Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms
Remote sensing technology provides an effective means for continuously assessing the chlorophyll content in plants on a broad scale. Given the challenges associated with satellite image quality and spatiotemporal resolution, spatiotemporal fusion algorithms for estimating vegetation chlorophyll cont...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000445 |
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| author | Annan Zeng Jianli Ding Jinjie Wang Lijing Han Haiyan Han Shuang Zhao Xiangyu Ge |
| author_facet | Annan Zeng Jianli Ding Jinjie Wang Lijing Han Haiyan Han Shuang Zhao Xiangyu Ge |
| author_sort | Annan Zeng |
| collection | DOAJ |
| description | Remote sensing technology provides an effective means for continuously assessing the chlorophyll content in plants on a broad scale. Given the challenges associated with satellite image quality and spatiotemporal resolution, spatiotemporal fusion algorithms for estimating vegetation chlorophyll content have garnered significant attention in recent years. In this study, we evaluated the performance of four fusion algorithms fusing Gaofen-1 WFV and MODIS data while also exploring their fusion accuracy. A random forest regression model was developed using the fused images and measured SPAD (Soil and Plant Analyzer Development) values, enabling large-scale, accurate, and dynamic monitoring of vegetation chlorophyll content. The results indicate that (1) all four fusion algorithms can effectively address the issue of missing images; (2) the constructed random forest regression model accurately estimates SPAD values; and (3) among the three vegetation indices that exhibit a strong correlation with SPAD values, the fusion strategy “Index-then-Blend” outperforms “Blend-then-Index.” This study provides comprehensive insights into dynamic and large-scale monitoring of vegetation chlorophyll content, particularly in scenarios in which satellite imagery is unavailable. |
| format | Article |
| id | doaj-art-f57c422014e944cd81981baf70fc27dc |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-f57c422014e944cd81981baf70fc27dc2025-02-03T04:16:38ZengElsevierEcological Informatics1574-95412025-05-0186103035Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithmsAnnan Zeng0Jianli Ding1Jinjie Wang2Lijing Han3Haiyan Han4Shuang Zhao5Xiangyu Ge6College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, ChinaXinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China; Xinjiang Institute of Technology, Aksu 843100, China; Corresponding author at: Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China. Xinjiang Institute of Technology, Aksu 843100, China.College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China; Corresponding author at: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, ChinaCollege of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China; Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, ChinaRemote sensing technology provides an effective means for continuously assessing the chlorophyll content in plants on a broad scale. Given the challenges associated with satellite image quality and spatiotemporal resolution, spatiotemporal fusion algorithms for estimating vegetation chlorophyll content have garnered significant attention in recent years. In this study, we evaluated the performance of four fusion algorithms fusing Gaofen-1 WFV and MODIS data while also exploring their fusion accuracy. A random forest regression model was developed using the fused images and measured SPAD (Soil and Plant Analyzer Development) values, enabling large-scale, accurate, and dynamic monitoring of vegetation chlorophyll content. The results indicate that (1) all four fusion algorithms can effectively address the issue of missing images; (2) the constructed random forest regression model accurately estimates SPAD values; and (3) among the three vegetation indices that exhibit a strong correlation with SPAD values, the fusion strategy “Index-then-Blend” outperforms “Blend-then-Index.” This study provides comprehensive insights into dynamic and large-scale monitoring of vegetation chlorophyll content, particularly in scenarios in which satellite imagery is unavailable.http://www.sciencedirect.com/science/article/pii/S1574954125000445Spatiotemporal fusionSPADGaofen-1 WFVBI or IB |
| spellingShingle | Annan Zeng Jianli Ding Jinjie Wang Lijing Han Haiyan Han Shuang Zhao Xiangyu Ge Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms Ecological Informatics Spatiotemporal fusion SPAD Gaofen-1 WFV BI or IB |
| title | Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms |
| title_full | Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms |
| title_fullStr | Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms |
| title_full_unstemmed | Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms |
| title_short | Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms |
| title_sort | exploring the feasibility of gf1 wfv data in estimating spad using spatiotemporal fusion algorithms |
| topic | Spatiotemporal fusion SPAD Gaofen-1 WFV BI or IB |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000445 |
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