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Arid Land Geography >

2025 , Vol. 48 >Issue 3: 455 - 466

DOI: https://doi.org/10.12118/j.issn.1000-6060.2024.169

Plant Ecology

Estimation of crop stubble biomass in the Bashang region of Zhangjiakou by combining optics and radar remote sensing

  • YU Kaixin ,
  • LI Jifeng ,
  • ZHEN Tianle ,
  • ZHANG Xialei ,
  • LI Huiru ,
  • GUO Zhongling ,
  • CHANG Chunping ,
  • ZHAO Xueqing
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  • 1. College of Geographical Sciences, Hebei Normal University, Shijiazhuang 050000, Hebei, China
    2. Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang 050000, Hebei, China
    3. Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050000, Hebei, China
    4. Hebei Key Research Institute of Humanities and Social Sciences at Universities “GeoComputation and Planning Center of Hebei Normal University”, Shijiazhuang 050000, Hebei, China

Received date: 2024-03-14

  Revised date: 2024-05-24

  Online published: 2025-03-14

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Abstract

Non-photosynthetic vegetation, such as crop stubble, plays a crucial role in material cycling and energy flow in arid and semi-arid ecosystems. It also significantly contributes to inhibiting soil erosion, retaining soil moisture, and promoting soil development. The Bashang region of Zhangjiakou Hebei Province, China is a core area for the ecological construction of Beijing-Tianjin sandstorm control and the development of the two capital areas. Estimating crop stubble biomass in this region using remote sensing is essential for evaluating regional wind erosion, the ecological environment, and the carbon and nitrogen cycles. This study utilized measured crop stubble biomass, Sentinel-2 optical images, and Sentinel-1 radar images to construct optical and radar remote sensing indices of crop stubble. Using optimal index normalization and multiple linear stepwise regression analysis, an estimation model combining optical and radar remote sensing was developed to calculate and analyze crop stubble biomass in the Bashang region from 2017 to 2023. The results show that: (1) Among the optical remote sensing indices, the RI(11,12) index, derived from Sentinel-2 short-wave infrared bands (B11 and B12), showed the highest correlation with crop stubble biomass, with a determination coefficient (R2) of 0.744. For radar remote sensing indices, the cross-polarization (VH) backscattering coefficient had the highest correlation with crop stubble biomass, achieving an R2 of 0.409. (2) The multivariate linear stepwise regression model demonstrated the highest accuracy, with an R2 of 0.796 and a root mean square error (RMSE) of 8.84 g·m-2, making it a reliable predictor of crop stubble biomass. (3) The estimation model incorporating both optical and radar remote sensing indices improved prediction accuracy by approximately 9.72% compared to optical remote sensing alone and by 66.74% compared to radar remote sensing alone. (4) From 2017 to 2023, the average annual crop stubble biomass in the Bashang region was 23.74×104 t, exhibiting a fluctuating downward trend. Annual variations in crop stubble biomass were influenced by air temperature and precipitation, while changes in planting structures driven by land transfer policies were a significant factor contributing to the decline in recent years.

Key words： crop stubble; biomass; optical remote sensing; radar remote sensing; the Bashang region of Zhangjiakou

Cite this article

YU Kaixin , LI Jifeng , ZHEN Tianle , ZHANG Xialei , LI Huiru , GUO Zhongling , CHANG Chunping , ZHAO Xueqing . Estimation of crop stubble biomass in the Bashang region of Zhangjiakou by combining optics and radar remote sensing[J]. Arid Land Geography, 2025 , 48(3) : 455 -466 . DOI: 10.12118/j.issn.1000-6060.2024.169

References

[1] Daughtry C S T, Hunt E R, Mcmurtrey J E. Assessing crop residue cover using shortwave infrared reflectance[J]. Remote Sensing of Environment, 2004, 90(1): 126-134.
[2] Kumar K, Goh K M. Crop residues and management practices: Effects on soil quality, soil nitrogen dynamics, crop yield, and nitrogen recovery[M]. United States: Academic Press, 1999: 197-319.
[3] Fu B, Chen L, Huang H Y, et al. Impacts of crop residues on soil health: A review[J]. Environmental Pollutants and Bioavailability, 2021, 33(1): 164-173.
[4] 赵明月, 刘源鑫, 张雪艳. 农田生态系统碳汇研究进展[J]. 生态学报, 2022, 42(23): 9405-9416.
  [Zhao Mingyue, Liu Yuanxin, Zhang Xueyan. A review of research advances on carbon sinks in farmland ecosystems[J]. Acta Ecologica Sinica, 2022, 42(23): 9405-9416. ]
[5] 常琳溪, 梁新然, 王磊, 等. 中国稻田土壤有机碳汇特征与影响因素的研究进展[J]. 土壤, 2023, 55(3): 487-493.
  [Chang Linxi, Liang Xinran, Wang Lei, et al. Research progress on characteristics and influencing factors of soil organic carbon sink in paddy fields in China[J]. Solis, 2023, 55(3): 487-493. ]
[6] 张越, 陈思宇, 毕鸿儒, 等. 干旱半干旱区农田土壤风蚀特征及参数化研究进展[J]. 中国沙漠, 2022, 42(3): 105-117.
  [Zhang Yue, Chen Siyu, Bi Hongru, et al. Characteristics and parameterization of farmland soil wind erosion in arid and semi-arid areas of China: Progress and challenges[J]. Journal of Desert Research, 2022, 42(3): 105-117. ]
[7] 吴盈盈, 王振亭. 河套平原土壤风蚀风险评估[J]. 干旱区地理, 2023, 46(3): 418-427.
  [Wu Yingying, Wang Zhenting. Risk assessment of soil wind erosion in Hetao Plain[J]. Arid Land Geography, 2023, 46(3): 418-427. ]
[8] Verhulst N, Nelissen V, Jespers N, et al. Soil water content, maize yield and its stability as affected by tillage and crop residue management in rainfed semi-arid highlands[J]. Plant and Soil, 2011, 344(1-2): 73-85.
[9] 张翼夫, 李洪文, 何进, 等. 玉米秸秆覆盖对坡面产流产沙过程的影响[J]. 农业工程学报, 2015, 31(7): 118-124.
  [Zhang Yifu, Li Hongwen, He Jin, et al. Effects of maize straw mulching on runoff and sediment process of slope[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(7): 118-124. ]
[10] 赵霞, 王秀萍, 刘天学, 等. 麦茬处理方式对土壤蒸发及夏玉米水分利用效率的影响[J]. 耕作与栽培, 2008(4): 9, 32.
  [Zhao Xia, Wang Xiuping, Liu Tianxue, et al. Effects of wheat stubble treatment methods on soil evaporation and water use efficiency of summer maize[J]. Tillage and Cultivation, 2008(4): 9, 32. ]
[11] 蔺阿荣, 周冬梅, 马静, 等. 基于RWEQ模型的疏勒河流域防风固沙功能价值评估[J]. 干旱区地理, 2024, 47(1): 58-67.
  [Lin Arong, Zhou Dongmei, Ma Jing, et al. Evaluation of wind prevention and sand fixation function in Shule River Basin based on RWEQ model[J]. Arid Land Geography, 2024, 47(1): 58-67. ]
[12] 谭锦, 吴秀芹, 阮永健, 等. 油莎豆(Cyperus esculentus)耕作区作物残茬对农田风蚀的影响[J]. 干旱区地理, 2022, 45(2): 546-556.
  [Tan Jin, Wu Xiuqin, Ruan Yongjian, et al. Effects of crop residues on farmland wind erosion in Cyperus esculentus planting area[J]. Arid Land Geography, 2022, 45(2): 546-556. ]
[13] 崔明, 赵立欣, 田宜水, 等. 中国主要农作物秸秆资源能源化利用分析评价[J]. 农业工程学报, 2008, 24(12): 291-296.
  [Cui Ming, Zhao Lixin, Tian Yishui, et al. Analysis and evaluation on energy utilization of main crop straw resources in China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(12): 291-296. ]
[14] 高利伟, 马林, 张卫峰, 等. 中国作物秸秆养分资源数量估算及其利用状况[J]. 农业工程学报, 2009, 25(7): 173-179.
  [Gao Liwei, Ma Lin, Zhang Weifeng, et al. Estimation of nutrient resource quantity of crop straw and its utilization situation in China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(7): 173-179. ]
[15] 谢光辉, 王晓玉, 任兰天. 中国作物秸秆资源评估研究现状[J]. 生物工程学报, 2010, 26(7): 855-863.
  [Xie Guanghui, Wang Xiaoyu, Ren Lantian. China’s crop residues resources evaluation[J]. Chinese Journal of Biotechnology, 2010, 26(7): 855-863. ]
[16] 赵越, 徐大伟, 范凯凯, 等. Landsat 8和机器学习估算蒙古高原草地地上生物量[J]. 农业工程学报, 2022, 38(24): 138-144.
  [Zhao Yue, Xu Dawei, Fan Kaikai, et al. Estimating above-ground biomass in grassland using Landsat 8 and machine learning in Mongolian Plateau[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(24): 138-144. ]
[17] 郭芮, 伏帅, 侯蒙京, 等. 基于Sentinel-2数据的青海门源县天然草地生物量遥感反演研究[J]. 草业学报, 2023, 32(4): 15-29.
  [Guo Rui, Fu Shuai, Hou Mengjing, et al. Remote sensing retrieval of nature grassland biomass in Menyuan County, Qinghai Province experimental area based on Sentinel-2 data[J]. Acta Prataculturae Sinica, 2023, 32(4): 15-29. ]
[18] Munyati C. Detecting the distribution of grass aboveground biomass on a rangeland using Sentinel-2 MSI vegetation indices[J]. Advances in Space Research, 2022, 69(2): 1130-1145.
[19] 雷步云. 基于全极化雷达数据的冬小麦作物残茬生物量估算研究[D]. 南京: 南京大学, 2017.
  [Lei Buyun. Estimation of winter wheat residue biomass based on full polarimetric radar data[D]. NanJing: Nanjing University, 2017. ]
[20] Kushwaha A, Dave R, Kumar G, et al. Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data[J]. Advances in Space Research, 2022, 70(12): 3833-3844.
[21] 张淼, 李强子, 蒙继华, 等. 作物残茬覆盖度遥感监测研究进展[J]. 光谱学与光谱分析, 2011, 31(12): 3200-3205.
  [Zhang Miao, Li Qiangzi, Meng Jihua, et al. Review of crop residue fractional cover monitoring with remote sensing[J]. Spectroscopy and Spectral Analysis, 2011, 31(12): 3200-3205. ]
[22] 冯哲, 刘瑞娟, 白宇晨, 等. 农田近地表风沙流风程效应变化特征研究[J]. 干旱区地理, 2022, 45(5): 1500-1512.
  [Feng Zhe, Liu Ruijuan, Bai Yuchen, et al. Variation characteristics of the fetch effect of near surface aeolian sand flux for farmlands[J]. Arid Land Geography, 2022, 45(5): 1500-1512. ]
[23] 刘之榆, 刘忠, 万炜, 等. SAR与光学遥感影像的玉米秸秆覆盖度估算[J]. 遥感学报, 2021, 25(6): 1308-1323.
  [Liu Zhiyu, Liu Zhong, Wan Wei, et al. Estimation of maize residue cover on the basisof SAR and optical remote sensing image[J]. National Remote Sensing Bulletin, 2021, 25(6): 1308-1323. ]
[24] Clark R N, Gallagher A J, Swayze G A, et al. Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra[J]. Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, 1990, 90-54: 176-186.
[25] Elvidge C D. Visible and near infrared reflectance characteristics of dry plant materials[J]. Remote Sensing, 1990, 11(10): 1775-1795.
[26] Gaparovi M, Dobrini D. Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal Sentinel-1 imagery[J]. Remote Sensing, 2020, 12(12): 1952, doi: 10.3390/rs12121952.
[27] Vreugdenhil M, Wagner W, Bauer-Marschallinger B, et al. Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study[J]. Remote Sensing, 2018, 10(9): 1396, doi: 10.3390/rs10091396.
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