LiuWangHuEtAl2020

Référence

Li, R., Wang, Y., Hu, J., Wang, Y., Min, Q., Bergeron, Y., Valeria, O., Gao, Z., Liu, J., Fu, Y. (2020) Spatiotemporal Variations of Satellite Microwave Emissivity Difference Vegetation Index in China Under Clear and Cloudy Skies. Earth and Space Science, 7(5):e2020EA001145. (URL )

Résumé

Abstract In this study, we used data from multiple sensors onboard NASA Aqua satellite to conduct a 10-year (2002–2011) remote sensing of microwave emissivity difference vegetation index (EDVI) over China. We investigated the spatial and temporal variations of EDVI in tropical and subtropical evergreen forest, deciduous forest, rice and wheat farmlands, grassland, and montane vegetation regions. The average of China's EDVI is positive in dense vegetation regions and negative in sparse vegetation regions, depending on the proportion of bare soil and open water. In all selected studying regions, the seasonal variation of EDVI follows the trend of vegetation phenology, even in regions with large proportion of open water. EDVI is positively correlated to the greenness of vegetation (normalized difference vegetation index [NDVI]) with certain phase difference in their seasonal cycle. In autumn, EDVI begins to decline earlier and faster than NDVI. In tropical rainforest, EDVI also starts to increase earlier than NDVI in spring. The large-scale spatial distribution of EDVI under clear sky and cloudy sky is similar. In montane vegetation regions, EDVI under heavy clouds (90\% fraction) conditions is significantly greater than that under clear sky (10\% fraction), indicating a possible cloud induced enhancement of vegetation water content. In forests and croplands in the plains, such effect is not remarkable.

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@ARTICLE { LiuWangHuEtAl2020,
    AUTHOR = { Li, R. and Wang, Y. and Hu, J. and Wang, Y. and Min, Q. and Bergeron, Y. and Valeria, O. and Gao, Z. and Liu, J. and Fu, Y. },
    TITLE = { Spatiotemporal Variations of Satellite Microwave Emissivity Difference Vegetation Index in China Under Clear and Cloudy Skies },
    JOURNAL = { Earth and Space Science },
    YEAR = { 2020 },
    VOLUME = { 7 },
    NUMBER = { 5 },
    PAGES = { e2020EA001145 },
    NOTE = { e2020EA001145 2020EA001145 },
    ABSTRACT = { Abstract In this study, we used data from multiple sensors onboard NASA Aqua satellite to conduct a 10-year (2002–2011) remote sensing of microwave emissivity difference vegetation index (EDVI) over China. We investigated the spatial and temporal variations of EDVI in tropical and subtropical evergreen forest, deciduous forest, rice and wheat farmlands, grassland, and montane vegetation regions. The average of China's EDVI is positive in dense vegetation regions and negative in sparse vegetation regions, depending on the proportion of bare soil and open water. In all selected studying regions, the seasonal variation of EDVI follows the trend of vegetation phenology, even in regions with large proportion of open water. EDVI is positively correlated to the greenness of vegetation (normalized difference vegetation index [NDVI]) with certain phase difference in their seasonal cycle. In autumn, EDVI begins to decline earlier and faster than NDVI. In tropical rainforest, EDVI also starts to increase earlier than NDVI in spring. The large-scale spatial distribution of EDVI under clear sky and cloudy sky is similar. In montane vegetation regions, EDVI under heavy clouds (90\% fraction) conditions is significantly greater than that under clear sky (10\% fraction), indicating a possible cloud induced enhancement of vegetation water content. In forests and croplands in the plains, such effect is not remarkable. },
    DOI = { 10.1029/2020EA001145 },
    EPRINT = { https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020EA001145 },
    KEYWORDS = { Satellite remote sensing, Microwave Emissivity Difference Vegetation Index (EDVI), Vegetation Water Content, Greenness of Vegetation, Seasonal Changes, Cloud-Vegetation Interaction },
    OWNER = { Daniel Lesieur },
    TIMESTAMP = { 2020-04-29 },
    URL = { https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020EA001145 },
}

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