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

2. 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

3. 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

4. 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
Zobler, L A World Soil File for Global Climate Modelling. NASA Technical Memorandum NASA Goddard Institute for Space Studies, New York, New York, U.S.A.

5. 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

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

7. 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 Topographic Position Index extension for ArcView 3.x, v1.2. Jenness Enterprises

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

9. 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
Tarboton, D. A New Method for the determination of flow directions and upslope areas in grid digital elevation models. WRR v.33 No. 2, Pages

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

11. 1981 French-made Soil Map

12. 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 Soil Moisture Effect on Thermal Infrared (8–13um) Emissivity," Geoscience and Remote Sensing, IEEE Transactions on , vol.48, no.5, pp
Apan, A., Kelly, R., Jensen, T., Butler, D., Strong, W., and Basnet, B Spectral Discrimination and Separability Analysis of Agricultural Crops and Soil Attributes using ASTER imagery. 11th ARSPC. Brisbane, Australia.

13. 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.

14. 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

15. Images courtesy of Fugro Airborne Surveys

16. 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
(Steve Arcone and Gary Koh will be the radar experts investigating this)

17. Effect of SM and texture of attenuation rates
Koh, G. and Wakeley, L Effect of Moisture on Radar Attenuation in Desert Soils

18. 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)