предыдущий|следующий

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

  • role： First author第一作者
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

  • Email：zhaow@imde.ac.cn
  • Introduction：赵伟，研究方向为山地地表水热过程遥感。E-mail：zhaow@imde.ac.cn
ZHAO Wei1,  
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

    University of Chinese Academy of Sciences, Beijing 100049, China

WEN Fengping12,  
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

    University of Chinese Academy of Sciences, Beijing 100049, China

CAI Junfei12

реферат

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.

ключеви́че слова́

passive microwave;soil moisture;spatial downscaling;Empirical method;Semi-empirical method;physical model-based method

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