基于TROPOMI叶绿素荧光遥感的冬小麦旱情监测

针对太阳诱导叶绿素荧光（Solar-Induced chlorophyll Fluorescence， SIF）可以有效指示陆表植被水分胁迫的特点，提出了归一化叶绿素荧光干旱指数（Normalized SIF Drought Index， NSDI）用于黄淮海地区冬小麦旱情监测。该方法首先基于哨兵-5p卫星（Sentinel-5p）对流层观测仪（Tropospheric Monitoring Instrument， TROPOMI）传感器反演得到的SIF原始产品集，通过0.1°等经纬步长栅格化处理为空间连续数据，然后基于时间序列分析进行了缺失值线性插补，再经过S-G滤波重建获得了高时空分辨率荧光数据集。以此数据集为基础，结合研究区冬小麦分布数据构建NSDI指数。通过选取典型旱情事件对比分析，NSDI指数与同期归一化植被指数（Normalized Difference Vegetation Index， NDVI）以及温度植被干旱指数（Temperature Vegetation Drought Index， TVDI）都有良好的相关性，其中与NDVI的R²为0.60，与TVDI的R²为0.41；NSDI指数与野外土壤水分调查结果也高度相关，其中河北样区R²为0.53，山东样区R²为0.54，整体R²为0.51；通过物联网监测数据分析显示，NSDI指数可以在优于2 d的滞后期内响应旱情的变化，其变化趋势与田间土壤水分保持高度相关。实验结果表明：NSDI指数可以在时空尺度上有效指示黄淮海地区冬小麦旱情。

A Winter Wheat Drought Index based on TROPOMI Solar-Induced Chlorophyll Fluorescence

According to the characteristic that Solar-Induced chlorophyll Fluorescence （SIF） can effectively indicate the water stress of land surface vegetation， we proposed a Normalized Solar-Induced Chlorophyll Fluorescence Drought Index （NSDI） for winter wheat drought monitoring in the Huang-Huai-Hai region. First， the original SIF data retrieved by the Sentinel-5p Tropospheric Instrument （TROPOMI） were processed into spatially continuous data with a spatial resolution of 0.1 degree. Missing values were then filled via the linear interpolation based on time series analysis， and S-G filters were applied to reconstruct high spatial and temporal resolution SIF dataset. The NSDI is developed using this reconstructed SIF dataset and winter wheat distribution data. The analysis of typical drought events revealed that the NSDI and the Normalized Difference Vegetation Index （NDVI） are strongly correlated with the R² of 0.60， the NSDI and the temperature vegetation drought index （TVDI） are also strongly correlated in different mature regions， with the highest R² of 0.66 in Yanshan region， and the lowest R² of 0.44 in Huanghuai plain region. The NSDI index is also highly correlated with the in-situ soil moisture data， with an R² of 0.53 and 0.54 respectively in Hebei and Shandong sample area， and an overall R² of 0.51. Analysis of monitoring data from the Internet of Things shows that the NSDI index can respond to changes of drought within a lag period of less than 2 days， and its change trend is highly correlated with soil moisture in the field. The experimental results show that the NSDI index can effectively indicate the drought of winter wheat in Huang-Huai-Hai region from the spatiotemporal perspective.