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).
Highlights
Conservation of post-harvest crop residue on agricultural land plays an important role in the protection of the soil surface against water and wind erosion [1,2,3]
The objective of this study was to investigate the potential of the Hyperion (EO-1) hyperspectral data and constrained linear spectral mixture analysis (CLSMA) for estimation and mapping of fractional crop residue cover, using ground data for validation purposes
The Hyperion data were corrected for the sensor spatial shift between the visible and near-infrared (VNIR) and short-wave infrared (SWIR) detectors, image striping, dead pixels, and gain and offset errors, and reduced for noise in the imagery
Summary
Conservation of post-harvest crop residue on agricultural land plays an important role in the protection of the soil surface against water and wind erosion [1,2,3]. Even though the relationship between the parameter in question and specific index can be re-established to address these differences, it is no guarantee that a satisfactory relationship can be found [9,10] These methods are not sufficiently rigorous and accurate for discriminating residue from bare soil and for estimating the fraction of residue cover [14]. They do not consider the spectral mixture of different materials in the same pixel [15,16]. Acquisition of imagery post-harvest or prior to spring seeding would simplify the extraction of percent residue cover by eliminating confusion with the crop canopy
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