长时序无缝地表温度重建方法研究  

Research on Long-term Gap-Free Land Surface Temperature Reconstruction Method

作　　者：鲍瑶 杨英宝[1] BAO Yao;YANG Yingbao(School of Earth Science and Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学地球科学与工程学院,江苏南京211100

出　　处：《遥感技术与应用》2024年第4期940-951,共12页Remote Sensing Technology and Application

基　　金：国家自然科学基金面上项目(42071346);江苏省研究生科研与实践创新计划项目(SJCX21_0210);中央高校基本科研业务费(学生项目)。

摘　　要：地表温度(Land Surface Temperature,LST)是全球气候变化研究的关键参数。热红外遥感LST产品是获取LST的理想数据源,但目前LST产品存在受云影响导致的大面积缺失,以及有限的时序无法满足历史时期气候变化研究的需求等问题,限制了LST产品的深入应用。采用ERA5的累积太阳辐射SOA表征云下LST的变化,结合三种用于LST重建的因子(辐射因子、地形因子和光谱因子),构建了无缝LST的随机森林重建模型,探讨了该模型在云空和时序迁移情形下的重建效果。研究结果表明:①将用于LST重建的因子进行重要性排序,地形和辐射因子表现出极高的重要性。构建的LST重建模型有较高的拟合度,R^(2)为0.97,RMSE为1.27 K。②重建的云空无缝LST修复了原始LST分布的破碎性。经地面站点LST验证,R^(2)在0.90以上。RMSE在2.67 K~3.15 K。将SOA与地面站点LST的变化趋势进行比较,发现两者表现出良好的连贯性,表明SOA能够充分反映云下LST的变化情况。③重建了经时序迁移的无缝LST,对于月时序迁移的LST,R^(2)在0.77~0.96,RMSE在1.35 K~4.02 K。对于年时序迁移的LST,R^(2)在0.86以上,RMSE在2.73 K~3.25 K。结合预测变量的统计图,发现辐射因子受时间迁移的影响较小,光谱因子NDVI随时间迁移发生变化,当用于LST重建的NDVI与用于LST重建模型训练的NDVI的范围相差较大时,重建的LST的精度将降低。本研究能为长时序、无缝的LST重建提供一定的理论支撑。Land Surface Temperature(LST)is a key parameter in the study of global climate change.Thermal infrared remote sensing LST products are an ideal data source for obtaining LST.However,at present,LST products suffer from large-area deletions caused by clouds,and the limited time series cannot meet the needs of climate change research in historical periods,which limit the in-depth application of LST products.In this pa⁃per,the cumulative solar radiation SOA of ERA5 is used to characterize the change of LST under cloud,and three factors(radiative factor,terrain factor and spectral factor)used for LST reconstruction are combined to construct a random forest reconstruction model of Gap-Free LST,and the model is discussed.Reconstruction effects in cloudy sky,and time-series migration.The research results show that:(1)the factors used for LST reconstruction are ranked by importance,and the topographic and radiative factors show high importance.and the topographic and radiative factors show high importance.The LST reconstruction model constructed has a high degree of fit,R^(2)is 0.97,RMSE is 1.27 K.(2)The reconstructed cloud-air Gap-Free LST fixes the frag⁃mentation of the original LST distribution.Verified by ground station LST,R^(2)is above 0.90.RMSE is be⁃tween 2.67 K and 3.15 K.Comparing the change trend of SOA and ground station LST,it is found that the two show good coherence,indicating that SOA can fully reflect the change of LST under the cloud.(3)The seam⁃less LST with sequential migration is reconstructed.For the LST with monthly sequential migration,R^(2)ranges from 0.77~0.96 and RMSE ranges from 1.35 K~4.02 K.For LST with annual sequential migration,R^(2)is above 0.86 and RMSE is between 2.73 K and 3.25 K.combined with the statistical map of predictor variables,it is found that the radiation factor is less affected by time migration,and the spectral factor NDVI migrates with time A change occurs,when the range of the NDVI used for LST reconstruction and the NDVI used for LST reconstruction model trainin

关 键 词：LST 重建 无缝 长时序 随机森林 

分 类 号：P237[天文地球—摄影测量与遥感]