Abstract
Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven’t been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value < 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.
Highlights
High spatial and temporal resolution remote sensing technology plays an important role in land-cover detection, crop growth monitoring and phenological parameter inversion [1]
It is impossible to obtain high temporal resolution and high spatial resolution images simultaneously from one sensor mounted on a satellite [2,3]
To resolve the difficulties that spatial and temporal adaptive reflectance fusion model (STARFM) presents from the mixed pixel of Moderate Resolution Imaging Spectroradiometer (MODIS) in heterogeneous regions, we developed an improved STARFM named Unmixing-based STARFM (USTARFM) with the help of an Unmixing-based algorithm
Summary
High spatial and temporal resolution remote sensing technology plays an important role in land-cover detection, crop growth monitoring and phenological parameter inversion [1]. Landsat series multi-spectral images at 30-m resolution have wide applications in extracting vegetation indices, monitoring land cover dynamic changes and ecological system variation studies. This system is widely used because of its finer spatial resolution, rich archive, and free availability [4,5,6]. The 16-day revisit cycle and the influence of bad weather, such as rain and clouds, make it difficult to acquire the continuous and cloudless remote sensing images that may be required to monitor certain Earth surface changes [7,8].
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