【目的】干涉合成孔径雷达测量(InSAR)技术近年来被广泛用于反演活动层厚度（ALT），然而现有研究较少考虑冻融对地表形变和土壤孔隙水热变化的影响，因此，本文构建了考虑土壤水热变化的ALT反演模型。【方法】使用InSAR技术和CNNBiLSTM-AM模型得到地表参数，顾及冻融驱动下活动层的变形和土壤孔隙及水分的变化构建了活动层厚度反演模型。首先，通过SBAS-InSAR技术提取研究区垂直向地表形变。然后，构建CNN-BiLSTM-AM模型，使用卷积神经网络（Convolutional Neural Networks, CNN）对多源遥感数据特征提取，采用双向长短期记忆网络（Bi-directional Long Short-term Memory,BiLSTM）对提取特征进行预测，添加多头自注意力层（Attention Mechanism, AM）提高模型对关键信息的提取，得到多特征约束下的土壤含水量预测值。最后，以垂直向地表形变作为表征活动层的主要参数，构建基于土壤孔隙比和土壤含水量的活动层厚度反演模型，得到兰新高铁冻土区活动层厚度的时空分布。【结果】模型估计值与俄博岭实测数据验证的...

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

冻土覆盖率高的小流域的径流形成受温度因素控制明显,普通水文模型不适用,而常规冻土水文模型因需要较多的气象观测要素而难以应用。考虑冻土流域产流机制,利用青藏高原腹地风火山小流域2017—2018年逐日降水、气温、径流观测数据,以降水、气温为输入,径流为输出,基于长短期记忆神经网络（LSTM）建立了适用于小流域尺度的冻土水文模型,并利用2019年观测数据进行验证。模型得益于LSTM特殊的细胞状态和门结构能够学习、反映活动层冻融过程和土壤含水量变化,具有一定的冻土水文学意义,能很好地模拟冻土区径流过程。模型训练期R2、NSE均为0.93,RMSE为0.63m3·s-1,验证期R2、NSE分别为0.81、0.77,RMSE为0.69m3·s-1。同时,为了验证模型可靠性,将模型应用于邻近的沱沱河流域,模型训练期（1990—2009年）R2、NSE均为0.73,验证期（2010—2019年）R2、NSE分别为0.66、0....