Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),whic...Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),which is a classical remote-sensing-based drought index,was proposed to capture typical drought characteristics across diverse regions and land-cover types.Relative soil moisture data obtained from the China Meteorological Administration Land Data Assimilation System(CLDAS)and vegetation net primary productivity(NPP)from the Atmosphere-Vegetation Interaction Model were further utilized to quantify TVDI sensitivity and evaluate its impacts.It was clear that TVDI successfully extracted the most severe drought events in southern China,exhibiting a significantly higher correlation coefficient with the minimum value of relative soil moisture than that obtained from the average value.In general,TVDI was negatively correlated with relative soil moisture and NPP,with the strength of these correlations gradually weakening as soil depth increased.Among land-cover types,TVDI performed best in depicting drought in cropland,followed by grassland and forest.These results can promote our acknowledgement of the typical drought characteristics and their impacts on vegetation,thereby providing valuable guidance for drought prevention strategies.展开更多
为建立中国风云三系列气象卫星长时间序列归一化植被指数数据集,选用滤波和函数拟合方法,针对林地、湿地、水稻、玉米、大豆、城市和水体7类地物开展数据重建效果定量分析,确定最佳数据重建方法,并在辽宁省开展时空变化分析。结果表明:...为建立中国风云三系列气象卫星长时间序列归一化植被指数数据集,选用滤波和函数拟合方法,针对林地、湿地、水稻、玉米、大豆、城市和水体7类地物开展数据重建效果定量分析,确定最佳数据重建方法,并在辽宁省开展时空变化分析。结果表明:非对称高斯函数拟合法(Asymmetric Gaussians,AG)、Savitzky-Golay滤波法(SG)、双Logistic函数拟合法(Double Logistic,DL)和时间序列谐波分析法(Harmonic Analysis of Time Series,HANTS)四种方法均表现出相对较好的去噪能力。SG方法对噪声比较敏感,HANTS方法在低值区受噪声影响大。AG和DL方法平滑效果较好,DL方法的峰值更接近于原始峰值。在高植被覆盖区和季节性作物区,SG方法相关系数最高(>0.93)、均方根误差最低(<0.1);在城市和水体低植被指数区,HANTS方法相关系数最高,为0.87,但四种方法的均方根误差均在0.06左右,差别不大。综合考虑曲线和定量分析结果,选取SG方法进行辽宁省植被指数数据集数据重建。辽宁省植被指数数值高低的空间分布与下垫面植被类型相符合,东部山区林地植被指数最高,达到0.75以上。2009—2020年,辽宁省NDVI年均值存在波动,不同地物植被指数变化存在差别,水体和城市植被指数变化相对较小,旱田作物(玉米、大豆)的植被指数受干旱年的影响植被指数变化稍大。辽宁省主要粮食作物植被指数年内均呈单峰分布,与一年一熟型吻合,均在8月上旬达到最大值。展开更多
For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for for...For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for forest ecological researches.In this paper,the processing of GLAS waveform data and the algorithm of forest canopy height in different terrain were implemented.The GLAS+MERSI joint inversion model of canopy height of different forest types in regional scale was established and used to map the forest canopy height of Jiangxi province.Overall,high accuracy was observed for the canopy height estimated by GLAS+MERSI joint inversion model with R^(2)=0.733 for the needle-leaf forest,following by the broadleaf forest(R^(2)=0.610).The results showed that the established model was workable.It was found that the GLAS+MERSI joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of forest canopy height at regional scale.The space distribution characteristic was found consistent with the data of land cover.展开更多
基金Innovation and Development Special Project of the China Meteorological Administration(CXFZ2024J051)National Key R&D Program of China(2022YFD2300200)。
文摘Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),which is a classical remote-sensing-based drought index,was proposed to capture typical drought characteristics across diverse regions and land-cover types.Relative soil moisture data obtained from the China Meteorological Administration Land Data Assimilation System(CLDAS)and vegetation net primary productivity(NPP)from the Atmosphere-Vegetation Interaction Model were further utilized to quantify TVDI sensitivity and evaluate its impacts.It was clear that TVDI successfully extracted the most severe drought events in southern China,exhibiting a significantly higher correlation coefficient with the minimum value of relative soil moisture than that obtained from the average value.In general,TVDI was negatively correlated with relative soil moisture and NPP,with the strength of these correlations gradually weakening as soil depth increased.Among land-cover types,TVDI performed best in depicting drought in cropland,followed by grassland and forest.These results can promote our acknowledgement of the typical drought characteristics and their impacts on vegetation,thereby providing valuable guidance for drought prevention strategies.
文摘为建立中国风云三系列气象卫星长时间序列归一化植被指数数据集,选用滤波和函数拟合方法,针对林地、湿地、水稻、玉米、大豆、城市和水体7类地物开展数据重建效果定量分析,确定最佳数据重建方法,并在辽宁省开展时空变化分析。结果表明:非对称高斯函数拟合法(Asymmetric Gaussians,AG)、Savitzky-Golay滤波法(SG)、双Logistic函数拟合法(Double Logistic,DL)和时间序列谐波分析法(Harmonic Analysis of Time Series,HANTS)四种方法均表现出相对较好的去噪能力。SG方法对噪声比较敏感,HANTS方法在低值区受噪声影响大。AG和DL方法平滑效果较好,DL方法的峰值更接近于原始峰值。在高植被覆盖区和季节性作物区,SG方法相关系数最高(>0.93)、均方根误差最低(<0.1);在城市和水体低植被指数区,HANTS方法相关系数最高,为0.87,但四种方法的均方根误差均在0.06左右,差别不大。综合考虑曲线和定量分析结果,选取SG方法进行辽宁省植被指数数据集数据重建。辽宁省植被指数数值高低的空间分布与下垫面植被类型相符合,东部山区林地植被指数最高,达到0.75以上。2009—2020年,辽宁省NDVI年均值存在波动,不同地物植被指数变化存在差别,水体和城市植被指数变化相对较小,旱田作物(玉米、大豆)的植被指数受干旱年的影响植被指数变化稍大。辽宁省主要粮食作物植被指数年内均呈单峰分布,与一年一熟型吻合,均在8月上旬达到最大值。
文摘For inversion of forest canopy height in large scale,it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively.The homemade satellite will provide a plentiful datum for forest ecological researches.In this paper,the processing of GLAS waveform data and the algorithm of forest canopy height in different terrain were implemented.The GLAS+MERSI joint inversion model of canopy height of different forest types in regional scale was established and used to map the forest canopy height of Jiangxi province.Overall,high accuracy was observed for the canopy height estimated by GLAS+MERSI joint inversion model with R^(2)=0.733 for the needle-leaf forest,following by the broadleaf forest(R^(2)=0.610).The results showed that the established model was workable.It was found that the GLAS+MERSI joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of forest canopy height at regional scale.The space distribution characteristic was found consistent with the data of land cover.
