结合光学与雷达遥感的张家口坝上地区作物残茬生物量估算

RichHTML

PDF (PC)

赞

可视化

结合光学与雷达遥感的张家口坝上地区作物残茬生物量估算

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

结合光学与雷达遥感的张家口坝上地区作物残茬生物量估算

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

摘要/Abstract

图/表 9

参考文献 27

Metrics

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 27

相关文章 15

Metrics

本文评价

推荐阅读 0

doi:10.12118/j.issn.1000-6060.2024.169

干旱区地理 ›› 2025, Vol. 48 ›› Issue (3): 455-466.doi: 10.12118/j.issn.1000-6060.2024.169 cstr: 32274.14.ALG2024169

于凯昕¹(), 李继峰¹^,²^,³^,⁴(), 甄天乐¹, 张夏蕾¹, 李慧茹¹^,³, 郭中领¹^,²^,³, 常春平¹^,²^,³, 赵雪晴¹

1.河北师范大学地理科学学院，河北石家庄 050000
2.河北省环境变化遥感识别技术创新中心，河北石家庄 050000
3.河北省环境演变与生态建设重点实验室，河北石家庄 050000
4.河北省高等学校人文社会科学重点研究基地“河北师范大学地理计算与规划研究中心”，河北石家庄 050000

收稿日期:2024-03-14 修回日期:2024-05-24 出版日期:2025-03-25 发布日期:2025-03-14
通讯作者: 李继峰（1987-），男，博士，副教授，主要从事水土保持与荒漠化防治研究. E-mail: lijifeng@hebtu.edu.cn
作者简介:于凯昕（2000-），女，硕士研究生，主要从事遥感信息机理与环境遥感应用研究. E-mail: 19832109246@163.com
基金资助:
国家自然科学基金项目(41901001);国家自然科学基金项目(42271002);河北省高等教育教学改革研究项目(2019GJJG140)

YU Kaixin¹(), LI Jifeng¹^,²^,³^,⁴(), ZHEN Tianle¹, ZHANG Xialei¹, LI Huiru¹^,³, GUO Zhongling¹^,²^,³, CHANG Chunping¹^,²^,³, ZHAO Xueqing¹

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:2024-03-14 Revised:2024-05-24 Published:2025-03-25 Online:2025-03-14

摘要：

作物残茬等非光合植被在干旱、半干旱地区生态系统物质循环、能量流动过程中承担着不可替代的角色，同时在阻抑土壤侵蚀、保持土壤水分、促进土壤发育等方面也具有重要作用。张家口坝上地区位于首都两区建设及京津风沙源治理的核心区域，利用遥感手段估算该地区作物残茬生物量，对区域风蚀状况评估、生态环境评价及碳、氮循环研究具有重要意义。基于实地测量的作物残茬生物量、Sentinel-2光学影像、Sentinel-1雷达影像构建作物残茬光学遥感指数和雷达遥感指数，采用最优指数归一化相乘和多元线性逐步回归分析方法，建立结合光学与雷达遥感的作物残茬生物量估算模型，计算并分析2017—2023年张家口坝上地区作物残茬生物量。结果表明：（1）光学遥感指数中，由Sentinel-2的短波红外波段（B11和B12）构建的RI_(11,12)指数与作物残茬生物量的相关性最高，模型决定系数（R²）为0.744。雷达遥感指数中，交叉极化（VH）后向散射系数与作物残茬生物量的相关性最高，R²为0.409。（2）结合光学与雷达遥感估算模型中，多元线性逐步回归模型精度最高，R²为0.796，均方根误差（RMSE）为8.84 g·m^-2，可较好地预测作物残茬生物量。（3）构建的作物残茬生物量估算模型精度较单纯使用光学遥感高约9.72%，较单纯使用雷达遥感高约66.74%。（4） 2017—2023年张家口坝上地区年均作物残茬生物量为23.74×10⁴ t，呈波动下降趋势，作物残茬生物量年际变化受气温和降水的影响，近年来，因土地流转政策造成的种植结构变化是导致该地区作物残茬生物量下降的重要因素。

关键词: 作物残茬, 生物量, 光学遥感, 雷达遥感, 张家口坝上

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

于凯昕, 李继峰, 甄天乐, 张夏蕾, 李慧茹, 郭中领, 常春平, 赵雪晴. 结合光学与雷达遥感的张家口坝上地区作物残茬生物量估算[J]. 干旱区地理, 2025, 48(3): 455-466.

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.

图1

图2

图3

表1

光学遥感指数与实测作物残茬生物量的线性回归"

光学遥感指数	公式	回归方程	R²	RMSE/g·m^-2
NDI_(11,12)	$\frac{R (11) - R (12)}{R (11) + R (12)}$ $R 11 - R 12 R 11 + R 12$	y=1025.8x+15.856	0.738^**	9.81
NDI_(9,12)	$\frac{R (9) - R (12)}{R (9) + R (12)}$ $R 9 - R 12 R 9 + R 12$	y=412.85x+127.98	0.479^**	13.85
NDI_(8A,12)	$\frac{R (8 A) - R (12)}{R (8 A) + R (12)}$ $R 8 A - R 12 R 8 A + R 12$	y=395.85x+127.38	0.401^**	14.36
RI_(11,12)	$\frac{R (11)}{R (12)}$ $R 11 R 12$	y=448.03x-425.06	0.744^**	9.70
RI_(12,11)	$\frac{R (12)}{R (11)}$ $R 12 R 11$	y=580.98x+595.84	0.732^**	9.92
RI_(9,12)	$\frac{R (9)}{R (12)}$ $R 9 R 12$	y=249.89x-118.13	0.483^**	13.79

表1

图4

表2

图5

图6

图7

[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. doi: 10.7522/j.issn.1000-694X.2021.00160
	[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. ] doi: 10.7522/j.issn.1000-694X.2021.00160
[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. doi: 10.12118/j.issn.1000-6060.2022.323
	[Wu Yingying, Wang Zhenting. Risk assessment of soil wind erosion in Hetao Plain[J]. Arid Land Geography, 2023, 46(3): 418-427. ] doi: 10.12118/j.issn.1000-6060.2022.323
[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. doi: 10.12118/j.issn.1000－6060.2023.333
	[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. ] doi: 10.12118/j.issn.1000－6060.2023.333
[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. doi: 10.12118/j.issn.1000–6060.2021.310
	[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. ] doi: 10.12118/j.issn.1000–6060.2021.310
[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. ] pmid: 20954384
[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. doi: 10.11686/cyxb2022147
	[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. ] doi: 10.11686/cyxb2022147
[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. ] pmid: 22295759
[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. doi: 10.12118/j.issn.1000-6060.2021.597
	[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. ] doi: 10.12118/j.issn.1000-6060.2021.597
[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.

Viewed

Full text

From	Others	local

Times	11	9
Rate	55%	45%

Abstract

Just accepted	Online first	Issue

0	0	41

From	Others	local

Times	30	11
Rate	73%	27%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

模型名称	回归方程	R²	RMSE/g·m^-2
VH×NDI_(11,12)	y=79.935x+65.077	0.701^**	10.48
VH×NDI_(9,12)	y=82.827x+67.081	0.606^**	12.03
VH×NDI_(8A,12)	y=84.679x+66.392	0.572^**	12.55
VH×RI_(11,12)	y=80.584x+65.463	0.705^**	10.41
VH×RI_(12,11)	y=-57.735x+97.404	0.100	18.20
VH×RI_(9,12)	y=84.533x+67.768	0.605^**	12.06
多元线性逐步回归模型	y=354.133RI_（11,12）+70.914RI_（9,12）+ 1.477（VH+VV）-321.174	0.796^**	8.84

Just accepted

Online first

Just accepted

Online first

[1]	张志明, 孙小妹, 包段红, 姚宝辉, 王志成, 苏军虎. 祁连山北麓荒漠草原5种优势植物生物量与土壤养分特征[J]. 干旱区地理, 2024, 47(4): 662-671.
[2]	罗新兰,孙悦,刘利民,王立为,杨丽桃,高西宁. 华北一作区马铃薯生长发育及产量对干旱胁迫响应的模拟研究——以武川县为例[J]. 干旱区地理, 2022, 45(3): 867-878.
[3]	谭锦,吴秀芹,阮永健,张欢,冯梦馨,莎日娜. 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.
[4]	叶静芸,吴波,贾晓红,费兵强,高君亮,成龙,庞营军,姚斌,孔德庸. 极干旱区稀疏荒漠植被地上生物量遥感估算[J]. 干旱区地理, 2022, 45(2): 478-487.
[5]	王清涛, 赵传燕, 王小平, 胡姗姗, 刘美艳, 史文宇, 王晓雨, 单文荣 . 基于 FAREAST 模型的青海云杉中-幼龄林生物量碳沿海拔梯度分布特征[J]. 干旱区地理, 2020, 43(5): 1316-1326.
[6]	张华, 张玉红, 张改改. 民勤绿洲青土湖植被优势种地上生物量估算[J]. 干旱区地理, 2020, 43(1): 201-210.
[7]	罗庆辉, 许仲林, 徐泽源, 李路, 常亚鹏, 徐昕亿, 宋昕妮. 天山雪岭云杉个体生物量分配及其变化规律的研究 [J]. 干旱区地理, 2019, 42(6): 1378-1386.
[8]	许佳, 祝晓瞳, 苑塏烨. 额河源流采金矿区不同恢复措施对矿区物种多样性和地上生物量的影响[J]. 干旱区地理, 2019, 42(3): 581-589.
[9]	王顺利, 王荣新, 敬文茂, 赵维俊, 牛赟, 马剑, 朱红. 祁连山干旱山地草地生物量对水分条件的响应[J]. 干旱区地理, 2017, 40(4): 772-779.
[10]	单立山, 李毅, 段桂芳, 张正中, 张荣, 种培芳. 模拟降雨变化对两种荒漠植物幼苗生长及生物量分配的影响[J]. 干旱区地理, 2016, 39(6): 1267-1274.
[11]	单立山, 李毅, 段桂芳, 张正中, 张荣, 种培芳. 模拟降雨变化对两种荒漠植物幼苗生长及生物量分配的影响[J]. 干旱区地理, 2016, 39(6): 1267-1274.
[12]	管海英，王权，赵鑫，靳佳，张思楠. 两种典型荒漠植被区土壤微生物量碳的季节变化及影响因素分析[J]. 干旱区地理, 2015, 38(1): 67-75.
[13]	王永吉，赵学春，来利明，朱林海，王健健，周继华，姜联合，马远见，赵春强，郑元润. 沙枣人工群落细根生物量和周转过程[J]. 干旱区地理, 2014, 37(3): 548-554.
[14]	陶冶，张元明. 中亚干旱荒漠区植被碳储量估算[J]. 干旱区地理, 2013, 36(4): 615-622.
[15]	吕笃康，史金，巴音山，赵玉. 伊犁粗茎驼蹄瓣生物量及生殖分配特性[J]. 干旱区地理, 2013, 36(3): 475-481.