Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data

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dc.contributor.authorBannari, Abderrazak
dc.contributor.authorStaenz, Karl
dc.contributor.authorChampagne, Catherine
dc.contributor.authorKhurshi, K. Shahid
dc.date.accessioned2017-03-24T22:35:58Z
dc.date.available2017-03-24T22:35:58Z
dc.date.issued2015
dc.descriptionSherpa Romeo green journal; open accessen_US
dc.description.abstractSoil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).en_US
dc.description.peer-reviewYesen_US
dc.identifier.citationBannari, A., Staenz, K., Champagne, C., & Khurshid, K. S. (2015). Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data. Remote sensing, 7, 8107-8127. doi:10.3390/rs70608107en_US
dc.identifier.urihttps://hdl.handle.net/10133/4813
dc.language.isoen_USen_US
dc.publisherMDPI AGen_US
dc.publisher.departmentDepartment of Geographyen_US
dc.publisher.facultyArts and Scienceen_US
dc.publisher.institutionArabian Gulf Universityen_US
dc.publisher.institutionUniversity of Lethbridgeen_US
dc.publisher.institutionAgriculture and Agri-Food Canadaen_US
dc.publisher.institutionMeteorological Service of Canadaen_US
dc.subjectCrop residueen_US
dc.subjectRemote sensingen_US
dc.subjectHyperspectralen_US
dc.subjectHyperionen_US
dc.subjectAgricultural landen_US
dc.subjectUnmixingen_US
dc.subjectCrop residue managementen_US
dc.subjectCrop residuesen_US
dc.titleSpatial variability mapping of crop residue using hyperion (EO-1) hyperspectral dataen_US
dc.typeArticleen_US
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