Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed (original) (raw)
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Advances in Space Research, 1993
Soil moisture estimates from a distributed hydrological model and two microwave remote sensors (Push Broom Microwave Radiometer and Synthetic Aperture Radar) were compared with the ground measurements collected during the MAC-HYDRO'90 experiment over a 7.4-km2 watershed in central Pennsylvania. Various information, including rainfall, soil properties, land cover, topography and remote sensing imagery, were integrated and analyzed using an image integration technique. It is found that the hydrological model and both microwave sensors successfully pick up the temporal variation of soil moisture. Results also indicate the spatial soil moisture pattern can be remotely sensed within reasonable accuracy using existing algorithms. Watershed averaged soil moisture estimates from the hydrological model are wetter than remotely sensed data. It is difficult to conclude which instrument yield better performance for the studied case. The choice will be based on the intended applications and information that is available.
Recent advances in radar remote sensing techniques illustrate the potential for monitoring soil moisture conditions at spatial and temporal scales required for regional and local modeling efforts. This research examined the feasibility of producing accurate and spatially distributed estimates of soil moisture using a time series of ERS-2 radar images for a tallgrass prairie ecosystem in northeast Kansas. Methods used included field data collection of soil moisture, digital image interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS-2) imagery, and environmental modeling in a raster geographic information system (GIS) and image processing environment. Critical to this study was determining the scattering behavior of overlying vegetation, or the contribution of vegetation backscatter (s o veg) to the total backscatter coefficient (s o total), which was simulated using a modified water cloud model. By removing s o veg from s o total , the amount of backscatter contributed by the soil surface (s o soil) was isolated and the linear relationship between s o soil and volumetric soil moisture determined. Single-date correlations averaged r = 0.62 and r = 0.67 for a burned and unburned watershed, respectively, within the study area. While previous studies have questioned the sensitivity of C-band radars to near-surface soil moisture conditions, these results show that ERS-2 data may be capable of monitoring soil moisture conditions over even extremely dense natural grassland vegetation.
Remote Sensing of Environment, 2002
The statistical structure of soil moisture fields was examined using large-scale images (40 Â 250 km) obtained during the Southern Great Plains 1997 (SGP'97) hydrology experiment. In particular, empirical scaling analysis was conducted to investigate the linkages between the spatial and temporal variability of soil moisture, and landscape characteristics including terrain, soils, and vegetation. The results show that the soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observation and hydrometeorological forcing. A break in statistical symmetry (multiscaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the aand b-scale ranges, respectively. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay content. The multifractal behavior is associated with the temporal evolution of drying and wetting regimes, reflecting the nonlinear character of soil moisture dynamics. Finally, Empirical Orthogonal Function (EOF) analysis was conducted to explain the relationship between the spatial structure of estimated soil moisture and that of ancillary data including topography, soil texture, and vegetation cover. Topography appears to dominate the spatial structure of soil moisture only during and immediately after rainfall. In interstorm periods, the spatial evolution of soil moisture is closely associated with the spatial variability of soil hydraulic properties when the soil is above field capacity, while vegetation dominates the evolution of soil moisture fields through evapotranspiration as the landscape dries down.
Advances in Geosciences, 2005
Hydrologic processes, such as runoff production or evapotranspiration, largely depend on the variation of soil moisture and its spatial pattern. The interaction of electromagnetic waves with the land surface can be dependant on the water content of the uppermost soil layer. Especially in the microwave domain of the electromagnetic spectrum, this is the case. New sensors as e.g. ENVISAT ASAR, allow for frequent, synoptically and homogeneous image acquisitions over larger areas. Parameter inversion models are therefore developed to derive bio-and geophysical parameters from the image products. The paper presents a soil moisture inversion model for ENVISAT ASAR data for local and regional scale applications. The model is validated against in situ soil moisture measurements. The various sources of uncertainties, being related to the inversion process are assessed and quantified.
Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale ( f 10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches. D
Remote Sensing of Environment, 2008
This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m 2 sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5 × 5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a sitespecific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy.
Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92
Remote Sensing of Environment, 1995
Wshita'92 was a large-scale study of remote sensing and hydrology conducted on the Little Washita watershed in southwest Oklahoma. Data collection during the experiment included passive microwave observations using an L-band electronically scanned thinned array radiometer (ESTAR) and surface soil moisture observations at sites distributed over the area. Data were collected on 8 days over a 9-day period in June 1992. The watershed was saturated with a great deal of standing water at the outset of the study. During the experiment there was no rainfall and surface soil moisture observations exhibited a drydown pattern over the period. Significant variations in the level and rate of change in surface soil moisture were noted over areas dominated by different soil textures. ESTAR data were processed to produce brightness temperature maps of a 740 sq. km. area on each of the 8 days. These data exhibited significant spatial and temporal patterns. Spatial patterns were clearly associated with soil textures and temporal patterns with drainage and evaporative processes. Relationships between the groundsampled soil moisture and the brightness temperatures were consistent with previous results. Spatial averaging of both variables was analyzed to study scaling of soil moisture over a mixed landscape. Results of these studies showed that a strong correlation is retained at these scales, suggesting that mapping surface moisture for large footprints may provide important information for regional studies.
Continental-Scale Evaluation of Remotely Sensed Soil Moisture Products
IEEE Geoscience and Remote Sensing Letters, 2007
A new data assimilation-based approach for the continental-scale evaluation of remotely sensed surface soil moisture retrievals is applied to four separate soil moisture products over the contiguous U.S. The approach is based on quantifying the ability of a given soil moisture product to correct for known rainfall errors when sequentially assimilated into a simple water balance model. Analysis results provide new insight into the continental-scale performance of surface soil moisture retrieval algorithms based on satellite passive microwave, scatterometer, and thermal remote sensing observations.