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介绍了＂黑河流域生态—水文过程综合遥感观测联合试验＂的背景、科学目标、试验组成和试验方案。试验的总体目标是显著提升对流域生态和水文过程的观测能力,建立国际领先的流域观测系统,提高遥感在流域生态—水文集成研究和水资源管理中的应用能力。由基础试验、专题试验、应用试验、产品与方法研究和信息系统组成。其中,①基础试验：搭载微波辐射计、成像光谱仪、热像仪、激光雷达等航空遥感设备,开展一系列关键生态和水文参量的观测;发展遥感正向模型及反演和估算方法。形成覆盖全流域的水文气象综合观测网,为流域生态—水文模型研究提供更有代表性的模型参数、驱动数据及更高精度的验证数据。构建无线传感器网络,度量生态水文模型所需的若干关键的驱动、参数和模型状态的空间异质性。开展航空遥感定标和地基遥感试验。依托传感器网络,并辅之以地面同步和加密观测,开展遥感产品真实性检验。②专题试验：开展＂非均匀下垫面多尺度地表蒸散发观测试验＂,采用密集的涡动相关仪、大孔径闪烁仪与自动气象站的观测矩阵,为揭示地表蒸散发的空间异质性,实现非均匀下垫面地表蒸散发的尺度扩展,发展和验证蒸散发模型提供基础数据。③应用试验：在流域上、中、下游分别开展针对积雪和冻土水文、灌溉水平衡和作物生长、生态耗水的综合观测试验,将观测数据和遥感产品用于上游分布式水文模型、中游地表水—地下水—农作物生长耦合模型、下游生态耗水模型,通过实证研究提升遥感在流域生态—水文集成研究和水资源管理中的应用能力。加强试验将在2012年5月起按中游、上游、下游的顺序展开,全流域持续观测期为2013—2015年。在各类试验的支持下,开展全流域生态—水文关键参量遥感产品生产,发展尺度转换方法,建立多源遥感数据同化系统。“,”This paper introduces the background, scientific objectives, experiment components and implemen- tation plan of the Heihe Watershed Allied Telemetry Experimental Research （HiWATER）. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class river basin observing system, and to increase the applicability of remote sensing and other new generation observation tech- niques in eco-hydrological studies and water resource management at basin scale. HiWATER encompasses funda- mental experiments, thematic experiments, application experiments, remote sensing methods development and products generation, and an integrated information system. （1） Fundamental experiments：① Microwave radiometer, imaging spectrometer, thermal imaging camera, Light Detection and Ranging （LiDAR） and other sensors will be used in the airborne missions to observe key eco- hydrological parameters and at the meantime to develop and improve the remote sensing models and methods for the retrieval of those variables. ② To establish a comprehensive hydrometeorological observation network that will cover the entire Heihe River Basin, in order to provide more representative model parameters and forcing data, and more accurate ground truths.③ An eco-hydrological Wireless Sensor Network （WSN） will be established to capture the spatial-temporal dynamics and variations of key forcing data, eco-hydrological parameters, and model state variables over heterogeneous land surfaces. ④ Airborne sensors calibration and ground-based remote sensing experiments will be conducted. Remote sensing products will be validated rest upon the WSN, in association with simultaneous in si- tu measurements and other intensive observations. （2） Thematic experiments： A muhi-scale observation experiment on evapotranspiration will be performed over heterogeneous land surfaces. Eddy Covariance （EC） systems, Large Aperture Scintillometer （LAS）, and Automat- ic Weather Stations （AWSs） will be intensively deployed to constitute an observing matrix to reveal the spatial het- erogeneities of ET, to identify the scale effects and achieve scale transformation of ET over heterogeneous land- scapes, and to provide elementary data sets for the development and validation of ET estimation models. （3） Application experiments： Purposeful observation experiments will be performed in the upstream, middle stream, and downstream of the Hiehe River Basin, aiming at snow and frozen soil hydrology, water balance in irri- gation management, and quantifying plant water consumption. Observed data sets and remote sensing products will be used in distributed hydrological model, coupled surface water-groundwater-crop growth model, and ecological water consumption model towards the areas of upstream, middle stream, and downstream. It is anticipated the ap- plicability of remote sensing in integrated eco-hydrologieal studies and water resource management can be enhanced by means of these empirical researches. The intensive observations and field campaigns will be orderly undertaken in the middle stream, upstream, and downstream of the Heihe River Basin from May, 2012 to 2015. Dependent on various experiments, remote sensing products at basin scale for the key eco-hydrological variables will be created, scale transformation approaches will be explored, and a multi-source remote sensing data assimilation system will be eventually built.