| Title: | Mapping intra-field yield variation using high resolution satellite imagery to integrate bioenergy and environmental stewardship in an agricultural watershed |
| Authors: | Hamada, Y., H. Ssegane and M.C. Negri |
| Year: | 2015 |
| Journal: | Remote Sensing |
| Volume (Issue): | 7(8) |
| Pages: | 9753-9768 |
| Article ID: | |
| DOI: | 10.3390/rs70809753 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic and pollutant |
| Primary Application Category: | bioenergy crop, tree and/or vegetation assessment |
| Secondary Application Category: | pollutant cycling/loss and transport |
| Watershed Description: | 207.4 km^2 Indian Creek located in east central Illinois, U.S. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Abstract: | Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red edge normalized difference vegetation index (RENDVI) had the highest correlation (R2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha−1 showed reduction of tile NO3-N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production. |
| Language: | English |
| Keywords: | predictive crop yield; red-edge; biofuel feedstock; sub-field scale; landscape design; future landscape patterns; hydrologic modeling; SWAT; water quality |