Quantitative driving factors and zoning patterns of GNSS-R land surface reflectivity spatial heterogeneity

doi:10.11947/j.AGCS.2026.20250451

Abstract

Abstract:

Global navigation satellite system reflectometry (GNSS-R) observations, such as surface reflectivity, have complex coupling effects with various parameters including soil moisture, vegetation, and terrain. There is strong spatial heterogeneity at the regional scale, making macroclimate-surface zoning method ineffective in depicting the responses under the varying influencing factors. This study quantitatively analyzes the driving factors of the spatial heterogeneity of GNSS-R surface reflectivity and proposes a zoning framework based on the quantification of the explanatory power (q value) of driving factors. Firstly, the geodetector model is used to quantitatively evaluate the q value of multiple surface driving factors (including topography, vegetation, soil moisture, and land cover) on the spatial differentiation of CYGNSS reflectivity. The results show that surface roughness, digital elevation model (DEM), and land cover type are the three dominant factors affecting the spatial heterogeneity of reflectivity. To examine the superimposition effect of multiple factors, based on the coupling relationship, the roughness factor with the strongest explanatory power is superimposed with soil moisture and vegetation water content respectively, to construct two types of composite zoning models: “roughness+soil moisture” and “roughness+vegetation water content”. Combined with the linear regression method, the fitting effects of different zoning strategies and surface parameter combinations on reflectivity are systematically compared (using weighted R² as the evaluation index). The research results show that: ①The model fitting goodness of the zoning superposition mode (especially the “roughness+vegetation water content” combination) is generally better than that of the single-factor zoning; ②The use of the PN value representing the scattering mechanism can significantly improve the model performance; ③The high weighted R² of the land cover zoning is due to the highprecision fitting in the large sample grassland area rather than the global optimum; ④The model performs well in low-altitude flat areas but is limited in accuracy in high-altitude complex terrain areas. Compared with macroclimate-surface zoning methods, the zoning model constructed based on the quantification of driving factors shows better fitting effects in the GNSS-R surface reflectivity linear regression model. This work provides important methodological support for the precise application of GNSS-R remote sensing data in the retrieval of surface parameters, environmental monitoring, and climate change research. It contributes to advancing the integration of multi-source remote sensing data and the coupled analysis of surface processes toward a more refined and mechanistic direction. Furthermore, it holds positive theoretical and practical significance for enhancing the simulation accuracy of global and regional-scale land surface hydrological, ecological, and climate models.

Key words: GNSS-R, driver factors, spatial heterogeneity, linear regression, partitioning mode

CLC Number:

P228

Qingyun YAN, Zixuan GUO, Yuanjin PAN, Yan JIA, Shuanggen JIN. Quantitative driving factors and zoning patterns of GNSS-R land surface reflectivity spatial heterogeneity[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(2): 315-327.

Figures/Tables 10

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表征类型	驱动因子	因变量
地形	坡向	地表反射率
	地形高程
	粗糙度
	坡度
植被	生物量
	叶面积指数
	归一化植被指数
	树干高
	植被含水量
	植被光学厚度
土壤	土壤湿度
地表覆盖	土地覆盖类型

驱动因子	q值
粗糙度	0.203 3
地形高程	0.180 0
土地覆盖类型	0.146 3
坡度	0.104 0
植被光学厚度	0.078 1
树干高	0.050 6
归一化植被指数	0.046 1
生物量	0.044 3
叶面积指数	0.042 1
植被含水量	0.040 0
土壤湿度	0.011 7
坡向	0.005 2

区域模型	决定系数	F检验	样本量
区域1	0.490	P<0.01	3 045 056
区域2	0.472	P<0.01	2 817 486
区域3	0.461	P<0.01	2 550 345
区域4	0.441	P<0.01	2 452 543
区域5	0.425	P<0.01	2 519 719
区域6	0.407	P<0.01	2 613 219
区域7	0.391	P<0.01	2 710 157
区域8	0.374	P<0.01	2 811 814
区域9	0.345	P<0.01	2 888 756
区域10	0.316	P<0.01	2 918 090
区域11	0.287	P<0.01	2 904 416
区域12	0.262	P<0.01	2 907 206
区域13	0.228	P<0.01	2 891 118
区域14	0.194	P<0.01	2 933 460
区域15	0.152	P<0.01	2 934 648
区域16	0.103	P<0.01	2 914 891
区域17	0.039	P<0.01	2 495 777

分区类型	输入参数	加权R²
粗糙度	SM	0.243 3
	SM、VWC	0.246 7
	SM、VWC、PN	0.315 2
地形高程	SM	0.225 0
	SM、VWC	0.228 9
	SM、VWC、PN	0.303 1
土地覆盖类型	SM	0.238 2
	SM、VWC	0.239 3
	SM、VWC、PN	0.316 5
粗糙度+土壤湿度	SM	0.250 3
	SM、VWC	0.252 7
	SM、VWC、PN	0.323 8
粗糙度+植被	SM	0.254 5
	SM、VWC	0.256 0
	SM、VWC、PN	0.326 8

分区组合	自变量	加权R²（9类分区）	加权R²（16类分区）
粗糙度+土壤湿度	SM	0.243 9	0.248 5
	SM、VWC	0.246 5	0.250 9
	SM、VWC、PN	0.318 1	0.322 3
粗糙度+植被	SM	0.246 6	0.252 0
	SM、VWC	0.248 8	0.253 5
	SM、VWC、PN	0.319 9	0.324 5