• Corpus ID: 41787676

Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy . Part 2 : Product evaluation

@inproceedings{Kolassaa2017SoilMR,
  title={Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy . Part 2 : Product evaluation},
  author={J. Kolassaa and P. Gentinea and C. Prigentb and F. Airesc and S. H. Alemohammada},
  year={2017}
}
A neural network (NN) soil moisture retrieval product computed from the synergy of AMSR-E brightness temperature and ASCAT backscatter observations is evaluated against in situ soil moisture observations from the International Soil Moisture Network (ISMN). The skill of the NN retrieval is compared to that of the ESA-CCI soil moisture retrieval as well as modeled soil moisture fields from ERA-interim/land. The NN retrieval is able to capture the observed soil moisture temporal variations with a… 

Figures and Tables from this paper

Assessment of EnKF data assimilation of satellite-derived soil moisture over the Indian domain with the Noah land surface model
Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained from LSM do not reflect the contribution of
Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA
Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux. However, soil moisture information has a significant importance in hydrological modelling
MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS : APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING
A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role
Spatially Distributed Evaluation of ESA CCI Soil Moisture Products in a Northern Boreal Forest Environment
Several previous studies have discussed the challenges in remotely sensed soil moisture retrievals over northern boreal environments. However, very few studies have focused solely on an evaluation of
A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability
TLDR
Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution.
Parameter Optimization of a Discrete Scattering Model by Integration of Global Sensitivity Analysis Using SMAP Active and Passive Observations
  • X. Bai, J. Zeng, Z. Su
  • Environmental Science, Mathematics
    IEEE Transactions on Geoscience and Remote Sensing
  • 2019
TLDR
The synergy of active radar and passive radiometer observations at the same spatial scale is explored to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands.
INVESTIGATING THE POSSIBILITY OF PREPARING SMALL SCALE SOIL MOISTURE MAP FROM COUPLED SENTINEL-1 AND SENTINEL-2 DATA
  • R. Attarzadeh, J. Amini
  • Environmental Science
    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 2019
Abstract. With the failure of the radar instrument on NASA's Soil Moisture Active Passive (SMAP) satellite, the Sentinel-1 sensor has been considered as an alternative for replacing the SMAP radar
The International Soil Moisture Network: serving Earth system science for over a decade
TLDR
The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures.
Development of a Daily Multilayer Cropland Soil Moisture Dataset for China Using Machine Learning and Application to Cropping Patterns
Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system.
Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence
TLDR
A machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment‐2 (GOME‐2), which is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh).
...
...

References

SHOWING 1-10 OF 66 REFERENCES
Soil moisture retrieval from multi‐instrument observations: Information content analysis and retrieval methodology
TLDR
An algorithm has been developed that employs neural network technology to retrieve soil moisture from multi-wavelength satellite observations and it was concluded that the temporal performance can be improved through incorporation of other existing retrieval approaches.
Soil moisture active and passive microwave products : intercomparison and evaluation over a Sahelian site
This paper presents a comparison and an evalu- ation of five soil moisture products based on satellite-based passive and active microwave measurements. Products are evaluated for 2005-2006 against
Evaluation of the ESA CCI soil moisture product using ground-based observations
Soil moisture retrieval from SMOS observations using neural networks
TLDR
The NN is able to capture the spatial and temporal dynamics of SM, and the SM computed with NNs compares well with the other SM datasets.
Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing
AbstractIn situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the
An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations
TLDR
The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters.
An evaluation of AMSR–E derived soil moisture over Australia
Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals
Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop
...
...