Large Scale High Fidelity Remote Soil Property Variability Principal Investigators: Mike Tischler, TEC Terry Sobecki, CRREL

Large Scale High Fidelity Remote Soil Property Variability WP # Status: Cont/Rev/New Purpose: Better model land-human interactions in COIN/ Stability Ops by characterizing spatial variability of soil properties influencing agricultural operations, water resource allocation, and soil loss through desertification and erosion. Results: Relate multi-source remote geophysical signatures to landscape processes and soil properties Filter complex sensor signatures to accentuate and discriminate for soil inferencing Capture wide-area low frequency changes in soil morphology Create continuous surface of soil physical properties Fusion of geophysical sensor measurements with DEM-based landscape characterization Payoff: Methods to characterize wide-area soil property variability without requiring direct sampling More informed COIN/Stability Ops decisions regarding land surface use and hydrology More accurate battlefield analysis from TDAs driven by soil properties Increased ability to effectively evaluate landscapes for tactical decisions (LZ’s, CCM, Sensor placement/performance, trafficability) Milestones FY11 FY12 FY13 FY14 Discriminate clay species/texture via γ-Ray   Isolate UHF subsurface backscatter component Decouple soil water/texture contributions to UFH signal Map soil group extents using terrain landform characterization Army Image courtesy Fugro Airborne Surveys Schedule and Cost 3 5 3 5 3 5 3 6 Total 2

Large Scale High Fidelity Remote Soil Property Variability Examples: Most comprehensive global soil dataset Harmonized Soil World Database (2009), Soil Texture attribute Derived (not measured) from 1:1,000,000 FAO using PTFs

Large Scale High Fidelity Remote Soil Property Variability Overall goal is to derive soil texture from available remotely sensed data, mostly DEM driven Initial effort will be to use supervised classification to create heuristic model of soil texture family (coarse, medium, fine – FAO/Zobler) Secondary effort will be to classify into 13 USDA Soil Texture Classes Two AOI’s SW Arizona SW Afghanistan Soil Texture Source Data STATSGO (Miller and White, 1998) 1981 French-made soil map of SW Afghanistan Excellent quality (spatially accurate, rich data source) In need of translation GSLs Afghanistan Soil Database (?) Miller, D.A. and R.A. White, 1998: A Conterminous United States Multi-Layer Soil Characteristics Data Set for Regional Climate and Hydrology Modeling. Earth Interactions, 2. [Available on-line at http://EarthInteractions.org] Zobler, L. 1986. A World Soil File for Global Climate Modelling. NASA Technical Memorandum 87802. NASA Goddard Institute for Space Studies, New York, New York, U.S.A.

Landform Characterization/Segmentation Relief is a fundamental soil forming property which includes slope position & landform element Slope controls water movement, which controls morphology Research will determine degree of statistical correlation between slope position, landform element, and soil texture classes at sites 4TB of terrain data; 30m Globally (courtesy of DIA) DEM (SRTM) Slope Aspect TPI (Topographic Position Index) TRI (Terrain Ruggedness Index) DEM and DEM derivatives can be used to segment the landscape into areas of homogeneity, which can be correlated to soil texture

Topographic Position Index (TPI) TPI compares elevation at each cell to mean elevation in a surrounding neighborhood Weiss, 2001. Topographic Position and Landforms Analysis.

Slope Position Classification Single TPI can be used to classify Slope Position (Jenness, 2006) Valley Lower Slope Flat Slope Middle Slope Upper Slope Ridge Jenness, J. 2006. Topographic Position Index extension for ArcView 3.x, v1.2. Jenness Enterprises

TPI When two scales of neighborhood are used to create two TPIs, landscape can be classified into landforms Weiss, 2001. Topographic Position and Landforms Analysis.

Topographic Wetness Index TWI – Topographic (Compound) Wetness Index Developed for TOPMODEL in ‘79 (Beven and Kirkby) Relationship of upslope contributing drainage area to slope a = upslope area draining through cell tan(b) = slope Studies show that TWI is correlated with depth to groundwater, soil pH, veg. species richness, and Soil Organic Matter Calculated using D-Inf flow direction (Tarboton, 1997), which is shown to have significantly higher correlation than D8 to Soil Organic Matter (Pei, Qin, Zhu, et. al., 2010). Tao Pei, Cheng-Zhi Qin, A-Xing Zhu, Lin Yang, Ming Luo, Baolin Li, Chenghu Zhou, Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods, Ecological Indicators, Volume 10, Issue 3, May 2010, Pages 610-619. Tarboton, D. A New Method for the determination of flow directions and upslope areas in grid digital elevation models. WRR v.33 No. 2, 1997. Pages 309-319

Classification Source Data Dominant Tex DEM (30m) Slope (30m) Parent Material Albedo TPI – small neighborhood TPI – large neighborhood TWI ~100km * ~100km

1981 French-made Soil Map

Additional Research ASTER soil moisture (Mira, Valor, Caselles, et al., 2010) In lab at SM < field capacity, emissivity exhibits variations at 8-9 microns Greatest variation in sandy soil ASTER soil texture - build on Apan et al. (2002) and include TIR bands of ASTER Spatial Similarity applied to soil typical location N-dimensional data analysis of site characteristics Possible to extrapolate from known areas into unknown areas Strength of correlation between TPI (slope position, landform element) and Soil Texture How closely are slope position and soil texture linked TPI computed at several scales, compare with soil texture classes Look for separability between soil texture classes TPI is scale dependant, must be matched with texture of similar scale Mira, M.; Valor, E.; Caselles, V.; Rubio, E.; Coll, C.; Galve, J.M.; Niclos, R.; Sanchez, J.M.; Boluda, R. 2010. Soil Moisture Effect on Thermal Infrared (8–13um) Emissivity," Geoscience and Remote Sensing, IEEE Transactions on , vol.48, no.5, pp.2251-2260. Apan, A., Kelly, R., Jensen, T., Butler, D., Strong, W., and Basnet, B. 2002. Spectral Discrimination and Separability Analysis of Agricultural Crops and Soil Attributes using ASTER imagery. 11th ARSPC. Brisbane, Australia.

Spatial Similarity Approach asks “is unknown location most like sandy soil sites, loamy soil sites, or fine soil sites?” At each cell in source area, value is measured for each n-dimensions (slope, aspect, ASTER band, TPI, etc.) for a particular soil texture category Outside source area, value distance is measured for each n-dimension and compared with source distribution for each soil texture category to determine spatial similarity Works well with ancillary datasets that are continuous (e.g., elevation), but not categorical (e.g., landcover classes) Result is a similarity surface for each input class; If source classification is (sandy, loamy, fine), then 3 surfaces will be created visualizing the spatial similarity to each class.

Large Scale High Fidelity Remote Soil Property Variability Gamma (γ) Ray Spectroscopy γ-Ray spectroscopy is a popular geophysical method in many fields, particularly mining. γ-Ray surveys measure percentages of Potassium, Thorium, and Uranium – the 3 most abundant radioactive elements in the earths surface Canadian and Australian governments have leveraged γ-Ray surveys for near surface mapping extensively, to the point of operational survey programs (Canada) Many private companies offer airborne γ-Ray surveys indicating that this is a mature technology Applications of γ-Ray survey to military challenges or soil property mapping are very few, though the potential certainly exists

Images courtesy of Fugro Airborne Surveys

Large Scale High Fidelity Remote Soil Property Variability Radar propagation velocity depends on soil moisture Radar Attenuation depends on both soil moisture and soil texture Measuring both of these properties over similar soil will yield conclusions about the soil texture Koh and Wakeley presented related work at Army Science Conference - 2010 (Steve Arcone and Gary Koh will be the radar experts investigating this)

Effect of SM and texture of attenuation rates Koh, G. and Wakeley, L. 2010. Effect of Moisture on Radar Attenuation in Desert Soils http://www.armyscienceconference.com/manuscripts/O/OO-002.pdf

Large Scale High Fidelity Remote Soil Property Variability Testable hypotheses UHF radar will have primarily subsurface backscatter over areas where surface roughness is less than wavelength of the radar Influence of soil moisture and soil texture on UHF signal can be decoupled UHF radar subsurface backscatter component varies spatially with soil texture Spatial variability in clay species (Illite, Kaolinite, Montmorillonite) are manifested through K-geochemistry, and can be detected by gamma ray spectroscopy Terrain based landform characterization & classification are correlated with soil texture groupings and spatial extents Soil texture spatial variability can be determined by investigating spatial soil water energy characteristics (e.g., 15-bar water content is directly proportional to clay content)