An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM
The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3673 |
| _version_ | 1827702578465472512 |
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| author | Mengxue Liu Xiangnan Liu Xiaobin Dong Bingyu Zhao Xinyu Zou Ling Wu Hejie Wei |
| author_facet | Mengxue Liu Xiangnan Liu Xiaobin Dong Bingyu Zhao Xinyu Zou Ling Wu Hejie Wei |
| author_sort | Mengxue Liu |
| collection | DOAJ |
| description | The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale. |
| first_indexed | 2024-03-10T14:59:13Z |
| format | Article |
| id | doaj.art-c9d4166b3838472ca0299b4ce780e5d6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:59:13Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c9d4166b3838472ca0299b4ce780e5d62023-11-20T20:21:13ZengMDPI AGRemote Sensing2072-42922020-11-011221367310.3390/rs12213673An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFMMengxue Liu0Xiangnan Liu1Xiaobin Dong2Bingyu Zhao3Xinyu Zou4Ling Wu5Hejie Wei6State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaFaculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaCollege of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, ChinaThe use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale.https://www.mdpi.com/2072-4292/12/21/3673spatiotemporal data fusionESTARFMmoving window strategysatellite images |
| spellingShingle | Mengxue Liu Xiangnan Liu Xiaobin Dong Bingyu Zhao Xinyu Zou Ling Wu Hejie Wei An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM Remote Sensing spatiotemporal data fusion ESTARFM moving window strategy satellite images |
| title | An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM |
| title_full | An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM |
| title_fullStr | An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM |
| title_full_unstemmed | An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM |
| title_short | An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM |
| title_sort | improved spatiotemporal data fusion method using surface heterogeneity information based on estarfm |
| topic | spatiotemporal data fusion ESTARFM moving window strategy satellite images |
| url | https://www.mdpi.com/2072-4292/12/21/3673 |
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