Remote Sensing of Soil
Moisture Content
Emery Three Irons
Gphy 426 Remote Sensing, Fall 2014
Photo: E. Three Irons
Significance of Remote Sensing in Detecting Soil Water
Content
 Estimates in crop production
 Contributes in prediction models
 Providing global maps of soil moisture
The Soil Moisture and Ocean Salinity (SMOS)
Overview
 Retrieval of soil moisture content
 Factor for weather and climate models
 Provide information on vegetation water content root zone
moisture content
 Using dual polarized L-band radiometer using aperture synthesis
(two-dimensional [2-D] interferometer)
The SMOS instantaneous field of view (FOV). Idea is a dual-polarized multi incidence angle
acquisitions.
Every 6 days, FOV is acquired.
Source: Yann H. Kerr et al./ IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 39
(2001) 1729-1735
Methods
 L-band signal (1.4 GHz) 2-D interferometric radiometer that is Yshaped with three 4.5 m arms
 Angular information allows retrieving soil moisture and
vegetation water content
 High temporal Sampling
 Spatial Resolution
 50 – 100 km climatic models
 10 – 20 km hydrology
 Obtaining surface temperature by means of thermal infrared
instruments
 Measurement performances are 30 km to more than 50 km for
ground resolution
Shows retrieval accuracy as a function of distance to the swath center
Source: Yann H. Kerr et al./ IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE
SENSING 39 (2001) 1729-1735
Results
 Soil Moisture obtained assimilation to assume root zone soil
moisture
 Incorporate into weather forecast models
Triangle method for obtaining surface soil water
content
Multispectral scanner image taken from NASA’s C-130 aircraft & used as a UTM map
Source Gillies R. R. et al. / International Journal of Remote Sensing, 18:15, 3145-3166
Overview
 Using Inversion Method to compare soil water content and surface
energy fluxes.
 Use to compute fractional vegetation cover and surface soil water
content.
 Measurements from surface radiant temperature and obtained
(NDVI).
Methods
 Measured and Obtained energy fluxes
 Field Programs, MONSOON ’90 and FIFE 1987 and
1989.
 Data acquired from ground and aircraft platforms.
 Remote Measurements from NS001 multispectral scanner on
NASA’s C-130 aircraft.
 MODTRAN used to calculate at surface values of reflectance and
radiant temperature.
 Reflectance used to calculate (NDVI).
 Inversion Algorithms
Scatter plots of NDVI versus surface radiant temperature (To )
Outline of pixel envelopes with isopleths of Mo derived from the simulations
Source: R. R. et al. / International Journal of Remote Sensing, 18:15, 3151
corresponding to the scatter plots
Source: R. R. et al. / International Journal of Remote Sensing, 18:15, 3154
Results
 Differences between the measured, stretched, and unstretched do
not overlap, one closest to 0 would be better.
 Stretching reduces the variability
 Surface moisture availability controlled by SVAT model modified
by stretching.
Latent heat ¯ uxes derived (stretching applied ) versus measured for FIFE & MONSOON
Source: R. R. et al. / International Journal of Remote Sensing, 18:15, 3160
Thermal Approach for Surface Water Content
Overview
 Thermal intertia method to retrieve surface soil water content.
 Remote sensing images used to obtain land surface temperature.
 Soil water content obtained by measuring variation of surface
temperature over time.
 Soil sample collection
Aerial View of Maganoce and location in Italy
Source: Maltese, A. et. al./ Hydrological Sciences Journal, 58 (5), 1147
Methods
 Georeferencing of remote sensing data using NERC’s Azgcorr
(release 5) software.
 Calibration of solar spectrum range band in spectral reflectance.
 Use of empirical line method to measure the spectral reflectance
simultaneously of the passing sensor above.
 Brightness temperature measured during day and night.
 Application of thermal inertia modelling.
Remote sensing images acquired by the ATM sensor on board the Dornier 228 101 (D-CALM)
airborne platform
Source: Maltese, A. et. al./ Hydrological Sciences Journal, 58 (5), 1147
Results
 Model was set up to retrieve the spatial distribution of soil water
content
 Compared the remote sensing-derived thermal inertia, PRS, and
the inertia P modelled as a function of the actual surface soil
water content.
 Methods used work on bare soil.
PRS model: thermal diffution eq. + boundary cond.
Pθ model: Ma and
Xue
Flow chart of the iterative model.
Source: Maltese, A. et. al./ Hydrological Sciences Journal, 58 (5), 1155
Influence of Surface Soil Moisture on Reflectance
Overview
 Effects of surface soil moisture and soil organic carbon.
 Influence of SSM and SOC on retrieval of accurate reflectance
measurements of surface soil roughness.
 Use of five plots to take reflectance measurements.
Methods
 Site location Basel, Switzerland.
 Soil tests of soil structure stability, bulk density and soil
moisture.
 Measurements of Soil Organic Carbon.
 Modelling soil surface roughness.
 Collection of hyperspectral reflectance by USB2000
spectroradiometer A frame device.
Five soil plots of Digital surface models
Source: Croft, H. et. al./ European Journal of Soil Science, July 2014, 608
Results





Absorption features have no variation on reflectance spectra.
Differences of baseline reflectance on all wavelengths.
Relationship of SSM, SOC and SSR on reflectance properties.
Problems distinguishing between SSR and SSM.
More tests to check accuracy.
Regression Values for reflectance by SSR, SSM and SOC
Source: Croft, H. et. al./ European Journal of Soil Science, July 2014, 609
References
Croft, H., Anderson, K., & Kuhn, N. J. (2014). Evaluating the influence of surface soil moisture
and soil surface roughness on optical directional reflectance factors. European Journal of Soil
Science.
Gillies, R. R., Kustas, W. P., & Humes, K. S. (1997). A verification of the'triangle'method for
obtaining surface soil water content and energy fluxes from remote measurements of the
Normalized Difference Vegetation Index (NDVI) and surface e. International Journal of Remote
Sensing, 18(15), 3145-3166.
Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Martinuzzi, J., Font, J., & Berger, M. (2001). Soil
moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission.
Geoscience and Remote Sensing, IEEE Transactions on, 39(8), 1729-1735.
Maltese, A., Bates, P. D., Capodici, F., Cannarozzo, M., Ciraolo, G., & La Loggia, G. (2013).
Critical analysis of thermal inertia approaches for surface soil water content retrieval.
Hydrological Sciences Journal, 58(5), 1144-1161.