1. ERS186: Environmental Remote Sensing Lecture 9: Soils

2. Overview Applications –Soil Science Physical Principles –Reflectance (specular and diffuse scattering) –Absorption bands –Dielectric constants Sensors –RADAR –Thermal –Hyperspectral

3. Definitions Soil: the weathered material between the surface of the Earth and the bedrock. Soils are composed of different composition and sizes of particles of inorganic mineral and organic matter Particles are about 50% of the soil volume, pores occupy the rest of the space. Pores can contain air or water (or ice!) Soils have vertical zonation (soil horizons) created by biological, chemical and physical processes

4. Soil Horizons O horizon: > 20% partially decayed organic matter (“humus”) A horizon: zone of eluviation/leaching; water leaches many minerals; often pale and sandy E horizon: mineral layer with loss of some combination of silicate clay, iron, aluminum B horizon: zone of illuviation; materials leached from other zones end up here; often lots of clay and iron oxides C horizon: weathered parent material; mostly mineral W horizon: water layer; Wf if permenantly frozen R horizon: bedrock

5. Soil Grain Size

6. Different size particles play different roles in soil: –Sand (0.05 to 2.0 mm): large air spaces, rapid drainage of water –Silt (0.002 to 0.05 mm): enhance movement and retention of soil capillary water –Clay (< 0.002 mm): enhance movement and retention of soil capillary water; carry electrical charges which hold ions of dissolved minerals (e.g. potassium and calcium)

7. Soil Texture Proportion of sand, silt and clay in a soil (or horizon), usually calculated as % weight for each type of particle. These %s can be broken up into different soil-texture classes.

8. Soil Taxonomy Similar to biological taxonomy -- dichotomous keys based on soil profiles, soil color, soil- texture class, moisture content, bulk density, porosity, and chemistry are used to ID different types of soils.

9. The Question What are the important properties of a soil in an RS image? –Soil texture –Soil moisture content –Organic matter content –Mineral contents, including iron-oxide and carbonates –Surface roughness

10. Exposed Soil Radiance L t = L p + L s + L v L t = at-sensor radiance of a pixel of exposed soil L p = atmospheric path radiance, usually needs to be removed through atmospheric correction L s = radiance reflected off the air-soil interface (boundary layer) –Soil organic matter and soil moisture content significantly impact L s ; typically characterize the O horizon, the A horizon (if no O), or lower levels if A and O are nonexistant. L v = volume scattering, EMR which penetrates a few mm to cm. –penetrates approximate 1/2 the wavelength –Function of the wavelength (so RADAR may penetrate farther), type and amount of organic/inorganic constituents, shape and density of minerals, degree of mineral compaction, and the amount of soil moisture present.

11. Exposed Soil Radiance

13. Basic Dry Soil Spectrum Key characteristic of soil spectrum: increasing reflectance with increasing wavelength through the visible, near and mid infrared portions of the spectrum

14. Soil Moisture Water is a strong absorber, so soils with more moisture will be darker over most of the VNIR and SWIR portions of the spectrum than drier soils. The depths of the water absorption bands at 1.4, 1.9 and 2.7  m can be used to determine soil moisture.

15. Soil Moisture and Texture Since clayey soil holds water more tightly than sandy soil, the water absorption features will be more prominent in clayey soils given the same amount of time since the last precipitation or watering. AVIRIS can be useful for quantifying these absorption features.

16. Soil Moisture from RADAR Different materials conduct electricity better than others (different complex dielectric constant). Higher dielectric constants (more moisture) yields higher RADAR backscatter. Melfort, Saskatchewan, Canada, ERS-1: Rainfall was incident on the lower half of the image but not on the upper half.

17. Soil Moisture from Thermal Sensors Water has a higher thermal capacity than soil and rock. Moist soils will change in temperature more slowly than dry soils.

18. Soil Moisture from Thermal Sensors Daedalus thermal image (night time). If we had a daytime image to compare it to, we could see the amount of change in temperature and make inferences on the soil moisture content (less change = more moisture).

19. Identifying Clayey Soils Soils with a large amount of clay exhibit hydroxyl absorption bands at 1.4 and 2.2  m. 2.2  m is more useful since it doesn’t overlap the water absorption feature.

20. Soil Organic Matter Organic matter is a strong absorber of EMR, so more organic matter leads to darker soils (lower reflectance curves).

21. Soil Organic Matter Organic matter content in the Santa Monica mountains mapped using AVIRIS (Palacios-Orueta et al. 1999).

22. Iron Oxide Recall that iron oxide causes a charge transfer absorption in the UV, blue and green wavelengths, and a crystal field absorption in the NIR (850 to 900 nm). Also, scattering in the red is higher than soils without iron oxide, leading to a red color.

23. Iron Oxide Iron content in the Santa Monica mountains mapped using AVIRIS (Palacios-Orueta et al. 1999).

24. Surface Roughness If a surface is smooth (particle size is small relative to wavelength), we expect a lot of specular reflection. –Only sensors positioned at the correct angle will see the bright reflectance. All other angles will see a dark surface (including all RADAR imagery). –Smooth surfaces are clayey or silty and often contain strong absorbers such as moisture, organic content, and iron oxide. A rough surface generates a lot of diffuse reflection. –Conversely, well drained sands are often very bright, regardless of angle.

25. Surface Roughness C/X-SAR (C-band) image of Oxford County, Ontario, Canada: Conservation tillage (the retention of crop residue on the soil surface) can diminish soil erosion. Conventional tillage produces a much rougher surface, and therefore brighter backscatter. The goal of this study was to determine if tillage practices could be identified using SAR imagery.