Methods， progresses， and challenges of passive microwave soil moisture spatial downscaling
- Vol. 26, Issue 9, Pages: 1699-1722(2022)
Received：20 November 2020，
Published：07 September 2022
DOI： 10.11834/jrs.20210515
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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。本文总结与分析了近20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3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。
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 of monitoring SM distribution at global scale
these kind products are generally limited by their low spatial resolution
which further prevents them from meeting the requirements of regional applications. On this basis
spatial downscaling has gradually become an alternative way of improving the spatial resolution of passive microwave SM products and a research hotspot in the field of remote sensing. Therefore
this paper reviewed and summarized the progresses on passive microwave SM spatial downscaling in the past 20 years. In terms of the downscaling methods
they can be divided into three key categories: empirical
semi-empirical
and physical model-based downscaling methods. The empirical downscaling method is simple and can easily achieve large-scale downscaling but lacks in physical background on the downscaling process. However
empirical methods have been widely used in passive microwave SM spatial downscaling study due to their simplicity and practicability. Physical model-based methods usually use data assimilation or/and land surface process models as the downscaling relational model. Usually
the process is complex
resulting in the low applicability of the physical model-based method
but this method can often obtain the downscaling results with good accuracy. The semi-empirical downscaling method generally can ensure the accuracy of the downscaling results and the operability of the method itself. However
the applicability of the semi-empirical method is still limited by its uncertainty related to the linking model for soil moisture expression and also some downscaling factors. Although numerous passive microwave SM spatial downscaling methods exist
the available downscaled SM products with good accuracy are limited. Currently
few passive microwave downscaling SM products are continuously produced
including the SMOS L4V5 SM product produced by BEC and the active and passive fusion SM products generated by NASA SMAP/Sentinel-1. Although the two kinds of downscaling SM products have the same spatial resolution (1 km)
both suffers from the poor spatial coverage. In general
there are still some problems and challenges should be considered for current spatial downscaling study for passive microwave SM product. Aiming at obtaining the downscaling results with high spatial resolution
good accuracy
seamless spatial coverage
and daily temporal resolution. The uncertainty in the downscaling model (the relationship between SM and the downscaling factors)
the uncertainty in the original passive microwave SM products and their incomplete spatial coverage
and the uncertainty in the downscaling factors (such as the influence from cloud cover and topography) are the issues should be well addressed. Overall
the development of the spatial downscaling study for passive microwave SM products will also provide more references and opportunities for promoting the application of SM products based on remote sensing in various fields including agro-forestry management
water resource assessment
and natural disaster monitoring.
关键词
Keywords
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Fengping WEN 中国科学院水利部成都山地灾害与环境研究所;中国科学院大学
Wei ZHAO 中国科学院水利部成都山地灾害与环境研究所
Lu HU 中国科学院空天信息创新研究院
Hongxin XU 上海航天技术研究院
Qian CUI 水利部信息中心
LI Ran 中国地质大学(武汉) 地理与信息工程学院
WANG Mengmeng 中国地质大学(武汉) 地理与信息工程学院;武昌理工学院 人工智能学院
ZHANG Zhengjia 中国地质大学(武汉) 地理与信息工程学院
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Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
Shanghai Academy of Spaceflight Technology
Information Center of Ministry of Water Resources of China
School of Geography and information Engineering, China University of Geosciences
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