# Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images

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## Abstract

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^{2}) 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 (R

^{2}= 0.570, RMSE = 6.560% and R

^{2}= 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 R

^{2}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 × ${\sigma}_{VV}^{0}$ and NDTI × ${\sigma}_{VH}^{0}$, with a R

^{2}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.

## 1. Introduction

_{2}in the soil and improving soil quality [3,4,5]. It also has positive influences on water infiltration and evaporation by improving soil structure and reducing daily variation of soil temperature [6,7]. Consequently, good residue management practices contribute significantly to an increase of crop yields [8,9,10]. From the view point of air quality protection, remaining crop residues in farmland can greatly reduce the emission of toxic gasses such as CO, SO

_{2}and NH

_{3}, while burning straws severely pollute the air [11,12]. Moreover, crop models need crop residue coverage (CRC) as an input parameter to simulate the impact of management practices on crop production. Therefore, it is of great importance to estimate CRC in crop planting areas and have a knowledge of the population of conservation tillage on a regional scale.

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Field Measurement Data

^{2}and distributed as Figure 2 shows. In the field measurement, iron wire frames (1 m × 1 m) were put on sample plots and photos were taken by camera to collect the CRC information of samples. Previous studies have proved that using cameras to collect CRC information (photo method) had a higher accuracy than the line transect method [16]. GPS was used to measure latitude and longitude information of each sample so that the field measurement location could be collocated with remote sensing images.

#### 2.3. Satellite Data and Pre-Processing

#### 2.3.1. Sentinel-1 Imagery

#### 2.3.2. Sentinel-2 Imagery

#### 2.4. Methods

#### 2.4.1. Calculations of Satellite Derived Variables

#### 2.4.2. Optimal Subset Regression Method

^{2}); (2) Bayes Information Criterion (BIC); (3) Akaike’s Information Criterion (AIC); and (4) model validation accuracy. The subset is better if the R

^{2}is larger and BIC, AIC, and model validation accuracy are smaller. Making a comprehensive assessment of the four indicators a method to obtain the best subset.

_{i}

^{2}is the coefficient of determination, when the ith independent variable is set as the dependent variable, and other independent variables as independent variables to regress. It is generally believed that serious colinearity exists when VIF is more than 10. The process of optimal subset regression is realized in the R software (leaps package: https://cran.r-project.org/web/packages/available_packages_by_name.html).

#### 2.4.3. Statistical Analysis

^{2}) was used in this study to measure the correlation between field measured CRC and satellite derived variables. The formula of R

^{2}is shown in Equation (4). The satellite derived variable with higher R

^{2}value was considered as the most suitable variables to estimate CRC:

_{i}is the estimated CRC for ith sample, y

_{i}is the field measured CRC for ith samples, while x′ and y′ are the mean values of them.

_{i}is the eimated CRC for ith sample, y

_{i}is the field measured CRC for ith samples, x′ and y′ are the mean values of them.

#### 2.4.4. Extraction of Winter Wheat Cultivation Area

## 3. Results

#### 3.1. Correlation between Optical Crop Residue Indices and Field Measured CRC

^{2}values of 0.570 and 0.157, respectively. The indices with R

^{2}from high to low were NDTI, NDI71, NDRI, NDI7, NDI72, NDI74 and NDI73. The corresponding R

^{2}values were 0.570, 0.472, 0.462, 0.425, 0.285, 0.238 and 0.157, respectively. Of the R

^{2}value, one was above 0.50, three were between 0.50 and 0.40 and three were below 0.40. The scatterplots between CRC and some OCRIs are shown in Figure 4.

^{2}value from highest to lowest. The RMSE values of NDTI, NDI71, NDRI, NDI7, NDI72, NDI74 and NDI73 were 6.560%, 7.317%, 7.430%, 7.597%, 8.498%, 8.714% and 9.155%, respectively. In conclusion, most OCRIs had the ability to estimate CRC. Among them, NDTI performed best.

#### 3.2. Correlation between Radar Parameters and Field Measured CRC

^{2}values, the best and worst RPs were RI2 (R

^{2}= 0.430) and VV_ME (R

^{2}= 0.123), respectively. The other RPs had similar R

^{2}values between 0.30 and 0.40, with an order from highest to lowest of RI1, VH_ME, ${\sigma}_{VV}^{0}$ and ${\sigma}_{VH}^{0}$. The scatterplots between CRC and some radar parameters are shown in Figure 5.

^{2}value. Nonetheless, RI2 turned out to be the most accurate RPs for CRC estimation.

#### 3.3. Correlation of Combined Optical Crop Residue Indices and Radar Parameters with CRC

^{2}value of 0.738, which increased by 0.168 when compared with the best results in OCRIs and RPs (R

^{2}= 0.570). Meanwhile, two OCRI-RPs had R

^{2}values above 0.70, eight OCRI-RPs had R

^{2}values between 0.60 and 0.70, and ten OCRI-RPs had R

^{2}values below 0.60. In most cases, OCRI-RPs performed better than OCRIs or RPs in CRC estimation, with the exception of NDTI × VH_ME, which had a R

^{2}value (R

^{2}= 0.551) lower than that of NDTI (R

^{2}= 0.570). The scatterplots between CRC and some OCRI-RPs are shown in Figure 6.

_{S}were between 5.140% and 7.522% (Table 4). NDTI × RI2 and NDI71 × VH_ME had the lowest and highest RMSE value (RMSE = 5.140% and 7.522%, respectively). The decrease of RMSE values proved the effectiveness of OCRI-RPs in CRC estimation.

#### 3.4. CRC Estimation Based on Optimal Subset Regression

^{2}, RMSE) in one submodel, it can obtain the best submodels. The detailed information of the eight submodels are shown in Table 5. It could easily find that submodel with two independent variables had a highest composite index. Therefore, it was the optimal model for CRC estimation. The equation using model 2 to estimate CRC is shown in Equation (6):

^{2}value of the optimal model was 0.770 and the RMSE of LOOCV was 4.846%. They both had certain improvements when compared with the best results in Table 5 (R

^{2}= 0.738, RMSE = 5.140%). Thus, the optimal model would be used as the final model to estimate CRC in study area. Figure 7 shows the scatterplot between field measured CRC and optimal model estimated CRC.

#### 3.5. CRC Mapping in Study Area

## 4. Discussion

^{2}= 0.570 and RMSE = 6.560%). This result was coincidental with the study of Jin et al. in 2015 [37]. Previous studies have shown that crop residues and soil had different reflection characteristics in the 1450–1960 nm and 2000–2100 nm ranges [1,44]. The reflectance curve of winter wheat crop residues and soil collected by an Analytical Spectral Device (ASD) spectrometer in the laboratory (Figure 9) also proved these reflection characteristics for the reflectance of crop residues having a peek of 1650 nm, and a valley of 2100 nm, while that of soil were flat and had a peak. Daughtry et al. [1] indicted that water absorption in the 1450 nm and 1960 nm range made crop residues have a peak of reflectance in 1650 nm. The broad absorption feature of crop residues in 2100nm may be associated with cellulose and lignin. NDTI were calculated from the bands b11 (1540–1680 nm) and b12 (2080–2320 nm) of Sentinel-2, which were sensitive bands for crop residue identification. Thus, NDTI performed better than any other OCRIs. NDRI was calculated by the b4 (645–683 nm) and b12 of Sentinel-2. It had a certain ability to estimate CRC. Gelder et al. [19] proved that NDRI performed better than NDTI when green vegetables appeared in farmland. However, in this study, there were little green vegetables on the farmland. Thus, the use of b4 made it more difficult to estimate CRC. NDI7 was calculated by the b8 (763–907 nm) and b12 bands of Sentinel-2. It has been proven that near infrared bands were sensitive to plant structure [37,45], which gives NDI7 the potential to distinguish crop residues from soil. Among the four new OCRIs, NDI71 performed better than NDI7, while NDI72, NDI73 and NDI74 performed worse than NDI7. It suggested that narrow bands were more sensitive to the reflectance of winter wheat. Therefore, more attention should be paid to the use of narrow bands for CRC estimation.

^{2}= 0.341, RMSE = 8.086% and R

^{2}= 0.319, RMSE = 8.241%). The results were similar to the findings of McNairn et al. in 1992, who held the view that cross-polarized backscattering coefficients can estimate CRC well and backscattering coefficients in the VV polarization direction can distinguish crop residues from soil [24]. However, the R

^{2}values were not very high between CRC and backscattering coefficient. It may because the incidence angles of Sentinel-1 images used in this study were both around 39°, but the best angle for CRC estimation was between 40° and 50° [26]. Radar indices have been popular indices for vegetable water content estimation and fresh weight estimation [46,47]. This study tentatively explored the potential of radar indices for CRC estimation, and interestingly found that RI1 and RI2 both performed better than ${\sigma}_{VV}^{0}$ and ${\sigma}_{VH}^{0}$, which proved the effectiveness of radar indices in CRC estimation. Sentinel-1 images data only have two polarization combinations. It has limited the study on exploring the relationships between CRC and radar indices constructed by backscattering coefficients in the other two polarization direction, which needed to be further explored in other microwave data.

^{2}value and a relatively lower RMSE value when compared with the OCRIs and RPs, which suggested that it was effective to improve the accuracy of CRC estimation by combining optical information and microwave information. For the purpose of further improvement of model accuracy, this study used optimal subset regression to estimate CRC. As an alternative method of stepwise regression technology, optimal subset regression can well fit the experimental data. It uses all combinations of independent variables to fit dependent variables, which can solve the problem of inconsistency made by forward stepwise regression and backward stepwise regression. The final results showed that the combinations of NDI71 × ${\sigma}_{VV}^{0}$ and NDTI × ${\sigma}_{VH}^{0}$ had the highest correlations with field measured CRC (R

^{2}= 0.770, RMSE = 4.846%). It had significant improvements in CRC estimation, when compared with the best results with OCRIs (R

^{2}= 0.570 and RMSE = 6.560%), the best results with RPs (R

^{2}= 0.430 and RMSE = 7.052%) and the best results with OCRI-RPs (R

^{2}= 0.738, RMSE = 5.140%). It was seen that synergistic use of optical and microwave data is very helpful to improve the accuracy of CRC estimation, which can open a new way for CRC estimation.

## 5. Conclusions

^{2}= 0.570, RMSE = 6.560%), while RI2 performed best in radar parameters (R

^{2}= 0.430 and RMSE = 7.052%). Combined optical information with microwave information can significantly improve the correlation between indices and field measured CRC, for most of the combined indices had improvements in prediction accuracy when compared with corresponding optical crop residue indices and radar parameters. Among the combined indices, NDTI × RI2 had a relatively higher R

^{2}value of 0.738 and a relatively lower RMSE value of 5.140%. The optimal multiple regression model was made up of NDI71 × ${\sigma}_{VV}^{0}$ and NDTI × ${\sigma}_{VH}^{0}$, which has the highest R

^{2}value of 0.770 and lowest RMSE value of 4.846%. The mean crop residues coverage in Yucheng County was 68.05%, which suggested conservation tillage had a great promotion in Yucheng County. The findings in this study demonstrated that synergistic use of optical remote sensing and microwave remote sensing is a good way to estimate CRC at a high accuracy. However, more studies are needed to test our method and findings in other regions for longer periods to achieve a general conclusion.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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Vegetation Index | Abbreviation | Formula | Reference |
---|---|---|---|

Normalized Difference Residue Index | NDRI | (b4 − b12)/(b4 + b12) | [19] |

Normalized Difference Index 7 | NDI7 | (b8 − b12)/(b8 + b12) | [20] |

Normalized Difference Tillage Index | NDTI | (b11 − b12)/(b11 + b12) | [17] |

Normalized Difference Index 71 | NDI71 | (b5 − b12)/(b5 + b12) | This paper |

Normalized Difference Index 72 | NDI72 | (b6 − b12)/(b6 + b12) | This paper |

Normalized Difference Index 73 | NDI73 | (b7 − b12)/(b7 + b12) | This paper |

Normalized Difference Index 74 | NDI74 | (b8A − b12)/(b8A + b12) | This paper |

Crop Residue Indices | Regression Equations | R^{2} | RMSE (%) |
---|---|---|---|

NDRI | Y = 1.391x + 1.064 | 0.462 ** | 7.430 |

NDI7 | Y = 1.299x + 0.820 | 0.425 ** | 7.597 |

NDTI | Y = 4.000x + 0.360 | 0.570 ** | 6.560 |

NDI71 | Y = 1.744x + 1.015 | 0.472 ** | 7.317 |

NDI72 | Y = 1.10x + 0.864 | 0.285 ** | 8.498 |

NDI73 | Y = 0.769x + 0.796 | 0.157 * | 9.155 |

NDI74 | Y = 0.935x + 0.770 | 0.238 ** | 8.714 |

**Note**: CRC means the field measured crop residue coverage; ** means model significant at the 0.01 probability level; * means model significant at the 0.05 probability level. RMSE means the root mean square error of leave-one-out cross validation.

Radar Parameters | Regression Equations | R^{2} | RMSE (%) |
---|---|---|---|

${\sigma}_{VV}^{0}$ | Y = −1.576x + 0.903 | 0.341 ** | 8.086 |

${\sigma}_{VH}^{0}$ | Y = 9.177x + 0.638 | 0.319 ** | 8.241 |

RI1 | Y = −0.010x + 0.848 | 0.365 ** | 8.515 |

RI2 | Y = −0.117x + 0.753 | 0.430 ** | 7.052 |

VV_ME | Y = −0.001x + 0.875 | 0.123 ^{n.s.} | 9.327 |

VH_ME | Y = 0.001x + 0.628 | 0.359 ** | 7.955 |

**Note:**Probability levels are indicated by n.s. and ** for “not significant” and p < 0.01, respectively. ${\sigma}_{VV}^{0}$ and ${\sigma}_{VH}^{0}$ means the normalized backscattering coefficients in VV, and VH polarization directions. RI1 and RI2 means radar indices defined in formula (1) and formula (2). VV_ME and VH_ME means the mean variables of ${\sigma}_{VV}^{0}$ and ${\sigma}_{VH}^{0}$ in gray level co-occurrence matrix.

Variables | Regression Equations | R^{2} | RMSE (%) |
---|---|---|---|

NDRI × ${\sigma}_{VV}^{0}$ | Y = 0.300x + 0.647 | 0.625 ** | 6.296 |

NDI7 × ${\sigma}_{VV}^{0}$ | Y = 0.303x + 0.662 | 0.552 ** | 6.846 |

NDTI × ${\sigma}_{VV}^{0}$ | Y = 0.298x + 0.643 | 0.671 ** | 5.741 |

NDI71 × ${\sigma}_{VV}^{0}$ | Y = 0.308x + 0.634 | 0.667 ** | 5.763 |

NDRI × ${\sigma}_{VH}^{0}$ | Y = 0.366x + 0.685 | 0.567 ** | 6.528 |

NDI7 × ${\sigma}_{VH}^{0}$ | Y = 0.451x + 0.685 | 0.576 ** | 6.599 |

NDTI × ${\sigma}_{VH}^{0}$ | Y = 0.324x + 0.690 | 0.585 ** | 6.435 |

NDI71 × ${\sigma}_{VH}^{0}$ | Y = 298x + 0.691 | 0.507 ** | 7.097 |

NDRI × RI1 | Y = 0.306x + 0.633 | 0.672 ** | 5.789 |

NDI7 × RI1 | Y = 0.309x + 0.651 | 0.595 ** | 6.423 |

NDTI × RI1 | Y = 0.294x + 0.634 | 0.728 ** | 5.221 |

NDI71 × RI1 | Y = 0.300x + 0.624 | 0.683 ** | 5.578 |

NDRI × RI2 | Y = 0.419x + 0.631 | 0.690 ** | 5.630 |

NDI7 × RI2 | Y = 0.433x + 0.647 | 0.623 ** | 6.198 |

NDTI × RI2 | Y = 0.393x + 0.635 | 0.738 ** | 5.140 |

NDI71 × RI2 | Y = 0.403x + 0.625 | 0.696 ** | 5.473 |

NDRI × VH_ME | Y = 0.395x + 0.685 | 0.568 ** | 6.606 |

NDI7 × VH_ME | Y = 0.443x + 0.689 | 0.525 ** | 6.936 |

NDTI × VH_ME | Y = 0.334x + 0.692 | 0.551 ** | 6.755 |

NDI71 × VH_ME | Y = 0.326x + 0.692 | 0.515 ** | 7.522 |

**Note**: OCRI-RPs means the indices combined optical crop residue indices and radar parameters; ** means model significant at the 0.01 probability level (p < 0.01).

Model | Adj R^{2} | AIC | BIC | RMSE | Composite Index | Selected Independent Variables in Model |
---|---|---|---|---|---|---|

1 | 0.73 | −3.04 | −33.34 | 5.14 | 2.34 | NDTI × RVI2 |

2 | 0.75 | −3.86 | −33.89 | 4.85 | 3.26 | NDI71 × ${\sigma}_{VV}^{0}$, NDTI × ${\sigma}_{VH}^{0}$ |

3 | 0.76 | −3.33 | −32.76 | 4.83 | 3.17 | NDI71 × ${\sigma}_{VV}^{0}$, NDTI × ${\sigma}_{VH}^{0}$, NDI71 × ${\sigma}_{VH}^{0}$ |

4 | 0.77 | −2.52 | −31.33 | 4.78 | 2.99 | NDI7 × RVI1, NDRI × ${\sigma}_{VV}^{0}$, NDI7 × ${\sigma}_{VH}^{0}$, NDTI × ${\sigma}_{VH}^{0}$ |

5 | 0.77 | −1.10 | −28.94 | 5.08 | 2.01 | NDRI × RVI1, NDI71 × RVI1, NDRI × ${\sigma}_{VV}^{0}$, NDI71 × ${\sigma}_{VV}^{0}$, NDI71 × ${\sigma}_{VH}^{0}$ |

6 | 0.77 | −0.05 | −27.28 | 5.42 | 1.17 | NDRI × VH_ME, NDRI × ${\sigma}_{VV}^{0}$, NDI71 × ${\sigma}_{VV}^{0}$, NDI71 × VH_ME, NDRI × RVI1, NDI71 × RVI1 |

7 | 0.79 | −0.17 | −28.15 | 5.73 | 1.31 | NDRI_RVI1, NDI71_${\sigma}_{VV}^{0}$, NDI71_RVI1, NDRI_VH_ME, NDI71_${\sigma}_{VH}^{0}$, NDRI_${\sigma}_{VV}^{0}$ NDI7_VH_ME |

8 | 0.79 | 1.31 | −25.89 | 5.31 | 1.09 | NDRI_RVI1, NDRI_${\sigma}_{VV}^{0}$, NDRI_${\sigma}_{VH}^{0}$, NDI71_${\sigma}_{VV}^{0}$, NDTI_${\sigma}_{VH}^{0}$, NDI71_RVI1, NDTI_VH_ME, NDI71_VH_ME, |

**Note:**Adj R

^{2}, AIC, BIC and RMSE represent adjusted coefficient of determination, Bayes Information Criterion, Akaike’s Information Criterion and root mean square error of leave-one-out cross validation, respectively.

Satellite Derived Variables | 0–60% | 60–70% | 70–100% |
---|---|---|---|

NDTI | 0.03 | 0.08 | 0.12 |

NDI71 | −0.18 | −0.17 | −0.15 |

RI1 | 0.37 | 0.60 | 0.44 |

RI2 | 0.19 | 0.19 | −0.13 |

NDI71 × ${\sigma}_{VV}^{0}$ | 0.02 | 0.05 | 0.30 |

NDTI × ${\sigma}_{VH}^{0}$ | −0.52 | 0.04 | 0.43 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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.
https://doi.org/10.3390/rs11101163

**AMA Style**

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.
https://doi.org/10.3390/rs11101163

**Chicago/Turabian Style**

Cai, Wenting, Shuhe Zhao, Yamei Wang, Fanchen Peng, Joon Heo, and Zheng Duan.
2019. "Estimation of Winter Wheat Residue Coverage Using Optical and SAR Remote Sensing Images" *Remote Sensing* 11, no. 10: 1163.
https://doi.org/10.3390/rs11101163