Abstract
Spatiotemporal fusion of remote sensing data is essential for generating high spatial and temporal resolution data by taking advantage of high spatial resolution and high temporal resolution imageries. At present, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is one of the most widely used spatiotemporal fusion technologies of remote sensing data. However, the quality of data acquired by STARFM depends on temporal information from homogeneous land cover patches at the MODIS (Moderate Resolution Imaging Spectroradiometer) imagery, and the estimation accuracy of STARFM degrades in highly fragmentated and heterogeneous patches. To address this problem, we developed an innovative method to improve fusion accuracy, especially in areas of high heterogeneity, by combining linear pixel unmixing and STARFM. This method derived the input data of STARFM by downscaling the MODIS data with a linear spectral mixture model. Through this fusion method, the complement effect of the advantages of remote sensing information can be realized, and the multi-source remote sensing data can be realized for visual data mining. The developed fusion method was applied in Bosten Lake, the largest freshwater lake in China, and our analysis of results suggests that (1) after introducing the linear spectral mixture model, the fusion images illustrated improved spatial details to a certain extent and can be employed to identify small objects, as well as their texture distribution information; (2) for fragmented and highly heterogeneous areas, a stronger correlation between the predicted results and the real images was observed when compared to STARFM with small bias; and (3) the predicted red band and near infrared band can generate high-precision 16-m NDVI (Normalized Difference Vegetation Index) data with advantages in both spatial resolution and temporal resolution. The results are generally consistent with the Gaofen-1 wide field of view cameras (GF-1 WFV) NDVI in the same period and therefore can reflect the spatial distribution of NDVI in detail.
Highlights
Due to the development of remote sensing technology and the application of different Earth observation satellite sensors, remote sensing data obtained from the same area can present multi-platform, multi-sensor, multi-spectral, multi-resolution, and multi-temporal characteristics
GF-1 uses the key technologies of high spatial resolution and multi-spectral and wide swath, and it is widely used in the applications of dynamic monitoring and change detection, geological and mineral resource surveying, ecological environment investigation, geological hazard investigation, and so on [12,13,14,15,16,17]
Considering that the estimation accuracy of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm is low in areas of high heterogeneity, this paper proposed a new fusion model
Summary
Due to the development of remote sensing technology and the application of different Earth observation satellite sensors, remote sensing data obtained from the same area can present multi-platform, multi-sensor, multi-spectral, multi-resolution, and multi-temporal characteristics. High temporal resolution sensors have more frequent revisit rates and produce wide area coverage with lower spatial resolution. MODIS data is widely used in the monitoring of land cover types on a large scale [18,19,20]. The spatial resolution of MODIS data is relatively low, and it cannot meet the requirements for the monitoring land cover and ecosystem changes in highly heterogeneous areas [21]. The availability and the application potential of data can be improved greatly
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