Data Description

Parameters

Surface soil moisture (0-5 cm) in cm³/cm³ derived from brightness temperature and sigma nought measurements is output on a fixed 9 km EASE-Grid 2.0.

Brightness temperature (TB) is a measure of the radiance of the microwave radiation welling upward from the top of the atmosphere to the satellite. The SMAP L-Band Radiometer measures four brightness temperature Stokes parameters: TH, TV, T3, and T4 at 1.41 GHz. TH and TV are the horizontally and vertically polarized brightness temperatures, respectively, and T3 and T4 are the third and fourth Stokes parameters, respectively.

Sigma nought (sigma0), or the backscatter coefficient, is a measure of the strength of radar signals reflected back to the instrument from a target, and is defined as per unit area on the ground. Usually expressed in dB, it is a normalized dimensionless number, comparing the strength observed to that expected from a defined area. The SMAP L-band Radar measures sigma0 using VV, HH, and HV transmit-receive polarizations, and uses separate transmit frequencies for the H (1.26 GHz) and V (1.29 GHz) polarizations. Sigma0 measurements are derived using Synthetic-Aperture Radar (SAR) processing.

Refer to the Data Fields document for details on all parameters.

File Information

Format

Data are in HDF5 format. For software and more information, including an HDF5 tutorial, visit the HDF Group's HDF5 Web site.

File Contents

As shown in Figure 1, each HDF5 file is organized into the following main groups, which contain additional groups and/or data sets:

Data Fields

Each file contains the main data groups summarized in this section. For a complete list and description of all data fields within these groups, refer to the Data Fieldsdocument.

All data element arrays are one-dimensional with a size N, where N is the number of valid cells from the swath that appear on the grid.

Soil Moisture Retrieval Data

Includes all combined radar and radiometer soil moisture data, ancillary data, and quality assessment flags.

Metadata Fields

Includes all metadata that describe the full content of each file. For a description of all metadata fields for this product, refer to the Metadata Fields document.

File Naming Convention

Files are named according to the following convention, which is described in Table 1:

SMAP_L3_SM_AP_yyyymmdd_RLVvvv_NNN.[ext]

For example:

SMAP_L3_SM_AP_20151225_R13171_002.h5

Where:

File Size

Each file is approximately 74 MB.

File Volume

The daily data volume is approximately 74 MB.

Spatial Information

Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N and 85.044°S. The gap in coverage at both the North and South Pole, called a pole hole, has a radius of approximately 400 km. The swath width is 1000 km, enabling nearly global coverage every three days.

Resolution

SMAP 3 km Synthetic Aperture Radar (SAR) backscatter data and 36 km radiometer brightness temperature data are combined using the SMAP Active-Passive algorithm to create soil moisture data that are then gridded using the 9 km EASE-Grid 2.0 global projection.

EASE-Grid 2.0

These data are provided on the global cylindrical EASE-Grid 2.0 (Brodzik et al. 2012). Each grid cell has a nominal area of approximately 9 x 9 km² regardless of longitude and latitude. The SPL3SMAP data product is posted on a 9 km EASE-Grid that is nested consistently with the 36 km brightness temperatures and 3 km radar backscatter cross-section data. Figure 2 shows a schematic of the nesting to a resolution of 3 km (4872 rows x 11568 columns on global coverage), 9 km (1624 rows x 3856 columns on global coverage) and 36 km (406 rows x 964 columns on global coverage).

EASE-Grid 2.0 has a flexible formulation. By adjusting a single scaling parameter, a family of multi-resolution grids that nest within one another can be generated. The nesting can be adjusted so that smaller grid cells can be tessellated to form larger grid cells. Figure 2 shows a schematic of the nesting.

This feature of perfect nesting provides SMAP data products with a convenient common projection for both high-resolution radar observations and low-resolution radiometer observations, as well as for their derived geophysical products.

For more on EASE-Grid 2.0, refer to the EASE-Grid 2.0 Format Description.

Temporal Information

Coverage

Coverage spans from 13 April 2015 through 07 July 2015.

Note: Temporal coverage for this data set is limited due to the premature failure of the SMAP L-Band Radar. On 07 July 2015, the radar stopped transmitting due to an anomaly involving the instrument's high-power amplifier (HPA). For details, refer to the SMAP News Release issued 02 September 2015 by the Jet Propulsion Laboratory (JPL).

Satellite and Processing Events

Due to instrument maneuvers, data downlink anomalies, data quality screening, and other factors, small gaps in the SMAP time series will occur. Details of these events are maintained on two master lists:

SMAP On-Orbit Events List for Instrument Data Users
Master List of Bad and Missing Data

Resolution

Each Level-3 file is a daily composite of half-orbit files/swaths.

Data Acquisition and Processing

Background

The goal of SMAP mission is to combine the favorable attributes of SMAP L-Band Radar and Radiometer observations in terms of their spatial resolution and sensitivity to soil moisture, surface roughness, and vegetation in order to estimate soil moisture at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km. Microwave radiometry and radar are well-established techniques for surface remote sensing. Combining passive and active sensors provides complementary information contained in the surface emissivity and backscatter signatures, which make it possible to obtain optimal accuracy of retrieved soil moisture at higher resolutions. Over land, it has been demonstrated that L-band radiometer and radar measurements both provide information to retrieve optimal soil moisture estimates (Das et al. 2011, Das et al. 2014, and Das et al. 2015).

Acquisition

SMAP Level-3 radar/radiometer soil moisture data (SPL3SMAP) are composited from SMAP L2 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 (SPL2SMAP).

Derivation Techniques and Algorithms

The SMAP Level-3 radar and radiometer soil moisture data set is a daily gridded composite of the SMAP L2 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 (SPL2SMAP) data set. The derivation of soil moisture from SMAP brightness temperatures occurs in the Level-2 processing of the separate radar and radiometer data sets.

Please refer to the Derivation Techniques section in the SPL2SMAP user guide for details on algorithms and ancillary data.

Processing

This product is generated by the SMAP Science Data Processing System (SDS) at the Jet Propulsion Laboratory (JPL) in Pasadena, California USA. To generate the standard SPL3SMAP product, the processing software ingests one day’s worth of SPL2SMAP files and creates individual global composites as two-dimensional arrays for each output parameter defined in the SPL2SMAP product. Wherever data overlap occurs (typically at high latitudes), data acquired closest to the 6:00 a.m. local solar time are chosen. Because the input SPL2SMAP files are available only for descending 6:00 a.m. passes, the resulting SPL3SMAP files are available only for descending 6:00 a.m. passes.

Quality, Errors, and Limitations

Error Sources

Anthropogenic Radio Frequency Interference (RFI), principally from ground-based surveillance radars, can contaminate both radar and radiometer measurements at L-band. Early measurements and results from ESA's Soil Moisture and Ocean Salinity (SMOS) mission indicate that in some regions RFI is present and detectable. The SMAP radar and radiometer electronics and algorithms include design features to mitigate the effects of RFI. The SMAP radar utilizes selective filters and an adjustable carrier frequency to tune to predetermined RFI-free portions of the spectrum while on orbit. The SMAP radiometer implements a combination of time and frequency diversity, kurtosis detection, and use of T4 thresholds to detect and, where possible, mitigate RFI.

Other sources of error, such as disaggregation process errors and calibration and gridding errors, are quantified analytically for the disaggregated brightness temperatures and retrieved soil moisture at 9 km and 3 km. (Entekhabi et al. 2012 and Das et al. 2015)

For more information, refer to the Error Sources section of the SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 3 (SPL2SMAP) user guide.

Quality Assessment

For in-depth details regarding the quality of these Version 3 Validated data, refer to the Beta Assessment Report.

Quality Overview

SMAP products provide multiple means to assess quality. Each product contains bit flags, uncertainty measures, and file-level metadata that provide quality information. For information regarding the specific bit flags, uncertainty measures, and file-level metadata contained in this product, refer to the Data Fields and Metadata Fields documents.

Each HDF5 file contains metadata with Quality Assessment (QA) metadata flags that are set by the SDS at the JPL prior to delivery to NSIDC. A separate metadata file with an .xml file extension is also delivered to NSIDC with the HDF5 file; it contains the same information as the file-level metadata.

A separate QA file with a .qa file extension is also associated with each data file. QA files are ASCII text files that contain statistical information in order to help users better assess the quality of the associated data file. If a product fails QA, it is never delivered to NSIDC DAAC.

Data Flags

Bit flags generated from input SMAP data and ancillary data are also employed to help determine the quality of the retrievals. Ancillary data help determine either specific aspects of the processing (such as corrections for transient water) or the quality of the retrievals (e.g. precipitation flag). These flags will provide information as to whether the ground is frozen, snow-covered, or flooded, or whether it is actively precipitating at the time of the satellite overpass. Other flags will indicate whether masks for steeply sloped topography, or for urban, heavily forested, or permanent snow/ice areas are in effect.

For a description of the data flag types and methods of flagging, refer to the Data Flags section in the SPL2SMAP user guide.

Instrumentation

Description

For a detailed description of the SMAP instrument, visit the SMAP Instrument page at the Jet Propulsion Laboratory (JPL) SMAP Web site.

Software and Tools

For tools that work with SMAP data, refer to the Tools Web page.

Version History

Document Creation Date

October 2015

Document Revision Date

April 2016

Related Data Sets

SMAP Data at NSIDC | Overview

SMAP Radar Data at the ASF DAAC

Related Websites

SMAP at NASA JPL

Contacts and Acknowledgments

Investigators

Dara Entekhabi, Narendra Das, Eni Njoku
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109 USA

Joel Johnson
Ohio State University
Columbus, OH 43210 USA

Jiancheng Shi
University of California
Santa Barbara, CA

References

Bolten, J., V. Lakshmi, and E. Njoku. 2003. Soil Moisture Retrieval Using the Passive/Active L- and S-band Radar/Radiometer. IEEE Trans. Geosci. Rem. Sens., 41:2792-2801.

Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie. 2012. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS Int. J. Geo-Inf. 1(1):32-45. http://dx.doi.org/10.3390/ijgi1010032.

Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie. 2014. Correction: Brodzik, M. J. et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS Int. J. Geo-Inf 2012. 1(1):32-45 ISPRS Int. J. Geo-Inf. 3(3):1154-1156. http://dx.doi.org/10.3390/ijgi3031154.

Das, N. N., D. Entekhabi, S. Dunbar, E. G. Njoku, and S. Yueh. 2015. Uncertainty Estimates in the SMAP Combined Active-Passive Downscaled Brightness Temperature. IEEE- TGARS. Accepted, in press.

Das, N. N., D. Entekhabi, E. G. Njoku, J. Johnston, J. C. Shi, and A. Colliander. 2014. Tests of the SMAP Combined Radar and Radiometer Brightness Temperature Disaggregation Algorithm Using Airborne Field Campaign Observations. IEEE-TGARS. 52:2018–2028.

Das, N. N., D. Entekhabi, and E. G. Njoku, 2011. An Algorithm for Merging SMAP Radiometer and Radar Data for High Resolution Soil Moisture Retrieval. IEEE-TGARS. 9: 1504-1512.

Das, N. N., and R. S. Dunbar. 2015. SMAP Level 3 Active/Passive Soil Moisture (L3_SM_AP) Product Specification Document. SMAP Project, JPL D-72552, Jet Propulsion Laboratory, Pasadena, CA. (SMAP L3_SM_AP PSD_10312015.pdf, 3 MB)

Das, N. N., et al. 2015. Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L2/3_SM_AP Beta-Release Data Products. SMAP Project, JPL D-93984. Jet Propulsion Laboratory, Pasadena, CA. (SMAP-AP_Assessment_Report_Final.pdf, 4 MB)

Entekhabi, D. et al. 2014. SMAP Handbook–Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw from Space. Pasadena, CA USA: SMAP Project, JPL CL#14-2285, Jet Propulsion Laboratory.

Entekhabi, D., N. Das, E. Njoku, J. Johnson, and J. Shi. 2014.SMAP Algorithm Theoretical Basis Document: L2 & L3 Radar/Radiometer Soil Moisture (Active/Passive) Data Products. SMAP Project, JPL D-66481. Jet Propulsion Laboratory, Pasadena, CA. (277_L2_3_SM_AP_RevA_web.pdf, 16.6 MB)