*3.1. Band Reflectivity Analysis*

All training samples (179, 68, 50, 19, 28, 49, and 46 samples of maize, buildings, woodlands, wastelands, water, roads, and other crops) were used to perform pixel information statistics for different feature types, and reflectivity curves of different land cover types were drawn, as shown in Figure 2. Vegetation and non-vegetation have significant differences in spectral characteristics. We found that buildings, roads, and wasteland exhibit higher reflectivity in visible wavelengths (B1–B3 and B7–B8), and buildings and other land cover types were strongly separated. Roads and wasteland were significantly different in B4–B6, which can be easily distinguished. Water exhibits strong NIR waves absorption, so the reflectance of water in B4 is lower than that of all other land cover types [36]. Maize, woodland, and other plants exhibit low reflectivity of visible wavelengths due to the absorption of chlorophyll, and their spectral characteristics are similar [37]. Thus, it is difficult to distinguish each plan type, although the reflectivity is higher in B4 (NIR) and B6 (Red-edge 2). In B4, the reflectivity of different land cover types was as follows: other plants > maize > wasteland > woodland > roads > building > water. The reflectivity of wasteland and woodland were similar, and the difference between roads and buildings was also small, but both were easily distinguished in B1–B3 and B5, as they have good separation.

separation.

sorption of chlorophyll, and their spectral characteristics are similar [37]. Thus, it is difficult to distinguish each plan type, although the reflectivity is higher in B4 (NIR) and B6 (Red-edge 2). In B4, the reflectivity of different land cover types was as follows: other plants > maize > wasteland > woodland > roads > building > water. The reflectivity of

was also small, but both were easily distinguished in B1–B3 and B5, as they have good

**Figure 2.** Spectral curves of different land cover types. **Figure 2.** Spectral curves of different land cover types.

### *3.2. Class Separability 3.2. Class Separability*

In this study, JM and TD were used to measure the separability between maize and other land cover types in the research area. The range of JM and TD are within [0, 2]. As a general rule, values in the range of [0.0, 1.0) indicate a very poor class separability; values in the range of [1.0, 1.9) indicate a poor separability; and values in the range of [1.9, 2.0] indicate a relatively good separability. In this study, JM and TD were used to measure the separability between maize and other land cover types in the research area. The range of JM and TD are within [0, 2]. As a general rule, values in the range of [0.0, 1.0) indicate a very poor class separability; values in the range of [1.0, 1.9) indicate a poor separability; and values in the range of [1.9, 2.0] indicate a relatively good separability.

The class separability of the samples in Table 3 was analyzed, and we found that the separability of different land cover types was significantly different, in whether there was the participation of the new band of GF-6 satellite. Compared with S1, the JM and TD between maize and other plants in S2 increased from 1.32 and 1.43 to 1.80 and 1.97, respectively, and the values between maize and woodland also increased from 1.43 and 1.81 to 1.67 and 1.95, which indicated that B5 can significantly enhance the separability of maize from other plants and wood-lands, but the spectra between them still exhibit a large overlap. By comparing the JM between maize and other land cover types in S1 and S2, we found that B5 also contributes partly to the separability of maize from wasteland and roads. The JM between maize and all land cover types in S3 was unchanged or decreased than that in S2, except between maize and woodland, which indicated that the contribution of B6 to the separability between maize and other land cover types was less than that of B5, but the contribution to the separability between maize and woodland was greater than that of B5. To sum up, the superposition of B5 and B6 can further increase the separability of maize and other land cover types, specifically for the distinction between maize and woodland. In S2, the JM between maize and all other land cover types was greater than 1.8, and the TD was more than 1.9, indicating that when the red-edge wavelength was involved in the calculation, the separability of maize and other land cover types was very high. The purple and yellow bands added in S4 and S5, respectively, can increase the The class separability of the samples in Table 3 was analyzed, and we found that the separability of different land cover types was significantly different, in whether there was the participation of the new band of GF-6 satellite. Compared with S1, the JM and TD between maize and other plants in S2 increased from 1.32 and 1.43 to 1.80 and 1.97, respectively, and the values between maize and woodland also increased from 1.43 and 1.81 to 1.67 and 1.95, which indicated that B5 can significantly enhance the separability of maize from other plants and wood-lands, but the spectra between them still exhibit a large overlap. By comparing the JM between maize and other land cover types in S1 and S2, we found that B5 also contributes partly to the separability of maize from wasteland and roads. The JM between maize and all land cover types in S3 was unchanged or decreased than that in S2, except between maize and woodland, which indicated that the contribution of B6 to the separability between maize and other land cover types was less than that of B5, but the contribution to the separability between maize and woodland was greater than that of B5. To sum up, the superposition of B5 and B6 can further increase the separability of maize and other land cover types, specifically for the distinction between maize and woodland. In S2, the JM between maize and all other land cover types was greater than 1.8, and the TD was more than 1.9, indicating that when the red-edge wavelength was involved in the calculation, the separability of maize and other land cover types was very high. The purple and yellow bands added in S4 and S5, respectively, can increase the separability of maize and other land cover types in a certain range because they still slightly increase JD, but their effect on improving TD was not obvious.


**Table 3.** Separability between maize and other land cover types under different schemes (S1–S5).

### *3.3. Maize Identification and Classification Results*

Using the same training samples, SVM and RF classification models were used to classify the remote sensing image under the S1–S5 schemes. The classification results are shown in Table 4.

**Table 4.** Classification results based on support vector machine (SVM) and random forest (RF) models in Qihe County.


The OA of the SVM and RF models under the S1 scheme was quite low at 84.05% and 85.57%, respectively, and the KC was 0.80 and 0.82, respectively. Under the S2 scheme, the classification accuracy of the two classification methods was significantly improved, reaching more than 90%; specifically, the OA of the RF model was 93.24%, and the KC was 0.91. This indicates that Red-edge 1 can effectively improve the classification accuracy of SVM and RF models in the research area. The OA of SVM in the S3 scheme was lower than that in the S2 scheme but remained 5.75% higher than that in the S1 scheme. The total accuracy of RF in the S3 scheme exhibited a significant decline and was far lower than that of the SVM model, but it remained 1.81% higher than that of the S1 scheme. These results indicate that Red-edge 2 can effectively improve the recognition ability of the SVM model on land cover types, but the improvement is lower than that of Red-edge 1. This may be because Red-edge 2 is significantly correlated with NIR (B4; *R* <sup>2</sup> = 0.991; Figure 3a), resulting in feature redundancy. The classification accuracy of the SVM model did not change significantly in the S4 and S5 schemes, and the OA was maintained at approximately 91%, with a KC of 0.89. The OA of the RF model in the S4 scheme increased rapidly compared with the S3 scheme and exceeded the classification result accuracy of the SVM model. The OA was 93.15%, and the KC was 0.91, which was almost the same as the result of the S2 scheme. The OA of the RF model in the S5 scheme was the highest, but it did not change significantly compared with the S4 scheme. The results showed that purple and yellow bands had a limited influence on the classification results. We speculate that one of the reasons may be that B7 was significantly correlated with B1 (*R* <sup>2</sup> = 0.986; Figure 3b), and B8 was significantly correlated with B2 and B3 (*R* <sup>2</sup> = 0.982 and *R* <sup>2</sup> = 0.981; Figure 3c,d).

**Figure 3.** Correlation analysis between various bands: (**a**) B4 and B6; (**b**) B1 and B7; (**c**) B2 and B8; (**d**) B3 and B8.

The impact of the newly added bands on the classification results of various land cover types was specifically analyzed, and a confusion matrix was established for the RF model results under the S1–S5 scheme through verification samples (Table 5). Difference analysis results for each land cover type between different band combinations are shown in Table 6.

The generated confusion matrix indicates that the PA of buildings and water under the S1 scheme was higher, reaching 97.50% and 97.49%, respectively. However, a misclassification between other crops and maize was observed, and the PA of maize is 82.36%. Furthermore, many wastelands and roads were mistakenly divided into buildings. From Figure 4b,h, we intuitively found that there were a large number of road and woodland spots in the RF classification results, as well as a large number of misclassified wastelands.

Under the S2 scheme, with the addition of Red-edge 1, the classification results were significantly improved, especially for woodlands and other plants, and the PA increased from 84.45% and 59.31% to 90.29% and 72.07%, respectively. In particular, the smoothness of the classification results of woodland on both sides of the road was improved (Figure 4c). The classification accuracy of maize reached 97.57%, which was an increase of 15.21% compared to the S1 scheme. However, the classification effect of roads was not significantly improved, and a large number of misclassified discontinuous roads were mixed in the maize fields. It showed that the Red-edge 1 band significantly contributed to vegetation classification, but its role in the recognition of non-vegetation land cover types was limited.

The classification results of S3 with the addition of Red-edge 2 were similar to those of S1 without a significant difference. Although the PA of maize, woodland, and other crops was improved, the PA of water bodies and roads decreased. These results indicated that Red-edge 2 had no obvious effect on improving the classification of land cover types in the research area, particularly for non-vegetation. We intuitively determined that the addition of Red-edge 2 significantly improved the recognition of continuous roads (Figure 4d) and the removal of wasteland patches (Figure 4f). However, the fragmentation of the roads in the field was not reduced. This may be because the resolution of the remote sensing image used was too low to identify a narrow road in the field.

The classification results of the S4 scheme with two red-edge bands were similar to those of the S2 scheme, but the misclassification and omission of almost all vegetation were significantly reduced—specifically, the ability to distinguish maize from other plants was very strong. The classification results exhibited better completeness, and the continuity and smoothness of the boundary of the spots were also improved (Figure 4e). However, Figure 4k shows that discontinuous road spots remained in the classification results. This indicates that when two red-edge bands exist simultaneously, Red-edge 1 plays a major role in classifying land cover types, and Red-edge 2 can be superimposed to improve the classification accuracy of buildings and wasteland.

**Table 5.** Confusion matrix for verifying classification accuracy of RF model (unit: %).



**Table 6.** Difference analysis results for each land cover type between different band combinations. **Table 6.** Difference analysis results for each land cover type between different band combinations.

significantly improved. But compared with S4, the PA of roads, wastelands, and water was significantly improved from 83.91%, 68.63%, and 97.46% to 94.89%, 75.97%, and 100%, respectively, indicating that the purple and yellow bands were more likely to respond to non-vegetation and increase their classification accuracy, but the PA of woodland and other vegetation decreased. From the perspective of the entire research area, the purple and yellow bands effectively reduced the "salt-and-pepper phenomenon" in the classification results (Figure 4f, l), thereby improving the accuracy of maize producers to

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Significance levels: 0.05. Significance levels: 0.05.

98.28%.

**Figure 4.** Classification results of RF models under different band combination schemes: (**a**) Original image of area A; (**b**–**f**) Classification results of S1–S5 in area A; (**g**) Original image of area B; (**h**– **l**) Classification results of S1–S5 in area B. **Figure 4.** Classification results of RF models under different band combination schemes: (**a**) Original image of area A; (**b**–**f**) Classification results of S1–S5 in area A; (**g**) Original image of area B; (**h**–**l**) Classification results of S1–S5 in area B.

To sum up, we found that the overall classification accuracy was the best when the RF model was used in the S5 scheme, and the land cover types of Qihe County were extracted as shown in Figure 5. The planting area of maize was estimated with the class The S5 scheme with the purple and yellow bands added to the S4 scheme is a remote sensing image that includes all bands of GF-6 WFV and the classification results were not significantly improved. But compared with S4, the PA of roads, wastelands, and water was significantly improved from 83.91%, 68.63%, and 97.46% to 94.89%, 75.97%, and 100%, respectively, indicating that the purple and yellow bands were more likely to respond to non-vegetation and increase their classification accuracy, but the PA of woodland and other vegetation decreased. From the perspective of the entire research area, the purple and yellow bands effectively reduced the "salt-and-pepper phenomenon" in the classification results (Figure 4f,l), thereby improving the accuracy of maize producers to 98.28%.

To sum up, we found that the overall classification accuracy was the best when the RF model was used in the S5 scheme, and the land cover types of Qihe County were extracted as shown in Figure 5. The planting area of maize was estimated with the class

statistical tool in ENVI, which was 774.18 km<sup>2</sup> . According to agricultural statistics, the maize planting area in Qihe County in 2018 was 757.73 km<sup>2</sup> , which falls within a 2.17% error from our calculated value. As shown in Figure 6, the maize plantation area was the largest, accounting for 54.87% in Qihe County in 2018. These results indicate that maize is a primary food crop in Qihe County; this facilitates a notably high quantity of maize straw being produced in the region, which exhibits great potential for recycling and utilization. statistical tool in ENVI, which was 774.18 km2. According to agricultural statistics, the maize planting area in Qihe County in 2018 was 757.73 km2, which falls within a 2.17% error from our calculated value. As shown in Figure 6, the maize plantation area was the largest, accounting for 54.87% in Qihe County in 2018. These results indicate that maize is a primary food crop in Qihe County; this facilitates a notably high quantity of maize straw being produced in the region, which exhibits great potential for recycling and utilization. statistical tool in ENVI, which was 774.18 km2. According to agricultural statistics, the maize planting area in Qihe County in 2018 was 757.73 km2, which falls within a 2.17% error from our calculated value. As shown in Figure 6, the maize plantation area was the largest, accounting for 54.87% in Qihe County in 2018. These results indicate that maize is a primary food crop in Qihe County; this facilitates a notably high quantity of maize straw being produced in the region, which exhibits great potential for recycling and utilization.

**Figure 5.** Land cover types in Qihe County based on the RF model in 2018. **Figure 5.** Land cover types in Qihe County based on the RF model in 2018. **Figure 5.** Land cover types in Qihe County based on the RF model in 2018.

**Figure 6.** Land cover type proportions based on the RF model in 2018. **Figure 6.** Land cover type proportions based on the RF model in 2018. **Figure 6.** Land cover type proportions based on the RF model in 2018.

### *3.4. Spatial Distribution of Maize Straw 3.4. Spatial Distribution of Maize Straw 3.4. Spatial Distribution of Maize Straw*

Averaging the field survey data in Table 7, the density of maize straw was calculated. According to Equation (1), the spatial distribution of maize straw in 2018 (Figure 7) was obtained by multiplying the planting area (each pixel is 16 m × 16 m) with the density of straw. The distribution of maize straw was the widest in southern and northeastern Qihe County, and the average distribution density in the northernmost and southernmost regions was slightly lower. The central and northern areas exhibit the lowest quantities and average densities because these areas are characterized by numerous towns. Averaging the field survey data in Table 7, the density of maize straw was calculated. According to Equation (1), the spatial distribution of maize straw in 2018 (Figure 7) was obtained by multiplying the planting area (each pixel is 16 m × 16 m) with the density of straw. The distribution of maize straw was the widest in southern and northeastern Qihe County, and the average distribution density in the northernmost and southernmost regions was slightly lower. The central and northern areas exhibit the lowest quantities and average densities because these areas are characterized by numerous towns. Averaging the field survey data in Table 7, the density of maize straw was calculated. According to Equation (1), the spatial distribution of maize straw in 2018 (Figure 7) was obtained by multiplying the planting area (each pixel is 16 m × 16 m) with the density of straw. The distribution of maize straw was the widest in southern and northeastern Qihe County, and the average distribution density in the northernmost and southernmost regions was slightly lower. The central and northern areas exhibit the lowest quantities and average densities because these areas are characterized by numerous towns.


**Table 7.** The density of maize straw in the sampling area. **No. Area (m × m) No. of Plants Weight of Straw (kg) Density of Straw (t/km2)** 

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**Table 7.** The density of maize straw in the sampling area.

**Figure 7.** Spatial distribution of maize straw in Qihe County in 2018. **Figure 7.** Spatial distribution of maize straw in Qihe County in 2018.

The total quantity and average density of maize straw in each township were further evaluated, as shown in Figure 8. Although the straw quantities of Liuqiao and An'tou were 47.12 kt and 39.07 kt, respectively, because of the small area of cultivated land, the average maize straw density was 543.46 t/km2 and 525.58 t/km2, respectively, so these two towns were very suitable for maize straw recycling and utilization. Larger townships in the central and southern regions, including Pandian, Renliji, Jiaomiao, and Huguantun, had relatively large maize straw yields and relatively high average density, so the potential for the utilization of maize straw resources was also notable. In northern towns such as Dahuang, Yizhangtun, Biaobaisi, and Huadian and the southern towns such as Zhaoguan and Maji, the total theoretical maize straw quantity was 25–32 kt, with the average density of straw being 380–470 t/km2, which indicates that these towns were also suitable for maize straw recycling and utilization. Yancheng had a small proportion of cultivated land and the lowest average density and yield of maize straw. Due to the development of towns and secondary industries, the average density of maize straw in Zhu'A and Yanbei was also low, indicating that the utilization potential of straw resources The total quantity and average density of maize straw in each township were further evaluated, as shown in Figure 8. Although the straw quantities of Liuqiao and An'tou were 47.12 kt and 39.07 kt, respectively, because of the small area of cultivated land, the average maize straw density was 543.46 t/km<sup>2</sup> and 525.58 t/km<sup>2</sup> , respectively, so these two towns were very suitable for maize straw recycling and utilization. Larger townships in the central and southern regions, including Pandian, Renliji, Jiaomiao, and Huguantun, had relatively large maize straw yields and relatively high average density, so the potential for the utilization of maize straw resources was also notable. In northern towns such as Dahuang, Yizhangtun, Biaobaisi, and Huadian and the southern towns such as Zhaoguan and Maji, the total theoretical maize straw quantity was 25–32 kt, with the average density of straw being 380–470 t/km<sup>2</sup> , which indicates that these towns were also suitable for maize straw recycling and utilization. Yancheng had a small proportion of cultivated land and the lowest average density and yield of maize straw. Due to the development of towns and secondary industries, the average density of maize straw in Zhu'A and Yanbei was also low, indicating that the utilization potential of straw resources is relatively lesser.

is relatively lesser.

**Figure 8.** Statistics of maize straw in each township of Qihe County in 2018. **Figure 8.** Statistics of maize straw in each township of Qihe County in 2018.

### **4. Conclusions 4. Conclusions**

The results of this study show that it is feasible to use SVM and RF models to estimate the yield and spatial distribution of maize straw by combining GF6/WFV, field survey data, and agricultural census. The land cover types in Qihe County were more accurately identified by the RF model, which consequently improved the estimation accuracy of the yield and spatial distribution of the maize straw. The main conclusions are as follows: The results of this study show that it is feasible to use SVM and RF models to estimate the yield and spatial distribution of maize straw by combining GF6/WFV, field survey data, and agricultural census. The land cover types in Qihe County were more accurately identified by the RF model, which consequently improved the estimation accuracy of the yield and spatial distribution of the maize straw. The main conclusions are as follows:


### sities. **5. Future Work**

**5. Future Work**  The research method of this study still exhibits some limitations that should be addressed. After inspection, it was found that the misjudged pixels were primarily those located at the junctions of various land cover types. Many mixed pixels that were difficult The research method of this study still exhibits some limitations that should be addressed. After inspection, it was found that the misjudged pixels were primarily those located at the junctions of various land cover types. Many mixed pixels that were difficult to distinguish, even by visual interpretation, were formed in the image because of the

superimposed spectral characteristics of different land cover types at the junctions [38]. Concurrently, the spatial resolution of the remote sensing image also limited the accuracy of extraction to a large extent [39]. Therefore, the focus of further research is to fuse higher resolution spatial information, and a discriminant model based on spatial relationship knowledge and mixed pixel decomposition will also be considered to improve extraction accuracy.

**Author Contributions:** The manuscript was written through the contributions of all authors. Conceptualization, Y.Z. and R.D.; Data curation, H.M.; Formal analysis, H.M. and H.L.; Funding acquisition, Y.Z.; Investigation, H.L.; Methodology, H.M.; Supervision, R.D.; Writing—original draft, H.M.; Writing—review and editing, H.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Natural Science Foundation of China (Grant No. U20A2086; 51806242), the Special Project on Innovation Methodology, Ministry of Science and Technology of China (No. 2020IM020900); and the Yantai Educational-Local Synthetic Development Project (Grant No. 2019XDRHXMXK25 and No. 2019XDRHXMQT36).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data that supported the findings of this study can be available from the corresponding author upon reasonable request.

**Acknowledgments:** We appreciate the supports from the Key Laboratory of Clean Production and Utilization of Renewable Energy, Ministry of Agriculture and Rural Affairs, at China Agricultural University; the National Joint R&D Center for International Research of BioEnergy Science and Technology, Ministry of Science and Technology, at China Agricultural University; the National Energy R&D Center for Biomass, National Energy Administration of China, at China Agricultural University; and Beijing Municipal Key Discipline of Biomass Engineering.

**Conflicts of Interest:** The authors declare no competing financial and non-financial interests.

### **References**

