Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds.

Evaluating Best Management Practices (BMPs) in watersheds using hydrologic and water quality models can help to establish an effective watershed water management. Soil and Water Assessment Tool (SWAT) was applied to Big Sunflower River Watershed (BSRW) and the Stovall Sherard Watershed (SSW) to evaluate BMP’s impact at watershed and field scale watersheds respectively. SWAT was calibrated and validated for streamflow, sediment yield, total nitrogen (TN), and total phosphorous (TP) at outlets of three sub-basins of the BSRW, and within the SSW. BMP scenarios of check-dam, tail water pond, vegetative filter strips (VFS), nutrient management, and tillage management were evaluated for their efficacy in reducing streamflow, sediment yield, and nutrient loads at field and watershed scales. The VFS was determined as the most effective BMP in decreasing sediment yield, TN, and TP at both field and watershed scales. At field scale, reduction of sediment yield, TN, and TP by VFS ranged from 8 to 12%, 71% to 98%, and 72% to 99% respectively and at watershed scale, reduction of sediment yield, TN, and TP by VFS ranged from 12 to 38%, 29% to 87%, and 42% to 99% respectively. The application of conservation and zero tillage operation showed reduction in sediment yield by 1% to 2% respectively but increased TN and TP by 2% to 25% at field and watershed scale watersheds. This study will help in managing water at field and watershed scale watersheds regarding BMPs selection and implementation. Graphical Abstract