奈曼旗玉米残茬覆盖度计算方法及其对土壤风蚀量计算的影响

doi:10.7522/j.issn.1000-694X.2025.00095

摘要/Abstract

摘要：

作物残茬可有效抑制农田土壤风蚀现象发生，获得准确的作物残茬覆盖度是提高土壤风蚀量计算精度的关键。以奈曼旗玉米残茬覆盖农田为研究对象，利用实测玉米残茬高光谱与覆盖度数据，结合Sentinel-2多光谱数据构建玉米残茬覆盖度计算模型，分析研究区2018—2022年风蚀季（10月至次年5月）玉米残茬覆盖度（FVC）的时空分布特征，通过修正土壤风蚀方程（RWEQ）评估FVC计算方法对农田土壤风蚀量计算的影响。结果表明：（1）研究区FVC与三波段高光谱反射率组合（2 190、740、443 nm波段）反演的纤维素吸收指数（CAI）相关性最强；对应Sentinel-2A的B₁₂、B₆、B₁波段构建FVC计算模型为FVC=1.344CAI-0.07，R² =0.77。（2）基于CAI计算的研究区玉米残茬覆盖度约为19.3%，较基于归一化植被指数（NDVI）计算的玉米残茬覆盖度更能反映玉米残茬覆盖的真实状况，后者明显低估玉米残茬覆盖度，基于CAI计算的玉米残茬覆盖度约是基于NDVI计算的玉米残茬覆盖度的1.47倍。（3）研究区平均土壤风蚀量约为6.702 t·hm^-2，基于CAI计算的土壤风蚀量更接近实际风蚀观测结果，基于NDVI计算的土壤风蚀量高估约0.55倍。（4）研究区农田土壤风蚀主要发生在4—5月，南部与北部以微度侵蚀、轻度侵蚀为主，中部与北部边缘以强烈侵蚀、极强烈侵蚀为主。

关键词: 奈曼旗, 纤维素吸收指数, 高光谱, 玉米残茬覆盖, 土壤风蚀

Abstract:

Crop residues can effectively restrain the occurrence of soil erosion in farmland， and obtaining accurate crop residue coverage is the key to improving the accuracy of soil erosion calculation. Taking the Maize residue coverage farmland in Naiman Banner as the research object， a Maize residue coverage calculation model was constructed using field-measured hyperspectral data and coverage data of Maize residue， combined with Sentinel-2 multispectral data. The spatiotemporal distribution characteristics of Maize Residue Coverage （FVC） during the wind erosion season from 2018 to 2022 （October to May of the following year） in the study area were analyzed. The impact of FVC calculation method on the calculation of soil erosion rate in farmland was evaluated by modifying the Revised Wind Erosion Equation （RWEQ）. The results showed that：（1） The correlation between FVC in the study area and the cellulose absorption index （CAI） inverted from three-band high-reflectance spectra （2 190 nm， 740 nm， 443 nm）. The FVC calculation model was constructed corresponding to the B₁₂， B₆， and B₁ bands of Sentinel-2A： FVC= 1.344CAI-0.07， R²=0.77. （2）The FVC in the study area is approximately 19.3%， FVC calculated based on CAI can better reflect the true situation of Maize residue coverage than the FVC calculated based on the Normalized Vegetation Index （NDVI）， which significantly underestimates FVC. The average annual FVC based on CAI is about 1.47 times that of FVC based on NDVI. （3） The average wind erosion rate in the study area is about 6.702 t·hm^-2. The wind erosion rate calculated based on CAI is closer to the wind erosion observation results， and the wind erosion rate calculated based on NDVI is significantly overestimated by about 0.55 times. （4）The wind erosion of farmland soil in the research area is mainly concentrated in April and May， with slight and mild erosion in the southern and northern parts， and strong and extremely strong erosion in the central and northern edges.

Key words: Naiman Banner, cellulose absorption index, hyperspectral, maize residue cover, soil wind erosion

中图分类号:

王飞翔, 甄天乐, 李继峰, 李慧茹, 郭中领, 常春平, 张夏蕾, 于凯昕. 奈曼旗玉米残茬覆盖度计算方法及其对土壤风蚀量计算的影响[J]. 中国沙漠, 2025, 45(4): 241-252.

Feixiang Wang, Tianle Zhen, Jifeng Li, Huiru Li, Zhongling Guo, Chunping Chang, Xialei Zhang, Kaixin Yu. Calculation method of maize residue coverage in Naiman Banner and its influence on the calculation of soil wind erosion rate[J]. Journal of Desert Research, 2025, 45(4): 241-252.

图/表 13

图1 研究区及样区分布（A）、野外数据采集场景（B）及不同玉米残茬覆盖度（C）照片注：基于自然资源部标准地图服务网站标准地图（审图号：蒙S（2024）17号）制作，底图边界无修改

Fig.1 Study area location and distribution of sample points （A）， and photos of field data acquisition scene （B）， and different corn residue coverage （C）

图2 不同覆盖度玉米残茬及土壤反射率（A）及其一阶微分（B）曲线

Fig.2 Spectral reflectance （A） and its first-order differential （B） curves of soil and maize residue with different coverage levels

表1 不同光谱指数及计算公式

Table 1 Different maize spectral indices and calculation formulas

光谱指数	简称	计算公式
归一化差值指数^[26]	NDI₁	$N D V 1 = λ i - λ j λ i + λ j$
比值指数^[27]	RI	$R I = λ i λ j$
归一化木质素指数^[28]	NDI₂	$N L D 2 = l g 1 λ i - l g 1 λ j l g 1 λ i + l g 1 λ j$
干枯燃料指数^[29]	DFI	$D F I = 100 × (1 - λ i / λ j) / λ k$
纤维素吸收指数^[30]	CAI	$C A I = 0.5 × (λ i + λ j) - λ k$

表1 不同光谱指数及计算公式

Table 1 Different maize spectral indices and calculation formulas

光谱指数	简称	计算公式
归一化差值指数^[26]	NDI₁	$N D V 1 = λ i - λ j λ i + λ j$
比值指数^[27]	RI	$R I = λ i λ j$
归一化木质素指数^[28]	NDI₂	$N L D 2 = l g 1 λ i - l g 1 λ j l g 1 λ i + l g 1 λ j$
干枯燃料指数^[29]	DFI	$D F I = 100 × (1 - λ i / λ j) / λ k$
纤维素吸收指数^[30]	CAI	$C A I = 0.5 × (λ i + λ j) - λ k$

图3 不同光谱指数与玉米残茬覆盖度相关性矩阵

Fig.3 Correlation matrix between different spectral indices and maize residue coverage

表2 Sentinel-2A光谱指数与玉米残茬覆盖度回归分析

Table 2 Regression analysis of Sentinel-2A spectral index and maize residue coverage

光谱指数	基于Sentinel-2A影像的指数计算公式	回归方程	n	R²	RMSE/%
NDI₁	NDI₁= $B 10 - B 8 a B 10 + B 8 a$	FVC=-0.0024NDI₁+0.1489	36	0.54	8.08
RI	RI= $B 12 B 4$	FVC=0.4418RI+93.319	36	0.52	8.24
NDI₂	$N D I 2 = l g 1 B 10 - l g 1 B 9 l g 1 B 10 + l g 1 B 9$	FVC=0.0031NDI₂-0.1244	36	0.50	8.45
DFI	DFI=100 $×$ (1-B₁₂/B₄)/B₈	FVC=0.097DFI+47.154	36	0.47	8.64
CAI	CAI=0.5 $×$ (B₁₂+B₁)-B₆	FVC=1.344CAI-0.07	36	0.77	5.77

表2 Sentinel-2A光谱指数与玉米残茬覆盖度回归分析

Table 2 Regression analysis of Sentinel-2A spectral index and maize residue coverage

光谱指数	基于Sentinel-2A影像的指数计算公式	回归方程	n	R²	RMSE/%
NDI₁	NDI₁= $B 10 - B 8 a B 10 + B 8 a$	FVC=-0.0024NDI₁+0.1489	36	0.54	8.08
RI	RI= $B 12 B 4$	FVC=0.4418RI+93.319	36	0.52	8.24
NDI₂	$N D I 2 = l g 1 B 10 - l g 1 B 9 l g 1 B 10 + l g 1 B 9$	FVC=0.0031NDI₂-0.1244	36	0.50	8.45
DFI	DFI=100 $×$ (1-B₁₂/B₄)/B₈	FVC=0.097DFI+47.154	36	0.47	8.64
CAI	CAI=0.5 $×$ (B₁₂+B₁)-B₆	FVC=1.344CAI-0.07	36	0.77	5.77

图4 根据不同玉米残茬指数计算的覆盖度与实测覆盖度对比

Fig.4 Comparison between the calculation results of maize residue indices and the field measured results

图5 2018—2022年研究区风蚀季玉米残茬覆盖度

Fig.5 Monthly maize residue coverage in the wind erosion season of 2018-2022

图6 2018—2022年研究区风蚀季玉米残茬覆盖度空间分布

Fig.6 Monthly spatial distribution of maize residue coverage in the wind erosion season of 2018-2022

图7 2018—2022年研究区风蚀季土壤风蚀量时间分布

Fig.7 Monthly soil wind erosion rate in the wind erosion season of 2018-2022

图8 2018—2022年研究区风蚀季土壤风蚀量空间分布

Fig.8 Monthly spatial distribution of soil wind erosion rate in the wind erosion season of 2018-2022

图9 使用两种指数计算的风蚀季玉米残茬覆盖度

Fig.9 Monthly average maize residue coverage calculated by two different indices in the wind erosion season of 2018-2022

图10 采用两种玉米残茬覆盖度计算的风蚀季土壤风蚀量对比

Fig.10 Monthly distribution of soil wind erosion rate calculated by two different maize residue coverage in the wind erosion season of 2018-2022

图11 采用NDVI和CAI计算的风蚀季土壤风蚀量差值空间分布

Fig.11 Differences in spatial distribution of monthly soil wind erosion rate calculated by two different maize residue coverage in the wind erosion season of 2018-2022

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