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Remote Sens. 2015, 7(6), 8107-8127; doi:10.3390/rs70608107

Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data

1
GeoInformatics Department, Arabian Gulf University, P.O. Box 26671, Manama, Kingdom of Bahrain
2
Alberta Terrestrial Imaging Centre (ATIC), Department of Geography, University of Lethbridge, 401, 817 4th Avenue South, Lethbridge, AB T1J 0P, Canada
3
Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
4
Earth Observation and Geomatics, Meteorological Service of Canada, Environment Canada, 373 Sussex Drive, Ottawa, ON K1A 0H3, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad Thenkabail
Received: 12 April 2015 / Revised: 10 June 2015 / Accepted: 15 June 2015 / Published: 18 June 2015
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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). View Full-Text
Keywords: crop residue; remote sensing; hyperspectral; Hyperion; agricultural land; unmixing crop residue; remote sensing; hyperspectral; Hyperion; agricultural land; unmixing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bannari, A.; Staenz, K.; Champagne, C.; Khurshid, K.S. Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data. Remote Sens. 2015, 7, 8107-8127.

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