Atmospheric data access for the geospatial user community

Index

NEWS

12-07-2018
ADAGUC Workshop 21-23 November 2018
KNMI organizes a 3-day workshop to provide hands-on experience with the ADAGUC software suite.
12-05-2017
ADAGUC Workshop 7 June 2017
KNMI organizes a 1-day workshop to provide hands-on experience with the ADAGUC software suite. Please check the announcement for the programme and how to signup.
12-05-2016
EGU, Geobuzz and Eumetsat
The latest presentations and posters from EGU, Geobuzz and Eumetsat have been added. See the presentations section here.
30-03-2015
ADAGUC Workshop 17-19 June 2015
KNMI organizes a 3-day workshop to provide hands-on experience with the ADAGUC software suite. Please check the announcement for the programme and how to signup.
03-11-2014
Application of polar orbiters with Pytroll and ADAGUC
Demonstration of Pytroll and ADAGUC displaying Suomi NPP Viirs. Read more here.
20-06-2013
Results ADAGUC Workshop 2013
KNMI has organized a 3-day workshop to get hands-on experience with the ADAGUC software suite: workshop June 2013.
16-04-2013
ADAGUC Workshop 17-19 June 2013
KNMI organizes a 3-day workshop to get hands-on experience with the ADAGUC software suite. Please read the announcement for the programme and how to signup.
29-03-2013
ADAGUC and OGC at KNMI
Status of ADAGUC and OGC services at KNMI.
18-09-2012
Launch of MSGCPP website
Launch of new website offering real-time MSG-CPP products using ADAGUC technology: http://msgcpp.knmi.nl/.
16-05-2010
Update of the precipitation service
The radar precipitation service has been updated. Besides better compliance to the WMS 1.1.1 specification, the Google Maps projection is now supported.
28-04-2009
ADAGUC presentations at EGU 2009
The ADAGUC presentations given at the EGU in Vienna are now online. You can access them here.
02-04-2009
Radar precipitation service available
The KNMI radar precipitation composite is now available in realtime through the ADAGUC services. The service can be found here.

16-02-2009
ADAGUC Products Standard V1.1 is now available
The ADAGUC Product Standard document (v1.1) is now publicly available.
09-12-2008
Presentations ADAGUC workshop 2008 available online
The presentations can be found in the
documentation section
04-12-2008
Final ADAGUC workshop
December 4-5 at VU, Amsterdam.
Workshop Announcement
03-10-2008
ADAGUC Products Standard V1.0 is now available
The ADAGUC Product Standard document (v1.0) is now publicly available.
22-08-2008
ADAGUC driver on GDAL Wiki (v0.1)
In agreement with Frank Warmerdam Maarten Plieger created an ADAGUC wiki on osgeo.org where the ADAGUC GDAL NetCDF4 driver can be found.
28-04-2008
ADAGUC presentations at EGU 2008
The presentations of the ADAGUC talks and posters at the EGU in Vienna are now online. You can access them here.
13-04-2008
ADAGUC at EGU 2008 - General Assembly & Congress in Vienna
3 talks and 3 posters will be presented by ADAGUC members
11-04-2008
First ADAGUC demonstrator service is available
Example datasets can be viewed and downloaded here
21-12-2007
Use Case and User Requirements documents published
The use case document (v2.1) and user requirements document (v1.3) are now publically available: Use cases V2.1, User Requirements v1.3
23-04-2007
ADAGUC at the ENVISAT 2007 conference
At the ENVISAT symposium 2007 in Montreux a poster is presented with goals and status of ADAGUC
16-04-2007
ADAGUC at EGU General Assembly 2007 in Vienna
Goals and current status of ADAGUC will be presented in an oral presentation
13-03-2007
ADAGUC at National Geo Information Days in Rotterdam
ADAGUC presented 2 posters to the Dutch Geoinformation community in the former van Nelle factory in Rotterdam. more
08-12-2006
ADAGUC success in Geo-Connected
ADAGUC will receive an additional grant for a top-up project, dealing with climate and forecast data. The Geo-Connected tender has been issued by RGI to establish bridges between research programs.
01-11-2006
Presentations workshop 2006 online
The presentations of the ADAGUC workshop are now online. You can access them here.

AMSR LPRMSM L3 Soilmoisture

Soilmoisture

1. Identification

1.1. Product description

1.1.1. Abstract

This dataset provides global soil moisture with a high temporal (day, night) and low (0.25 degree) spatial resolution, but are limited to approximately the top few cm of the soil. The soil moisture data is expressed in volumetric values (m3 m-3).

This product is based on AMSR-E satellite observations and is derived according to the Land Surface Parameter Model (LPRM) (Owe et al. 2008). The LPRM is a three-parameter retrieval model for passive microwave data and is based on a microwave radiative transfer model that links surface geophysical variables (i.e. soil moisture, vegetation water content, and soil/canopy temperature) to the observed brightness temperatures. It uses the dual polarized channel (either 6.925 or 10.65GHz) for the retrieval of both surface soil moisture and vegetation water content. The land surface temperature is derived separately from the vertically polarized 36.5GHz channel.

1.1.2. Purpose

Soil moisture, as the state variable of the water cycle over land, determines water flux between the atmosphere, the surface and subsurface. Because a large amount of heat is exchanged when water changes phase, the water cycle is also fundamental to the dynamics of the Earth's energy cycle. Furthermore, since water is the universal solvent in the Earth system, biogeochemical cycles (e.g., carbon, nitrogen, methane) are embedded in the water cycle. Soil moisture information will be important for elements of Earth system science, for water resource assessment, and for natural hazards mitigation.

Soil moisture is the key control on evaporation and transpiration at the land-atmosphere boundary. Since large amounts of energy are required to vaporize water, soil moisture control also has a significant impact on the surface energy flux. Thus, soil moisture variations affect the evolution of weather and climate particularly over continental regions. Initialization of numerical weather prediction (NWP) models, and seasonal climate models with accurate soil moisture information enhances their prediction skill.

Soil moisture and its freeze/thaw state are also key determinants of the global carbon cycle. Carbon uptake and release in boreal landscapes is one of the major sources of uncertainty in assessing the carbon budget of the Earth system (the so-called missing carbon sink).

1.1.3. Application

Satellite derived soil moisture products are already used successfully for drought prediction (Loew et al., 2008), crop yield prediction (de Wit and van Diepen ,2007), and flood Forecasting (Parajka et al., 2006) and are planned to be used in Numerical Weather Prediction Models (Holmes et al., 2008).

1.2. Time period of content

The dataset spans the period of June 19, 2002 to present. The soil moisture retrievals are derived from a sun synchronous satellite platform, resulting in descending orbits at approximately 1:30 AM solar time and ascending orbits at ~1:30 PM solar time. The soil moisture products are produced in a near-real time mode, with a time lag of approximately 24 hours (depends on the online facilities of the Level 2 AMSR-E brightness temperatures).

1.3. Status

1.3.1. Progress

The dataset is validated extensively (i.e. De Jeu et al., 2008) and additional data validation will continue. The algorithm development has been completed although updated versions with improvements might follow.

1.3.2. Maintenance and update frequency

Every 24 hours new soil moisture maps will be produced.

1.4. Spatial Domain

1.4.1. Bounding coordinates

Global coverage: Longitude [-180,180], latitude [-90.,90.]

1.5. Keywords

1.5.1. Theme

Soil Moisture

Hydrology

Climate

Satellite Remote Sensing

Passive Microwave

AMSR-E

1.5.2. Place

Global.

1.5.3. Stratum

Land.

1.5.4. Temporal

June 19 2002 – Now.

1.6. Access constraints

None.

1.7. Use constraints

Use citation.

1.8. Point of contact

Dr. Richard de Jeu

VU University Amsterdam

Faculty of Earth and Life Sciences

De Boelelaan 1085, 1081 HV Amsterdam

Tel: +31 20 5987287

Email: richard.de.jeu@falw.vu.nl

1.9 Citation

Owe M., RAM. De Jeu, and TRH Holmes (2008). Multi-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture, J. Geophys. Res., 113, F01002, doi:1029/2007JF000769.

1.10 Preview

Figure 1. Twenty-four-hour global gridded ascending (daytime; ~ 1:30 PM) and descending (nighttime; ~1:30 AM) surface soil moisture retrievals at 6.9 GHz from AMSR-E using LPRM.

1.11. Data set credit

Dr. Manfred Owe

NASA GSFC

Greenbelt MD

USA

Email: manfred.owe@gsfc.nasa.gov

Dr. Richard de Jeu

VU University Amsterdam

Dept of Hydrology and Geo-Environmental Sciences

Amsterdam

the Netherlands

Email: richard.de.jeu@falw.vu.nl

Dr. Thomas Holmes

VU University Amsterdam

Dept of Hydrology and Geo-Environmental Sciences

Amsterdam

the Netherlands

Email: thomas.holmes@falw.vu.nl

1.12. Cross reference

NASA-AMSR-E L3 Global Soil Moisture product:

Njoku EG, Jackson T, Lakshmi V, Chan T, Nghiem SV (2003) Soil moisture retrieval from AMSR-E, IEEE Trans. Geosci. Rem. Sens., 41:215–229.

JAXA-AMSR-E L3 Global Soil Moisture product:

Koike T, Y Nakamura, I Kaihotsu, G Davva, N Matsuura, K Tamagawa, H Fujii (2004). Development of an advanced microwave scanning radiometer (AMSR-E) algorithm of soil moisture and vegetation water content. Annual Journal of Hydraulic Engineering, JSCE, 48, 217–222

ERS global soil moisture product

Wagner W, Lemoine G, Rott H (1999) A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data, Remote Sensing of Environment, 70:191-207. doi:10.1016/S0034-4257(99)00036-X

Wagner W, Scipal K, Pathe C, Gerten D, Lucht W, Rudolf B (2003) Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res., 108, DOI 10.1029/2003JD003663.

Validation and cross comparison activities

De Jeu RAM, WW Wagner, TRH Holmes, AJ Dolman, NC van de Giesen, and J Friesen. Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers, in review.

Draper CS, JR Walker, PJ Steinle, RAM De Jeu, and TRH Holmes (2007) Remotely Sensed Soil Moisture over Australia from AMSR-E. In MODSIM07, 10-13 Dec. 2007, Christchurch, New Zealand.

Rudiger C, JC Calvet, C Gruhier, TRH Holmes, RAM De Jeu, and WW Wagner (2008) An Intercomparison of ERS-Scat, AMSR-E Soil Moisture Observations with Model Simulations over France, in review

Wagner WW, V Naeimi, K Scipal, RAM De Jeu, J Martinez Fernandez (2007) Soil Moisture from Operational Meteorological Satellites, Hydrogeology Journal, 15, doi: 10.1007/s10040-006-0104-6.

1.13. Literature

Ashcroft, P., and F. Wentz, 2003. (updated daily). AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001, September to October 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

De Jeu RAM (2003) Retrieval of Land Surface Parameters Using Passive Microwave Observations, PhD Dissertation, VU Amsterdam, 120 pp, ISBN 90-9016430-8 [PDF].

De Jeu RAM and M Owe (2003) Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. International Journal of Remote Sensing, 24, 4559-4578, doi: 10.1080/0143116031000095934.

De Wit AJW, and CA van Diepen (2007) Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts, Agricultural and Forest Meteorology, 146, Pages 38-56.

Holmes TRH, M Drusch, JP Wigneron and RAM De Jeu (2008) A global simulation of microwave emission: Error structures based on output from ECMWF’s operational Integrated Forecast System, IEEE Transactions on Geoscience and Remote Sensing, 46, doi: 10.1109/TGARS.2007.91498.

Kerr YH, Waldteufel P, Wigneron JP, Martuzzi, JM, Font J, Berger M (2001) Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens. 39: 1729-1735.

Kerr YH, (2007) Soil moisture from space: Where are we? Hydrogeology Journal. 15:117-120.

Liu Y, RAM De Jeu AIJM Van Dijk and M Owe (2007) TRMM-TMI satellite observed soil moisture and vegetation density (1998–2005) show strong connection with El Nino in eastern Australia. Geophysical Research Letters, 34, doi:10.1029/2007GL030311.

Loew, A, TRH Holmes, and RAM de Jeu (2008) The European heat wave 2003: early indicators from 1multisensoral microwave remote sensing? In Review.

Meesters AGCA, RAM De Jeu and M Owe (2005) Analytical Derivation of the Vegetation Optical Depth from the Microwave Polarization Difference Index, IEEE Geoscience and Remote Sensing Letters, 2, 121-123.

Njoku EG, Kong JA (1977) Theory for passive microwave remote sensing of near-surface soil moisture, Journal of Geophysical Research, 82: 3108-3118.

Owe M, RAM De Jeu, and JP Walker (2001) A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index. IEEE Transactions on Geoscience and Remote Sensing, 39, 1643-1654.

Owe M., RAM. De Jeu, and TRH Holmes (2008). Multi-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture, J. Geophys. Res., 113, F01002, doi:1029/2007JF000769.

Parajka, J, V Naeimi, G Bloschl, W Wagner, R Merz, and K Scipal. (2006). Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrol. Earth Syst. Sci., 10, 353–368.

Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, 2004“The Global Land Data Assimilation System”, Bull. Amer. Meteor. Soc., 85 (3):381-394.

Wang, J.R., and T.J. Schmugge, 1980, “An empirical model for the complex dielectric permittivity of soil as a function of water content”, IEEE Trans. Geosci. Remote Sensing, 18:288-295.

2. Data Quality

2.1 Lineage

2.1.1. Source information

The global soil moisture product is based on level 2 resampled microwave brightness temperatures from the AMSR-E radiometer on the AQUA Earth observation satellite. The sensor is 12 channels (six frequencies), with 4 bands relevant to soil moisture retrieval. AMSR-E has a swath width of 1445 km. Daily Earth coverage is nearly 100 percent above and below 45 degrees north and south latitude, while mid-latitudes experience about 80 percent coverage. Sensor data were downloaded as brightness temperatures from their public source archives and are available from the National Snow and Ice Data Center (NSIDC) in Boulder Colorado (http://nsidc.org/). Users who use this data should cite:

Ashcroft, P., and F. Wentz, 2003. (updated daily). AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001, September to October 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

2.1.2. Processing steps

The soil moisture retrieval methodology uses a nonlinear iterative procedure in a forward modelling approach to partition the surface emission into its primary source components, i.e. the soil emission and the canopy emission, and then optimizes on the canopy optical depth and the soil dielectric constant. Once convergence between the calculated and observed brightness temperatures is achieved, the model uses a global data base of soil physical properties (Rodell et al., 2004) together with a soil dielectric mixing model (Wang and Schmugge, 1980) to solve for the surface soil moisture. No field observations of soil moisture, canopy biophysical properties, or other observations are used for calibration purposes, resulting in a model that is largely physically-based with no regional dependence, and is applicable at any microwave frequency suitable for soil moisture monitoring (i.e. L–, C–, X–, or Ku–band).

A data mask was developed on the AMSR-E data products to eliminate those data cells where data values were either meaningless due to frozen soil conditions, snow cover or excessive vegetation, or were unreliable because the residual between observed and modelled brightness temperature exceeds 0.25 K. Pixels with snow and frozen soils were detected with a simple surface temperature algorithm (De Jeu and Owe, 2003). Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in decreased sensor sensitivity to soil moisture variations (e.g. Figure 1). For this reason pixels with a vegetation optical depth exceeding 0.8 were removed.

The effect of vegetation density on the decrease of sensitivity to soil moisture variations is inversely proportional to the wavelength and therefore higher at X-band than at C-band.

Surface soil moisture retrievals from the LPRM have been evaluated in several previous studies against both observational and model simulation data sets from a variety of global test sites, and compared quite well. De Jeu et al 2008 presented an overview of all validation activities and they demonstrated the strong relationship between soil moisture accuracy and vegetation density.

A poor accuracy in soil moisture can be found in the vegetated regions. This can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. De Jeu et al. (2008) estimated a soil moisture accuracy of about 0.06 m3m-3 for sparse to moderate vegetated regions.

3. Spatial Data Organization

3.1. Indirect Spatial Reference

Map covers the world.

3.2. Direct Spatial Reference Method

Raster.

3.3. Point and vector object information

N/A

3.4. Raster object information

3.4.1. Row count

720

3.4.2. Column count

1440

3.4.3. Vertical count

1

4. Spatial Reference

4.1. Coordinate System

4.1.1. Geographic coordinate units

Degrees.

4.1.2. Map projection

Latitude_longitude.

4.1.3. Datum

WGS84

4.1.4. EPSG Code


4.1.5. PROJ4 parameters


5. Product Description Reference Information

5.1. Product Description Date

28-06-2008

5.2. Product Description Review Date

28-06-2008

5.3. Product Description Contact

Dr. Richard de Jeu (email Richard.de.jeu@falw.vu.nl)