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Data Set ID:
NSIDC-0712

SMAP/In Situ Core Validation Site Land Surface Parameters Match-Up Data, Version 1

SMAP radiometer and radar soil moisture data products are matched with in situ-based soil moisture estimates from core validation sites to produce this data set. These data provide performance assessments of various SMAP soil moisture products.

This is the most recent version of these data.

Version Summary:

First release of data set

STANDARD Level of Service

Data: Data integrity and usability verified

Documentation: Key metadata and user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools

See All Level of Service Details

Parameter(s):
  • SOILS > SOIL MOISTURE/WATER CONTENT > Soil Moisture
  • SOILS > SOIL TEMPERATURE > Soil Temperature
Data Format(s):
  • ASCII Text
Spatial Coverage:
N: 85.044, 
S: -85.044, 
E: 180, 
W: -180
Platform(s):GROUND STATIONS, SMAP
Spatial Resolution:
  • 3 km x 3 km
  • 9 km x 9 km
  • 36 km x 36 km
Sensor(s):SMAP L-BAND RADAR, SMAP L-BAND RADIOMETER, SOIL MOISTURE PROBE, SOIL TEMPERATURE PROBE
Temporal Coverage:
  • 1 April 2015 to 1 June 2019
Version(s):V1
Temporal ResolutionVariesMetadata XML:View Metadata Record
Data Contributor(s):Andreas Colliander, Aaron Berg, Carsten Montzka, Jose Martinez-Fernandez, Mark Seyfried, Hala Al Jassar, Wouter Dorigo, Jeffrey Walker, Mehrez Zribi

Geographic Coverage
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Once you have logged in, you will be able to click and download files via a Web browser. There are also options for downloading via a command line or client. For more detailed instructions, please see Options Available for Bulk Downloading Data from HTTPS with Earthdata Login.

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Colliander, A., H. Al Jassar, W. Dorigo, J. Martinez-Fernandez, C. Montzka, M. Seyfried, et al. 2017. SMAP/In Situ Core Validation Site Land Surface Parameters Match-Up Data, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/DXAVIXLY18KM. [Date Accessed].
Created: 
25 September 2019
Last modified: 
8 October 2019

Data Description

SMAP radiometer and radar soil moisture data products are matched with in situ-based soil moisture estimates from core validation sites to produce this data set. These data provide performance assessments of various SMAP soil moisture products. The SMAP radiometer and radar soil moisture retrieval algorithms are described in corresponding Algorithm Theoretical Basis Documents (ATBD).

The SMAP products matched with in situ data are:

  • SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP)
  • SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture (SPL2SMA)
  • SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (SPL2SMAP)
  • SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (SPL2SMP_E)

Parameters

Parameters for this data set are surface soil moisture (0-5 cm) in m3/m3 and surface soil temperature (0-50) in °C.

Sample Data Record

Figure 1. Sample of SPL2SMP Matching Data

File Information

Format

Data are provided in ASCII text (.txt).

Extensible Markup Language (.xml) files with associated metadata are also provided. 

File Contents

Table 1 lists the information necessary to identify the contents of the data files, such as SMAP product IDs, versions, and Composite Release (CRIDs).

CRIDs indicate processing changes (such as algorithm and software) shared by a particular version. Note that major versions (Version 3, for example) can have multiple CRIDs. Refer to the SMAP Data Versions page for more information on versions and CRIDs.

Table 1. File Subdirectories, Contents, Versions, and CRIDs
Subdirectory SMAP Product ID SMAP ID in File Names for this Product Version CRID in this Match-Up Product1 CRID(s) in Operational SMAP Products2 Assessment Reports3
2015.04.01 SPL2SMP SMAPL2SMP Version 3 T12323 R13080 Report
2015.04.01 SPL2SMP SMAPL2SMP Version 4 R13080 R14010, R15060, R15152, R15180, R15181, R15182 Report
2015.04.01 SPL2SMP SMAPL2SMP Version 5 T15600 R16000, R16010 Report
2015.04.01 SPL2SMP SMAPL2SMP Version 6 T16500 R16510 Report
2015.04.01 SPL2SMP_E SMAPL2SMPE Version 1 D14000 R14010, R15060, R15152, R15180, R15181, R15182 Report
2015.04.01 SPL2SMP_E SMAPL2SMPE Version 2 R16010 R16000, R16010 Report
2015.04.01 SPL2SMP_E SMAPL2SMPE Version 3 R16510 R16510 Report
2015.04.13 SPL2SMA SMAPL2SMA Version 3 T12400 R13080 Report
2015.04.13 SPL2SMAP SMAPL2SMAP Version 3 D12000 R13080 Report
1 CRIDs in this product are usually different than those in SMAP operational products (e.g. SPL2MP). To generate this match-up product, a separate offline (non-operational) processor is used for validation grid processing; this results in separate, corresponding CRIDs.
2 The SMAP Data Versions page lists CRIDs used in operational SMAP products only.
3 Match-up files correspond to the data used in the assessment reports.

Table 2 describes the fields contained in the data files.

Data Field Unit Valid Range Definition Description Source
Table 2. Description of Data Fields
ID N/A N/A Reference pixel ID Unique ID of the reference pixel N/A
Yr N/A N/A Year N/A N/A
Mo N/A N/A Month N/A N/A
Day N/A N/A Day N/A N/A
Hr N/A N/A Hour N/A N/A
Min N/A N/A Minute N/A N/A
TOY N/A N/A Time of Year Fractional day of year, where 01 January is 1 and 31 December is 365 (non leap year) N/A
WASM m3/m3 0-0.6 Weighted Average Soil Moisture of reference pixel Obtained from the sensors deployed within the SMAP reference pixel area; includes arithmetic average and weighted average; weighting scheme independently decided for each pixel (default approach is Voronoi diagrams); soil moisture measurements are taken at a depth of 0-5 cm in situ
ASM m3/m3 0-0.6 Average Soil Moisture of reference pixel Obtained from the sensors deployed within the SMAP reference pixel area in situ
WAST °C 0-50 Weighted Average Soil Temperature of reference pixel Obtained from the sensors deployed within the SMAP reference pixel area; includes arithmetic average and weighted average; weighting scheme independently decided for each pixel (default approach is Voronoi diagrams) in situ
AST °C 0-50 Average Soil Temperature of reference pixel Obtained from the sensors deployed within the SMAP reference pixel area in situ
sWASM m3/m3 0-0.2 Standard deviation of Weighted Average Soil Moisture of reference pixel Used in computing the averages WASM and ASM. For weighted average, the weights are accounted for in the computation of the standard deviation N/A
sASM m3/m3 0-0.2 Standard deviation of Average Soil Moisture of reference pixel Non-weighted standard deviation N/A
NUM N/A N/A Number of sensors used to compute average N/A N/A
Q-RP N/A 0=valid; 1=invalid Reference pixel quality flag Each in situ sensor is quality controlled. During the averaging process, these quality flags are aggregated to determine whether the averaged value is ok. If less than 10% of the contributing stations (after weighting) are compromised, the value is deemed valid (0) otherwise invalid (1). N/A
SM-1 m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-1 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
Tsurf (°C) N/A 0-50 Surface Temperature from SMAP product Model-based soil temperature from the Global Modeling and Assimilation Office (GMAO) for estimating effective surface temperature and computing emissivity SMAP
S-FLG N/A N/A SMAP surface flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to the SPL2SMP user guide for more information. SMAP
SM-2  m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-2 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
SM-3 m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-3 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
SM-4 m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-4 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
SM-5 m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-5 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
SM-6 m3/m3 0-0.6 SMAP soil moisture Refer to Table 3 SMAP
Q-FLG-6 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
SM-7 m3/m3 0-0.6 SMAP soil moisture N/A SMAP
Q-FLG-7 N/A N/A SMAP retrieval quality flag Can convert to a 16-bit binary to retrieve the individual flag values1. Refer to Table 3 SMAP
ORB N/A N/A Orbit number Values are either ascending (positive) or descending (negative) orbits N/A
1Users should only consider the defined bits when interpreting data flags, not the entire 16 bit integer

Table 3 provides descriptions of the SMAP soil moisture and quality data fields for each of the SMAP match-up products.

Data Fields SPL2SMP/SPL2SMP_E SPL2SMA SPL2SMAP
Table 3. SMAP Soil Moisture Data and Quality Fields
SM-1 Soil moisture retrieval from optional algorithm 1 - SCA-H. Corresponds to soil_moisture_option1 data field; refer to the SPL2SMP user guide for more information. Soil moisture retrieval from the time series algorithm. Corresponds to soil_moisture data field; refer to the SPL2SMA user guide for more information. Soil moisture retrieval from v-pol option1 algorithm – disaggregated/downscaled vertical polarization brightness temperature. Corresponds to soil_moisture_v_option1 data field; refer to the SPL2SMAP user guide for more information.
Q-FLG-1 Quality flag for optional algorithm 1, SCA-H.* Corresponds to retrieval_qual_flag_option1 data field (refer to SPL2SMP user guide). Quality flag for the soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMA user guide). Quality flag for the baseline soil moisture retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMAP user guide). 
SM-2 Soil moisture retrieval from optional algorithm 2 - SCA-V. Corresponds to soil_moisture_option2 data field (refer to SPL2SMP user guide). Soil moisture retrieval from normalized change. Corresponds to soil_moisture_change_index data field (refer to SPL2SMA user guide). Soil moisture retrieval from v-pol option2 algorithm – disaggregated/ downscaled vertical polarization brightness temperature. Corresponds to soil_moisture_v_option2 data field (refer to SPL2SMAP user guide).
Q-FLG-2 Quality flag for baseline algorithm 2, SCA-V.* Corresponds to retrieval_qual_flag_option2 data field (refer to SPL2SMP user guide). Quality flag for the soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag_change_index data field (refer to SPL2SMA user guide). Quality flag for the soil moisture retrieval. Corresponds to retrieval_qual_flag_option2 data field (refer to SPL2SMAP user guide). 
SM-3 Soil moisture retrieval from optional algorithm 3. Through SPL2SMP Version 5 and SPL2SMP_E Version 2, this algorithm was DCA. Beginning with Version 6 of SPL2SMP and Version 3 of SPL2SMP_E (released in August 2019), DCA was replaced by the MDCA algorithm. For all versions of SPL2SMP and SPL2SMP_E, this field corresponds to soil_moisture_option3 data field (refer to SPL2SMP user guide). Soil moisture retrieval from the Kim/van Zyl time series algorithm. Corresponds to soil_moisture_kvz data field (refer to SPL2SMA user guide). Soil moisture retrieval from v-pol option 3 algorithm – disaggregated/downscaled vertical polarization brightness temperature. Corresponds to soil_moisture_v_option3 data field (refer to SPL2SMAP user guide.
Q-FLG-3 Quality flag for optional algorithm 3, DCA or MDCA.* Corresponds to retrieval_qual_flag_option3 data field (refer to SPL2SMP user guide). Quality flag for the soil moisture and freeze-thaw retrieval. Corresponds to the retrieval_qual_flag_kvz data field (refer to SPL2SMA user guide). Quality flag for the baseline soil moisture retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMAP user guide). 
SM-4 Soil moisture retrieval from optional algorithm 4 - MPRA. Corresponds to soil_moisture_option4 data field (refer to SPL2SMP user guide). Note: the MPRA algorithm was retired with the launch of SPL2SMP Version 6 and SPL2SMP_E Version 3; this field is no longer used. Soil moisture retrieval from the Shi snapshot algorithm. Corresponds to soil_moisture_snapshot_shi data field (refer to SPL2SMA user guide). Soil moisture retrieval from h-pol option1 – disaggregated/downscaled horizontal polarization brightness temperature. Corresponds to soil_moisture_h_option1 data field (refer to SPL2SMAP user guide).
Q-FLG-4 Quality flag for optional algorithm 4, MPRA.* Corresponds to retrieval_qual_flag_option4 data field (refer to SPL2SMP user guide). Note: the MPRA algorithm was retired with the launch of SPL2SMP Version 6 and SPL2SMP_E Version 3; this field is no longer used. Quality flag for soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag data value (refer to SPL2SMA user guide). Quality flag for the baseline soil moisture retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMAP user guide).
SM-5 Soil moisture retrieval from optional algorithm 5 - E-DCA. Corresponds to soil_moisture_option5 data field (refer to SPL2SMP user guide). Note: the E-DCA algorithm was retired with the launch of SPL2SMP Version 6 and SPL2SMP_E Version 3; this field is no longer used. Soil moisture retrieval from the snapshot algorithm. Corresponds to soil_moisture_snapshot data field (refer to SPL2SMA user guide). Soil moisture retrieval from h-pol option2 – disaggregated/downscaled horizontal polarization brightness temperature. Corresponds to soil_moisture_h_option2 data field (refer to SPL2SMAP user guide).
Q-FLG-5 Quality flag for optional algorithm 5, E-DCA.* Corresponds to retrieval_qual_flag_option5 data field (refer to SPL2SMP user guide). Note: the E-DCA algorithm was retired with the launch of SPL2SMP Version 6 and SPL2SMP_E Version 3; this field is no longer used. Quality flag for soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMA user guide). Quality flag for soil moisture retrieval. Corresponds to retrieval_qual_flag_option2 data field (refer to SPL2SMAP user guide). 
SM-6 N/A Soil moisture retrieval from the Dubois/van Zyl snapshot algorithm. Corresponds to soil_moisture_snapshot_DVZ data field (refer to SPL2SMA user guide). Soil moisture retrieval from h-pol option3 soil moisture – disaggregated/downscaled horizontal polarization brightness temperature. Corresponds to soil_moisture_h_option3 data field (refer to SPL2SMAP user guide). 
Q-FLG-6 N/A Quality flag for soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMA user guide). Quality flag for the baseline soil moisture retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMAP user guide). 
SM-7 N/A Soil moisture retrieval from the time series algorithm. Corresponds to soil_moisture_time_series data field (refer to SPL2SMA user guide). N/A
Q-FLG-7 N/A Quality flag for soil moisture and freeze-thaw retrieval. Corresponds to retrieval_qual_flag data field (refer to SPL2SMA user guide). N/A
* Provided as a numerical value; can convert to a 16-bit binary to retrieve the individual flag values

Naming Convention

Files are named according to the following convention and as described in Table 4:

NSIDC-0712_[Pixel ID]_SMAPL2SM[XXX]_[X]LVvvv_YYYYMMDD_yyyymmdd.[.txt/MET.xml]

Variable Description
Table 4. File Name Description
NSIDC-0712 Data Set ID
[Pixel ID] Pixel ID with four 2-digit variables; 
ID: Site Principal Investigator ID1
SN: Site Number1
GS: Grid Scale
PN: Pixel number
SMAPL2SM[XXX]
Associated SMAP product:
SMAPL2SMP: SMAP L2 Soil Moisture Passive
SMAPL2SMA: SMAP L2 Soil Moisture Active
SMAPL2SMAP: SMAP L2 Active/Passive
SMAPL2SMAPE: SMAP L2 Active/Passive Enhanced
[X]LVvvv CRID [a 5-digit ID usually preceded by an R (for Release), which indicates processing changes (i.e. algorithm and software) shared by a particular version)] of SMAP product:
[X] Usually R (for Release)
L Launch Indicator (1: post-launch standard data)
V 1-Digit Major CRID Version Number
vvv 3-Digit Minor CRID Version Number
Refer to the SMAP Data Versions page for version information.
YYYYMMDD Start date (4-digit year, 2-digit month, 2-digit day)
YYYYMMDD End date (4-digit year, 2-digit month, 2-digit day)
.txt ASCII text file extension
.MET.xml Metadata XML file extension
1Together the Site Principal Investigator ID and Site Number make up the Site ID (see Table 5 for more details).

Example file: 
NSIDC-0712_03013602_SMAPL2SMP_T12323_20150401_20160229.txt

File Size

The total data set volume is approximately 9.7 MB.

The average file size is approximately 114 KB.

Spatial Information

Coverage

Spatial coverage for the SMAP data is global (N: 85.044, S: -85.044, E: 180, W: -180). Coverage for the in situ data varies based on the location of core sites, which are dispersed globally. Table 5 contains a list of the various core sites and their latitude and longitude.

Table 5. Core Validation Sites
Site Name Site ID Site PI Location Latitude, Longitude (approximate) Core Site Scales [km]I Climate RegimeII IGBPIII Land Cover References
1 Tonzi Ranch 2501 M. Moghaddam USA (California) 38.43° N, 120.97° W (Not a core site) Temperate Savannas woody Clewley et al., 2017
2 Walnut Gulch* 1601 D. C. Goodrich USA (Arizona) 31.72° N, 110.68° W 3, 9, 36 Arid Shrub open Keefer et al., 2008
3 Reynolds Creek* 0401 M. Seyfried USA (Idaho) 31.72° N, 110.68° W 9, 36 Arid Grasslands Seyfried et al., 2001
4 TxSON** 4801 T. Caldwell USA (Texas) 30.5° N, 98.5° W 3, 9, 36 Temperate Grasslands (Not available)
5 Fort Cobb* 1603 P. J. Starks USA (Oklahoma) 35.36° N, 98.55° W 36 Temperate Grasslands (Not available)
6 Little Washita* 1602 P. J. Starks USA (Oklahoma) 34.97° N, 97.97° W 9, 36 Temperate Grasslands Cosh et al., 2006
7 South Fork* 1607 M. H. Cosh/J. Prueger USA (Iowa) 42.44° N, 93.44° W 3, 9, 36 Cold Croplands Coopersmith et al., 2015
8 St. Josephs* 1606 S. Livingston USA (Indiana) 41.449° N, 85.011° W 9 Cold Croplands (Not available)
9 Little River* 1604 D. Bosch USA (Georgia) 31.64° N, 83.65° W 3, 9, 36 Temperate Cropland/natural mosaic Bosch et al., 2007
10 Millbrook 2601 M. Temimi USA (New York) 41.78° N, 73.73° W (Not a core site) Cold Forest deciduous broadleaf (Not available)
11 Kenaston* 2701 A. Berg Canada 50.45° N, 106.38° W 3, 9, 36 Cold Croplands Rowlandson et al,. 2015
12 Carman* 0901 H. McNairn Canada 49.62° N, 97.98° W 9, 36 Cold Croplands McNairn et al., 2015
13 Casselman* 0902 H. McNairn Canada 45.47° N, 74.73° W 9 Cold Croplands (Not available)
14 Tabasco 3201 J. Ramos Mexico 17.93° N, 92.84° W (Not a core site) Tropical Croplands (Not available)
15 Monte Buey* 1902 M. Thibeault Argentina 32.96° S, 62.52° W 3, 9, 36 Arid Croplands (Not available)
16 Bell Ville 1901 M. Thibeault Argentina 32.54° S, 62.61° W 36 Arid Croplands (Not available)
17 REMEDHUS* 0301 J. Martínez-Fernández Spain 41.3° N, 5.4° W 9, 36 Temperate Croplands Martinez-Fernandez and Ceballos, 2005
18 Valencia* 4101 E. Lopez-Baeza Spain 39.57° N, 1.29° W 3, 9 Arid Savannas woody (Not available)
19 EURAC 4401 C. Notarnicola Italy 46.68° N, 10.59° E (Not a core site) Polar Shrub open Pasolli et al., 2015
20 Twente* 1204 Z. Su The Netherlands 52.27° N, 6.67° E 36 Temperate Cropland/natural mosaic Dente et al., 2012
21 TERENO 0201 C. Montzka Germany 50.5° N, 6.33° E (Not a core site) Temperate Forest mixed Zacharias et al., 2011
22 HOAL 0601 M. Vreugdenhil/W. Dorigo Austria 48.2° N, 15.07° E (Not a core site) Temperate Mixed forest Blöschl et al., 2016
23 Sodankyla 1701 J. Pulliainen Finland 67.37° N, 26.65° E (Not a core site) Cold Savannas woody Rautiainen et al., 2012; Ikonen et al., 2016
24 Saariselka 1702 J. Pulliainen Finland 67.97° N, 24.12° E (Not a core site) Cold Savannas woody (Not available)
25 Kuwait 0501 H. Jassar Kuwait 29.3° N, 47.33° E (Not a core site) Temperate Barren/sparse (Not available)
26 Mpala 2401 K. Caylor Kenya 0.49° N, 36.87° E (Not a core site) Temperate Grasslands (Not available)
27 Niger 4501

B. Cappelaere/T. Pellarin

 Niger 13.575° N, 2.663° E (Not a core site) Arid Grasslands Louvet et al., 2015
28 Benin 4502

S. Galle/ T. Pellarin

 Benin 9.789° N, 1.679° E (Not a core site) Arid Savannas Louvet et al., 2015
29 Ngari 1203 Z. Su Tibet 32.5° N, 79.97° E (Not a core site) Arid Barren/sparse Su et al., 2011; Su et al., 2013
30 Naqu 1201 Z. Su Tibet 31.37° N, 91.88° E (Not a core site) Polar Grasslands Su et al., 2011; Su et al., 2013
31 Maqu 1202 Z. Su Tibet 33.88° N,102.13° E (Not a core site) Cold Grasslands Su et al., 2011; Su et al., 2013
32 Mongolian grasslands** 5301 J. Asanuma Mongolia 46.063° N,106.774° E 36 Cold Grasslands Wen et al., 2014
33 Yanco* 0701 J. Walker Australia 34.8° S, 146.11° E 3, 9, 36 Semi-Arid Croplands/Grasslands Panciera et al., 2014
34 Kyeamba* 0702 J. Walker Australia 35.35° S, 147.52° E 3, 9, 36 Temperate Grasslands Smith et al., 2012
35 HOBE 6701 K. H. Jensen Denmark 55.97° N, 9.10° E 36 Temperate Croplands Bricher et al., 2012
* Core site status at launch
** Core site status acquired after launch
I (Not a core site) = currently not a core site at any scale (3, 9, or 36 km), but this may change in the future
II Koeppen-Geiger climate classification (Peel et al., 2007)
II International Geosphere-Biosphere Programme (IGBP)

Resolution

Spatial resolution for the in situ data is the same as the match-up SMAP data: 3, 9, and 36 km.

Temporal Information

Coverage

Approximate start and end dates for the match-up SMAP data are listed below. Note that not all files start or end on those exact dates.

  • SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) — 01 April 2015 to 01 June 2019
  • SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture (SPL2SMA) — 13 April 2015 to 08 July 2015
  • SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (SPL2SMAP) — 14 April 2015 to 06 July 2015
  • SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture (SPL2SMP_E) — 01 April 2015 to 01 June 2019

Resolution

Temporal resolution varies due to the coincidence of the in situ and SMAP data.

Data Acquisition and Processing

Background

In an effort to ensure the geographic distribution and diversity of conditions of the core validation sites (CVS), SMAP partnered with investigators around the globe. These Calibration/Validation (Cal/Val) partners play a crucial role in the execution of the SMAP Cal/Val Plan (Jackson et al., 2013). CVS candidates were selected based on a minimum requirement of continuous soil moisture measurements at 5 cm depth; the measurements also needed to be replicated within a grid cell of at least one of the SMAP spatial scales (although some sites have multiple pixels at 9 km and 3 km scale). Table 5 lists the site candidates. The sites are divided into two categories: (a) those where confidence that they are representative of a site at a certain spatial scale is high enough for using the site as a basis of computing the performance metrics (CVS), and (b) sites that can be utilized for algorithm testing but the confidence that they are representative is not high enough for using the site in the metrics computations (candidate sites).

Criteria for determining whether a site is a CVS include:

  • Number of sensors within the pixel
  • Geographical distribution of sensors within the pixel
  • Calibration of the soil moisture sensors
  • Quality assessment of the measured soil moisture time-series
  • Spatial up-scaling function
  • Maturity as a large scale reference

Sites that initially did not meet these requirements have the option of performing supplemental investigations such as additional field sampling and modeling studies to eventually reach CVS status. Because different SMAP surface soil moisture products have different spatial scales, the suitability of the various sites for validation of the different products must be evaluated separately. Currently qualified core validation sites represent land cover types that together extend over about 70% of the retrieval domain defined for the products. Upgrading some of the current candidate sites to CVS status would raise this figure close to 100% (Colliander et al., 2017).

Acquisition

The SMAP soil moisture data products in this data set are:

​Information regarding SMAP data versions and CRIDs are provided in Table 1.

For information regarding algorithms and product specifications, refer to the Assessment Reports provided in Table 1.

Processing

As shown in Figure 2, in situ data provided by the Cal/Val partners goes through several processing steps before being matched with corresponding SMAP products.

  • The in situ data are run through an automatic Quality Control (QC) procedure before determining the up-scaled soil moisture values for each pixel. The QC is implemented largely following the approach presented in Dorigo et al., 2012. The in situ data are checked for various issues, including missing data, out of range values, spikes, sudden drops and physical temperature limits.
    • Additionally, since some sensors begin to exhibit unpredictable behavior below 4°C, the physical temperature is checked.
    • Finally, some stations are excluded because they do not represent the surrounding environment. As an example, this exclusion may be based on irrigation activities or the location of the station.
  • Next, the up-scaling function is applied to the data. The up-scaling function is developed using the set of sensors that function properly for the majority of the time period under consideration. This means that the Voronoi diagrams are determined with only functioning sensors, and the sensors that fail during the time period are left outside the process entirely.
  • Coincident overpasses of SMAP data in time and space are then matched with the up-scaled in situ timeseries that are closest to the overpass time.

The high level of automation in this process allows tracking the performance of the soil moisture products periodically and with low latency because repetitive, manual involvement is minimized. Match-up products are then used for validation and further development of SMAP algorithms.

Figure 2. Processing Steps

Quality, Errors, and Limitations

Error sources and data quality are discussed in Jackson et al., 2016, Chan et al., 2016, and Colliander et al., 2017. SMAP retrieval algorithm uncertainties are discussed in the corresponding ATBD (O’Neill et al., 2015).

Instrumentation

Description

For a detailed description of the SMAP instrument, visit the SMAP Instrument page at the JPL SMAP Web site.  For the in situ data, investigators used various soil moisture and soil temperature probes.

Related Data Sets

SMAP Data at NSIDC | Overview

SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture

SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture

SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture

SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture

Related Websites

SMAP at NASA JPL

Contacts and Acknowledgments

Andreas Colliander
Jet Propulsion Laboratory, California Institute of Technology
4800 Oak Grove Dr.
Pasadena, CA 91109

Data Contributors
Carsten Montzka, Research Center Julich
Jose Martinez-Fernandez, University of Salamanca
Mark Seyfried, USDA Agriculture Research Service
Hala Al Jassar, Kuwait University
Wouter Dorigo, Technical University of Wien
Jeffrey Walker, Monash University
Mehrez Zribi, CNES
Heather McNairn, Agriculture and Agri-food Canada
Zhongbo Su, University of Twente
Michael Cosh, USDA Agriculture Research Service
Jouni Pulliainen, Finnish Meteorological Institute
Marc Thibeault, CONAE
Kelly Caylor, Princeton University
Mahta Moghaddam, University of Southern California
Marouane Temimi, City College of New York
Aaron Berg, University of Guelph
Judith Ramos Hernandez, National Autonomous University of Mexico
Ernesto Lopez-Baeza, University of Valencia
Claudia Notarnicola, European Academy of Bozen
Thierry Pellarin, University Joseph Fourier
Todd Caldwell, University of Texas Austin

References

Bircher, S., N. Skou, K. H. Jensen, J. P. Walker, and L. Rasmussen. 2012. A soil moisture and temperature network for SMOS validation in Western Denmark. Hydrol. Earth Syst. Sci. 16:1445-1463. https://dx.doi.org/10.5194/hess-16-1445-2012

Blöschl, G., et al. 2016. The hydrological open air laboratory (HOAL) in Petzenkirchen: a hypothesis-driven observatory. Hydrology and Earth System Sciences 20:227-255. doi: 10.5194/hess-20-227-2016.

Bosch, D. D., J. M. Sheridan, and L. K. Marshall. 2007. Precipitation, soil moisture, and climate database, Little River Experimental Watershed, Georgia, United States. Water Resources Research 43(9):Art.#W09472. doi: 10.1029/2006WR005834.

Chan, S., et al. 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Transactions on Geoscience and Remote Sensing 54(8):4994-5007.

Clewley, D. et al. 2017. A method for upscaling in situ soil moisture measurements to satellite footprint scale using random forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(6):2663-2673. doi: 10.1109/JSTARS.2017.2690220.

Colliander, et al. 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment 191:215-231. doi: 10.1016/j.rse.2017.01.021.

Coopersmith, Evan J., et al. 2015. Soil moisture model calibration and validation: an ARS watershed on the South Fork Iowa River. Journal of Hydrometeorology. 16(3): 1087–1101. doi: 10.1175/JHM-D-14-0145.1.

Cosh, Michael H., et al. 2006. Temporal stability of surface soil moisture in the Little Washita River Watershed and its applications in satellite soil moisture product validation. Journal of Hydrology 323:168–177. doi: 10.1016/j.jhydrol.2005.08.020.

Dente, L., et al. 2012. Maqu network for validation of satellite-derived soil moisture products. International Journal of Applied Earth Observation and Geoinformation 17:55-65. doi: 10.1016/j.jag.2011.11.004.

Dorigo, W. A. A. Xaver, M. Vreugdenhil, A. Gruber, A. Hegyiova, A. D. Sanchis-Dufau, D. Zamojski, C. Cordes, W. Wagner, M. Drush. 2012. Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone Journal 12(3). doi: 10.2136/vzj2012.0097.

Ikonen, Jaakko, et al. 2016. The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation. Geoscientific Instrumentation Methods, and Data Systems 5(1):95-108. doi: 10.5194/gi-5-95-2016.

Jackson, T. J., A. Colliander, J. Kimball, R. Reichle, W. Crow, D. Entekhabi, P. O’Neill, and E. Njoku. 2013. SMAP Science Data Calibration and Validation Plan. SMAP Project, JPL D-52544. Jet Propulsion Laboratory, Pasadena, CA.

Jackson, T., et al. 2016. Calibration and Validation for the L2/3_SM_P Version 3 Data Products. SMAP Project, JPL D-93720, Jet Propulsion Laboratory, Pasadena, CA.

Keefer, T. O., M. S. Moran, and G. B. Paige. 2008. Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resources Research 44(5):Art. #W05S07. doi: 10.1029/2006WR005702.

Louvet, S., et al. 2015. SMOS soil moisture product evaluation over West-Africa from local to regional scale. Remote Sensing of Environment 156:383–394. doi: 10.1016/j.rse.2014.10.005.

Martínez-Fernández, J., and A. Ceballos. 2005. Mean soil moisture estimation using temporal stability analysis. Journal of Hydrology 312(1-4):28-38. doi: 10.1016/j.jhydrol.2005.02.007.

McNairn, H., et al. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): prelaunch calibration and validation of the SMAP soil moisture algorithms. IEEE Transactions on Geoscience and Remote Sensing 53(5):2784-2801. doi: 10.1109/TGRS.2014.2364913.

O’Neill, P., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish. September 14, 2015. Algorithm Theoretical Basis Document (ATBD): L2 & L3 Radiometer Soil Moisture (Passive) Data Products. SMAP Project, JPL D-66480, Rev. B, Jet Propulsion Laboratory, Pasadena, CA.

Panciera, R., et al. 2014. The Soil Moisture Active Passive Experiments (SMAPEx): toward soil moisture retrieval from the SMAP mission. IEEE Transactions on Geoscience and Remote Sensing 52(1):490-507. doi: 10.1109/TGRS.2013.2241774.

Pasolli, L. et al. 2015. Estimation of soil moisture in mountain areas using SVR technique applied to multiscale active radar images at C-Band. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(1):262-283. doi: 10.1109/JSTARS.2014.2378795.

Peel M. C., B. L. Finlayson, and T. A. McMahon. 2007. Updated world map of the Koppen-Geiger climate classification. Hydrol. Earth. Syst. Sci 11:1633–1644. doi:  10.1038/srep18018.

Rautiainen, K., et al. 2012. L-band radiometer observations of soil processes in boreal and subarctic environments. IEEE Transactions on Geoscience and Remote Sensing 50(5):1483-1497. doi: 10.1109/TGRS.2011.2167755.

Rowlandson, T., et al. 2015. Use of in situ soil moisture network for estimating regional-scale soil moisture during high soil moisture conditions. Canadian Water Resources Journal 40(4):343-351. doi: 10.1080/07011784.2015.1061948.

Seyfried, M. S., et al. 2001. Long-term soil water content database, Reynolds Creek Experimental Watershed, Idaho, United States. Water Resources Research 37(11):2847–2851.

Smith, A. B., et al. 2012. The Murrumbidgee Soil Moisture Monitoring Network data set. Water Resources Research 48(7):Art. #W07701. doi: 10.1029/2012WR011976.

Su, Z., et al. 2011. The Tibetan Plateau Observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrology and Earth System Sciences 15(7):2303-2316. doi: 10.5194/hess-15-2303-2011.

Su, Z., et al. 2013. Evaluation of ECMWF's soil moisture analyses using observations on the Tibetan Plateau. Journal of Geophysical Research - Atmospheres 118(11):5304–5318. doi: 10.1002/jgrd.50468.

Wen, Jun, et al. 2014. New evidence for the links between the local water cycle and the underground wet sand layer of a mega-dune in the Badain Jaran Desert, China. Journal of Arid Land 6(4):371–377. doi: 10.1007/s40333-014-0062-0.

Zacharias, S., et al. 2011. A network of terrestrial environmental observatories in Germany. Vadose Zone Journal 10(3):955-973.

Technical References

For additional references, see the SMAP Technical References page.

No technical references available for this data set.

How To

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