Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data
As an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally conti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/19/2324 |
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| author | Guofeng Tao Kun Jia Xiang Zhao Xiangqin Wei Xianhong Xie Xiwang Zhang Bing Wang Yunjun Yao Xiaotong Zhang |
| author_facet | Guofeng Tao Kun Jia Xiang Zhao Xiangqin Wei Xianhong Xie Xiwang Zhang Bing Wang Yunjun Yao Xiaotong Zhang |
| author_sort | Guofeng Tao |
| collection | DOAJ |
| description | As an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally continuous FVC with high spatio-temporal resolution based on a single remote-sensing data source. Therefore, the objective of this study is to explore the feasibility of generating high spatio-temporal resolution FVC based on the fusion of GaoFen-1 Wide Field View (GF-1 WFV) data and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Two fusion strategies were employed to identify a suitable fusion method: (i) fusing reflectance data from GF-1 WFV and MODIS firstly and then estimating FVC from the reflectance fusion result (strategy FC, Fusion_then_FVC). (ii) fusing the FVC estimated from GF-1 WFV and MODIS reflectance data directly (strategy CF, FVC_then_Fusion). The FVC generated using strategies FC and CF were evaluated based on FVC estimated from the real GF-1 WFV data and the field survey FVC, respectively. The results indicated that strategy CF achieved higher accuracies with less computational cost than those of strategy FC both in the comparisons with FVC estimated from the real GF-1 WFV (CF:R<sup>2</sup> = 0.9580, RMSE = 0.0576; FC: R<sup>2</sup> = 0.9345, RMSE = 0.0719) and the field survey FVC data (CF: R<sup>2</sup> = 0.8138, RMSE = 0.0985; FC: R<sup>2</sup> = 0.7173, RMSE = 0.1214). Strategy CF preserved spatial details more accurately than strategy FC and had a lower probability of generating abnormal values. It could be concluded that fusing GF-1 WFV and MODIS data for generating high spatio-temporal resolution FVC with good quality was feasible, and strategy CF was more suitable for generating FVC given its advantages in estimation accuracy and computational efficiency. |
| first_indexed | 2024-12-20T11:37:34Z |
| format | Article |
| id | doaj.art-0e0dc278fa864e959d4bc4e63718b3b9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:37:34Z |
| publishDate | 2019-10-01 |
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| series | Remote Sensing |
| spelling | doaj.art-0e0dc278fa864e959d4bc4e63718b3b92022-12-21T19:42:04ZengMDPI AGRemote Sensing2072-42922019-10-011119232410.3390/rs11192324rs11192324Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS DataGuofeng Tao0Kun Jia1Xiang Zhao2Xiangqin Wei3Xianhong Xie4Xiwang Zhang5Bing Wang6Yunjun Yao7Xiaotong Zhang8State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaKey Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaAs an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally continuous FVC with high spatio-temporal resolution based on a single remote-sensing data source. Therefore, the objective of this study is to explore the feasibility of generating high spatio-temporal resolution FVC based on the fusion of GaoFen-1 Wide Field View (GF-1 WFV) data and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Two fusion strategies were employed to identify a suitable fusion method: (i) fusing reflectance data from GF-1 WFV and MODIS firstly and then estimating FVC from the reflectance fusion result (strategy FC, Fusion_then_FVC). (ii) fusing the FVC estimated from GF-1 WFV and MODIS reflectance data directly (strategy CF, FVC_then_Fusion). The FVC generated using strategies FC and CF were evaluated based on FVC estimated from the real GF-1 WFV data and the field survey FVC, respectively. The results indicated that strategy CF achieved higher accuracies with less computational cost than those of strategy FC both in the comparisons with FVC estimated from the real GF-1 WFV (CF:R<sup>2</sup> = 0.9580, RMSE = 0.0576; FC: R<sup>2</sup> = 0.9345, RMSE = 0.0719) and the field survey FVC data (CF: R<sup>2</sup> = 0.8138, RMSE = 0.0985; FC: R<sup>2</sup> = 0.7173, RMSE = 0.1214). Strategy CF preserved spatial details more accurately than strategy FC and had a lower probability of generating abnormal values. It could be concluded that fusing GF-1 WFV and MODIS data for generating high spatio-temporal resolution FVC with good quality was feasible, and strategy CF was more suitable for generating FVC given its advantages in estimation accuracy and computational efficiency.https://www.mdpi.com/2072-4292/11/19/2324fractional vegetation coverspatial and temporal fusiongf-1 wfv dataradiative transfer modelrandom forest regression |
| spellingShingle | Guofeng Tao Kun Jia Xiang Zhao Xiangqin Wei Xianhong Xie Xiwang Zhang Bing Wang Yunjun Yao Xiaotong Zhang Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data Remote Sensing fractional vegetation cover spatial and temporal fusion gf-1 wfv data radiative transfer model random forest regression |
| title | Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data |
| title_full | Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data |
| title_fullStr | Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data |
| title_full_unstemmed | Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data |
| title_short | Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data |
| title_sort | generating high spatio temporal resolution fractional vegetation cover by fusing gf 1 wfv and modis data |
| topic | fractional vegetation cover spatial and temporal fusion gf-1 wfv data radiative transfer model random forest regression |
| url | https://www.mdpi.com/2072-4292/11/19/2324 |
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