Crop Residue Discrimination Using Ground-Based Hyperspectral Data

Singh, Rimjhim Bhatnagar; Ray, S. S.; Bal, Santanu Kumar; Sekhon, B. S.; Gill, G. S.; Panigrahy, Sushma

doi:10.1007/s12524-012-0226-3

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In previous studies, these bands were used for index computation to map crop residues with different reference wavelengths in the FSWIR [29,34,37]. The best performing NDI of H2013 utilized the wavelength centered at 1981 nm, a wavelength which may be sensitive to lignin, nitrogen [10,25] or plant residues [38]. Similarly, Oldeland et al [11] state an importance of the SWIR spectral region for dry-matter analysis in an African savanna.…”

Section: Hyperspectral Indices For Dwarf Shrub Biomass Detectionmentioning

confidence: 99%

Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

2015

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Abstract:In spite of considerable efforts to monitor global vegetation, biomass quantification in drylands is still a major challenge due to low spectral resolution and considerable background effects. Hence, this study examines the potential of the space-borne hyperspectral Hyperion sensor compared to the multispectral Landsat OLI sensor in predicting dwarf shrub biomass in an arid region characterized by challenging conditions for satellite-based analysis: The Eastern Pamirs of Tajikistan. We calculated vegetation indices for all available wavelengths of both sensors, correlated these indices with field-mapped biomass while considering the multiple comparison problem, and assessed the predictive performance of single-variable linear models constructed with data from each of the sensors. Results showed an increased performance of the hyperspectral sensor and the particular suitability of indices capturing the short-wave infrared spectral region in dwarf shrub biomass prediction. Performance was considerably poorer in the area with less vegetation cover. Furthermore, spatial transferability of vegetation indices was not feasible in this region, underlining the importance of repeated model building. This study indicates that upcoming space-borne hyperspectral sensors increase the performance of biomass prediction in the world's arid environments.

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Section: Hyperspectral Indices For Dwarf Shrub Biomass Detectionmentioning

confidence: 99%

Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

2015

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“…Crop residue plays a crucial role in mitigating soil erosion caused by wind and runoff [1], retains moisture and nutrients in the soil [2], and increases the organic content of the soil [3,4]. Additionally, crop residue improves water use efficiency, enhances soil fertility [5], and ultimately contributes to overall soil quality [6].…”

Section: Introductionmentioning

confidence: 99%

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Azimi,

Jung

2024

Remote Sensing

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Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

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Spectral Signature-Based Water Stress Characterization and Prediction of Wheat Yield under Varied Irrigation and Plant Bio-regulator Management Practices

Bal

Wakchaure

Potekar

et al. 2021

J Indian Soc Remote Sens

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Crop Residue Discrimination Using Ground-Based Hyperspectral Data

Cited by 4 publications

References 13 publications

Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Spectral Signature-Based Water Stress Characterization and Prediction of Wheat Yield under Varied Irrigation and Plant Bio-regulator Management Practices

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