Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (24): 5005-5016.doi: 10.3864/j.issn.0578-1752.2020.24.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta

XI Xue1(),ZHAO GengXing1(),GAO Peng1,CUI Kun1,LI Tao2   

  1. 1College of Resources and Environment, Shandong Agricultural University/National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai’an 271018, Shandong
    2Soil and Fertilizer Working Station of Shandong, Jinan 250100
  • Received:2020-03-07 Accepted:2020-05-05 Online:2020-12-16 Published:2020-12-28
  • Contact: GengXing ZHAO E-mail:1349637259@qq.com;zhaogx@sdau.edu.cn

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Abstract:

【Objective】 The purpose of this paper was to explore an accurate and efficient remote sensing method for soil salinity extraction of wheat field in the Yellow River Delta, and obtain the degree and distribution of soil salinization of wheat fields.【Method】This study took Kenli District as the research area, and set 77 sample points in winter wheat growing area evenly. At the same time, two representative test areas and 99 grid sample points were set, and the surface soil salinity data in wheat field and the multi-spectral images of UAV in the test area were collected. The sensitive spectral parameters were screened from four spectral bands (red, green, red edge, and near-infrared) and five spectral indexes (SI, NDVI, DVI, RVI, and GRVI). Stepwise regression, partial least squares, BP neural network and support vector machine methods were used to establish models for predicting the soil salinity, and the band ratio mean method was used to obtain the correction coefficient of the corresponding band of sentinel-2A satellite image. And then the selected soil salinity estimation model was converted into an inversion model based on satellite image. After using the data from the wheat field sample points to verify the models, the best soil salinity inversion model in wheat field was selected, and two scales of soil salinity inversion are realized in the test areas and the research area.【Result】The results showed that the four bands of UAV and the spectral indexes NVDI, RVI and SI were significantly correlated with soil salinity. Among the 13 models of the four modeling methods, the four index models established by NDVI, RVI and SI were better than the other models in modeling and verifying R 2; The best inversion model was the spectral index model obtained by partial least square method: Y=-9.4774×NDVI1+ 0.4794×RVI1+ 3.0747×SI1+ 5.0604, and the accuracy R2 was 0.513 and RMSE was 1.379. By using this model, the soil salinity distribution map of the test area and the whole wheat area was obtained. Combined with the measured interpolation and the survey, the inversion model and spatial distribution results were proved to be accurate and reliable. 【Conclusion】In this study, the soil salinity inversion model of the coastal wheat area based on the integration of satellite and UAV was constructed, which had positive reference value for the production and management of crops in the coastal saline area.

Key words: winter wheat, unmanned aerial vehicle, sentinel-2A satellite, soil salinity, inversion model

Fig. 1

Distribution of study area and test area"

Table 1

Calculation formula of spectral index"

光谱指数
Spectral index		 计算公式
Computational formula		 参考文献
Reference
归一化植被指数NDVI (bNIR-bR)/( bNIR+bR) [32]
比值植被指数RVI bNIR/bR [32]
差值植被指数DVI bNIR-bR [32]
绿波段比值植被指数GRVI bNIR/bG [32]
盐分指数SI $\sqrt{bG\times bR}$ [33]

Table 2

Grade standard of soil salinization degree in Kenli district"

土壤盐渍化等级
Soil salinity grade		 非盐渍化
Non-salinized		 轻度盐渍化
Mild salinized		 中度盐渍化
Moderate salinized		 重度盐渍化
Severe salinized		 盐土
Salinized soil
分级标准
Grade standard (g·kg-1)		 SS<1 1≤SS<2 2≤SS<4 4≤SS<6 SS>6
等级 Grade 1 2 3 4 5

Table 3

Correlation between the bands of sample points and the measured soil salinity content in test area"

变量 Variable SS G R REG NIR
SS 1
G 0.677** 1
R 0.667** 0.506** 1
REG 0.616** 0.431** 0.578** 1
NIR -0.671** -0.561** -0.449** -0.486** 1

Table 4

Correlation between the spectral indexes of sample points and the measured soil salinity content in test area"

变量 Variable SS SI NDVI DVI RVI GRVI
SS 1
SI 0.770** 1
NDVI -0.787** -0.808** 1
DVI -0.780** -0.768** 0.953** 1
RVI -0.560** -0.696** 0.890** 0.844** 1
GRVI -0.432** -0.690** 0.468** 0.557** 0.398** 1

Table 5

Soil salinity estimation model based on multi-spectral of UAV"

建模方法
Modeling approach		 光谱参量
Spectral parameter		 估测模型
Estimating model		 建模精度
Modeling accuracy		 验证精度
Verification accuracy
R2 RMSE R2 RMSE
逐步回归
Stepwise regression		 bG Y=18.609×bG+0.169 0.458 1.267 0.401 0.882
bG,bR Y=12.546×bG+19.044×bR-1.120 0.599 1.089 0.600 0.729
bG,bR,bNIR Y=8.360×bG+15.775×bR-9.912×bNIR +3.116 0.673 0.983 0.650 0.847
bG,bR,bNIR,bREG Y=7.988×bG+12.282×bR-8.525×bNIR+6.979×bREG+2.101 0.694 0.951 0.648 0.771
NDVI Y=-7.507×NDVI+6.308 0.620 1.061 0.668 0.685
NDVI,RVI Y=-13.261×NDVI+0.69×RVI+6.734 0.715 0.918 0.692 0.897
NDVI,RVI,SI Y=-10.287×NDVI+0.651×RVI+13.486×SI+3.843 0.756 0.850 0.710 0.907
偏最小二乘法
Partial least squares		 bG,bR,bNIR,bREG Y=6.021×bG+6.5986×bR+6.2650×bNIR-4.1737×bREG+1.3260 0.689 1.114 0.719 1.177
NDVI,RVI,SI Y=-9.4774×NDVI+0.4794×RVI+3.0747×SI+5.0604 0.734 0.954 0.784 0.769
BP神经网络
The BP neural network		 bG,bR,bNIR,bREG — — — 0.714 0.893
NDVI,RVI,SI — — — 0.753 0.993
支持向量机
Support vector machine		 bG,bR,bNIR,bREG — 0.804 0.590 0.467 0.473
NDVI,RVI,SI — 0.835 0.353 0.640 0.512

Table 6

Soil salinity estimation model based on spectral index of satellite image"

建模方法
Modeling approach		 估测模型
Estimating model		 建模精度
Modeling accuracy		 验证精度
Verification accuracy
R2 RMSE R2 RMSE
逐步回归Stepwise regression Y=-97.012×NDVI+22.298×RVI-13.905×SI-8.489 0.509 1.190 0.434 0.874
偏最小二乘法Partial least squares Y=-57.4889×NDVI+13.3418×RVI+21.9667×SI-9.0323 0.555 0.940 0.414 0.964
BP神经网络The BP neural network — — — 0.602 0.900
支持向量机Support vector machine — 0.612 1.166 0.438 0.432

Fig. 2

Interpolation diagram (left) and inversion diagram (right) of soil salinity in the test area"

Table 7

Grade-area statistics of soil salinity in test area (%)"

土壤盐分等级分布图
Distribution diagram of soil salinity grade		 试验区A Test area A 试验区B Test area B
1 2 3 4 5 1 2 3 4 5
实测图Interpolation diagram 0 0 47.63 36.16 16.21 32.74 0 9.81 57.45 0
反演图Inversion diagram 0 0 47.34 36.38 16.28 31.27 7.84 8.10 50.63 2.16

Fig. 3

Spectral curve of typical features in the study area (left) and extraction model of winter wheat decision tree (right)"

Fig. 4

Interpolation diagram (left) and upscaling inversion diagram (right) of soil salinity in wheat area"

Table 8

Grade-area statistics of soil salinity in wheat area (%)"

土壤盐分等级分布图
Distribution diagram of soil salinity grade		 1 2 3 4 5
实测图Interpolation diagram 0 63.07 18.28 14.45 4.20
反演图Inversion diagram 0 73.09 14.01 10.08 2.82
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