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

doi:10.12118/j.issn.1000-6060.2024.169

Abstract

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

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.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Tab. 1

Linear regression between optical remote sensing indices and measured crop stubble biomass"

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

Tab. 1

Fig. 4

Tab. 2

Fig. 5

Fig. 6

Fig. 7

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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

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

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