HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level
Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a high-r...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9165877/ |
| _version_ | 1818890100766932992 |
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| author | Jiale Jiang Qiaofeng Zhang Xia Yao Yongchao Tian Yan Zhu Weixing Cao Tao Cheng |
| author_facet | Jiale Jiang Qiaofeng Zhang Xia Yao Yongchao Tian Yan Zhu Weixing Cao Tao Cheng |
| author_sort | Jiale Jiang |
| collection | DOAJ |
| description | Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a high-resolution spatiotemporal image fusion method (HISTIF) consisting of filtering for cross-scale spatial matching (FCSM) and multiplicative modulation of temporal change (MMTC). In FCSM, we considered both point spread function effect and geo-registration errors between fine and coarse resolution images. Subsequently, MMTC used pixel-based multiplicative factors to estimate the temporal change between reference and prediction dates without image classification. The performance of HISTIF was evaluated using both simulated and real datasets with one from real Gaofen-1 (GF-1) and simulated Landsat-like/Sentinel-like images, and the other from real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was compared with the existing methods spatial and temporal adaptive reflectance fusion model (STARFM), FSDAF, and Fit-FC. The results demonstrated that HISTIF produced substantial reduction in the fusion error from cross-scale spatial mismatch and accurate reconstruction in spatial details within fields, regardless of simulated or real data. The images predicted by STARFM exhibited pronounced blocky artifacts. While the images predicted by HISTIF and Fit-FC both showed clear within-field variability patterns, HISTIF was able to reduce the spectral distortion more significantly than Fit-FC. Furthermore, HISTIF exhibited the most stable performance across sensors. The findings suggest that HISTIF could be beneficial for the frequent and detailed monitoring of crop growth at the subfield level. |
| first_indexed | 2024-12-19T17:19:33Z |
| format | Article |
| id | doaj.art-00a7ee053bdf46aa9bffe849bf775f4b |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-19T17:19:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-00a7ee053bdf46aa9bffe849bf775f4b2022-12-21T20:12:42ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01134607462610.1109/JSTARS.2020.30161359165877HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield LevelJiale Jiang0https://orcid.org/0000-0002-2214-7009Qiaofeng Zhang1Xia Yao2Yongchao Tian3https://orcid.org/0000-0002-6106-4505Yan Zhu4Weixing Cao5Tao Cheng6https://orcid.org/0000-0002-4184-0730National Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaSatellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a high-resolution spatiotemporal image fusion method (HISTIF) consisting of filtering for cross-scale spatial matching (FCSM) and multiplicative modulation of temporal change (MMTC). In FCSM, we considered both point spread function effect and geo-registration errors between fine and coarse resolution images. Subsequently, MMTC used pixel-based multiplicative factors to estimate the temporal change between reference and prediction dates without image classification. The performance of HISTIF was evaluated using both simulated and real datasets with one from real Gaofen-1 (GF-1) and simulated Landsat-like/Sentinel-like images, and the other from real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was compared with the existing methods spatial and temporal adaptive reflectance fusion model (STARFM), FSDAF, and Fit-FC. The results demonstrated that HISTIF produced substantial reduction in the fusion error from cross-scale spatial mismatch and accurate reconstruction in spatial details within fields, regardless of simulated or real data. The images predicted by STARFM exhibited pronounced blocky artifacts. While the images predicted by HISTIF and Fit-FC both showed clear within-field variability patterns, HISTIF was able to reduce the spectral distortion more significantly than Fit-FC. Furthermore, HISTIF exhibited the most stable performance across sensors. The findings suggest that HISTIF could be beneficial for the frequent and detailed monitoring of crop growth at the subfield level.https://ieeexplore.ieee.org/document/9165877/Cropsheterogeneityimage fusionspatiotemporal fusionsubfield monitoring |
| spellingShingle | Jiale Jiang Qiaofeng Zhang Xia Yao Yongchao Tian Yan Zhu Weixing Cao Tao Cheng HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Crops heterogeneity image fusion spatiotemporal fusion subfield monitoring |
| title | HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level |
| title_full | HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level |
| title_fullStr | HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level |
| title_full_unstemmed | HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level |
| title_short | HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level |
| title_sort | histif a new spatiotemporal image fusion method for high resolution monitoring of crops at the subfield level |
| topic | Crops heterogeneity image fusion spatiotemporal fusion subfield monitoring |
| url | https://ieeexplore.ieee.org/document/9165877/ |
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