Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images
AbstractAs 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 effective 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 coefficient 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 ×
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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. Remote Sens. 2019, 11, 1163.
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. Remote Sensing. 2019; 11(10):1163.Chicago/Turabian Style
Cai, Wenting; Zhao, Shuhe; Wang, Yamei; Peng, Fanchen; Heo, Joon; Duan, Zheng. 2019. "Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images." Remote Sens. 11, no. 10: 1163.
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