prediction. Using seven performance metrics, experiments involving actual Landsat and MODIS images demonstrated that the proposed approach achieves comparable or better fusion performance than that of STARFM, STI-FM, and FSDAF.

Highlights

Fusing high spatial resolution/low temporal resolution Landsat images with low spatial resolution/high temporal resolution MODIS images will have many applications, such as drought monitoring, fire damage assessment, flood damage monitoring, etc
Since our main interest is in forward prediction, we only compare with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SpatioTemporal Image-Fusion Model (STI-FM), and Flexible Spatiotemporal DAta Fusion (FSDAF)
We present a simple, and high performance forward prediction approach to generating Landsat images with high temporal resolution

Summary

Introduction

Fusing high spatial resolution/low temporal resolution Landsat images with low spatial resolution/high temporal resolution MODIS images will have many applications, such as drought monitoring, fire damage assessment, flood damage monitoring, etc. Several alternative algorithms [3,4] were published to further improve the fusion performance. The Bayesian prediction approach [5,6,7], which was proposed for fusing satellite images with complementary characteristics, can be an alternative fusion method for Landsat and MODIS. According to the survey paper [8], the STAARCH [3] approach can handle abrupt changes, but requires two pairs of MODIS and Landsat images. Similar to STAARCH, ESTARFM requires two pairs of images. As a result, both STAARCH and ESTARFM may not be suitable for forward prediction

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Conclusion