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Description

This Level-2B AMSR Unified product contains surface soil moisture at 25 km resolution, estimated from AMSR-E and AMSR2 brightness temperature observations using two different approaches: the Normalized Polarization Difference (NPD) algorithm and the Single Channel Algorithm (SCA). Data are provided in the NSIDC EASE-Grid Global projection.

Soil moisture estimates are derived from AMSR-E and AMSR2 data which have been spatially resampled by the Japan Aerospace Exploration Agency (JAXA) to intercalibrate, or “unify,” the two sensors.

Version Description

Initial release

Product Summary

Platforms
Aqua ,
GCOM-W1
Instruments
AMSR-E ,
AMSR2
Projects
AMSR Unified
Spatial Extent

N: 89.24 S: -89.24 E: 180 W: -180

Spatial Resolution
25 Kilometers x 25 Kilometers
Spatial Reference System(s)
NSIDC EASE-Grid Global (EPSG:3410)
Location
GLOBAL
Coordinate System
CARTESIAN
Granule Spatial Representation
ORBIT
Temporal Extent
2012-07-02
Temporal Resolution
50 Minute
Concept ID
C3243533185-NSIDC_CPRD
Data State
COMPLETE
Number of Files/Granules
237848
Processing Level
2B
Published
Science Keywords
Brightness Temperature ,
Soil Moisture/Water Content ,
Vegetation Water Content

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Jackson, T., Chan, S., Bindlish, R., & Njoku, E. (2018). AMSR-E/AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture, Version 1 [Dataset]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/IKQ0G7ODMLC7 Date Accessed: 2026-09-30

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Publications Citing This Dataset

Filters
Title Authors Year Referenced Datasets
Satellite Microwave Radiometry for the Observation of Land Surfaces: A General Review
  • Vittucci, Cristina, Picchiani, Matteo
Soil Moisture Monitoring Method and Data Products: Current Research
  • Liu, Ruihao, Chang, Cun, Zhong, Ruisen, Lu, Shiyang
Mapping global floods with 10 years of satellite radar data
  • Misra, Amit, White, Kevin, Nsutezo, Simone Fobi, Straka, William, Lavista, Juan
Global L-band equivalent AI-based vegetation optical depth dataset
  • Skulovich, Olya, Li, Xiaojun, Wigneron, Jean-Pierre, Gentine, Pierre
A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset
  • Skulovich, Olya, Gentine, Pierre
The Carbon Cycle Influence on Tornadoes
Using Sentinel-1 data for monitoring of soil moisture
  • Garkusha, Igor N., Hnatushenko, Volodymyr V., Vasyliev, Volodymyr V.
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