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dc.contributor.author Bannari, Abderrazak
dc.contributor.author Staenz, Karl
dc.contributor.author Champagne, Catherine
dc.contributor.author Khurshi, K. Shahid
dc.date.accessioned 2017-03-24T22:35:58Z
dc.date.available 2017-03-24T22:35:58Z
dc.date.issued 2015
dc.identifier.citation Bannari, 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/rs70608107 en_US
dc.identifier.uri https://hdl.handle.net/10133/4813
dc.description Sherpa Romeo green journal; open access en_US
dc.description.abstract Soil 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.language.iso en_US en_US
dc.publisher MDPI AG en_US
dc.subject Crop residue en_US
dc.subject Remote sensing en_US
dc.subject Hyperspectral en_US
dc.subject Hyperion en_US
dc.subject Agricultural land en_US
dc.subject Unmixing en_US
dc.subject Crop residue management en_US
dc.subject Crop residues en_US
dc.title Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data en_US
dc.type Article en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geography en_US
dc.description.peer-review Yes en_US
dc.publisher.institution Arabian Gulf University en_US
dc.publisher.institution University of Lethbridge en_US
dc.publisher.institution Agriculture and Agri-Food Canada en_US
dc.publisher.institution Meteorological Service of Canada en_US


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