Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion
Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We a...
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4349 |
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| author | Jianping Wu Zhongbing Chang Yongxian Su Chaoqun Zhang Xiong Wu Chongyuan Bi Liyang Liu Xueqin Yang Xueyan Li |
| author_facet | Jianping Wu Zhongbing Chang Yongxian Su Chaoqun Zhang Xiong Wu Chongyuan Bi Liyang Liu Xueqin Yang Xueyan Li |
| author_sort | Jianping Wu |
| collection | DOAJ |
| description | Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming. |
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| language | English |
| last_indexed | 2024-03-10T01:18:26Z |
| publishDate | 2022-09-01 |
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| series | Remote Sensing |
| spelling | doaj.art-c613239aa788477ea12bee2f814aeee62023-11-23T14:05:10ZengMDPI AGRemote Sensing2072-42922022-09-011417434910.3390/rs14174349Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous EcoregionJianping Wu0Zhongbing Chang1Yongxian Su2Chaoqun Zhang3Xiong Wu4Chongyuan Bi5Liyang Liu6Xueqin Yang7Xueyan Li8Guangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Laboratory of Tropical and Subtropical Natural Resources in South China, Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510663, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Provincial Key Lab of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaMultiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming.https://www.mdpi.com/2072-4292/14/17/4349satellite-derived phenologyspring green-up datevegetation indexasynchronous phenologyheterogeneous ecoregion |
| spellingShingle | Jianping Wu Zhongbing Chang Yongxian Su Chaoqun Zhang Xiong Wu Chongyuan Bi Liyang Liu Xueqin Yang Xueyan Li Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion Remote Sensing satellite-derived phenology spring green-up date vegetation index asynchronous phenology heterogeneous ecoregion |
| title | Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion |
| title_full | Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion |
| title_fullStr | Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion |
| title_full_unstemmed | Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion |
| title_short | Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion |
| title_sort | identification of the spring green up date derived from satellite based vegetation index over a heterogeneous ecoregion |
| topic | satellite-derived phenology spring green-up date vegetation index asynchronous phenology heterogeneous ecoregion |
| url | https://www.mdpi.com/2072-4292/14/17/4349 |
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