基于无人机影像分层建模的土壤深层盐渍化空间预测

doi:10.3864/j.issn.0578-1752.2025.20.004

Abstract

Abstract:

【Background】Soil salinization severely constrains crop growth and ecological balance, and its accurate monitoring is essential for saline-alkali land reclamation, yield forecasting, and precision farmland management. Driven by natural and anthropogenic factors, salinization is governed by the redistribution of water and salt within the soil profile, exhibiting pronounced vertical migration and strong spatial heterogeneity. Although unmanned aerial vehicle (UAV) remote sensing is now widely used for field-scale salinity mapping, it essentially captures surface information and fails to characterize salt gradients in deeper layers. 【Objective】To develop a UAV-image-based, layer-specific modeling framework that integrates machine learning with Kriging interpolation for high-resolution 3-D mapping of subsurface soil salinity.【Method】Firstly, the UAV was equipped with multispectral sensors to obtain high-resolution images of the test field, and the soil salinity data at different depths were measured synchronously, supplemented by real-time dynamic differential positioning technology to ensure spatial accuracy. Then, a spectral feature set including the red-edge band was constructed, and the feature optimization was carried out based on the random forest algorithm. On this basis, machine learning and Kriging interpolation method were combined to establish a stratified soil salinity prediction model and generate a high-resolution salinity distribution map. Finally, the advantages of the proposed method in the spatial representation of deep salinization were verified by comparing it with the cubic fitting depth function prediction method.【Result】The prediction accuracy R² of each depth of deep soil salinization spatial prediction by the mixed model hierarchical modeling was 0.68 (0-10 cm), 0.51 (10-20 cm), 0.58 (20-40 cm), 0.56 (40-60 cm) and 0.52 (60-80 cm), respectively, and the prediction effect of 0-10 cm surface layer was the best. The red-edged salinity index was an important predictor at all depths, which verified the applicability and effectiveness of the constructed red-edged index. By comparing the prediction results of the mixed model with the cubic fitting depth function, the spatial prediction accuracy of deep soil salinization in the layered model of the mixed model was higher, and it could more truly reflect the salinization degree at different depths in the experimental area.【Conclusion】UAV remote sensing technology is the best in shallow (0-10 cm) soil salinity prediction, and the prediction accuracy of soil properties decreases with the increase of depth, and the depth accuracy still needs to be improved. From the prediction results, the average soil salinity gradually increases with the increase of depth, indicating that there is an accumulation phenomenon of salt in the soil profile. Compared with the cubic fitting depth function method, the hybrid model based on random forest stratification modeling and Kriging residual correction shows higher spatial prediction accuracy in each soil layer, which is more reasonable and practical, and provides a scientific basis for dynamic monitoring of regional soil salinization and accurate layered soil salinity mapping.

Key words: UAV remote sensing, soil salinization, depth, hybrid model, random forest, Kriging interpolation, salinity prediction

LEI MingKuo, ZHA Yan, WANG Li, CHENG Gang, WEN CaiYun, YIN ZuoTang, LU Miao. Spatial Prediction of Deep Soil Salinization Based on Layered Modeling Using UAV Imagery[J].Scientia Agricultura Sinica, 2025, 58(20): 4070-4084.

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Figures/Tables 10

Fig. 1

Fig. 2

Table 1

Formulas for calculating index features"

数据Data	特征Feature	简称Abbreviation	计算公式Calculation formula
光谱值 Spectral value	绿波段Green band	G	—
	红波段Red band	R	—
	红边Red edge	REG	—
	近红外Near-infrared	NIR	—
盐度指数 Salinity index	盐度指数Salinity index	SI	（R×NIR）/G^[27]
	盐度红边指数Salinity red-edged index	SIREG	（REG×NIR）/G
	归一化盐度指数Normalized salinity index	NDSI	（R-NIR）/（R+NIR）^[27]
	归一化盐度红边指数 Normalized salinity red-edged index	NDSIREG	（REG-NIR）/（REG+NIR）
	盐度指数1 Salinity index 1	SI1	$ \sqrt{G \times R}$^[28]
	盐度红边指数1 Salinity index red-edged 1	SI1REG	$ \sqrt{G \times R E G}$
	盐度指数2 Salinity index 2	SI2	$ \sqrt{G^{2}+R^{2}+N I R^{2}}$^[28]
	盐度红边指数2 Salinity index red-edged 2	SI2REG	$ \sqrt{G^{2}+R E G^{2}+N I R^{2}}$
	盐度指数3 Salinity index 3	SI3	$ \sqrt{G^{2}+R^{2}}$^[28]
	盐度红边指数3 Salinity index red-edged 3	SI3REG	$ \sqrt{G^{2}+R E G^{2}}$
	土壤指数T Soil index T	SIT	100（R-NIR）^[29]
	红边土壤指数T Red-edged soil index T	SITREG	100（REG-NIR）
强度指数 Intensity index	强度指数1 Intensity index 1	Int1	（G+R）/2^[29]
	强度红边指数1 Intensity red-edged index 1	Int1REG	（G+REG）/2
	强度指数2 Intensity index 2	Int2	（G+R+NIR）/2^[29]
	强度红边指数2 Intensity red-edged index 2	Int2REG	（G+REG+NIR）/2
植被指数 Vegetation index	归一化植被指数 Normalized difference vegetation index	NDVI	$ \frac{N I R-R}{N I R+R}$^[27]
	归一化植被红边指数 Normalized difference vegetation red-edged index	NDVIREG	$ \frac{N I R-R E G}{N I R+R E G}$
	土壤调节植被指数 Soil adjusted vegetation index	SAVI	$ \frac{(1+L) \times(N I R-R)}{N I R+R+L}$^[30]
	红边土壤调节植被指数 Soil adjusted vegetation red-edged index	SAVIREG	$ \frac{(1+L) \times(N I R-R E G)}{N I R+R E G+L}$
	亮度指数Brightness index	BI	$ \sqrt{R^{2}+N I R^{2}}$^[28]
	红边高度指数Brightness red-edged index	BIREG	$ \sqrt{R E G^{2}+N I R^{2}}$
	氧化铁指数 Iron oxide index	IFe₂O₃	$ \frac{R}{N I R}$^[30]
	红边氧化铁指数 Iron oxide red-edged index	IFe₂O₃REG	$ \frac{R E G}{N I R}$
	差异植被指数Differential vegetation index	DVI	NIR-R^[29]
	红边差异植被指数 Differential vegetation red-edged index	DVIREG	NIR-REG

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 2

Fig. 7

Table 3

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深度 Depth (cm)	测试集Test set			验证集Validation set
深度 Depth (cm)	R²	RMSE	MAE	R²	RMSE	MAE
0-10	0.73	0.77	0.51	0.68	0.93	0.69
10-20	0.71	1.74	1.41	0.51	2.25	2.12
20-40	0.78	4.87	3.93	0.58	4.37	3.68
40-60	0.74	4.46	3.32	0.56	4.44	3.56
60-80	0.69	3.05	2.55	0.52	4.81	3.46

深度Depth (cm)	三次拟合公式Cubic fitting formula (y=)	R²	F检验（P值）F-test (P value)
10-20	0.7722x³-9.3995x²+33.548x-25.728	0.22	0.12
20-40	0.9934x³-11.766x²+40.284x-25.292	0.26	0.09
40-60	0.1979x³-2.0289x²+6.9204x+9.7859	0.27	0.07
60-80	-0.118x³+2.0183x²-8.4252x+26.231	0.26	0.08

Spatial Prediction of Deep Soil Salinization Based on Layered Modeling Using UAV Imagery

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