Remote Sensing of Soil Moisture Lecture 7

What is soil moisture? Soil moisture is the water that is held in the spaces between soil particles. Surface soil moisture is the water that is in the upper 10 cm of soil, whereas root zone soil moisture is the water that is available to plants, which is generally considered to be in the upper 200 cm of soil. Is defined as the ratio of liquid water content to the soil in percentage of volume or weight, is an in heritage and memory of previous precipitations. –Gravimetric water content on mass (weight) basis: ratio of the mass of liquid phase to solid soil mass –Volumetric water content on volume basis: ratio of the liquid phase in soil to total volume of the soil. –Degree of saturation ratio of water volume over total soil pore volume Commonly this is used as a measure of the amount of water in the vadose zone (above the water table). Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface

How to get soil moisture (in situ) Directly in the laboratory, it is measured gravimetrically; by weighing the moist volume of soil, drying it, and then weighing it again. Indirectly: Time Domain Reflectometry (TDR), neutron probe, capacitance probe, etc. these methods must be calibrated against gravimetric measurements. Global soil moisture data bank – USA SCAN –

Soil moisture: Ratio of liquid water content to soil in volume or weight

An NRCS soil scientist placing a piezometer in the ground to measure soil moisture

Measuring soil moisture content using cosmic-ray neutrons Inferred from measurements of low- energy cosmic-ray neutrons that are generated within soil, moderated mainly by hydrogen atoms, and diffused back to the atmosphere. Non-invasive, spatial scale of ~ 700 m horizontal, depth of decimeters (Zreda 2008)

Thermal infrared techniques –Through assimilation/modeling to get root-zone soil moisture Microwave –SAR –Passive –Top 2-5 cm, shallower than 10cm, could be modeling to root-zone soil moisture Optical (visible/near infrared) –Using solar radiation as a direct energy source, is a passive remote sensing method covering visible and near infrared –Indirectly to root-zone soil moisture Remote sensing soil moisture

Passive microwave remote sensing Passive microwave remotely sensed data providing estimates of soil moisture with good temporal resolution on a daily basis and on a regional scale (~10 km) Vegetation cover, soil temperature, snow cover, topography and surface roughness play a significant role in the microwave emission from the surface. Other parameters: soil texture, bulk soil density, and atmospheric effects have a smaller influence. Many approaches to retrieve soil moisture from microwave radiometric measurements –Statistical approaches –Forward model inversion –Neural networks –Data assimilation

Advantages of Microwave RS Transparent atmosphere, all weather coverage in decimeter range of EMR Vegetation semitransparent Microwave measurement strongly dependent on dielectric properties of soil water Not dependent on solar illumination

Basis for Microwave Remote Sensing of Soil Moisture Basis for microwave remote sensing of soil moisture is contrast in dielectric constant of water (80) and dry soil (<5), causing emissivity contrast of 0.4 for water and 0.95 for dry land (Schmugge 2002) Research concludes surface layer sm can be determined to about ¼ wavelength, i.e. 0-5 cm layer using microwave λ = 21 cm Longer λ better for increased depth, less noise Jensen, 2007

Soil moisture in pasture λ = 21 cm responded Soil moisture  λ = 21 cm  Schmugge 2002

Emissivity and Soil Moisture Brightness temperature related to emissivity for 0 to 5 cm surface layer ε M is soil surface emissivity, T M is soil surface temperature (1-ε M )T sky is ~ 2K, therefore ε M ~ T B /T M If T M estimated independently, ε M can be determined Typical range for ε M is 0.9 for dry soil to 0.6 for smooth wet soil T B = ε M T M + (1-ε M )T sky Schmugge 2002

Factors affecting accuracy Vegetation cover –Most important, dense vegetation (corn, forest) can obscure soil surface –Greater effect at shorter λ Soil properties –Density and texture Surface roughness –Commonly 10 to 20% reduction in response range Density and roughness relatively constant

Radar Remote Sensing— Soil Moisture HYDROS ( –Back-up ESSP mission for global soil moisture. L-band radiometer. L-band radar. –Died mission Courtesy: Tom Jackson, USDA SGP’97 Radar Pol: VV, HH & HV Res – 3 and 10 km Radiometer Pol: H, V Res =40 km, dT= 0.64º K

SAR for surface soil moisture Can map soil moisture at high resolution over large areas Affected by surface roughness, vegetation cover, and incidence angle

Linear relation between soil moisture and radar signal (backscatter) Zribi et al. 2005

Radar signal has an inverse relation with incidence angle, so soil moisture has a relation between both radar signal and incidence angle. Zribi et al. 2005

An algorithm for merging SMAP radiometer and radar data for high resolution soil moisture retrieval - Das, N., Entekhabi, D., Njoku, E., IEEE- Transactions on Geoscience and Remote Sensing, In press. Download FileDownload File

Total Upwelling Radiance (L t ) Recorded by a Remote Sensing System over Exposed Soil is a Function of Electromagnetic Energy from Several Sources Jensen, 2007 Optical Remote Sensing Soil Moisture

Soil Properties Spectral reflectance function of –Soil texture, moisture, salinity, surface roughness, organic matter, iron oxide Jensen, 2007

Radiant energy may be reflected from the surface of the dry soil, or it penetrates into the soil particles, where it may be absorbed or scattered. Total reflectance from the dry soil is a function of specular reflectance and the internal volume reflectance. As soil moisture increases, each soil particle may be encapsulated with a thin membrane of capillary water. The interstitial spaces may also fill with water. The greater the amount of water in the soil, the greater the absorption of incident energy and the lower the soil reflectance. Reflectance from Dry versus Wet Soils Jensen, 2007

Reflectance from Moist Sand and Clay Soils Higher moisture content in (a) sandy soil, and (b) clayey soil results in decreased reflectance throughout the visible and near-infrared region, especially in the water- absorption bands at 1.4, 1.9, and 2.7 mm. Jensen, 2007

Estimating SM with vegetation Surface soil moisture correlation with sub-surface sm decreases with depth

Soil Climate Analysis Network (SCAN) Sites

Soil Climate Analysis Network SCAN soil moisture (hourly) at –5cm, 10cm, 20cm, 50cm, and 100cm –3 sites, humid grassland, semi-arid grassland, semi-arid shrubland Terra-MODIS 250 m 8-day NDVI Analysis based on both raw time series and deseasonalized time series

Results (1) The deseasonalized time series results in consistent and significant correlation (0.46–0.55) between NDVI and root- zone soil moisture at the three sites; (2) Vegetation (NDVI) at the humid site needs longer time (10 days) to respond to soil moisture change than that at the semi-arid sites (5 days or less); (3) The time-series of root-zone soil moisture estimated by a linear regression model based on deseasonalized time series accounts for 42–71% of the observed soil moisture variations for the three sites; and (4) In the semi-arid region, root-zone soil moisture of shrub- vegetated area can be better estimated using NDVI than that of grass-vegetated area.

Correlation coefficient between soil moisture and NDVI versus time lag of NDVI during growing season.

Humid grassland Estimated versus observed soil moisture at the 10 cm, 20 cm, and 50 cm depths during May to October of 2000– 2003 at the Prairie View site (TX) using delta method (left panel) and raw method (right panel).

Semi-arid grassland Estimated versus observed soil moisture at 20 cm depths during May to September of 2000–2003 at the Adams Ranch site (NM) using delta method (left panel) and raw method (right panel).

Semi-arid shrub Estimated versus observed soil moisture at the 20 cm depths during May to September of 2000–2003 at the Walnut Gulch site (AZ) using delta method (left panel) and raw method (right panel).

Mapping Time Series Root Zone Soil Moisture in a Semi-Arid Region Using Satellite Images

Land Cover