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Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

机译:使用Sentinel-1,Sentinel-2和Landsat-8在南部克鲁格国家公园进行干旱监测的地表水分和植被覆盖度分析

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During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Ni?o event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.
机译:在2015年和2016年的南部夏季季节,由于发生了异常强烈的厄尔尼诺事件,南非经历了自气候记录开始以来最严重的气象干旱之一。为了调查地表水分和植被结构的时空动态,利用了ESA的Copernicus Sentinel-1 / -2和NASA的Landsat-8的2015年3月至2017年11月期间的数据。结合起来,这些雷达和光学卫星系统可提供具有高时空分辨率的有希望的数据。利用Sentinel-1 C波段数据基于超时共极化(垂直-垂直-VV)雷达后向散射变化检测方法得出地表水分,描述了干燥季节和潮湿季节之间的动态。利用来自TLS(陆地激光扫描仪)的树冠高度模型(CHM)的植被信息以及Sentinel-2和Landsat-8的归一化植被指数(NDVI),来分析植被结构类型和动态到表面湿度指数(SurfMI)。我们的结果表明,我们的雷达与光学相结合的方法可以分离和恢复适合干旱监测的地表水分条件。此外,我们得出结论,对于开发稀树草原生态系统的干旱监测系统而言,至关重要的是整合土地覆盖和植被信息以分析从地球观测时间序列得出的地表水分动态。

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