黑河综合遥感联合试验研究进展:概述

“黑河综合遥感联合试验”是在我国典型的内陆河流域--黑河流域开展的大型航空、卫星遥感和地面同步观测试验。28个单位的280多名科研人员、研究生和工程技术人员参加了试验。介绍了2008年加强试验期以来的研究工作进展。“黑河综合遥感联合试验”最主要的成果是一套多尺度、高质量的综合观测数据集,该数据集已正式对外发布,十分有力地支持了一系列生态、水文、定量遥感模型的发展、改进和验证,试验所期望突破“数据瓶颈”的目标已经实现。此外,已在积雪参数提取、地表冻融微波遥感、森林结构参数的遥感反演、蒸散发观测与遥感估算、土壤水分反演、生物物理参数和生物化学参数反演、水文气象观测、尺度推绎、流域水文模拟和同化应用等方面取得了丰富的研究成果。试验亮点包括蒸散发观测及其遥感模型改进、机载激光雷达的应用、多角度热红外传感器研制和应用；航空遥感在获取高质量、高分辨率数据方面依然具有不可替代的作用。

Progresses on the Watershed Allied Telemetry Experimental Research (WATER)

WATER experiment is a simultaneous airborne,satellite-borne and ground-based remote sensing experiment taken place in the Heihe river Basin,a typical inland river basin in northwest China.Many teams contributed to the WATER through their participation.In total,28 institutions and more than 280 scientists,students,engineers and aircrews participated in the field campaigns of WATER.This paper introduces the research progresses since the intensive observation period of WATER,which was completed in 2008.The most important outcome of the WATER is its multi-scale,high-quality,and integrated observational data sets,which was officially released and opened to public from July 2010.It offers supports for the development,improvement and validation of a series of ecological,hydrological,and quantitative remote sensing models.The designed objective to break through the data bottleneck in integrated watershed study has been accomplished.In addition,the WATER has achieved abundant research results in ① retrieving snow parameters,surface soil freeze/thaw state,forest structure parameters,evapotranspiration,soil water content and biogeophysical parameters from remote sensing;② hydrometeorology observation;③ and development of scaling methods,hydrological models and data assimilation system.The highlights of WATER include improvement of remote sensing models on estimating evapotranspiration,application of airborne LiDAR,and development and application of a multi-angle thermal infrared remote sensing sensor.It is also illustrated that the airborne remote sensing is still playing an irreplaceable role in obtaining high-quality and high-resolution data.