The watershed-scale effects of agricultural conservation practices are not well understood. A baseline calibration and an input parameter sensitivity analysis were conducted for simulation of watershed-scale hydrology in the Little River Experimental Watershed (LREW) in the Coastal Plain near Tifton, Georgia. The Soil and Water Assessment Tool (SWAT) was manually calibrated to simulate the hydrologic budget components measured for the 16.9 km 2 subwatershed K of the LREW from 1995 to 2004. A local sensitivity analysis was performed on 16 input variables. The sum of squares of the differences between observed and simulated annual averages for baseflow, stormflow, evapotranspiration, and deep percolation was 19 mm 2 ; average annual precipitation was 1136 mm. The monthly Nash-Sutcliffe model efficiency (NSE) for total water yield (TWYLD) was 0.79 for the ten-year period. Daily NSE for TWYLD was 0.42. The monthly NSE for three years with above-average rainfall was 0.89, while monthly NSE was 0.59 for seven years with below annual average rainfall, indicating that SWAT's predictive capabilities are less well-suited for drier conditions. Monthly average TWYLD for the high-flow winter to early spring season was underpredicted, while the low-flow late summer to autumn TWYLD was overpredicted. Results were negatively influenced when seasonal tropical storms occurred during a dry year. The most sensitive parameters for TWYLD were curve number for crop land (CN2(crop)), soil available water content (SOL_AWC), and soil evaporation compensation factor (ESCO). The most sensitive parameters for stormflow were CN2(crop), curve number for forested land (CN2(forest)), soil bulk density (SOL_BD), and SOL_AWC. The most sensitive parameters for baseflow were CN2(crop), CN2(forest), ESCO, and SOL_AWC. Identification of the sensitive SWAT parameters in the LREW provides modelers in the Coastal Plain physiographic region with focus for SWAT calibration.

The U.S. Department of Agriculture Agricultural Research Service Southeast Watershed Research Laboratory (SEWRL) initiated a hydrologic research program on the Little River Experimental Watershed in south‐central Georgia, United States, in 1967. The primary intent of the program was to develop an improved understanding of basic hydrologic and water quality processes on Coastal Plain watersheds and to evaluate the effects of agricultural management practices on the region's natural resources and environment. Long‐term (up to 37 years), research‐quality streamflow data have been collected for up to eight flow measurement sites within the Gulf‐Atlantic Coastal Plain physiographic region, an important agricultural production area in the southeastern United States. Forty‐six precipitation gauges and three climate stations are currently in operation to collect data in support of the hydrologic network. Over the past 20 years, sediment and agrichemical concentrations in streamflow have also been monitored to permit evaluation of the impacts of agriculture on regional surface and groundwater quality. Along with the hydrologic and water quality data, geographic spatial data layers for terrain, soils, geology, vegetation, and land management have also been developed. These databases, described in five accompanying data reports, can be accessed via an ftp site supported by the SEWRL (ftp://www.tiftonars.org/).

Vegetation indices based solely on visible reflectance may simplify and decrease the cost of crop growth estimates compared to visible and near‐infrared (NIR) indices. Ground‐based and aerial visible and visible/NIR vegetation indices based on aerial images were compared for sensitivity to ground cover fraction (GCF) of cotton (Gossypium hirsutum L.) under four irrigation treatments in 2004 and five treatments in 2005 and 2006. In‐season cotton imagery was collected using an unmodified Nikon COOLPIX 4300 camera and a COOLPIX 4300 camera modified for NIR imaging attached to a tethered blimp. GCF imagery was collected at 45 to 60 m and compared with normalized difference vegetation index (NDVI) and green/red ratio values from imagery collected at 180 to 250 m. Ground‐based (1.5 m) spectrometer NDVI measurements using multiple spectral regions were also evaluated. Spectrometer (r2 = 0.40 to 0.80) and camera (r2 = 0.68 to 0.90) indices were highly correlated with season‐wide GCF between fractions of 0.20 and 0.80 and were sensitive to irrigation treatments. Camera green/red ratio was linearly correlated with GCF throughout the 3 yr. The pooled comparison for the 3 yr was strongly linear (r2 = 0.86). Our results suggest that the green/red ratio index might allow quick, simple, and accurate crop growth estimates for production.

Off-the-shelf consumer digital cameras are convenient and user-friendly. However, the use of these cameras in remote sensing is limited because convenient methods for concurrently determining visible and near-infrared (NIR) radiation using these cameras have not been developed. Two Nikon Coolpix 4300 digital cameras were evaluated in tandem to determine the effectiveness of a cross-camera calibration procedure that would allow concurrent use of an unmodified digital camera and a NIR-sensitive digital camera without preset shutter speeds or aperture settings. The NIR-sensitive camera was modified to detect NIR radiation by replacing the internal hot mirror with a Hoya RM72 filter. Each camera was calibrated at five exposure levels using a Gretag-Macbeth ColorChecker TM reflectance panel, and raw camera brightness values were converted to relative reflectance by exposure compensation equations. The method was tested on a series of 26 diffuse reflectance targets, which also yielded the same exposure compensation relationships. The relationship between camera channel brightness and target reflectance was nonlinear within each exposure, but sensitivity was linear between exposures. The procedure was tested on 36 cotton plots (Gossypium hirsutum) in an irrigation study in 2006. Images obtained on eight dates during the season were corrected for exposure and converted to relative reflectance values. The normalized difference vegetation index (NDVI) values from the plots were then compared with ground-based spectrometer measurements of NDVI. Corrected camera-based NDVI values were closely correlated with the spectrometer NDVI values (r 2 = 0.72), suggesting that the camera system can more consistently estimate crop reflectance characteristics if exposure compensation is applied.