Spatiotemporal Data Fusion Method Research Articles

An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images

The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

A spatiotemporal satellite image fusion model with autoregressive error correction (AREC)

ABSTRACTTo overcome the trade-off between spatial and temporal resolutions of satellite sensors, various spatiotemporal image fusion methods have been developed to generate synthetic imagery with both fine spatial and temporal resolutions. While such methods have achieved different levels of success, they have not considered spatiotemporal autocorrelation in the fusion process. Herein, we propose a novel spatiotemporal model incorporating autoregressive error correction (AREC) for fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images. This AREC model minimizes autoregressive errors when fitting a relationship between coarse- and fine-resolution pixels in an existing MODIS–Landsat image pair (on a base date ). The derived relationship is then applied to the corresponding pixel in the prediction pair (on a prediction date ). This method enables the relationship to be optimized and the phenological and land-cover changes to be treated in a unified manner. The AREC model was tested using a simulated data set and two actual data sets from Shenzhen, China, and Saskatchewan, Canada. The model was compared with the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatiotemporal data fusion (FSDAF) method in terms of changed and unchanged regions, and whole data sets, using visual analysis and quantitative indices. The results show that the AREC model can effectively predict phenological and land-cover changes with higher accuracies than other algorithms as a result of AREC.

Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles

Tree mortality caused by outbreaks of the bark beetle Ips typographus (L.) plays an important role in the natural dynamics of Norway spruce (Picea abies L.) stands, which could cause far-reaching changes in the occurrence and duration of vegetation phenology. Field-based early detection of tree disturbances is hampered by logistic, terrain, and technical shortcomings, and by the inability to continuously monitor disturbances over large areas. Despite achievements in remote mapping of bark-beetle-induced tree mortalities, early warning has been mostly unsuccessful mainly because of the lack of spectral sensitivity and discrepancies in definitions of field- and image-based disturbance classes. Here we applied a method based on inter-annual phenology of Norway spruce stands derived from synthetic multispectral data to part of the Bavarian Forest National Park in Germany. We fused temporally continuous Moderate Resolution Imaging Spectroradiometer and discrete RapidEye data using a flexible spatiotemporal data fusion method to achieve validated 8-day RapidEye-like composites of normalized difference vegetation index for 2011. We assumed that the dead trees delineated on 2012 aerial photographs were those in which bark beetle infestations were initiated in 2011. Samples were drawn with variable-sized buffering to represent the areas prone to infestations and their surroundings. We applied a conditional inference random forest to select the best image date among the entire 46 synthetic datasets to best discriminate between the core infestation patches and their surroundings from the subsequent year. Of the discrete time points identified, day 281 of the year represented the highest discrepancy between aerial image-based dead trees and their surroundings. Classification results were significantly correlated with beetle count data obtained using pheromone traps. Our method provided valuable information for management purposes and enabled wall-to-wall mapping of stands prone to infestation and its uncertainty. The results offer potential implications for rapid and cost-effective monitoring of bark beetle outbreaks using satellite data, which would be of great benefit for both management and research tasks.

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions. The STVIFM aims to predict the fine-resolution NDVI through understanding the contribution of each fine-resolution pixel to the total NDVI change, which was calculated from the coarse-resolution images acquired on two dates. On the one hand, it considers the difference in relationships between the fine- and coarse-resolution images on different dates and the difference in NDVI change rates at different growing stages. On the other hand, it neither needs to search similar pixels nor needs to use land cover maps. The Landsat-8 and MODIS data acquired over three test sites with different landscapes were used to test the spatial and temporal performance of the proposed model. Compared with the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method, the proposed STVIFM outperforms the STARFM and ESTARFM at three study sites and different stages when the land cover or NDVI changes were captured by the two pairs of fine- and coarse-resolution images, and it is more robust and less computationally intensive than the FSDAF.

A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets.

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., 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 (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

A flexible spatiotemporal method for fusing satellite images with different resolutions

Abstract Studies of land surface dynamics in heterogeneous landscapes often require remote sensing data with high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial resolution images through blending two types of data, i.e., frequent coarse spatial resolution data, such as that from MODIS, and less frequent high spatial resolution data such as that from Landsat. The proposed method is based on spectral unmixing analysis and a thin plate spline interpolator. Compared with existing spatiotemporal data fusion methods, it has the following strengths: (1) it needs minimum input data; (2) it is suitable for heterogeneous landscapes; and (3) it can predict both gradual change and land cover type change. Simulated data and real satellite images were used to test the performance of the proposed method. Its performance was compared with two very popular methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an unmixing-based data fusion (UBDF) method. Results show that the new method creates more accurate fused images and keeps more spatial detail than STARFM and UBDF. More importantly, it closely captures reflectance changes caused by land cover conversions, which is a big issue with current spatiotemporal data fusion methods. Because the proposed method uses simple principles and needs only one fine-resolution image as input, it has the potential to increase the availability of high-resolution time-series data that can support studies of rapid land surface dynamics.

Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data

The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors. Global space-borne sensors are not good enough to acquire an image at fine spatial and high temporal (FSHT) resolutions simultaneously. In this article, we propose a computationally efficient technique to create a FSHT resolution image using a ground-based data processing system. Resourcesat-2, part of the Indian Space Research Organization’s (ISRO’s) mission, carries linear imaging self-scanners (LISS III and LISS IV) and an advanced wide-field sensor (AWiFS) on board. The spatial and temporal resolutions of LISS III are 23.5 m and 24 days and those of AWiFS are 56 m and 5 days, respectively. The proposed method creates a synthetic FSHT resolution image with 23.5 m spatial and 5 day temporal resolution. This method is referred to as ‘LISS III spatial and AWiFS temporal’ (LSAT) data fusion. The LSAT data fusion method is based on a sub-pixel relationship between the images of a single AWiFS–LISS III image pair, which was acquired before or after the ‘prediction date’: a synthetic LISS III image for the time is ‘predicted’ (synthesized) using an AWiFS image at time and a single AWiFS–LISS III image pair at time , where . The LSAT model was tested on simulated and real data sets acquired by the LISS III and AWiFS sensors. The proposed method was compared with the recently developed spatiotemporal data fusion methods. The experimental results demonstrate that the method is computationally efficient and shows consistent prediction accuracy in retrieving surface reflectance changes.