Abstract
Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
Highlights
Crop residues, such as stalks, stems, leaves, and seed pods, are materials left on the surface of agricultural fields after harvest
We focused on the estimation of Crop residue cover (CRC) based on empirical regressions and machine learning methods from Sentinel-2 imagery
The results show that 3BI1, 3BI2, and 3BI3 improved the sensitivity to CRC and the estimation accuracy compared to the published crop residue indices (CRIs)
Summary
Crop residues, such as stalks, stems, leaves, and seed pods, are materials left on the surface of agricultural fields after harvest. Remote sensing techniques used to estimate CRC can be classified into empirical regression based on crop residue indices (CRIs), classification [9,10], spectral unmixing [11], and spectral angle methods [12,13]. CRIs that are designed to enhance the discrimination of crop residue from soil are reportedly more correlated to CRC than spectral reflectances [31] It is worth exploring the use of CRIs as input features in the ML models for the prediction of CRC. They found that textural features were correlated with CRC and the regression based on a combination of textural features and CRIs yields a better result than the other two approaches Based on these studies, it is necessary to evaluate the potential of textural features used as input predictors in the machine learners for the prediction of CRC. We evaluated and compared the performance of univariate regression against individual MSI bands, CRIs and texture features, as well as the retrieval accuracies of partial least squares regression (PLSR), ANN, support vector regression (SVR), GPR, and random forest (RF) associated with MSI bands, CRIs, and their combinations with textural features
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