Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather sta...Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.展开更多
近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法...近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法结合,分析了江苏海岸带NPP和LST的时空变化及影响因素,结果表明:①由于人类对沿海滩涂资源的利用以及养殖业的发展等,江苏海岸带范围随岸线不断变化,岸线逐步向海推进,且南部向海推进范围大于北部。②近30年来,江苏海岸带NPP和LST呈现出显著的时空异质性特征。时间上1990、2000、2010、2020年代的NPP月均值分别为102.88、88.23、156.62、98.90 g C·m^(−2),呈现下降-上升-下降趋势,而LST月均值分别为32.6、31.7、28.3、37.6℃,呈现先下降后上升的趋势。空间上,NPP与LST在江苏海岸带南北分布呈现出一定差异性。③地表覆盖类型是影响江苏海岸带NPP和LST时空异质性的主要因素。林地的NPP最高,养殖池塘NPP最低;人工建筑的LST值最高,湿地、水域与养殖池塘的LST值相对较低。此外,随着气温升高,NPP和LST有逐渐上升的趋势,而植被覆盖度的升高则导致NPP上升和LST下降。展开更多
基金Under the auspices of the‘Beautiful China’Ecological Civilization Construction Science and Technology Project(No.XDA23100203)National Natural Science Foundation of China(No.42071289)Henan Postdoctoral Foundation(No.20180087)。
文摘Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.
文摘近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法结合,分析了江苏海岸带NPP和LST的时空变化及影响因素,结果表明:①由于人类对沿海滩涂资源的利用以及养殖业的发展等,江苏海岸带范围随岸线不断变化,岸线逐步向海推进,且南部向海推进范围大于北部。②近30年来,江苏海岸带NPP和LST呈现出显著的时空异质性特征。时间上1990、2000、2010、2020年代的NPP月均值分别为102.88、88.23、156.62、98.90 g C·m^(−2),呈现下降-上升-下降趋势,而LST月均值分别为32.6、31.7、28.3、37.6℃,呈现先下降后上升的趋势。空间上,NPP与LST在江苏海岸带南北分布呈现出一定差异性。③地表覆盖类型是影响江苏海岸带NPP和LST时空异质性的主要因素。林地的NPP最高,养殖池塘NPP最低;人工建筑的LST值最高,湿地、水域与养殖池塘的LST值相对较低。此外,随着气温升高,NPP和LST有逐渐上升的趋势,而植被覆盖度的升高则导致NPP上升和LST下降。
