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全文摘要次数: 196 全文下载次数: 180
引用本文:

DOI:

10.11834/jrs.20210515

收稿日期:

2020-11-13

修改日期:

2021-05-24

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被动微波土壤水分遥感产品空间降尺度研究:方法、进展及挑战
赵伟, 文凤平, 蔡俊飞
中国科学院、水利部成都山地灾害与环境研究所
摘要:

土壤水分不仅在地表水、能量以及碳循环中发挥着非常重要的作用,其时空变化也是影响和反映气候变化的关键因子。虽然被动微波遥感技术是目前监测大尺度范围土壤水分变化最为成熟的技术手段,但是其土壤水分产品空间分辨率往往较低(几十千米不等),不能满足区域和局地尺度的应用需求。鉴于这一问题,空间降尺度逐渐成为了提高被动微波土壤水分遥感产品空间分辨率的主要方式,也是当前遥感研究领域的热点之一。为此,本文总结、分析了近二十多年来国内外被动微波土壤水分遥感产品空间降尺度研究进展,系统归纳了经验性、半经验性和基于物理机理的三大类降尺度方法,并就各方法特征进行了详细说明,概述了各方法的优势和缺点。归纳而言,虽然被动微波土壤水分遥感产品空间降尺度方法众多,但可靠的高分辨率降尺度土壤水分产品仍较少,这与被动微波土壤水分遥感产品、降尺度关系模型方法、以及降尺度辅助因子等有着直接的关联。未来相关研究应重点结合多源遥感数据建立适用性强、精度高的降尺度关系模型,进而实现时空无缝的高时空分辨率降尺度土壤水分产品的生产,为推动土壤水分遥感产品在农林业管理、自然灾害监测、水文过程分析等区域应用中提供支持。

Methods, progresses and challenges of passive microwave soil moisture spatial downscaling
Abstract:

Soil moisture (SM) plays an important role in the global water, energy and carbon cycle, and its spatial distribution is also one of the key components of global climate change. Although passive microwave remote sensing technology is the most effective way to monitor the distribution of SM on a large scale, the passive microwave SM products are generally limited by their low spatial resolution which makes it cannot meet the requirements of regional applications. On this basis, spatial downscaling has gradually become an alternative way to improve the spatial resolution of passive microwave SM products, and it also became one of the research hotspots in the field of remote sensing. Therefore, this paper analyzed and summarized researches on passive microwave SM spatial downscaling in the past more than 20 years. In terms of the classifications of downscaling methods, the existing methods can be divided into three categories: empirical, semi-empirical and physical model-based downscaling method. The empirical downscaling method is simple and easy to achieve large-scale downscaling study, but it lacks physical background in the downscaling process. However, empirical method has been widely used in passive microwave SM spatial downscaling study due to their simplicity and practicability. Physical model-based method usually uses data assimilation or/and land surface process models as the downscaling relational model. Usually, its process is complex, which makes physical model-based method has low applicability, but such method can often obtain more accurate downscaling SM results. The semi-empirical downscaling method can give consideration to both the accuracy of downscaling results and the operability of the method itself. However, the semi-empirical method still has the problem of insufficient applicability. Although passive microwave SM spatial downscaling method is numerous, but the downscaled SM products with good accuracy are unusual. Currently, there are only a few passive microwave downscaling SM products continuously produced, mainly the SMOS L4V5 SM product published by BEC and the active and passive SM product calculated by NASA SMAP/Sentinel-1. Although the two kinds of downscaling SM products have the same spatial resolution (1 km), both have some defects in spatial coverage. There are still some problems and challenges need to be solved in the downscaling research of passive microwave SM, which makes it difficult to obtain the downscaling results with high spatial resolution, good accuracy, completely spatial coverage, and daily temporal resolution. This is mainly related to the uncertainty in the downscaling model (the relationship between SM and downscaling factors), passive microwave SM product (uncertainty in the original product and incomplete spatial coverage) and downscaling factors (the influence from cloud cover). Therefore, in the future, the research focus should be to establish downscaling relationship model with strong applicability and high accuracy based on multi-source remote sensing data and to obtain high-resolution downscaled SM product with completed spatial coverage. The development of passive microwave SM spatial downscaling will also provide more references and opportunities for promoting application of SM product based on remote sensing in fields such as agro-forestry management and natural disaster monitoring.

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