利用 Sentinel-3 SLSTR 数据研究地表温度(land surface temperature,LST)、植被覆盖度(fractional vegetation cover,FVC)、总柱水汽含量(total column water vapor content,TCWV)等气象参数及其与 DEM 的相互关系,分析了 2018-2022年江西短期干旱等气象事件.分析结果表明:全省气候事件中FVC之间呈现显著正相关,相关系数在0.86～0.93且FVC逐年在增加;LST之间存在显著正相关;干旱事件中TCWV之间存在显著正相关,洪涝和干旱事件间TCWV之间存在中度负相关.同时,LST和FVC呈负相关,TCWV和FVC呈正相关,且FVC之间和TCWV之间的变化趋势相似.另外,DEM与FVC存在中度正相关,DEM与LST存在负相关,DEM与TCWV存在双重相关.因此,Sentinel-3 SLSTR数据可作为江西省气象异常事件监测的有效数据源,TCWV和FVC数据的利用对异常数据监测具有重要意义.
Analysis of Short-term Drought Events in Jiangxi Province Utilizing Sentinel-3 SLSTR Climate Parameters
This study utilizes Sentinel-3 SLSTR data to investigate surface temperature(LST),fractional vegetation cover(FVC),total column water vapor content(TCWV),and their correlation with digital elevation model(DEM).The research focuses on short-term droughts and other meteorological events in Jiangxi from 2018 to 2022.The findings reveal that FVC exhibits a significant positive correlation in provincial climate events,with correlation coefficients ranging from 0.86 to 0.93,and displays an increasing trend over the years.Positive correlations are also observed between LST and TCWV in drought events,with a moderate negative correlation between TCWV in flood and drought events.Additionally,LST and FVC are negatively correlated,while TCWV and FVC show a positive correlation,indicating a similar trend of change between FVC and TCWV.Besides,DEM demonstrates a moderate positive correlation with FVC,a negative correlation with LST,and a dual correlation with TCWV.Consequently,Sentinel-3 SLSTR data emerge as a valuable data source for monitoring meteorological anomalies in Jiangxi province.The utilization of TCWV and FVC data holds significant implications for anomalous data monitoring.
droughtSentinel-3 SLSTRfractional vegetation coverland surface temperatureatmospheric contentJiangxi province
万方数据
维普
