Fusing microwave and optical satellite observations for high resolution soil moisture data products

2017 ◽  
Author(s):  
Xiwu Zhan ◽  
Li Fang ◽  
Jicheng Liu ◽  
Chris Hain ◽  
Jifu Yin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Satellite Observations ◽  
Soil Moisture Data ◽  
Data Products

scholarly journals Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products

2020 ◽  
Author(s):  
Chen Zhang ◽  
Zhengwei Yang ◽  
Liping Di ◽  
Eugene Yu ◽  
Li Lin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products ◽  
Geoprocessing Services

Estimation of Soil Moisture Content Using Deep Learning and High-Resolution Satellite Imagery (Sentinel-1 and 2)

2020 ◽  
Author(s):  
Soo-Jin Lee ◽  
Yang-Won Lee
Keyword(s):  
Soil Moisture ◽  
Deep Learning ◽  
High Resolution ◽  
Forest Fires ◽  
Optimal Solution ◽  
Local Scale ◽  
Soil Moisture Data ◽  
Local Optimal Solution ◽  
Salinity Data

<p>Soil moisture is an important factor affecting global circulation (climate, carbon, and water), disasters (drought, floods, and forest fires), and crop growth, so the production of soil moisture data is important. Currently, satellite-based soil moisture data is available from NASA’ SMAP (Soil Moisture Active Passive) and ESA’ SMOS (Soil Moisture and Ocean Salinity) data. Since these data are based on passive microwave sensor, they have low spatial resolution. Therefore, it is difficult to observe the distribution of soil moisture on a local scale. The purpose of this study is to produce high resolution soil moisture for monitoring on a local scale. For this purpose, we performed soil moisture modeling using high resolution satellite data (Sentinel-1 SAR (synthetic-aperture radar), Sentinel-2 MSI (multispectral instrument)) and deep learning. Deep learning is a method improving the problems of traditional neural networks such as overfitting, gradient vanishing, and local optimal solution through development of learning methods such as dropout, ReLU (Rectified Linear Unit), and so on. Recently, it has been used for estimation of surface hydrologic factors (soil moisture, evapotranspiration, etc.). The study area is an agricultural area located in Manitoba and Saskatoon, Canada. In-situ soil moisture data was constructed from RISMA (Real-Time In-Situ Soil Monitoring for Agriculture). In order to develop an optimal soil moisture model, various condition experiments on hyper-parameters affecting the performance of model were carried out and their performance was evaluated.</p>

scholarly journals Potential and limitations of multidecadal satellite soil moisture observations for climate model evaluation studies

2013 ◽  
Vol 10 (3) ◽  
pp. 3541-3594 ◽  
Author(s):  
A. Loew ◽  
T. Stacke ◽  
W. Dorigo ◽  
R. de Jeu ◽  
S. Hagemann
Keyword(s):  
Soil Moisture ◽  
Model Evaluation ◽  
Climate Model ◽  
Global Climate Models ◽  
Satellite Observations ◽  
Data Set ◽  
Soil Moisture Dynamics ◽  
Soil Moisture Data ◽  
Climate Model Evaluation ◽  
Moisture Dynamics

Abstract. Soil moisture is an essential climate variable of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observational based soil moisture data set has become available that provides information on soil moisture dynamics from satellite observations (ECVSM). The present study investigates the potential and limitations of this new dataset for several applications for climate model evaluation. We compare soil moisture data from satellite observations, reanalysis data and simulation results from a state-of-the-art climate model and analyze relationships between soil moisture and precipitation anomalies in the different datasets. In a detailed regional study, we show that ECVSM is capable to capture well interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

scholarly journals A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals Impact of Soil Moisture Initialization in the Simulation of Indian Summer Monsoon Using RegCM4

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1148
Author(s):  
Suman Maity ◽  
Sridhara Nayak ◽  
Kuvar Satya Singh ◽  
Hara Prasad Nayak ◽  
Soma Dutta
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Careful Examination ◽  
Soil Moisture Data ◽  
Statistical Measures ◽  
The Impact ◽  
Soil Moisture Initialization

Soil moisture is one of the key components of land surface processes and a potential source of atmospheric predictability that has received little attention in regional scale studies. In this study, an attempt was made to investigate the impact of soil moisture on Indian summer monsoon simulation using a regional model. We conducted seasonal simulations using a regional climate model (RegCM4) for two different years, viz., 2002 (deficit) and 2011 (normal). The model was forced to initialize with the high-resolution satellite-derived soil moisture data obtained from the Climate Change Initiative (CCI) of the European Space Agency (ESA) by replacing the default static soil moisture. Simulated results were validated against high-resolution surface temperature and rainfall analysis datasets from the India Meteorology Department (IMD). Careful examination revealed significant advancement in the RegCM4 simulation when initialized with soil moisture data from ESA-CCI despite having regional biases. In general, the model exhibited slightly higher soil moisture than observation, RegCM4 with ESA setup showed lower soil moisture than the default one. Model ability was relatively better in capturing surface temperature distribution when initialized with high-resolution soil moisture data. Rainfall biases over India and homogeneous regions were significantly improved with the use of ESA-CCI soil moisture data. Several statistical measures such as temporal correlation, standard deviation, equitable threat score (ETS), etc. were also employed for the assessment. ETS values were found to be better in 2011 and higher in the simulation with the ESA setup. However, RegCM4 was still unable to enhance its ability in simulating temporal variation of rainfall adequately. Although initializing with the soil moisture data from the satellite performed relatively better in a normal monsoon year (2011) but had limitations in simulating different epochs of monsoon in an extreme year (2002). Thus, the study concluded that the simulation of the Indian summer monsoon was improved by using RegCM4 initialized with high-resolution satellite soil moisture data although having limitations in predicting temporal variability. The study suggests that soil moisture initialization has a critical impact on the accurate prediction of atmospheric circulation processes and convective rainfall activity.

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