Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection
Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/2/266 |
| _version_ | 1827601275377680384 |
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| author | Yiting Wang Donghui Xie Yinggang Zhan Huan Li Guangjian Yan Yuanyuan Chen |
| author_facet | Yiting Wang Donghui Xie Yinggang Zhan Huan Li Guangjian Yan Yuanyuan Chen |
| author_sort | Yiting Wang |
| collection | DOAJ |
| description | Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images. |
| first_indexed | 2024-03-09T04:51:58Z |
| format | Article |
| id | doaj.art-61d8bf675709441e9794ad17b81ab63f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:51:58Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-61d8bf675709441e9794ad17b81ab63f2023-12-03T13:09:49ZengMDPI AGRemote Sensing2072-42922021-01-0113226610.3390/rs13020266Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data SelectionYiting Wang0Donghui Xie1Yinggang Zhan2Huan Li3Guangjian Yan4Yuanyuan Chen5College of Geomatics, Xi’an University of Science and Technology, Shaanxi 710054, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Shaanxi 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Shaanxi 710054, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Shaanxi 710054, ChinaDespite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.https://www.mdpi.com/2072-4292/13/2/266spatiotemporal data fusionMODISLandsatNDVIESTARFMLMGM |
| spellingShingle | Yiting Wang Donghui Xie Yinggang Zhan Huan Li Guangjian Yan Yuanyuan Chen Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection Remote Sensing spatiotemporal data fusion MODIS Landsat NDVI ESTARFM LMGM |
| title | Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection |
| title_full | Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection |
| title_fullStr | Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection |
| title_full_unstemmed | Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection |
| title_short | Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection |
| title_sort | assessing the accuracy of landsat modis ndvi fusion with limited input data a strategy for base data selection |
| topic | spatiotemporal data fusion MODIS Landsat NDVI ESTARFM LMGM |
| url | https://www.mdpi.com/2072-4292/13/2/266 |
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