Dense time series of remote sensing images with high spatio-temporal resolution are critical for monitoring land surface dynamics in heterogeneous landscapes. Spatio-temporal fusion is an effective solution to obtaining such time series images. Many spatio-temporal fusion methods have been developed for producing high spatial resolution images at frequent intervals by blending fine spatial images and coarse spatial resolution images. Previous studies have revealed that the accuracy of fused images depends not only on the fusion algorithm, but also on the input image pairs being used. However, the impact of input images dates on the fusion accuracy for time series with different temporal variation patterns remains unknown. In this paper, the impact of input image pairs on the fusion accuracy for monotonic linear change (MLC), monotonic non-linear change (MNLC), and non-monotonic change (NMC) time periods were evaluated, respectively, and the optimal selection strategies of input image dates for different situations were proposed. The 16-day composited NDVI time series (i.e., Collection 6 MODIS NDVI product) were used to present the temporal variation patterns of land surfaces in the study areas. To obtain sufficient observation dates to evaluate the impact of input image pairs on the spatio-temporal fusion accuracy, we utilized the Harmonized Landsat-8 Sentinel-2 (HLS) data. The ESTARFM was selected as the spatio-temporal fusion method for this study. The results show that the impact of input image date on the accuracy of spatio-temporal fusion varies with the temporal variation patterns of the time periods being fused. For the MLC period, the fusion accuracy at the prediction date (PD) is linearly correlated to the time interval between the change date (CD) of the input image and the PD, but the impact of the input image date on the fusion accuracy at the PD is not very significant. For the MNLC period, the fusion accuracy at the PD is non-linearly correlated to the time interval between the CD and the PD, the impact of the time interval between the CD and the PD on the fusion accuracy is more significant for the MNLC than for the MLC periods. Given the similar change of time intervals between the CD and the PD, the increments of R² of fusion result for the MNLC is over ten times larger than those for the MLC. For the NMC period, a shorter time interval between the CD and the PD does not lead to higher fusion accuracies. On the contrary, it may lower the fusion accuracy. This study suggests that temporal variation patterns of the data must be taken into account when selecting optimal dates of input images in the fusion model.

The Sentinel-2 MSI and Landsat-8 OLI are often used as data sources to form temporally dense harmonization observations for accurate quantitative studies. However, challenges remain in resolving surface reflectance differences between both sensors due to inconsistencies in spectral band positions, widths, and spectral response functions. In this study, we developed the matching bands adjustment before missing bands prediction method (MA-MP) for performing spectral harmonization between Landsat-8 and Sentinel-2A to reduce such surface reflectance differences. This method uses the global representative samples from the harmonized Landsat Sentinel-2 (HLS) product to build the spectral harmonization model of Landsat-8 and Sentinel-2A at the cluster level for band-pass adjustment in the bands where Landsat-8 and Sentinel-2A are matched, and for prediction in the band which is unilaterally missing bands in Landsat-8 image relative to Sentinel-2A image. Experimental results show robust spectral harmonization in four selected study areas with a variety of land cover types, and the spectral harmonization results are significantly better than those of current mainstream methods. Overall, the proposed MA-MP can adjust the observations of Landsat-8 and Sentinel-2A in matching bands and predict the observations of Landsat-8 in a unilaterally missing band, which has the potential to be used flexibly for building the spectral harmonization model between any two different sensor images.

The Landsat optical images have provided a unique long-term record of the terrestrial surface and have always been an important data source for Earth observation. However, due to variable solar and view geometry, Landsat's observations have directional reflectance effects over non-Lambertian surfaces across all spectral bands, known as BRDF effects, which will become a significant source of noise for fine applications. In this study, we developed the Combining Optimal BRDF Archetype with Spectral Characteristics method (COBASC) to effectively reduce BRDF effects in Landsat directional reflectance data. COBASC builds a high-accuracy BRDF parameter Look-Up Table (BRDF LUT) for Landsat pixels by determining the optimal BRDF archetypes of Landsat pixel samples and combining them with the spectral characteristics of the pixel samples, considering landscape heterogeneity at the Landsat scale. Based on the BRDF LUT built, high-accuracy BRDF parameters for Landsat pixels are obtained to implement BRDF correction. Experimental results show that under varying degrees of BRDF effects in Landsat observations, the proposed method consistently and effectively reduces the directional reflectance differences caused by BRDF. Moreover, the accuracy of the BRDF-corrected results is higher than that achieved by current mainstream methods, thereby demonstrating the superiority of the proposed method in correcting BRDF effects in Landsat directional reflectance data. The COBASC has the potential to be used flexibly for BRDF correction on other medium- to high-spatial-resolution satellite remote sensing images.