Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing images. In this paper, an object-based spatiotemporal fusion model (OBSTFM) is proposed to produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology changes and land cover changes without shape changes). Considering different changes that might occur in different regions, multi-resolution segmentation is first employed to produce segmented objects, and then a linear injection model is introduced to produce preliminary prediction. In addition, a new optimized strategy to select similar pixels is developed to obtain a more accurate prediction. The performance of proposed OBSTFM is validated using two remotely sensed dataset experiencing phenology changes in the heterogeneous area and land cover type changes, experimental results show that the proposed method is advantageous in such areas with non-shape changes, and has satisfactory robustness and reliability in blending large-scale abrupt land cover changes. Consequently, OBSTFM has great potential for monitoring highly dynamic landscapes.

Spatiotemporal fusion technique provides a cost-efficient way to achieve dense time series observation. Among all categories of spatiotemporal fusion methods, the weight function-based method attracted considerable attention. However, this kind of method selects similar pixels in a regular window without considering the distribution of features, which will weaken its ability to preserve the structure information. Besides, the weight function-based method carries out pixel-by-pixel fusion computation, which leads to computational inefficiency. To solve the aforementioned issues, a flexible object-level (OL) processing strategy is proposed in this article. Three popular spatiotemporal fusion methods include the spatial and temporal adaptive reflectance fusion model (STARFM), the enhance STARFM (ESTARFM) and the three-step method (Fit-FC) were selected as examples to analyze and validate the effectiveness of the OL processing strategy. Four study sites with different surface landscapes and change patterns were adopted for experiments. Experimental results indicated that the OL fusion versions of STARFM, ESTARFM, and Fit-FC can better preserve the structural information, and were 102.89–113.71, 92.77–115.73, and 30.51–36.15 times faster than their original methods. Remarkably, the OL fusion versions of Fit-FC outperform all competing methods in one-pair case fusion experiments, especially in Poyang lake wetland (PY) area (root mean square error (RMSE) is 0.0343 versus 0.0380, correlation coefficient ( $r$ ) is 0.7469 versus 0.6986 compare with Fit-FC). Additionally, the OL processing strategy can also be adopted to enhance other methods which use the principle of combining similar adjacent information. The program and test data are available at https://github.com/Andy-cumt .

Spatiotemporal fusion (STF) is a cost-effective way to complement the spatiotemporal resolution of multi-source images, which has been employed in various applications requiring image sequences. In real-world applications, the spectral accuracy, spatial accuracy and efficiency of STF play a critical role. Despite this, most STF methods focus on improving the spectral accuracy, while the challenges of spatial information loss and low efficiency have received limited attention. Additionally, the improvements of spectral accuracy, spatial accuracy and efficiency in STF are contradictory, and existing STF methods cannot balance them well, which limits their reliability and applicability for various STF tasks. To solve above issues, this study proposes an object-level hybrid spatiotemporal fusion method (OL-HSTFM), which incorporates the efficiency advantage of object-level fusion strategy, spectral accuracy advantage of the three-step method (Fit-FC), and the spatial accuracy advantage of the spatial and temporal adaptive reflectance fusion model (STARFM). The performance of OL-HSTFM was compared with two classic STF methods and eight state-of-the-art STF methods at two sites. The experimental results indicate that OL-HSTFM outperforms the other 10 methods in overall performance and has excellent efficiency. Furthermore, this study proposes a new metric that can assess the accuracy of both spatial and spectral domains in STF, which provides a more comprehensively and intuitively measurement of the quality of fused images compared to commonly used metrics. The program of OL-HSTFM is openly available on https://github.com/Andy-cumt/Object-level-spatiotemporal-fusion-models .