Landsat satellites have provided high-quality Earth observations for more than 40 years, which significantly benefits much research on agriculture, environment, ecology, and so on. However, its low temporal resolution (16-day) and disturbances such as cloud contamination, prevent its usage in some scenarios. Therefore, reconstructing the Landsat image series is always an important topic. Currently, the approaches for reconstructing that are massive, which mainly include the filling-based methods and fusion-based ones. However, their scalability and applicability in large-scale applications are limited. To address this problem, based on the Google Earth engine (GEE), which is a powerful cloud platform, this article introduces a GEE-based fusion and filling model (GFFM) for generating high-quality synthetic Landsat surface reflectance time series data. This model adopts a pixel-wise regression technique to fuse the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data, providing a synthetic image series first. Then, a harmonic analysis is used to densify the Landsat image series. Finally, we utilize a Bayesian model average (BMA) as a weight function to integrate and adjust the previously obtained data to acquire the final seamless image series. We compare the proposed GFFM with some state-of-the-art fusion and filling approaches on various datasets. The experimental results demonstrate that the GFFM not only outperforms these fusion and filling approaches on different datasets, but also shows more robustness in cases of less and cloudy input data.

Hyperspectral image change detection (HSI-CD) is a technique that intelligently checks the changed details in bitemporal hyperspectral images (Bi-HSIs). Deep learning (DL), with the ability to model nonlinear changing features, has achieved promising results in HSI-CD, but the feature mining mechanism is unclear and the architecture design lacks transparency in such DL models. To alleviate this problem, this paper proposes a new low-rank and sparse representation-based deep unfolding network (LRSRNet) for HSI-CD. For feature mining mechanism, the LRSRNet adopts a low-rank and sparse subnetwork (LRSnet) and a change detection sub-network (CDnet). The former is responsible for extracting low-rank features with valuable information and suppressing sparse features containing interference information, while the latter aims to obtain change information from low-rank features. For architecture design, the LRSnet formulates the HSI as a low-rank estimation, sparse estimation, and hyperspectral reconstruction in a low-rank and sparse model, and iteratively optimizes and updates the above sub-problems through deep networks. A new CDnet is designed as a concise convolutional architecture to extract change information from representative Bi-HSIs features. Experiments on three real datasets demonstrate the performance superiority of the proposed LRSRNet method over nine model-driven, datadriven, and model-data-joint-driven HSI-CD algorithms in both qualitative and quantitative evaluations. The proposed LRSRNet is available online: https://github.com/chengle-zhou/LRSRNet.

Normalized Difference Vegetation Index (NDVI) data from optical satellites have been widely used in the field of remote sensing. However, due to cloud cover and other extreme weather reasons, NDVI time series are often missing. In order to fill the observation gap under severe weather conditions, an NDVI time series reconstruction method that fuses optical and synthetic aperture radar (SAR) images has been developed. However, optical images often suffer from long-term data missing, severely limiting the usability of this reconstruction method. To solve this problem, this paper proposes a NDVI-like image strategy to avoid reliance on optical data. Specifically, the strategy obtains NDVI-like images by fitting the multivariate linear relationship between VV, VH polarization bands of SAR images and NDVI images. Then, the optical image information is represented by NDVI-like images, and the SAR and NDVI-like time series are input into the time series reconstruction network to reconstruct the NDVI time series in cloudy areas. Experimentation has revealed that the NDVI-like image strategy can effectively supplements optical image information for the NDVI reconstruction network, reduce the the influence of SAR image speckle noise on reconstruction outcomes, and obtain better reconstruction effects.

Spatiotemporal fusion is an important means to reconstruct the medium spatial resolution remote sensing image series. Presently, many spatiotemporal fusion approaches have been developed and adopted in research on agriculture, ecology, environment, and so on. Although these approaches have achieved remarkable performance in experiments and applications, most of them are designed to fuse all involved bands using the same model with the same parameters, which ignores the band difference. The ignorance may limit the fusion quality for some bands. To address this problem, we propose a novel spatiotemporal data fusion approach based on parameter selection (PSDFA) in this article. The core idea of the newly proposed PSDFA is producing the synthetic image pairs using available data via three means first and then selecting a similar image pair for each band to provide the parameters that are needed for their fusion. The PSDFA can not only be applied in local computers, and its simplified version can also be implemented in Google Earth Engine (GEE), which is a powerful and widely used cloud platform for remote sensing data computing. To test the PSDFA, we conduct two experiments, one in local computers and another in GEE. In local computers, the PSDFA is compared with five state-of-the-art fusion methods on two public Landsat–Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. In GEE, it is used to produce the monthly 30-m image series in two study sites in the USA and compared with another GEE-based fusion approach. The experimental results demonstrate the outstanding performance of the proposed PSDFA in both local computers and GEE.

Spatiotemporal fusion (STF) can produce synthetic remote sensing data with both high spatial and high temporal resolution by blending the temporally sparse fine resolution data and temporally dense coarse resolution data, for which it has been adopted in many fields, such as agriculture, environment and ecology. In the literature, Moderate Resolution Imaging Spectroradiometer (MODIS) images are the most frequently employed as the coarse resolution images in STF due to their high quality and availability. However, the MODIS images contain significant strip noise sometimes, which will lead to unfavorable fusion results. In this letter, a MODIS strip noise removal strategy for STF (NRSTF) is suggested. Specifically, the NRSTF first removes the strip noise of MODIS image of the base time utilizing the bands relationship from the base fine resolution image, then reconstructs that of the predicted time via an adaptive convolution-based noise estimation approach. Finally, the reconstructed, noise-free MODIS images are fused with the base fine resolution image to predict the missing fine resolution images. Four wildly used STF approaches, including STARFM, FSDAF, Fit-FC, and RASDF are selected to test the NRSTF on an actual Landsat-MODIS dataset, whose MODIS images suffers from serious strip noise in two short wave infrared (SWIR) bands. The experiment results demonstrate that the proposed NRSTF can not only effectively eliminate strip noise, but also significantly enhance the accuracy and applicability of the fusion results. The source code of NRSTF is available at https://github.com/lijli26/NRSTF.git.

Spatio-temporal fusion is a feasible way to generating the synthetic remote sensing data with high spatial resolution and high temporal resolution simultaneously by blending the fine and coarse resolution satellite images. To date, dozens of spatio-temporal fusion approaches have been developed. A basic rule of these approaches is the bands of coarse and fine images must be corresponding, which means the quality of fused images depends on that of both fine and coarse images. In the literature, the MODIS images are the most wildly used coarse images in spatio-temporal fusion. However, the MODIS images may suffer from serious stripe noises in the short-wave infrared-1 and short-wave infrared-2 bands, which will lead to undesired results of spatio-temporal fusion. To address this problem, we develop a noise proof strategy in this paper, which takes advantage of the spectral correlation of base fine image to remove the stripe noises of the base MODIS image, then the spatial correlation of base MODIS image is exploited to restore the MODIS image of the predicted time. Finally, the reconstructed MODIS images are fused with the base fine image to predict the missing fine images. The strategy is tested via real Landsat and MODIS images, and the experimental result demonstrates it is not only effective in removing the stripe noises of MDOIS short-wave infrared-1 and short-wave infrared-2 bands, but also able to improve the fusion accuracy.

Spatio-temporal fusion is a feasible way to provide synthetic satellite images with high spatial and high temporal resolution simultaneously. Due to its practicability, spatio-temporal fusion has gotten increasing attention, for which many spatio-temporal fusion approaches have been developed. Most spatio-temporal fusion methods follow the “base fine image guided” (BFIG) fusion mode, resulting in the fact that their fusion results are similar to the base fine images. Therefore, these methods can perform well in areas with limited surface changes due to high similarity between the base and the predicted fine images. However, they might not be applicable in areas with intense surface changes. In this article, we develop a pansharpening-based spatio-temporal fusion model (PSTFM) by introducing the pansharpening fusion mode, which is “coarse image guided” (CIG), into spatio-temporal fusion. PSTFM first trains a pansharpening convolutional neural network (CNN), which then fuses the coarse images and reconstructed panchromatic (Pan) images of the predicted time to recover the missing fine images. The newly proposed PSTFM is compared with three representative BFIG spatio-temporal fusion methods on two Landsat–Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, both of which contain intense surface changes. After that, the experimental results are analyzed and discussed in detail. The experiments and the analysis demonstrate that the newly proposed PSTFM has remarkably qualitative and quantitative performance in predicting the intense surface changes while it is mediocre in areas with low surface change intensity.

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat–MODIS image pairs and one publicly available dataset of Sentinel–MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.

Spatio-temporal fusion has been developed to generate the synthetic remote sensing data with high spatial resolution and high temporal resolution simultaneously. To date, a number of spatio-temporal fusion methods have been developed and most of them are remarkable. However, most the methods are designed to achieve better fusion performance and higher fusion accuracy, while the fusion speed is always ignored. As a matter of fact, most current spatio-temporal fusion methods are time-consuming. To address this defect of spatiotemporal fusion, in this paper we propose a extremely fast spatiotemporal fusion method. The core idea of this method is extracting the spatial information from the prior high-spatial-resolution images and embedding that into the low-spatial-resolution images by local normalization to predict the missing high-spatial-resolution images. In the experiments, two dataset, including a Landsat-MODIS dataset and a Sentienl-MODIS dataset, are adopted to testing this method. The experimental results demonstrate this method can achieve great performance with extremely fast speed.

In some remote sensing applications such as change detection, satellite images with both high spatial and high temporal resolution are required. However, no single satellite sensor can currently provide such images due to technical specifications. To solve this problem, spatio-temporal fusion provides a cost-effective solution. In this paper, we propose a new spatio-temporal fusion approach, based on convolutional neural networks (CNNs), for Landsat and MODIS image fusion. Specifically, the proposed approach utilizes CNNs to model the heterogeneity of fine pixels from the coarse MODIS images. Here, the heterogeneity of fine pixels is defined as the difference between the reflectance changes obtained from the two types of images. After that, two transition-predicted images can be obtained using the trained CNNs, which are then fused in order to obtain a fi-nal prediction. In our newly proposed approach, CNNs are only used to learn the heterogeneity of fine pixels rather than the whole images, thus providing a more stable and less time-consuming strategy as compared to other available approaches. We evaluated the proposed approach on a public spatio-temporal fusion dataset and the obtained results suggest that our newly developed method achieves state-of-the-art performance.