Search

Due to the limitations inherent in satellite sensor capabilities, it remains a challenge to balance the temporal and spatial resolutions of current remote sensing observations. Spatiotemporal fusion has emerged as a viable approach to address this issue. Many spatiotemporal fusion models have been proposed to fuse surface reflectance initially; however, their adaption for land surface temperature (LST), particularly with the 'fusion-then-retrieval (FTR)' or 'retrieval-then-fusion (RTF)' strategy, has not yet been comprehensively explored. This paper describes a comparative study of the two strategies within the context of LST fusion using three classic fusion methods including the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion model (FSDAF). The findings illuminate nuanced applicability across different models: (1) the FTR strategy is more suitable for STARFM; (2) the strategic applicability for ESTARFM and FSDAF is strongly related to the reference images; (3) the STARFM and FSDAF models are easily affected by the time interval between the predicted date and the reference date. This work is expected to provide practical guidance for synthesizing high spatiotemporal LST data with fusion models. [ABSTRACT FROM AUTHOR]

Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an essential role in the hydrological cycle, drought monitoring, and water resources management. However, passive microwave remote sensing products offer coarse spatial resolutions (approximately 25-40 km), greatly limiting their applications. In this study, we considered vegetation memory and increased the amount of vegetation data by spatiotemporal fusion, integrated lagged vegetation index and fusion data as model inputs, and downscaled the spatial resolution of SMAP SM using the Random Forest (RF) model as well as multi-source remote sensing data. The downscaled SM was validated with 30 in situ SM measurements and daily precipitation data and explored the contributions of NDVIlagged and NDVIfused in downscaling. The results show that the downscaled SM is highly consistent with the in situ SM measurements. The correlation ranges from 0.628 to 0.849 with an average of 0.689. The root mean square error (RMSE) ranges from 0.024 to 0.094 m³/m³ with an average of 0.057 m3/m3. The proposed method leverages the strengths of the RF model, fusion vegetation data, and lagged vegetation data to improve high-resolution SM accuracy. [ABSTRACT FROM AUTHOR]

Spatiotemporal fusion (STF) is a cost-effective way to reconstruct time-series images. In recent years, deep learning-based (DL-based) STF methods have received substantial attention. However, two limitations of DL-based STF methods still remain: (1) existing methods require simultaneous learning of both the multi-source images correction model and the STF model, which complicates the training task. The high complexity poses a challenge for the network to accurately learn the underlying mathematical principles of STF, thereby reducing the method's reliability and generalization ability; (2) existing methods tend to generate blurry predictions. To address these limitations, this study proposes a task decoupled (TD) framework that offers a simple yet effective solution for enhancing DL-based STF method. The framework consists of a correction model and a STF model, which are trained using actual and simulated image pairs, respectively, to learn the multi-source images correction model and STF mathematical model. The loss of edge feature is added in the loss function of the STF model to ameliorate its detailed information preservation ability. The proposed framework is evaluated on three DL-based STF methods in five different sites using root-mean-square error (RMSE) and Robert's edge (Edge) to assess spectral and spatial accuracy. The experimental results indicate that the proposed framework can significantly improve the models' ability to predict spectral information (average increase rate = 5.3% in spectral accuracy), preserve spatial information (average increase rate = 16.2% in spatial accuracy), retrieve land cover change, and generalize to new data. These findings demonstrate the effectiveness of the proposed framework in addressing the limitations of existing DL-based STF methods and its potential for advancing STF applications. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are useful models for spatiotemporal fusion, especially under strong temporal changes. Convolutional layers with receptive fields of various sizes produce hierarchical features that can improve prediction performance. In this study, we aimed to develop a CNN model for spatiotemporal fusion, which can extract hierarchical features and fully use them. The proposed network is composed of residual dense blocks with local dense connections that effectively utilize the hierarchical features. We conducted the experiments in the newly proposed Kansas dataset, which includes Sentinel-2 and Sentinel-3 image pairs with large resolution differences and strong temporal changes. Based on both quantitative and qualitative evaluation, experiments revealed that the STFRDN model outperformed the Flexible Spatiotemporal DAta Fusion (FSDAF) 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), Fit-FC, and DMNet methods in all of the test groups. The second major finding was that the STFRDN model was able to produce sufficiently successful predictions against cases with such large resolution differences. To get more accurate prediction results when there is a significant resolution difference, it is important to extract the right features that can model the discrepancy. Loss of spatial detail can be avoided by making full use of hierarchical features without any manipulation after extraction. The results of this study provide insights into the significance of extracting and fully utilizing the hierarchical features. [ABSTRACT FROM AUTHOR]

Numerous spatiotemporal fusion models have been developed to fuse dense time-series data with a high spatial resolution for monitoring land surface dynamics. Nonetheless, enhancing spatial details of fused images, eliminating the obvious 'plaque' phenomenon and image blurring in fused images, and developing relatively simple and1 easy-to-implement algorithms remain a challenge for spatiotemporal fusion algorithms. Therefore, this paper presents a newly proposed spatial enhanced spatiotemporal reflectance fusion model (SE-STRFM) for image fusions in heterogeneous regions with land cover change. The SE-STRFM model predicts temporal changes of reflectance in sub-pixel details based on the spectral unmixing theory, and allocates reflectance changes caused by abrupt land cover change in fine-resolution images with a relatively simple algorithm and easy implementation. SE-STRFM only needs one pair of input data, comprising one fine-resolution image and one coarse-resolution image, to achieve high-precision reflectance prediction with spatial details. To verify the reliability and applicability of the SE-STRFM, we use Landsat image and simulated MODIS-like image to fuse high spatial and temporal resolution images and select two study areas with heterogeneous landscape and land cover type change for fusion experiments and accuracy evaluation. The results show that the images fused by SE-STRFM have clearer spatial details and a more accurate spectral distribution compared with those fused by the most widely used STARFM, ESTARFM and FSDAF. In two study areas with heterogeneous landscape and land cover type change, compared with STARFM, ESTARFM and FSDAF, the RMSE of SE-STRFM is 10.52%, 28.39% and 6.58% lower on average, respectively; r is 3.67%, 10.33% and 1.65% higher on average, respectively; AAD is 9.05%, 24.58% and 7.29% lower on average, respectively; and SSIM is 3.16%, 10.16% and 1.92% higher on average, respectively. SE-STRFM can accurately capture temporal changes with spatial details and effectively predict abrupt land-cover changes. [ABSTRACT FROM AUTHOR]

Accurate and detailed identification of land cover types is beneficial to the ecological environment and sustainable urban development, yet the extraction of urban land use/land cover (LULC) information with high accuracy is challenged by the high degree of landscape fragmentation. Therefore, this study is based on the Google Earth Engine (GEE) cloud platform and uses the U-Net model combined with spectral image data and SAR data to conduct a LULC classification study of highly heterogeneous urban areas in central China. The results indicated that the overall accuracy of classification result by using the U-Net model and the optimal combination of image features was 95.58%, which was 1.37%, 4.84%, and 7.85% higher than that of random forest (RF), support vector machine (SVM) and k- Nearest Neighbor (kNN), respectively. It showed that the U-Net model can effectively extract LULC information and obtain better classification results in urban areas than the machine learning algorithms. The results of this study could provide technical support to improve the accuracy of information extraction in urban areas with fragmented features. [ABSTRACT FROM AUTHOR]

Spatiotemporal fusion technology provides a feasible, economical solution for generating remote sensing images with high spatiotemporal resolution. The recently proposed learning-based method achieved high accuracy; however, its network structure is relatively simple, and the deep features of the input image cannot be obtained, so that the fused image cannot restore good landform details and the quality is not very good. Moreover, most methods use a single pixel-level (MSE) loss, which makes recovering high-frequency details difficult, resulting in a reduction in the fusion accuracy. In this paper, we propose an edge structure loss, which is added to a spatiotemporal fusion network without pre training model. To fully extract the spectral information and spatial details of the image, we propose a DenseNet-BC module for image fusion tasks, which makes the features more easily transmittable in the whole network. This improvement also enables the network to perform spatiotemporal fusion with better generalizability and robustness. In addition, we propose an edge loss to further improve the accuracy of the model fusion results. Experiments with existing spatiotemporal fusion algorithms in different regions show that our proposed method is more fault tolerant and achieve a higher accuracy in terms of quality evaluation indicators and better visual effects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user's accuracies of sedge swamp and paddy respectively. [ABSTRACT FROM AUTHOR]

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases. [ABSTRACT FROM AUTHOR]