融合多源遥感信息的黑河流域绿洲区逐日田块尺度（30m）地表蒸散发估算与耗水规律研究

Accurate estimation of surface evapotranspiration (ET) with high quality and fine spatio-temporal resolution is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale．Integrating multi-source remote sensing data is one of the main ideas for many scholars to obtain synthesized frequent high spatial resolution surface ET. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces, etc. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism need further investigation, including the applicability and the influencing factors, such as local environment, advection, and heterogeneity of the landscape, for improving estimation accuracy. Due to technical and budget limitations, so far, no single sensor provides both high spatial resolution and high temporal resolution. Optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions. The accurate "all-weather" ET estimation method will be proposed through blending multi-source remote sensing data acquired by optical, thermal infrared (TIR) and passive microwave remote sensors on board polar satellite platforms. The estimation will be carried out for daily ET of the oasis at field scales (30 m) in the Heihe River Basin, and then the remotely sensed ET results are overlapped with the footprint-weighted images of EC (eddy correlation) for ground-based validation. Finally, this research will develop a multi-scale evaluation method for monitoring irrigation water efficiency at the irrigation district scale and village scale. And then further analyzed the changing characteristics of irrigation water efficiency at different scales.

高质量、高时空分辨率地表蒸散发（Evapotranspiration，ET）是遥感在流域生态-水文集成研究和水资源管理中面临的巨大障碍之一。多源遥感数据协同，是当前众多学者获取时间连续、高空间分辨率地表蒸散发的主要思路。然而，在蒸散发遥感估算模型及其参数化方案、时间尺度扩展、时空融合模型和异质性地表的精度验证等方面急需深入研究。本项目以物理基础坚实的SEBS模型为蓝本，开展遥感估算蒸散发的机理研究，提高模型估算精度。同时，克服单一遥感数据源不能兼顾时间和空间分辨率方面的矛盾以及光学遥感在阴雨天的缺失。基于光学、热红外和被动微波等多源遥感，结合蒸散发估算模型与时空融合方法，建立“全天候”蒸散发估算方案，实现黑河流域绿洲区逐日田块尺度（30m）地表蒸散发的估算，利用足迹模型进行验证。最后，构建多尺度（灌区尺度、村社尺度）灌溉水资源利用效率评价方法，探讨黑河绿洲耗水的时空变化规律。

高质量、高时空分辨率（逐日田块尺度）地表蒸散发的遥感估算在流域水资源管理等研究中具有重要价值。本项目目标是利用多源卫星遥感信息及融合方法，开展逐日田块尺度地表蒸散发估算研究，分析流域（绿洲区）蒸发耗水变化规律。.项目认真按照总目标执行，研究内容和结果：（1）地表蒸散发遥感模型参数化方案优化。针对绿洲-荒漠水热异质性强的下垫面，利用全局敏感性分析确定SEBS模型的敏感变量，开展了模型敏感变量的参数化方案优化。同时使用全天候地表温度数据，克服了在阴雨天无法获取地表温度数据的困难，准确地估算了空间全覆盖的瞬时地表蒸散发。最后通过考虑水汽压差、短波辐射比等多因子约束，提高瞬时到日地表蒸散发的尺度扩展方法精度，获取了黑河流域空间全覆盖、高时空分辨率地表蒸散发，验证结果较理想，其MBE值为-0.13mm/d，MAPE值32.21%，RMSE值为0.84mm/d，R值为0.90。（2）时空融合技术应用于高时空分辨率地表蒸散发估算。一是比较了模型输入参数融合方法(IPFA)和蒸散发融合方法(ETFA)，发现两种方案对于均质表面的性能相似，而IPFA方案对于非均质表面的表现稍好。与观测值比较，二者的MPAE分别是12.38%和13.40%。二是针对现有再分析数据空间分辨率较低的情况，利用回归克里格插值方法，结合站点数据，制备了与遥感数据尺度更加匹配的气象要素数据。（3）地表蒸散发产品的制备与耗水特征分析。基于以上构建的地表蒸散发估算方案，生产了2010-2016年黑河流域（绿洲区）逐日田块尺度的地表蒸散发，并完成了绿洲区灌溉水利用效率（IWE）和水分利用效率（WUE）的评估。.该项目生成的黑河流域高时空分辨率地表蒸散发数据已在国家青藏高原科学数据中心(http://data.tpdc.ac.cn)共享发布。该数据集可支撑黑河流域水资源的精细管理，更好服务于黑河绿色发展途径制定与可持续发展决策。.在项目支持下，已在国际遥感领域TOP期刊发表1篇SCI论文和1篇中文核心期刊（已接收）；在审SCI论文2篇和即将出版合著一部（已向出版社提交书稿）。参加国内外会议5次并做报告。培养本科生3人，硕士生2人。