Depending on the topography and soil characteristics of an area, soil moisture, an important factor in crop productivity, can be quite variable over the land surface. Thus, a method for determination of soil moisture without the necessity for exhaustive manual measurements would be beneficial for characterizing soil moisture within a given region or field. In this study, soil surface reflectance data in the visible and near−infrared regions were analyzed in conjunction with surface moisture data in a field environment to determine the nature of the relationship between the two, and to identify potential methods for estimation of soil moisture from remotely sensed data in these wavelengths. Results indicate that it is feasible to estimate surface (0 to 7.6 cm) soil moisture from visible and near−infrared reflectance, although estimating moisture regimes rather than precise water content is perhaps more likely. Furthermore, an exponential model was appropriate to describe soil moisture from spectral reflectance data. In particular, the visible region of the electromagnetic spectrum works well with such a model. A partial least squares analysis with improved R 2 values over the single−band models indicated that mulitspectral data may add more useful information about soil moisture as compared to single−band data. The results also suggested that the performance of reflectance models for moisture estimation is a function of soil types; the estimation results were better for the lighter of the two soils in this study.
Abstract. Subsurface tile drainage systems are widely used in agricultural watersheds in the Midwestern US and enable the Midwest area to become highly productive agricultural lands, but can also create environmental problems, for example nitrate-N contamination associated with drainage waters. The Soil and Water Assessment Tool (SWAT) has been used to model watersheds with tile drainage. SWAT2012 revisions 615 and 645 provide new tile drainage routines. However, few studies have used these revisions to study tile drainage impacts at both field and watershed scales. Moreover, SWAT2012 revision 645 improved the soil moisture based curve number calculation method, which has not been fully tested. This study used long-term (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) field site and river station data from the Little Vermilion River (LVR) watershed to evaluate performance of tile drainage routines in SWAT2009 revision 528 (the old routine) and SWAT2012 revisions 615 and 645 (the new routine). Both the old and new routines provided reasonable but unsatisfactory (NSE < 0.5) uncalibrated flow and nitrate loss results for a mildly sloped watershed with low runoff. The calibrated monthly tile flow, surface flow, nitrate-N in tile and surface flow, sediment and annual corn and soybean yield results from SWAT with the old and new tile drainage routines were compared with observed values. Generally, the new routine provided acceptable simulated tile flow (NSE = 0.48-0.65) and nitrate in tile flow (NSE = 0.48-0.68) for field sites with random pattern tile and constant tile spacing, while the old routine simulated tile flow and nitrate in tile flow results for the field site with constant tile spacing were unacceptable (NSE = 0.00-0.32 and −0.29-0.06, respectively). The new modified curve number calculation method in revision 645 (NSE = 0.50-0.81) better simulated surface runoff than revision 615 (NSE = −0.11-0.49). The calibration provided reasonable parameter sets for the old and new routines in the LVR watershed, and the validation results showed that the new routine has the potential to accurately simulate hydrologic processes in mildly sloped watersheds.
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