Abstract
Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.
Highlights
Satellite data with a high spatiotemporal resolution are highly desired for studying ecological processes and impacts in heterogeneous landscapes
When the maximum and minimum intervals are both large (i.e., >300), the fusion accuracy is still high with R ≥ 0.8 and root mean squared error (RMSE) ≤ 0.2
This finding is explainable using the phenological similarity strategy (PSS) because the base image pairs, vals are both large (i.e., >300), the fusion accuracy is still high with R ≥ 0.8 and RMS
Summary
Satellite data with a high spatiotemporal resolution are highly desired for studying ecological processes and impacts in heterogeneous landscapes. A single satellite sensor provides data with either a high temporal frequency or a high spatial resolution due to the trade-offs among the spatial, temporal, and spectral resolutions [1]. The Moderate Resolution Imaging Spectroradiometer (MODIS) has a frequent revisit cycle but a coarse resolution ranging from 250 m to 1 km, whereas Landsat satellite data have a finer spatial resolution of 30 m but a restricted temporal resolution [1]. To exceed the physical limitations of individual satellite data, a feasible solution has been to spatiotemporally fuse frequent coarse-resolution images (hereafter referred to as coarse images) with less frequent fine-resolution images (hereafter referred to as fine images) [2,3,4]. A detailed review can be found in [4]
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