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Satellite Soil Moisture and Its Applications

Call for Papers

Soil moisture is an important land surface state variable that has long memory to impact the exchanges of water, energy, and carbon between the land surface and atmosphere. It can be used to define agricultural drought, assess wildfire risk, monitor flooding development, and map dust emissions. In situ soil moisture observations are generally limited to site scale and provide insufficient data coverage. Because of the uncertainties associated with meteorological forcing data, faulty estimates of relevant land surface parameters, and deficient model formulations, the model-based estimations may not represent observed soil moisture. However, active and passive microwave remote sensing has been proven to be a reliable tool for remotely monitoring surface soil moisture.

Satellite soil moisture data assimilation has significant positive impacts on the model performance and in turn improving the accuracy of weather forecasts. Following a “bottom-up” approach, satellite soil moisture explores good performance in rainfall retrieval with addressing inherently intermittent nature of the “top-down” techniques. Additionally, enhanced soil moisture can prevent soil erosion and consequently increase the threshold friction velocity of dust outbreak. Soil moisture is also highly required to account for land surface emissivity due to its impacts on the spatial and temporal variation of bare soil emissivity.

Presently, there are a number of operational satellite soil moisture products (ASCAT, SMOS, SMAP, AMRS-2 (X) AMSR-2 (O), etc.). Given the real time records of remote sensing soil moisture, it is desirable to promote the use of the data in improving model performance, accuracy of weather forecasts, and capability of monitoring natural disasters (e.g., drought, dust storm, wildfire, and flood). There are also lots of open scientific questions related to understanding soil moisture-precipitation feedback, the role of soil moisture in climate change, and impacts of soil moisture on thermal radiation. To address these questions, there is a need to improve accuracy of in situ soil moisture observations, retrieve higher resolution products, develop algorithm of soil moisture retrieval, and construct long time series of satellite soil moisture data.

This special issue is aimed at providing assessments on the advances in the development and validations of satellite soil moisture products and their applications in meteorology. We sincerely invite authors to contribute original research and review manuscripts focused on the continuing effort to develop satellite soil moisture products, illustrating their applications in data assimilation and natural disasters monitoring/warning and investigating their roles in water cycle and climate, as well as other interdisciplinary areas.

Potential topics include but are not limited to the following:

  • Remote sensing soil moisture retrieval
  • Downscaling satellite soil moisture retrieval
  • In situ soil moisture observations
  • Long time series of satellite soil moisture
  • Validation of spaceborne soil moisture products
  • Improvements on land surface model skills via assimilating soil moisture retrieval
  • Enhancement on accuracy of weather forecasts with satellite soil moisture
  • Natural disasters monitoring using remotely sensed soil moisture
  • Characteristics of soil moisture in climate change and global warming
  • Impacts of soil moisture on thermal radiation
  • Rainfall estimation by inverting soil moisture
  • Soil moisture-precipitation feedback
  • Interaction of soil moisture and airborne dust

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 6 April 2018
Publication DateAugust 2018

Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

  • Jifu Yin, NOAA/NESDIS/STAR, College Park, USA

Guest Editors

  • Runping Shen, Nanjing University of Information Science and Technology, Nanjing, China
  • Qingyan Meng, Chinese Academy of Sciences, Beijing, China
  • Hui Xu, University of Maryland, College Park, USA
  • Heshun Wang, University of Maryland, College Park, USA