{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:12:49Z","timestamp":1769843569290,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2015,7,31]],"date-time":"2015-07-31T00:00:00Z","timestamp":1438300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u00b7ha\u22121 showed reduction of tile NO3-N and sediment exports by 15.9%\u201325.9% and 25%\u201339%, 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.<\/jats:p>","DOI":"10.3390\/rs70809753","type":"journal-article","created":{"date-parts":[[2015,7,31]],"date-time":"2015-07-31T10:12:24Z","timestamp":1438337544000},"page":"9753-9768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed"],"prefix":"10.3390","volume":"7","author":[{"given":"Yuki","family":"Hamada","sequence":"first","affiliation":[{"name":"Environmental Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herbert","family":"Ssegane","sequence":"additional","affiliation":[{"name":"Energy Systems Division, Argonne National Laboratory, 9700 South Cass Argonne, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Negri","sequence":"additional","affiliation":[{"name":"Energy Systems Division, Argonne National Laboratory, 9700 South Cass Argonne, IL 60439, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s12155-014-9412-1","article-title":"The impact of corn stover removal on N2O emission and soil respiration: An investigation with automated chambers","volume":"7","author":"Baker","year":"2014","journal-title":"Bioenergy Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s12155-014-9414-z","article-title":"Assessing the soil carbon, biomass production, and nitrous oxide emission impact of corn stover management for bioenergy feedstock production using DAYCENT","volume":"7","author":"Campbell","year":"2014","journal-title":"Bioenergy Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1002\/hyp.8280","article-title":"Simulated watershed scale impacts of corn stover removal for biofuel on hydrology and water quality","volume":"26","author":"Cibin","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"431","DOI":"10.2489\/jswc.66.6.431","article-title":"Multiple corn stover removal rates for cellulosic biofuels and long-term water quality impacts","volume":"66","author":"Thomas","year":"2011","journal-title":"J. Soil Water Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"418","DOI":"10.2136\/sssaj2008.0141","article-title":"Corn stover removal for expanded uses reduces soil fertility and structural stability","volume":"73","author":"Lal","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.biombioe.2013.12.020","article-title":"Corn stover for bioenergy production: Cost estimates and farmer supply response","volume":"62","author":"Thompson","year":"2014","journal-title":"Biomass Bioenergy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5199","DOI":"10.1021\/es303437c","article-title":"Estimating net anthropogenic nitrogen inputs to us watersheds: Comparison of methodologies","volume":"47","author":"Hong","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.2134\/jeq2002.1610","article-title":"Relating net nitrogen input in the Mississippi river basin to nitrate flux in the lower Mississippi river: A comparison of approaches","volume":"31","author":"McIsaac","year":"2002","journal-title":"J. Environ. Qual."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"329","DOI":"10.2134\/jeq2001.302329x","article-title":"Nitrogen input to the gulf of Mexico","volume":"30","author":"Goolsby","year":"2001","journal-title":"J. Environ. Qual."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10095","DOI":"10.1021\/es402181y","article-title":"US federal agency models offer different visions for achieving renewable fuel standard (RFS2) biofuel volumes","volume":"47","author":"Keeler","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.2134\/jeq2010.0539","article-title":"A novel framework to classify marginal land for sustainable biomass feedstock production","volume":"40","author":"Gopalakrishnan","year":"2011","journal-title":"J. Environ. Qual."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.rser.2013.08.079","article-title":"Renewable energy potential on marginal lands in the United States","volume":"29","author":"Milbrandt","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.biombioe.2007.07.003","article-title":"Comparison of corn and switchgrass on marginal soils for bioenergy","volume":"32","author":"Varvel","year":"2008","journal-title":"Biomass Bioenergy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.rser.2010.11.041","article-title":"Assessment of bioenergy potential on marginal land in China","volume":"15","author":"Zhuang","year":"2011","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/BF00122911","article-title":"An analysis of a silvopastoral system for the marginal land in the southeast United States","volume":"10","author":"Dangerfield","year":"1990","journal-title":"Agrofor. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1038\/nature11811","article-title":"Sustainable bioenergy production from marginal lands in the US Midwest","volume":"493","author":"Gelfand","year":"2013","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/gcbb.12078","article-title":"Using existing landscape data to assess the ecological potential of Miscanthus cultivation in a marginal landscape","volume":"6","author":"Harvolk","year":"2014","journal-title":"GCB Bioenergy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1021\/es3033132","article-title":"Historical US cropland areas and the potential for bioenergy production on abandoned croplands","volume":"47","author":"Zumkehr","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.biombioe.2015.04.012","article-title":"Multifunctional landscapes: Site characterization and field-scale design to incorporate biomass production into an agricultural system","volume":"80","author":"Ssegane","year":"2015","journal-title":"Biomass Bioenergy"},{"key":"ref_20","unstructured":"Basso, B., Cammarano, D., and Carfagna, E. Review of Crop Yield Forecasting Methods and Early Warning Systems. Available online:http:\/\/www.fao.org\/fileadmin\/templates\/ess\/documents\/meetings_and_workshops\/GS_SAC_2013\/Improving_methods_for_crops_estimates\/Crop_Yield_Forecasting_Methods_and_Early_Warning_Systems_Lit_review.pdf."},{"key":"ref_21","unstructured":"Campbell, J.B. (2002). Introduction to Remote Sensing, CRC Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2307\/2657019","article-title":"Estimating near-infrared leaf reflectance from leaf structural characteristics","volume":"88","author":"Slaton","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2004.05.017","article-title":"Crop condition and yield simulations using Landsat and MODIS","volume":"92","author":"Doraiswamy","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, Y., Liu, X., Shen, Y., and Jin, J. (2014, January 11\u201314). Assessment of agricultural drought indicators impact on soybean crop yield: A case study in Iowa, USA. Proceedings of the Third International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2014), Beijing, China.","DOI":"10.1109\/Agro-Geoinformatics.2014.6910573"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2134\/agronj2013.0314","article-title":"Testing remote sensing approaches for assessing yield variability among maize fields","volume":"106","author":"Sibley","year":"2014","journal-title":"Agron. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.14358\/PERS.73.10.1149","article-title":"Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques","volume":"73","author":"Li","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Topaloglou, C., Monachou, S., Strati, S., Alexandridis, T., Stavridou, D., Silleos, N., Misopolinos, N., Nunes, A., and Araujo, A. (2013). Modeling LAI based on land cover map and NDVI using SPOT and Landsat data in two Mediterranean sites: Preliminary results. Proc. SPIE, 8795.","DOI":"10.1117\/12.2028367"},{"key":"ref_29","first-page":"760","article-title":"Mapping of summer crops in the state of Parana, Brazil, through the 10-day SPOT vegetation NDVI composites","volume":"31","author":"Araujo","year":"2011","journal-title":"Eng. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/0143116031000102485","article-title":"Within-field wheat yield prediction from IKONOS data: A new matrix approach","volume":"25","author":"Enclona","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.fcr.2013.12.006","article-title":"Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs","volume":"157","author":"Alganci","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Konecny, G. (2014). Geoinformation: Remote Sensing, Photogrammetry and Geographic Information Systems, CRC Press.","DOI":"10.1201\/b15765"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"583","DOI":"10.2134\/agronj2001.933583x","article-title":"Use of remote-sensing imagery to estimate corn grain yield","volume":"93","author":"Shanahan","year":"2001","journal-title":"Agron. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_35","unstructured":"Baret, F., Guyot, G., and Major, D. (1989, January 10\u201314). TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing, Geoscience and Remote Sensing Symposium, IGARSS\u201989, Vancouver, BC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.agwat.2010.08.019","article-title":"Using vegetation indices from satellite remote sensing to assess corn and soybean response to controlled tile drainage","volume":"98","author":"Cicek","year":"2010","journal-title":"Agric. Water Manag."},{"key":"ref_38","unstructured":"Maier, M. (2014, January 23\u201324). Protecting water with on-farm conservation: The Indian creek watershed project. Proceeding of the National Workshop on Large Landscape Conservation, Washington, DC, USA."},{"key":"ref_39","unstructured":"USDA-NASS (2012). Cropscape-Cropland Data Layer, Available online:http:\/\/nassgeodata.gmu.edu\/CropScape."},{"key":"ref_40","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral vegetation indices and their relationships with agricultural crop characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1080\/01431160701395252","article-title":"The early explanatory power of NDVI in crop yield modelling","volume":"29","author":"Wall","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ssegane, H., and Negri, M.C. (2015). Designing a sustainable integrated landscape for commodity and bioenergy crops in a tile-drained agricultural watershed. GCB Bioenergy, under review.","DOI":"10.2134\/jeq2015.10.0518"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s00477-002-0101-9","article-title":"Power of the mann-whitney test for detecting a shift in median or mean of hydro-meteorological data","volume":"16","author":"Yue","year":"2002","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Harmel, R., Cooper, R., Slade, R., Haney, R., and Arnold, J. (2006). Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Trans. Am. Soc. Agric. Eng., 49.","DOI":"10.13031\/2013.20488"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0924-2716(92)90030-D","article-title":"Remote sensing and crop production models: Present trends","volume":"47","author":"Maas","year":"1992","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.3390\/rs5041704","article-title":"Using low resolution satellite imagery for yield prediction and yield anomaly detection","volume":"5","author":"Rembold","year":"2013","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JSTARS.2010.2091492","article-title":"Evaluation of sentinel-2 spectral sampling for radiative transfer model based LAI estimation of wheat, sugar beet, and maize","volume":"4","author":"Richter","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2010.05.006","article-title":"Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat","volume":"73","author":"Atzberger","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.still.2012.02.007","article-title":"Long-term rotation and tillage effects on soil structure and crop yield","volume":"127","author":"Munkholm","year":"2013","journal-title":"Soil Tillage Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0378-4290(03)00141-2","article-title":"Crop management factors influencing yield and quality of crop residues","volume":"84","author":"Reddy","year":"2003","journal-title":"Field Crops Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2012.12.017","article-title":"MODIS-based corn grain yield estimation model incorporating crop phenology information","volume":"131","author":"Sakamoto","year":"2013","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/8\/9753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:50:01Z","timestamp":1760215801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/8\/9753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,31]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2015,8]]}},"alternative-id":["rs70809753"],"URL":"https:\/\/doi.org\/10.3390\/rs70809753","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7,31]]}}}