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Estimation of winter wheat residue coverage using optical and SAR remote sensing images

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Estimation of winter wheat residue coverage using optical and SAR remote sensing images. / Cai, W.; Zhao, S.; Wang, Y.; Peng, F.; Heo, J.; Duan, Z.

In: Remote Sensing, Vol. 11, No. 10, 1163, 15.05.2019.

Research output: Contribution to journalJournal articlepeer-review

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Cai, W, Zhao, S, Wang, Y, Peng, F, Heo, J & Duan, Z 2019, 'Estimation of winter wheat residue coverage using optical and SAR remote sensing images', Remote Sensing, vol. 11, no. 10, 1163. https://doi.org/10.3390/rs11101163

APA

Cai, W., Zhao, S., Wang, Y., Peng, F., Heo, J., & Duan, Z. (2019). Estimation of winter wheat residue coverage using optical and SAR remote sensing images. Remote Sensing, 11(10), [1163]. https://doi.org/10.3390/rs11101163

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Author

Cai, W. ; Zhao, S. ; Wang, Y. ; Peng, F. ; Heo, J. ; Duan, Z. / Estimation of winter wheat residue coverage using optical and SAR remote sensing images. In: Remote Sensing. 2019 ; Vol. 11, No. 10.

Bibtex

@article{37156709c1034c098d28d913651b8409,
title = "Estimation of winter wheat residue coverage using optical and SAR remote sensing images",
abstract = "As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effiective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coeffcient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ VV0 and NDTI × σ VH0, with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation. {\textcopyright} 2019 by the authors.",
keywords = "Crop residues coverage, Optical crop residue indices, Optimal subset regression, Radar parameters, Sentinel-1, Sentinel-2, Winter wheat, Agricultural wastes, Crops, Ecosystems, Mean square error, Microwaves, Optical correlation, Radar imaging, Radar measurement, Regression analysis, Satellites, Soil moisture, Space-based radar, Synthetic aperture radar, Crop residue, Optimal subsets, Remote sensing",
author = "W. Cai and S. Zhao and Y. Wang and F. Peng and J. Heo and Z. Duan",
year = "2019",
month = may,
day = "15",
doi = "10.3390/rs11101163",
language = "English",
volume = "11",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "10",

}

RIS

TY - JOUR

T1 - Estimation of winter wheat residue coverage using optical and SAR remote sensing images

AU - Cai, W.

AU - Zhao, S.

AU - Wang, Y.

AU - Peng, F.

AU - Heo, J.

AU - Duan, Z.

PY - 2019/5/15

Y1 - 2019/5/15

N2 - As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effiective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coeffcient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ VV0 and NDTI × σ VH0, with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation. © 2019 by the authors.

AB - As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effiective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coeffcient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ VV0 and NDTI × σ VH0, with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation. © 2019 by the authors.

KW - Crop residues coverage

KW - Optical crop residue indices

KW - Optimal subset regression

KW - Radar parameters

KW - Sentinel-1

KW - Sentinel-2

KW - Winter wheat

KW - Agricultural wastes

KW - Crops

KW - Ecosystems

KW - Mean square error

KW - Microwaves

KW - Optical correlation

KW - Radar imaging

KW - Radar measurement

KW - Regression analysis

KW - Satellites

KW - Soil moisture

KW - Space-based radar

KW - Synthetic aperture radar

KW - Crop residue

KW - Optimal subsets

KW - Remote sensing

U2 - 10.3390/rs11101163

DO - 10.3390/rs11101163

M3 - Journal article

VL - 11

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 10

M1 - 1163

ER -