Editorial Type:
Article
Article Type:
Research Article

Ingestion of Simulated SMAP L3 Soil Moisture Data into Military Maneuver Planning

Susan Frankenstein Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Hanover, New Hampshire

Search for other papers by Susan Frankenstein in
Current site
Google Scholar
PubMed Close
,
Maria Stevens Geotechnical and Structures Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Vicksburg, Mississippi

Search for other papers by Maria Stevens in
Current site
Google Scholar
PubMed Close
, and
Constance Scott Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Hanover, New Hampshire

Search for other papers by Constance Scott in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2015
Collections:
NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research
DOI:
https://doi.org/10.1175/JHM-D-14-0032.1
Page(s):
427–440
Restricted access

Abstract

This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.

Corresponding author address: Susan Frankenstein, ERDC CRREL, 72 Lyme Rd., Hanover, NH 03755. E-mail: susan.frankenstein@usace.army.mil

This article is included in the NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research Special Collection.

Abstract

This paper uses simulated SMAP level-3 (L3) soil moisture data to calculate soil strength directly and compares the results against the current Noah Land Information System–based climatology approach. Based on the availability of data, three sites were chosen for the study: Cheorwon, South Korea; Laboue, Lebanon; and Asham, Nigeria. The simulated SMAP satellite data are representative of May conditions. For all three regions, this is best represented by the “average” soil moisture used in the current climatology approach. The cumulative distribution frequency of the two soil moisture sources indicates good agreement at Asham, Nigeria; mixed agreement at Cheorwon, South Korea; and no agreement at Laboue, Lebanon. Soil strengths and resulting vehicle speeds for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097 were calculated based on the Harmonized World Soil Database soil types used by the two soil moisture sources, as well as with a finer-resolution National Geospatial-Intelligence Agency product. Better agreement was found in soil strengths using the finer-resolution soil product. Finally, fairly large differences in soil moisture become muted in the speed calculations even when all factors except soil strength, slope, and vehicle performance are neglected. It is expected that the 0.04 volumetric uncertainty in the final SMAP L3 soil moisture product will have the greatest effect at low vehicle speeds. Field measurements of soil moisture and strength as well as soil type are needed to verify the results.

Corresponding author address: Susan Frankenstein, ERDC CRREL, 72 Lyme Rd., Hanover, NH 03755. E-mail: susan.frankenstein@usace.army.mil

This article is included in the NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research Special Collection.

Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Ahlvin, R. B., and Haley P. W. , 1992: NATO Reference Mobility Model Edition II, NRMM II user’s guide. U.S. Army Waterways Experiment Station Tech. Rep. GL-92-19, 286 pp.

  • Baylot, E. A., Stevens M. T. , Patterson J. A. , Brandon G. M. , and Green J. G. , 2013: Arc-of-instability and combatant command terrain geostatistics and ground vehicle mobility predictions. U.S. Army Corps of Engineers ERDC/GSL Rep. TR-13-3, 208 pp.

  • Bullock, C. D., 1994: Methodology for the development of inference algorithms for worldwide application of interim terrain data to the NATO Reference Mobility Model. U.S. Army Waterways Experiment Station Tech. Rep. GL-94-37, 128 pp.

  • Chen, F., and Coauthors, 1996: Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251–7268, doi:10.1029/95JD02165.

    • Search Google Scholar
    • Export Citation

  • Chung, S. O., Sudduth K. , Motavalli P. , and Kitchen N. , 2013: Relating mobile sensor soil strength to penetrometer cone index. Soil Tillage Res., 129, 9–18, doi:10.1016/j.still.2012.12.004.

    • Search Google Scholar
    • Export Citation

  • Corps of Engineers, 1956: Trafficability of soils: A summary of trafficability studies through 1955. USACE Tech Memo. TM-3-240-14, 62 pp.

  • Das, N., 2013: Soil Moisture Active Passive (SMAP) ancillary data report: Soil attributes, preliminary, v.1. SMAP Science Doc. 044/JPL-D-53058, 13 pp. [Available online at http://smap.jpl.nasa.gov/files/smap2/044_soil_attrib.pdf.]

  • Desilets, D., Zreda M. , and Ferré T. P. A. , 2010: Nature’s neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resour. Res.,46, W11505, doi:10.1029/2009WR008726.

  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grummann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational Mesoscale Eta Model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation

  • Entekhabi, D., Das N. , Njoku E. , Johnson J. , and Shi J. , 2012: Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document L2 & L3 radar/radiometer soil moisture (active/passive) data products. Initial release, v.1, 71 pp. [Available online at http://smap.jpl.nasa.gov/files/smap2/L2&3_SM_AP_InitRel_v11.pdf.]

  • Fatherree, B. H., 2006: The History of Geotechnical Engineering at the Waterways Experiment Station 1932–2000. U.S. Army Corps of Engineers, 362 pp.

  • Frankenstein, S., 2014: User’s manual for FASST (Fast All-season Soil STrength). 42 pp. [Available online at https://webcam.erdc.dren.mil/FASST/.]

  • Frankenstein, S., and Koenig G. G. , 2004a: Fast All-season Soil STrength (FASST). U.S. Army Corps of Engineers ERDC/CRREL Rep. SR-04-1, 107 pp.

  • Frankenstein, S., and Koenig G. G. , 2004b: FASST vegetation models. U.S. Army Corps of Engineers ERDC/CRREL Rep. TR-04-25, 57 pp.

  • Frankenstein, S., and Koenig G. G. , 2008: FASST soil moisture, soil temperature: Original vs. new. U.S. Army Corps of Engineers ERDC/CRREL Rep. TR-08-7, 38 pp.

  • Fulton, J. P., Wells L. G. , Shearer S. A. , and Barnhisel R. I. , 1996: Spatial variation of soil physical properties: A precursor to precision tillage. Proc. ASAE Int. Meeting, Phoenix, AZ, American Society of Agricultural Engineering, Paper 961002.

  • Green, J. G., Stevens M. T. , Joshi A. , and Mason G. , 2014: Mobility suitability of MRAP variants in selected worldwide environments. U.S. Army Corps of Engineers ERDC/GSL Rep. TR-14-25, 117 pp.

  • Haley, P. W., Jurkat M. P. , and Brady P. M. Jr., 1979: NATO Reference Mobility Model, Edition I: Users guide, volume I. TARADCOM R&D Laboratory Tech. Rep. 12503, 389 pp. [Available online at www.dtic.mil/dtic/tr/fulltext/u2/b047979.pdf.]

  • Jurkat, M. P., Nuttall C. J. , and Haley P. W. , 1975a: The U.S. Army Mobility Model (AMM-75). U.S. Army/FRG Army Mobility Symposium Proceedings, U.S. Army Tank Automotive Command, Mobility Systems Laboratory Tech. Rep. 12101, 3–50. [Available online at www.dtic.mil/dtic/tr/fulltext/u2/a021702.pdf.]

  • Jurkat, M. P., Nuttall C. J. , and Haley P. W. , 1975b: The AMC ’74 Mobility Model. U.S. Army Tank Automotive Command, Mobility Systems Laboratory Tech. Rep. 11921 (LL149), 430 pp.

  • Kumar, A., Chen Y. , Sadek A. , and Rahman S. , 2012: Soil cone index in relation to soil texture, moisture content, and bulk density for no-tillage and conventional tillage. Agric. Eng. Int.: CIGR J., 14, 26–37.

    • Search Google Scholar
    • Export Citation

  • Kumar, S. V., and Coauthors, 2006: Land information system: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 1402–1415, doi:10.1016/j.envsoft.2005.07.004.

    • Search Google Scholar
    • Export Citation

  • Kumar, S. V., Peters-Lidard C. D. , Eastman J. L. , and Tao W.-K. , 2008: An integrated high resolution hydrometeorological modeling testbed using LIS and WRF. Environ. Modell. Software, 23, 169–181, doi:10.1016/j.envsoft.2007.05.012.

    • Search Google Scholar
    • Export Citation

  • Margulis, S. A., McLaughlin D. , Entekhabi D. , and Dunne S. , 2002: Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment. Water Resour. Res.,38, 1299, doi:10.1029/2001WR001114.

  • Meyer, M. P., Ehrlich I. R. , Sloss D. , Murphy N. R. , Wismer R. D. , and Czako T. , 1977: International Society for Terrain–Vehicle Systems standards. J. Terramech., 14, 153–182, doi:10.1016/0022-4898(77)90013-1.

    • Search Google Scholar
    • Export Citation

  • Peters-Lidard, C. D., and Coauthors, 2007: High-performance Earth system modeling with NASA/GSFC’s Land Information System. Innovations Syst. Software Eng.,3, 157–165, doi:10.1007/s11334-007-0028-x.

    • Search Google Scholar
    • Export Citation

  • Priddy, J. D., and Willoughby W. E. , 2006: Clarification of vehicle cone index with reference to mean maximum pressure. J. Terramech., 43, 85–96, doi:10.1016/j.jterra.2004.10.001.

    • Search Google Scholar
    • Export Citation

  • Rula, A. A., and Nuttall C. J. Jr., 1971: An Analysis of Ground Mobility Models (ANAMOB). U.S. Army Waterways Experiment Station Tech. Rep. M-71-4, 330 pp.

  • Sahoo, A. K., De Lannoy G. J. M. , Reichle R. H. , and Houser P. R. , 2013: Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA. Adv. Water Resour., 52, 19–33, doi:10.1016/j.advwatres.2012.08.007.

    • Search Google Scholar
    • Export Citation

  • Shoop, S., 1993: Terrain characterization for trafficability. U.S. Army CRREL Rep. 93-6, 30 pp.

  • Stevens, M. T., Towne B. W. , Mason G. L. , Priddy J. D. , Osorio J. E. , and Barela C. A. , 2013: Procedures for one-pass vehicle cone index (VCI1) determination for acquisition support. U.S. Army Corps of Engineers ERDC/GSL Rep. SR-13-2, 59 pp.

  • Sullivan, P. M., Bullock C. D. , Renfroe N. A. , Albert M. R. , Koenig G. G. , and O’Neill K. , 1997: Soil Moisture Strength Prediction Model version II (SMSP II). U.S. Army Waterways Experiment Station Tech. Rep. GL-97-15, 117 pp.

  • Trewartha, G. T., 1968: An Introduction to Climate. McGraw-Hill, 408 pp.

  • U.S. Army, 1973: The AMC ’71 Mobility Model. U.S. Army Tank Automotive Command, Mobility Systems Laboratory Tech. Rep. 11789 (LL 143), 70 pp.

  • Wang, L., and Qu J. J. , 2009: Satellite remote sensing applications for surface soil moisture monitoring: A review. Front. Earth Sci. China, 3, 237–247, doi:10.1007/s11707-009-0023-7.

    • Search Google Scholar
    • Export Citation

  • Yi, Y., Kimball J. S. , Jones L. A. , Reichle R. H. , and McDonald K. C. , 2011: Evaluation of MERRA land surface estimates in preparation for the Soil Moisture Active Passive mission. J. Climate, 24, 3797–3816, doi:10.1175/2011JCLI4034.1.

    • Search Google Scholar
    • Export Citation

  • Zreda, M., Desilets D. , Ferré T. P. A. , and Scott R. L. , 2008: Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophys. Res. Lett.,35, L21402, doi:10.1029/2008GL035655.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1300 364 30
PDF Downloads 498 167 20