{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:42:26Z","timestamp":1772761346509,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T00:00:00Z","timestamp":1588032000000},"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>Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November\u2013May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015\u20132016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R2) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R2 of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area.<\/jats:p>","DOI":"10.3390\/rs12091397","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T01:29:15Z","timestamp":1588123755000},"page":"1397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-5025","authenticated-orcid":false,"given":"Ahmed","family":"Laamrani","sequence":"first","affiliation":[{"name":"Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco"},{"name":"Guelph Science and Technology Branch, Agriculture and Agri-Food Canada (AAFC), Guelph, ON N1G 4S9, Canada"},{"name":"Department of Geography, Environment &amp; Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1532-7175","authenticated-orcid":false,"given":"Pamela","family":"Joosse","sequence":"additional","affiliation":[{"name":"Guelph Science and Technology Branch, Agriculture and Agri-Food Canada (AAFC), Guelph, ON N1G 4S9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1006-0018","authenticated-orcid":false,"given":"Heather","family":"McNairn","sequence":"additional","affiliation":[{"name":"Ottawa Research and Development Centre, AAFC, Ottawa, ON K1A 0C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-5662","authenticated-orcid":false,"given":"Aaron","family":"Berg","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment &amp; Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer","family":"Hagerman","sequence":"additional","affiliation":[{"name":"Guelph Science and Technology Branch, Agriculture and Agri-Food Canada (AAFC), Guelph, ON N1G 4S9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn","family":"Powell","sequence":"additional","affiliation":[{"name":"Guelph Science and Technology Branch, Agriculture and Agri-Food Canada (AAFC), Guelph, ON N1G 4S9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Berry","sequence":"additional","affiliation":[{"name":"Geomatics, AAFC, Regina, SK S4P 0M3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,28]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Crop residue biomass effects on agricultural runoff","volume":"2013","author":"Mailapalli","year":"2013","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_2","first-page":"25","article-title":"Impact of residue management and subsurface drainage on non-point source pollution in the Arroyo Colorado. Sustain","volume":"3\u20134","author":"Enciso","year":"2014","journal-title":"Water Qual. Ecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55A","DOI":"10.2489\/jswc.70.3.55A","article-title":"Sequestering carbon and increasing productivity by conservation agriculture","volume":"70","author":"Lal","year":"2015","journal-title":"J. Soil Water Conserv."},{"key":"ref_4","unstructured":"Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) (2020, March 31). New Horizons: Ontario\u2019s Agricultural Soil Health and Conservation Strategy, Available online: http:\/\/www.omafra.gov.on.ca\/english\/landuse\/soil-strategy.pdf."},{"key":"ref_5","unstructured":"(2020, March 31). Canada-Ontario Lake Erie Action Plan. Available online: https:\/\/www.canada.ca\/content\/dam\/eccc\/documents\/pdf\/great-lakes-protection\/dap\/action_plan.pdf."},{"key":"ref_6","unstructured":"USDA (2020, March 31). Environmental Quality Incentives Program, Available online: https:\/\/www.nrcs.usda.gov\/wps\/portal\/nrcs\/main\/national\/programs\/financial\/eqip."},{"key":"ref_7","unstructured":"Statistics Canada (2020, March 31). Table: 32-10-0162-01, Selected Land Management Practices and Tillage Practices Used to Prepare Land for Seeding, Historical Data, Available online: https:\/\/doi.org\/10.25318\/3210016201-eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.4141\/cjss10005","article-title":"Context for re-evaluating agricultural source phosphorus loadings to the Great Lakes","volume":"91","author":"Joosse","year":"2011","journal-title":"Can. J. Soil Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.agwat.2014.12.010","article-title":"Sediment-assisted nutrient transfer from a small, no-till, tile drained watershed in Southwestern Ontario, Canada","volume":"152","author":"Molder","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2134\/jeq2016.07.0248","article-title":"Increased soluble phosphorus loads to Lake Erie: Unintended consequences of conservation practices?","volume":"46","author":"Jarvie","year":"2016","journal-title":"J. Environ. Qual."},{"key":"ref_11","first-page":"478","article-title":"Residue measurement techniques","volume":"48","author":"Morrison","year":"1993","journal-title":"J. Soil Water Conserv."},{"key":"ref_12","unstructured":"Dickey, E.C., Shelton, D.P., Meyer, G.E., and Fairbanks, K.T. (1989). Determining crop residue cover with electronic image analysis. Biol. Syst. Eng., 238. Available online: https:\/\/digitalcommons.unl.edu\/biosysengfacpub\/238."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.2489\/jswc.72.5.471","article-title":"Determining the number of measurements required to estimate crop residue cover by different methods","volume":"72","author":"Laamrani","year":"2017","journal-title":"J. Soil Water Conserv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Laamrani, A., Pardo Lara, R., Berg, A.A., Branson, D., and Joosse, P. (2018). Using a Mobile Device \u201cApp\u201d and Proximal Remote Sensing Technologies to Assess Soil Cover Fractions on Agricultural Fields. Sensors, 18.","DOI":"10.3390\/s18030708"},{"key":"ref_15","unstructured":"Crop Residue Management Survey (CRM) (2020, March 31). Conservation Technology Information Centre. Available online: https:\/\/www.ctic.org\/resource_display\/?id=255."},{"key":"ref_16","unstructured":"(2020, March 31). Operational Tillage Information System (OpTIS). Available online: https:\/\/www.ctic.org\/OpTIS."},{"key":"ref_17","unstructured":"(2020, March 31). Mapping Conservation Practices and Outcomes in the Corn Belt. Available online: https:\/\/www.ctic.org\/files\/FinalReport_CBPPP_CTIC-TNC_v5.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/07038992.1993.10874543","article-title":"Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper","volume":"19","author":"Mcnairn","year":"1993","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2003.10.023","article-title":"Assessing crop residue cover using shortwave infrared reflectance","volume":"90","author":"Daughtry","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.2489\/jswc.63.3.112","article-title":"Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia","volume":"63","author":"Sullivan","year":"2008","journal-title":"J. Soil Water Conserv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.rse.2007.08.006","article-title":"Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover","volume":"112","author":"Daughtry","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"971","DOI":"10.3390\/rs1040971","article-title":"An improved ASTER index for remote sensing of crop residue","volume":"1","author":"Serbin","year":"2009","journal-title":"Remote Sens."},{"key":"ref_23","first-page":"446","article-title":"Crop residue modeling and mapping using Landsat, ALI, Hyperion, airborne remote sensing data","volume":"6","author":"Galloza","year":"2013","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8107","DOI":"10.3390\/rs70608107","article-title":"Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data","volume":"7","author":"Bannari","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14559","DOI":"10.3390\/rs71114559","article-title":"Estimation of maize residue cover using Landsat-8 OLI image spectral information and textural features","volume":"7","author":"Jin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","unstructured":"Rundquist, S., and Carlson, S. (2020, March 31). Mapping Cover Crops on Corn and Soybeans in Illinois, Indiana and Iowa, 2015\u20132016. Available online: https:\/\/www.agri-pulse.com\/ext\/resources\/pdfs\/EWG_CoverCropReport_C05.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"064033","DOI":"10.1088\/1748-9326\/aac4c8","article-title":"Satellite detection of cover crops and their effects on crop yield in the Midwestern United States","volume":"13","author":"Seifert","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"925","DOI":"10.13031\/trans.59.11489","article-title":"Quantification and Mapping of Surface Residue Cover for Maize and Soybean Fields in South Central Nebraska","volume":"59","author":"Sharma","year":"2016","journal-title":"Trans. ASABE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1016\/j.rse.2010.04.024","article-title":"Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping","volume":"114","author":"Pacheco","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hively, W.D., Lamb, B.T., Daughtry, C.S.T., Shermeyer, J., McCarty, G.W., and Quemada, M. (2018). Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices. Remote Sens., 10.","DOI":"10.3390\/rs10101657"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"864","DOI":"10.2134\/agronj2003.0291","article-title":"Remote Sensing the Spatial Distribution of Crop Residues","volume":"97","author":"Daughtry","year":"2005","journal-title":"Agron. J."},{"key":"ref_32","first-page":"154","article-title":"Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data: Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands","volume":"76","author":"Wang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","first-page":"306","article-title":"A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis","volume":"78","author":"Yue","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5721\/EuJRS20164907","article-title":"Measuring intensity of tillage and plant residue cover using remote sensing","volume":"49","author":"Sonmez","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.rse.2011.09.016","article-title":"Remote sensing of crop residue cover using multi-temporal Landsat imagery","volume":"117","author":"Zheng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120","DOI":"10.2489\/jswc.68.2.120","article-title":"Multitemporal remote sensing of crop residue cover and tillage practices: A validation of the minNDTI strategy in the United States","volume":"68","author":"Zheng","year":"2013","journal-title":"J. Soil Water Conserv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hively, W.D., Shermeyer, J., Lamb, B.T., Daughtry, C.T., Quemada, M., and Keppler, J. (2019). Mapping Crop Residue by Combining Landsat and WorldView-3 Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11161857"},{"key":"ref_38","unstructured":"Schut, L.W. (1992). Soils of Elgin County, Agric. Canada Research Branch."},{"key":"ref_39","unstructured":"Kludze, H., Deen, B., and Dutta, A. (2011). Report on Literature Review of Agronomic Practices for Energy Crop Production under Ontario Conditions, University of Guelph. Available online: https:\/\/ofa.on.ca\/wp-content\/uploads\/2017\/11\/OFA-PROJECT-FINAL-REPORT-JULY-04-2011-RAAC1.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.biombioe.2013.05.036","article-title":"Estimating sustainable crop residue removal rates and costs based on soil organic matter dynamics and rotational complexity","volume":"56","author":"Kludze","year":"2013","journal-title":"Biomass Bioenergy"},{"key":"ref_41","unstructured":"Environment Canada (2020, February 18). Canadian Climate Normals 1971\u20132000 St. Thomas Weather Station, Available online: http:\/\/climate.weather.gc.ca\/climate_normals."},{"key":"ref_42","unstructured":"(2020, March 31). PCI Geomatica 2015 Software. Available online: https:\/\/www.pcigeomatics.com\/pressnews\/2015_PCI_Geomatica-15.pdf."},{"key":"ref_43","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2015). Atmospheric\/Topographic Correction for Satellite Imagery; ATCOR-2\/3 User Guide, Version 9.0.0, ReSe Applications Schl\u00e4pfer. DLR Report DLR-IB."},{"key":"ref_44","unstructured":"Boardman, J.M., Kruse, F.A., and Green, R.O. (1995). Mapping target signature via partial unmixing of AVIRIS data, Summaries of the Fifth JPL Airborne Earth Science Workshop, JPL Publication 95\u20131."},{"key":"ref_45","unstructured":"ENVI Image Analysis Software, Harris Geospatial Solutions, Inc.. Available online: https:\/\/www.harrisgeospatial.com\/Software-Technology\/ENVI."},{"key":"ref_46","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"Volume I","author":"Freden","year":"1974","journal-title":"Third Earth Resources Technology Satellite\u20131 Syposium"},{"key":"ref_47","first-page":"87","article-title":"Using thematic mapper data to identify contrasting soil plains and tillage practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_48","unstructured":"R Development Core Team A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: http:\/\/www.r-project.org\/."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fisk, C., Clarke, K.D., and Lewis, M.M. (2019). Comparison of hyperspectral versus traditional field measurements of fractional ground cover in the Australian arid zone. Remote Sens., 11.","DOI":"10.3390\/rs11232825"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Koirala, B., Khodadadzadeh, M., Contreras, C., Zahiri, Z., Gloaguen, R., and Scheunders, P. (2019). A supervised method for nonlinear hyperspectral unmixing. Remote Sens., 11.","DOI":"10.3390\/rs11202458"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fisk, C., Clarke, K.D., Delean, S., and Lewis, M.M. (2019). Distinguishing photosynthetic and non-photosynthetic vegetation: How do traditional observations and spectral classification compare?. Remote Sens., 11.","DOI":"10.3390\/rs11212589"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:24:03Z","timestamp":1760361843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,28]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091397"],"URL":"https:\/\/doi.org\/10.3390\/rs12091397","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,28]]}}}