Advances in Meteorology

Satellite Soil Moisture and Its Applications


Status
Published

Lead Editor

1NOAA/NESDIS/STAR, College Park, USA

2Nanjing University of Information Science and Technology, Nanjing, China

3Chinese Academy of Sciences, Beijing, China

4University of Maryland, College Park, USA


Satellite Soil Moisture and Its Applications

Description

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

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 4328574
  • - Editorial

Satellite Soil Moisture and Its Applications

Jifu Yin | Runping Shen | ... | Heshun Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 5636932
  • - Research Article

Spatiotemporal Variation Characteristics of Vegetative PUE in China from 2000 to 2015

Haitao Xu | Peng Hou | ... | Bing Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 4832423
  • - Research Article

Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

Daeun Kim | Heewon Moon | ... | Minha Choi
  • Special Issue
  • - Volume 2018
  • - Article ID 9436438
  • - Research Article

Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

Qiuxia Xie | Qingyan Meng | ... | Shaohua Zhao
  • Special Issue
  • - Volume 2018
  • - Article ID 9315132
  • - Research Article

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

Qingyan Meng | Linlin Zhang | ... | Ying Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 8746068
  • - Research Article

Analysis of SO2 Pollution Changes of Beijing-Tianjin-Hebei Region over China Based on OMI Observations from 2006 to 2017

Zhifang Wang | Fengjie Zheng | ... | Shutao Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 1908570
  • - Research Article

Bias Correction in Monthly Records of Satellite Soil Moisture Using Nonuniform CDFs

Shan Wang | Huiling Shan | ... | Chunxiang Shi
  • Special Issue
  • - Volume 2018
  • - Article ID 2158168
  • - Research Article

Estimation of Actual Evapotranspiration Distribution in the Huaihe River Upstream Basin Based on the Generalized Complementary Principle

Jiaqi Gao | Miao Qiao | ... | Mustapha Adamu
  • Special Issue
  • - Volume 2018
  • - Article ID 5712046
  • - Research Article

Spatiotemporal Variability of Arctic Soil Moisture Detected from High-Resolution RADARSAT-2 SAR Data

Adam Collingwood | François Charbonneau | ... | Paul Treitz
  • Special Issue
  • - Volume 2018
  • - Article ID 9310838
  • - Research Article

Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging

Shan Wang | Yuexing Wang | ... | Chun-Xiang Shi
Advances in Meteorology
 Journal metrics
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Acceptance rate14%
Submission to final decision121 days
Acceptance to publication18 days
CiteScore4.600
Journal Citation Indicator0.490
Impact Factor2.9
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