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.