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Data Set ID:
SPL4CMDL

SMAP L4 Global Daily 9 km Carbon Net Ecosystem Exchange, Version 2

The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.

Version Summary:

Changes to this version include:

  • Transitioned to Validated-Stage 2
  • Using SPL4SMAU V2 Validated and SPL4SMGP V2 Validated data as input
  • Update to process radiometer data from 2015-03-31 to 2015-04-12
  • Some data fields renamed from *_av to *_mean
  • Updated to have have continuous RMSE-based "quality" fields instead of the categorical quality flag in V1

Geographic Coverage

Parameter(s):
  • Soils > Soil Productivity > Gross Primary Productivity (GPP)
  • Soils > Soil Respiration > Heterotrophic Respiration (Rh)
  • Soils > Carbon > Net Ecosystem CO2 Exchange (NEE)
  • Soils > Carbon > Soil Organic Carbon (SOC)
Spatial Coverage:
  • N: 85.044, S: -85.044, E: 180, W: -180

Spatial Resolution:
  • 9 km x 9 km
Temporal Coverage:
  • 31 March 2015
Temporal Resolution: 1 day
Data Format(s):
  • HDF5
Platform(s) AQUA, MODELS, SMAP Observatory , SUOMI-NPP, TERRA
Sensor(s): MODIS, SMAP L-Band Radiometer , VIIRS
Version: V2
Data Contributor(s): Kimball, J. S., L. A. Jones, J. Glassy, and R. Reichle.

Data Citation

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.

Kimball, J. S., L. A. Jones, J. Glassy, and R. Reichle. 2016. SMAP L4 Global Daily 9 km Carbon Net Ecosystem Exchange, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/UBKO5ZUI7I5V. [Date Accessed].

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Detailed Data Description

Parameter Description

This SMAP data product contains daily estimates of global ecosystem productivity, including net ecosystem exchange (NEE), gross primary production (GPP), heterotrophic respiration (Rh), and soil organic carbon (SOC), along with quality control metrics. The NEE of CO2 with the atmosphere is a fundamental measure of the balance between carbon uptake by vegetation GPP, and carbon losses through autotrophic respiration (Ra) and heterotrophic respiration (Rh). The sum of Ra and Rh defines the total ecosystem respiration rate (Rtot), which encompasses most of the annual terrestrial CO2 efflux to the atmosphere. All parameters are expressed in units of g C m-2 day-1. The CO2 flux state variable outputs are provided in SPL4CMDL files as eight vegetated land-cover classes called Plant Function Types (PFTs). For example, the CO2 flux state variable outputs are provided in NEE/nee_pft{1..8}_meanGPP/gpp_pft{1..8}_mean, and RH/gpp_pft{1..8}_mean. The soil carbon pool state variable output are provided in SOC/soc_pft{1..8}_mean. Refer to Table 1 for descriptions of the eight PFTs.

Table 1. Plant Function Type (PFT) Classifier Summary
PFT Class Label PFT Code PFT Description PFT Class used in SPL4CMDL
Water 0 For all ocean and perennial inland water bodies No
Evergreen needleleaf 1 Evergreen needle-leaf trees (mostly conifers) Yes
Evergreen broadleaf 2 Evergreen broadleaf trees Yes
Deciduous needleleaf 3 Deciduous needle-leaf trees Yes
Deciduous broadleaf 4 Deciduous broad-leaf trees Yes
Shrub 5 Shrub (woody perennial) Yes
Grass 6 Grasses (native Graminoids) Yes
Cereal crop 7 Cereal cropland (domesticated agricultural crops such as wheat, oats, barley, rye ) Yes
Broadleaf crop 8 Broadleaf crop (domesticated agricultural) Yes
Urban and Built-up 9 Urban and built-up (cities, towns, highways, etc) No
Snow and ice 10 Snow and ice (may or may not be perennial) No
Barren (rock) or sparsely vegetated 11 Barren, rock, or very sparsely vegetated land No
Unclassified 254 Areas otherwise not classified as per above No

Totals for each vegetated land class (i.e. count of vegetated 1 km grid cells contained within each 9 km grid cell) are provided in each SPL4CMDL file (QA/qa_count_pft{1..8}). Non-vegetated grid cells are determined by the union of specified vegetation PFT classes in Table 1 and availability of long-term MODIS fPAR (MOD15A2) for production of the fPAR climatology (refer to the Baseline Algorithm). Vegetated PFT grid cells lacking sufficient fPAR retrievals to produce the fPAR climatology and non-vegetated PFT grid cells with otherwise valid fPAR climatology are excluded from SPL4CMDL simulations and QA counts. QA counts are time-static and are therefore identical across files because the PFT classification does not change over the course of data generation within each SPL4CMDL version.

Users may use the QA count information to compute total non-vegetated 1 km grid cell coverage, compute percent coverage for each PFT, and account for non-vegetated regions when computing areal averages from SPL4CMDL state variables. For example, when computing the total GPP within a 9 km grid cell, a user would multiply the mean GPP (i.e. /GPP/gpp_mean in g C m-2 d-1) by the vegetated PFT total QA count (i.e. /QA/qa_count).

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

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Format

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

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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:

Figure 1. Subset of File Contents
For a complete list of file contents for the SMAP Level-4 carbon product, refer to the Data Fields page. 

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 Fields document.

All global data arrays have dimensions of 1624 rows and 3856 columns (6,262,144 pixels per layer). Note: The EASE-Grid 2.0 global 1 km reference grid is defined as 14616 lines by 34704 samples (507,233,664 pixels per layer).

EC

Environmental Constraints Data

GEO

Geolocation data, including latitude/longitude coordinate variables in decimal degree units that enable convenient geo-referenced viewing and analysis.

GPP

Gross Primary Production Data

NEE

Net Ecosystem CO2 Exchange Data

QA

QA includes quality control flags, quality assessment, and valid grid cell counts.

RH

Heterotrophic Respiration Data

SOC

Soil Organic Carbon Data

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.

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File Naming Convention

Files are named according to the following convention, which is described in Table 2:

SMAP_L4_C_MDL_yyyymmddThhmmss_VLMmmm_NNN.[ext]

For example:

SMAP_L4_C_mdl_20151007T000000_Vv2020_001.h5

Where:

Table 2. File Naming Conventions
Variable Description
SMAP Indicates SMAP mission data
L4_C_MDL Indicates specific product (L4: Level-4; C: Carbon; MDL: Model)
yyyymmddThhmmss Date/time in Universal Coordinated Time (UTC) of the first data element that appears in the product, where:
yyyymmdd 4-digit year, 2-digit month, 2-digit day
T Time (delineates the date from the time, i.e. yyyymmddThhmmss)
hhmmss 2-digit hour, 2-digit minute, 2-digit second
VLMmmm

Science Version ID, where:

Variable Description
V Version
L Launch Indicator (V: Validated Data)
M 1-Digit Major Version Number
mmm 3-Digit Minor Version Number

Example: Vv2020 indicates a Validated product with a version of 2.020. Refer to the SMAP Data Versions page for version information.

Note: The data product Science Version ID (example: Vv2020) consists of the first six characters of the data product Composite Release ID (CRID). The full CRID includes four additional digits that are to be found in individual granule metadata within the DataIdentifcation / DatasetIdentification / CompositeReleaseID field. These additional digits denote minor processing changes, such as runtime configuration and other minor changes that do not impact the science of the data product.

NNN Number of times the file was generated under the same version for a particular date/time interval (002: 2nd time)
.[ext] File extensions include:
.h5 HDF5 data file
.xml XML Metadata file
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File Size

Each file is approximately 133 MB.

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Volume

The daily data volume is approximately 133 MB.

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Spatial Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N and 85.044°S.

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Spatial Resolution

Level-4 carbon model inputs include the following spatial resolutions:

  • 500 m resolution MODIS-based global PFT classification (from MCD12Q1 Type 5)
  • 1 km Fraction of Photosynthetically Active Radiation (fPAR) data (from MOD15A2)
  • 9 km resolution SMAP Level-4 soil moisture data (SPL4SMGP)
  • ¼ degree pre-processed global, daily averaged meteorology data from the GEOS-5 Forward Processing (FP) system

Level-4 carbon model processing is conducted at 1 km EASE-Grid 2.0 resolution consistent with MODIS PFT and fPAR inputs. Level-4 carbon model daily global outputs are gridded using a 9 km EASE-Grid 2.0 projection consistent with the SMAP L4 soil moisture data used as input.

Note that while this product has a 9 km spatial resolution, it also retains sub-grid scale heterogeneity information as determined from the 1 km resolution MODIS PFT and FPAR inputs.

For more details regarding inputs used in the carbon model, refer to the Data Sources section.

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Projection and Grid Description

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.

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 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). Note that the grid used for this product has been adjusted using a scaling parameter in order to accommodate a resolution of 1 km.

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.

Perfect Nesting in EASE-Grid 2.0
Figure 2. Perfect Nesting in EASE-Grid 2.0
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Temporal Coverage

Coverage spans from 31 March 2015 to present. For the analytical variables, the ancillary MODIS fPAR 8-day climatology provides a fallback input source to help ensure there are no spatio-temporal gaps in the modeled data record. Therefore no gaps are expected to occur in the SPL4CMDL output data stream.

Temporal Coverage Gaps

Satellite and Processing Events

Due to instrument maneuvers, data downlink anomalies, data quality screening, and other factors, small gaps in the time series will occur. Refer to the SMAP On-Orbit Events List for Instrument Data Users page for details regarding these gaps.

Latencies

FAQ: What are the latencies for SMAP radiometer data sets?

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Temporal Resolution

Each Level-4 file is a daily composite. Calculations for this product are conducted at a daily time step in order to provide the necessary precision for resolving dynamic boreal vegetation phenology and carbon cycles (Kimball et al. 2009, Kim et al. 2012).

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Software and Tools

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

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Data Acquisition and Processing

This section has been adapted from Kimball et al. (2014), the ATBD for this product.

Sensor or Instrument Description

For a detailed description of the SMAP instrument, visit the SMAP Instrument page at the JPL SMAP Web site.

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Data Sources

The following data sources are used as input to calculating this Level-4 carbon product:

  1. SMAP L4 Carbon Model ancillary MODIS fPAR 8-day Climatology: Primary back-up source
  2. NASA EOS Aqua MODIS fPAR 8-day Data (MYD15A2): Canopy fPAR and land cover classification: Secondary back-up source
  3. VIIRs NDVI Data (VVI3P): Secondary back-up source

In addition, ancillary data sources used as input to calculating this Level-4 carbon product are listed in Table 4.

Table 4. Primary Ancillary Inputs to the SPL4CMDL Algorithm
Parameter Units Type Spatial Resolution Source
fPAR % Dynamic (8-day) 1 km MODIS (MOD15A2*)
Rsw MJ m - 2 d - 1 Dynamic (daily) 9 km** GEOS-5
Tmn °C Dynamic (daily) 9 km** GEOS-5
VPD Pa Dynamic (daily) 9 km** GEOS-5
SM % Sat. Dynamic (daily) 9 km SPL4SMGP
SMrz % Sat. Dynamic (daily) 9 km SPL4SMGP
Ts °C Dynamic (daily) 9 km SPL4SMGP
F/T Discrete class Dynamic (daily) 9 km** GEOS-5***
Land Cover Class Discrete class Static 1 km MODIS (MOD12Q1)
fPAR Climatology % Static (8-day) 1 km MODIS (MOD15A2)
Additional Inputs for Algorithm Options
VI (NDVI) Dimensionless Dynamic (8-day) 1 km MODIS (MOD13A2MYD13A2), VIIRS (VVI3P)
Recovery Status Years Static 1 km MODIS (MOD13A2MYD13A2)

* MOD indicates data acquired by the MODIS instrument on the Terra satellite; MYD indicates data acquired by the MODIS instrument on the Aqua satellite.

** The native resolution of GEOS-5 FP fields is ¼ degree (latitude) by 3/8 degree (longitude); SPL4CMDL processing internally resamples these fields to 9 km.

*** Due to the loss of the SMAP radar instrument and operational freeze/thaw (F/T) classification product, SPL4CMDL uses the GMAO GEOS-5-modeled TSURF parameter to define F/T conditions in the carbon model.

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Theory of Measurements

Current capabilities for regional assessment and monitoring of NEE are limited by mismatches between bottom-up and top-down information sources. Atmospheric transport model inversions of CO2 concentrations from sparsely distributed measurement stations provide information on seasonal patterns and trends in atmospheric CO2 but little information on underlying processes; these methods are also too coarse to resolve carbon source-sink activity at scales finer than broad latitudinal and continental domains (Piao et al. 2007, Dargaville et al. 2002). Tower CO2 flux measurement networks provide detailed information on stand-level NEE and associated biophysical processes, but little information regarding spatial variability in these processes over heterogeneous landscapes (Running et al. 1999). Estimates of NEE and component carbon fluxes from satellite remote sensing provide a means for scaling between relatively intensive stand-level measurement and modeling approaches, and top-down assessments from atmospheric model inversions.

Science Objectives

The primary science objectives of the SPL4CMDL product are to:

  • Determine NEE regional patterns and temporal behavior (daily, seasonal, and annual) to within the accuracy range of in situ tower measurement estimates of these
    processes;
  • Link NEE estimates with component carbon fluxes (GPP and Rtot) and the primary environmental constraints to ecosystem productivity and respiration.

The SPL4CMDL algorithm addresses carbon cycle science objectives by enabling detailed mapping and monitoring of spatial patterns and temporal dynamics of land-atmosphere CO2 exchange, and the underlying carbon fluxes and environmental drivers of these processes. The SPL4CMDL product also links SMAP land parameter measurements to global terrestrial CO2 exchange, including boreal ecosystems, reducing uncertainties about the "missing sink" on land for atmospheric CO2.

Atmospheric transport model inversions of CO2 concentrations indicate that the Northern Hemisphere terrestrial biosphere is responsible for much of the recent terrestrial sink strength for atmospheric carbon (Dargaville et al. 2002). Variability in land-atmosphere CO2 exchange is strongly controlled by climatic fluctuations and disturbance, while uncertainty regarding the magnitude and stability of the sink are constrained by a lack of detailed knowledge on the response of underlying processes at regional scales (Denman et al. 2007, Houghton 2003). The SMAP mission provides the potential for much improved spatial resolution and L-band microwave sensitivity to land surface processes for monitoring soil moisture and thermal dynamics of global ecosystems, including boreal and arctic biomes.

The baseline SPL4CMDL algorithm uses daily inputs from the SMAP Level-4 soil moisture stream to define soil moisture and frozen temperature constraints to vegetation productivity, ecosystem respiration, and NEE. The algorithm provides estimates of NEE (g C m-2 day-1) and component carbon fluxes for global vegetated land areas at mean daily intervals; the product defines sub-grid scale mean and variability in carbon fluxes for dominant and sub-dominant vegetation classes within each grid cell as determined from finer scale ancillary land cover classification and fPAR inputs. The target accuracy for the SPL4CMDL product is to attain a mean annual unbiased RMSE (ubRMSE) accuracy for NEE within 30 g C m-2 yr-1 or 1.6 g C m-2 day-1, commensurate with the estimated accuracy of in situ tower measurements (Baldocchi 2008, Richardson 2005, Richardson 2008). The baseline 1 km SPL4CMDL spatial resolution is similar to the sampling footprint of CO2 flux measurements from the global tower network (Running et al. 1999, Baldocchi et al. 2008). Secondary products of scientific value produced during SPL4CMDL processing include surface (<10 cm depth) Soil Organic Carbon (SOC) stocks (g C m-2), vegetation Gross Primary Production (GPP), heterotrophic soil and litter respiration (Rh), dimensionless (0-100 percent) environmental constraint indices for GPP and Rh, and detailed data Quality Assessment (QA) metrics for NEE.

The SPL4CMDL product enables quantification and mechanistic understanding of spatial and temporal variations in NEE over a global domain. NEE represents the primary measure of carbon (CO2) exchange between the land and atmosphere, and the SPL4CMDL product is directly relevant to a range of applications including regional mapping and monitoring of terrestrial carbon stocks and fluxes, climate and drought related impacts on vegetation productivity, and atmospheric transport model inversions of terrestrial source-sink activity for atmospheric CO2. The SPL4CMDL product also satisfies carbon cycle science objectives of the NRC Decadal Survey to advance our understanding of the way in which global ecosystems, including boreal-Arctic biomes, respond to climate anomalies and their capacity to reinforce or mitigate global warming.

For more background information, refer to Section 2.3: Historical Perspective in the ATBD for this product.

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Derivation Techniques and Algorithms

The SPL4CMDL algorithm consists of Light Use Efficiency (LUE) and terrestrial carbon flux model components used to estimate GPP, respiration, residual NEE carbon fluxes, and underlying SOC pools on a daily basis. The baseline SPL4CMDL algorithm is summarized in Figures 4a and 4b for respective LUE and carbon flux model components. The approach has structural elements similar to the Century (Parton et al. 1987, Ise and Moorcroft 2006) and CASA (Potter et al. 1993) soil decomposition models and the operational MOD17 GPP algorithm (Zhao et al. 2005, Zhao and Running 2010), but is adapted for use with daily biophysical inputs derived from both global satellite and model analysis data (Kimball et al. 2009Yi et al. 2013). The current SPL4CMDL algorithm baseline was developed from earlier versions and pre-launch development and testing, and incorporates recommendations from external SPL4CMDL algorithm reviews (for example, Kimball et al. 2009).

Figure 4a. Baseline Light Use Efficiency (LUE) Carbon Model Structure for Estimating GPP 
(Click image for high-resolution version)
Arrows denote the primary pathways of data flow, while boxes denote the major process calculations. Primary inputs include daily root zone soil moisture (SMrz) and landscape freeze/thaw (FT) status from SMAP Level-4 soil moisture products (in red), and other dynamic ancillary inputs (in green) including MODIS (MOD15) fPAR and GMAO GEOS-5 daily surface meteorology, including vapor pressure deficit (VPD), minimum air temperature (Tmn) and incident solar shortwave radiation (Rsw). Model calculations are performed at 1 km spatial resolution using dominant vegetation class and BPLUT response characteristics for each grid cell defined from a global land cover classification. The resulting GPP calculation is a primary input to the Level-4 carbon terrestrial carbon flux model below (Figure 4b).

Figure 4b. Terrestrial Carbon Flux Model for Estimating NEE
(Click image for high-resolution version)
Primary algorithm inputs (in red) include daily GPP from the LUE model (Figure 4a), and surface soil moisture (SM) and surface temperature (Ts) from the SMAP Level-4 product. NEE is the primary (validated) output, while GPP, respiration (Rh + Ra), and SOC are secondary (research) outputs. 

Dynamic daily inputs to the SPL4CMDL algorithms include satellite optical-IR remote sensing MODIS-based fPAR, GEOS-5 surface meteorology (Rsw, Tmn, VPD) and associated SPL4SMGP soil moisture (SMrz) which provide primary inputs to a LUE algorithm to determine GPP, where Rsw is incoming shortwave solar radiation (MJ m-2 d-1); Tmn is minimum daily 2-m air temperature (°C), VPD is atmosphere vapor pressure deficit (Pa), and SMrz is the integrated surface to root zone (0-1m depth) soil moisture (% Sat.). The SPL4CMDL dynamic inputs also include GEOS-5 freeze/thaw classification defined by surface temperature (Ts, °C) or the SMAP sensor constraints to productivity and autotrophic respiration calculations. SMAP Level-4 surface soil moisture (≤ 5 cm depth) and soil temperature are used as primary drivers of the soil decomposition and Rh calculations. Static inputs to the SPL4CMDL algorithms include a global land cover classification, which is used to define the major plant functional types and associated biome-specific Biome Properties Look-Up Table (BPLUT) response characteristics for each vegetated grid cell within the product domain. The BPLUT parameters are defined for up to 8 global vegetation (PFT) classes; the model parameters for each global PFT class were calibrated prior to SPL4CMDL operational production by optimizing carbon model NEE calculations against tower eddy covariance measurement based daily NEE observations from global FLUXNET sites representing the major PFT classes (Baldocchi 2008). The land cover classification used for SPL4CMDL processing is consistent with the one used in the production of the fPAR inputs. All model inputs are available as satellite remote sensing derived products or from model (GEOS-5) analysis.

Although the SPL4CMDL product is global in extent, product accuracy requirements and validation activities were primarily specified for northern (≥45°N) land areas consistent with NRC objectives for better understanding of terrestrial carbon source/sink activity in boreal regions (NRC 2007, Jackson et al. 2011). The SPL4CMDL calculations are conducted at 1 km spatial scale consistent with the resolution of ancillary fPAR and land cover inputs. The simulations have also been conducted in a consistent global EASE-Grid Version 2 projection format. Model simulations for each 1 km grid cell are conducted using the corresponding (nearest-neighbor) 9 km resolution SMAP Level-4 Soil Moisture and GEOS-5 inputs. The MODIS (MOD/MYD15) fPAR product is produced at 1 km spatial resolution and 8-day temporal fidelity from both NASA EOS Terra and Aqua sensor records.

The MODIS fPAR operational products are obtained in a tile-based sinusoidal projection. Preprocessing of these data prior to the SPL4CMDL ingestion involves reprojecting from sinusoidal to 1 km resolution global cylindrical EASE-Grid projection formats, followed by trailing nearest-neighbor temporal interpolation of MOD15A2 best Quality Control (QC; cloud-free, favorable surface conditions) 8-day fPAR series to each daily time step. Missing or low QC 8-day fPAR data are gap filled on a grid cell-wise basis using an ancillary fPAR mean 8-day climatology constructed from the long-term (10+ year) MODIS record. The resulting fPAR data are combined with daily biophysical inputs from GEOS-5 and SPL4SMGP data to estimate NEE, component carbon fluxes (GPP and Rh) and surface SOC pools. SPL4CMDL computes daily Environmental Constraint (EC) indices which influence the GPP and NEE flux calculations, including the estimated bulk environmental reduction to PAR conversion efficiency (εmult), low soil moisture and temperature constraints (Wmult, Tmult) to soil decomposition and Rh calculations, and freeze/thaw (F/T) status within each 9 km grid cell. These environmental constraint indices are provided in SPL4CMDL files as the EC/emult_mean, EC/wmult_mean, EC/tmult_mean and EC/frozen_area respective SDS fields.

The resulting SPL4CMDL parameters enable characterization of spatial patterns and daily temporal fidelity in NEE, underlying carbon fluxes and SOC pools, and their primary environmental drivers. The resulting fine scale (1 km resolution) SPL4CMDL outputs are spatially aggregated to the coarser 9 km resolution final product grid by weighted linear averaging of outputs according to the fractional cover of individual PFT classes represented within each 9 km grid cell and defined by the underlying 1 km resolution MODIS PFT map; the sub-grid scale means from individual PFT classes are preserved for each 9 km grid cell, while proportional vegetation cover information is included in the product metadata, allowing the coarse resolution data to be decomposed into the relative contributions from individual PFT classes within each cell. These outputs are designed to facilitate improved algorithm and product accuracy over heterogeneous land cover areas, and product outputs that are more consistent with the mean sampling footprint of most tower CO2 flux measurement sites (Baldocchi 2008, Chen et al. 2012).

Algorithm Options

For more information regarding algorithm options, refer to the ATBD, for this product.

Ancillary Data

Ancillary data required as input for the algorithms are summarized in Table 4. For in-depth information on ancillary data, refer to the ATBD, Section 3.2: Ancillary Data Requirements.

For more information regarding the algorithm, refer to the ATBD for this product.

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Processing Steps

Written by the University of Montana's Numerical Terradynamic Simulation Group (NTSG), the SPL4CMDL science code was transferred from NTSG to the NASA Global Modelling and Assimilation Office (GMAO) for translation and implementation as operational code in conjunction with SMAP Level-4 soil moisture production within the GMAO Level-4 SMAP Science Data Processing System (SDS). To generate the SPL4CMDL product, the processing software ingests SPL4SMGP daily files, MODIS-derived 8-day fPAR files, and GEOS-5 daily surface meteorology data. The ingested data are then inspected for retrievability criteria according to input data quality, ancillary data availability, and land cover conditions. Two pre-processor codes, one for fPAR data and one for global meteorology data, are then executed each day to temporally aggregate and resample these respective inputs for use by the baseline algorithm software. When retrievability criteria are met, the production software invokes the baseline retrieval algorithm to generate the daily carbon model outputs.

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Error Sources

Many sources of error contribute to the uncertainty in the SPL4CMDL product. The key sources of error or uncertainty to the SPL4CMDL algorithm are:

  1. Errors in the ancillary 8 day fPAR inputs
  2. Errors in the SPL4SM soil moisture and temperature inputs
  3. Errors in the GEOS-5 daily surface meteorology inputs
  4. Uncertainty in the internal model parameterization, initialization, and calibration parameters

For more information about error sources refer to the ATBD for this product.

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Quality Assessment

For in-depth details regarding the quality of these Version 2 data, refer to the following reports: 
Validated Assessment Report 
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 GMAO prior to delivery to National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). A separate metadata file with an .xml file extension is also delivered to NSIDC DAAC with the HDF5 file; it contains the same information as the HDF5 file-level metadata.

Quality Flags

Quality Assessment (QA) fields are also provided with metadata from MODIS fPAR and SPL4SM inputs to the SPL4CMDL algorithms. These QA fields incorporate expected model uncertainty propagating from input driver uncertainty including SPL4SMGP, GEOS-5 FP, and MODIS fPAR. This QA input error information was assigned by comparing unbiased Root Mean Square Errors (ubRMSE) relative to global historical flux tower benchmark data during SPL4CMDL pre-launch calibration. Input errors are propagated during SPL4CMDL 1 km model calculations using standard error propagation procedures employing the SPL4CMDL model Jacobian and simplifying independence assumptions. Resulting 1 km NEE ubRMSE fields are quadratically averaged to 9 km output fields for each PFT class as defined from 1 km MOD12Q1 land cover and then posted as the NEE QA ubRMSE geophysical variable (g C m-2 d-1). The resulting QA information has been evaluated and refined through post-launch SPL4CMDL Cal/Val activities using concurrent eddy covariance CO2 flux measurements from global tower measurement networks (Baldocchi 2008), comparisons with other similar global carbon products, and algorithm sensitivity studies over the observed range of environmental variability. The above-described QA fields are provided in SPL4CMDL files as the QA/nee_rmse_mean and QA/nee_rmse_pft{1..8}_mean fields. Refer to the SPL4CMDL Product Specification Document (PSD) Version 2.0 for additional details.

Quality control bit flags are provided in SPL4CMDL files to identify retrieval conditions including use of alternative ancillary data sets and exceedance of expected output field value ranges. Alternative ancillary conditions indicated in the QC bit flags include the use of alternative fPAR sources in place of baseline MODIS (MOD15) fPAR inputs, potential gaps in the SPL3SMA input stream, and instances where the ancillary fPAR 8-day climatology is used in place of the dynamic best QC MODIS fPAR input stream to estimate GPP. Expected PFT class specific range thresholds for each state variable (NEE, GPP, Rh, and SOC) have been established from dynamic algorithm simulations using long-term (10+ year) daily data input records from pre-launch data sources similar to those used for post-launch SPL4CMDL production, including MODIS (MOD15) fPAR, freeze-thaw status (Kim et al. 2012), and MERRA surface meteorology (Yi et al. 2011). These post-launch diagnostics are provided in SPL4CMDL files in the QA/carbon_model_bitflag data field for additional user evaluation. Table 7 indicates the bit-field positions for the above-described flags. A copy of Table 7 is also provided within each SDS file as metadata for quick reference; refer to the QA/carbon_model_bitflag data field.

Table 7. QC Bit Flag Fields, Names, Positions, and Description Metadata

Bit Flag Name

Bit Positions
{Start, End}

Number
of Bits

Value Range

Description

NEE bit 00 – 00 1 {0|1} 0 = NEE within valid range; 1 = out of valid range
GPP bit 01 – 01 1 {0|1} 0 = GPP within valid range; 1 = out of valid range
Rh bit 02 – 02 1 {0|1} 0 = Rh within valid range; 1 = out of valid range
SOC bit 03 – 03 1 {0|1} 0 = SOC within valid range; 1 = out of valid range
PFT dominant 04 – 07 4 {1..8} Most frequently occurring (dominant) vegetated PFT class as defined from qa_count
QA score 08 – 11 4 {0,1,2,3} Relative nee_mean error as ranked by nee_rmse_mean: 0 = (RMSE<1 g C m-2 d-1); 1 = (1<=RMSE<2 g C m-2 d-1); 2= (2=<RMSE<3 g C m-2 d-1); 3 = (RMSE> = 3 g C m-2 d-1)
GPP method 12 – 12 1 {0|1} 0 = derived GPP using 8-day FPAR or NDVI input, 1 = derived GPP via FPAR or NDVI climatology
NDVI method 13 – 13 1 {0|1} 0 = derived GPP using FPAR; 1 = derived GPP using NDVI
F/T method 14 – 14 1 {0|1} 0 = used SPL3SMA F/T; 1 = used GEOS-5 surface temperature
IsFill* 15 – 15 1 {0|1} 0 = is NOT fill value (simulation performed for one or more 1 km grid cells within 9 km grid cell), 1 = is fill value (no 1 km simulation performed within 9 km grid cell). Fill values occur for non-land, non-vegetated, and/or grid cells otherwise lacking valid FPAR data record.
* When IsFill = 1, then all other bit fields will have value 1 and the entire uint16 integer will evaluate to 65534. Users should therefore check the value of IsFill prior to referencing other bit fields.

For more information, such as algorithm testing procedures and Calibration and Validation (Cal/Val) activities, refer to the ATBD. For more information regarding data flags, refer to the Data Fields document.

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References and Related Publications

Contacts and Acknowledgments

Investigators

John Kimball, Lucas Jones, Joe Glassy
Numerical Terradynamic Simulation Group (NTSG)
College of Forestry & Conservation
The University of Montana
Missoula, MT 59812-1049 USA

Rolf Reichle
NASA Goddard Space Flight Center
Global Modeling and Assimilation Office
Mail Code 610.1
8800 Greenbelt Rd
Greenbelt, MD 20771 USA

Document Information

Document Creation Date

October 2015

Document Revision Date

April 2016

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