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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 205-212     DOI: 10.6046/zrzyyg.2021427
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Extraction and analysis of soil salinization information of Alar reclamation area based on spectral index modeling
DAI Yunhao1(), GUAN Yao1, FENG Chunyong2, JIANG Min1, HE Xinghong1()
1. College of Water Conservancy and Architecture Engineering, Tarim University, Alar 843300, China
2. Department of Geographical Sciences, Beijing Normal University, Beijing 100088, China
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Abstract  

This study aims to explore the optimal remote sensing salinization detection index (SDI) model for the inversion of soil salinization in the Alar reclamation area. Based on Landsat8 OLI remote sensing images and field measured data, this study built the remote sensing SDI models using the salinity index (SI), the normalized difference vegetation index (NDVI), the modified soil adjusted vegetation index (MSAVI), and the surface albedo. Then, using these models, this study extracted the soil salinization information on the Alar reclamation area and verified the model precision. Finally, this study determined the optimal remote sensing-based SDI model through comparative analysis. The results are as follows. The four types of remote sensing-based SDI models SDI1 (SI-NDVI), SDI2 (SI-MSAVI), SDI3 (SI-Albedo), and SDI4 (Albedo-MSAVI)had general classification precision of 83.45%, 69.78%, 53.23%, and 71.94%, respectively. Model SDI1 was the most suitable for the inversion of the degree of soil salinization in the Alar reclamation area. Models SDI2 and SDI4 can be utilized as a reference for soil salinization monitoring of the Alar reclamation area. As revealed by the inversion results of the SDI model, the reclamation area is dominated by non-saline and lightly saline soils, with heavily saline soil and saline soil primarily distributed in the northeast and southeast. Model SDI1 established based on SI and NDVI has high accuracy in extracting the soil salinization information of the Alar reclamation area and can be used as the remote sensing-based SDI model for the inversion of soil salinization in reclamation areas. This study can provide an effective technical reference for the control and prevention of soil salinization.
Keywords spectral index      Alar reclamation area      soil salinization      remote sensing salinity detection index model     
ZTFLH:  TP79  
  S153  
Issue Date: 20 March 2023
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Yunhao DAI
Yao GUAN
Chunyong FENG
Min JIANG
Xinghong HE
Cite this article:   
Yunhao DAI,Yao GUAN,Chunyong FENG, et al. Extraction and analysis of soil salinization information of Alar reclamation area based on spectral index modeling[J]. Remote Sensing for Natural Resources, 2023, 35(1): 205-212.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021427     OR     https://www.gtzyyg.com/EN/Y2023/V35/I1/205
Fig.1  General situation and sampling point distribution of Alar reclamation area
光谱指数 公式① 参考文献
NDVI N D V I = N I R - R N I R + R [18]
Albedo Albedo=0.356B+0.130R+0.373NIR+0.085SWIR1+0.072SWIR2-0.0018 [20]
MSAVI M S A V I = ( 2 N I R + 1 ) - ( 2 N I R - 1 ) 2 - 8 ( N I R - R ) 2 [20]
SI S I = B · R [18]
SDI1模型(SI-NDVI) SDI1= ( N D V I - 1 ) 2 + S I 2 [18]
SDI2模型(SI-MSAVI) SDI2= ( M S A V I - 1 ) 2 + S I 2 [19]
SDI3模型(SI-Albedo) SDI3= A l b e d o 2 + S I 2 [28]
SDI4模型(Albedo-MSAVI) SDI4= ( 1 - A l b e d o ) 2 + M S A V I 2 [20???????][28]
Tab.1  Remote sensing spectral index and model
Fig.2  Two dimensional scatter diagram
二维散
点图		 光谱指数拟合公式 拟合度
SI-
NDVI		 线性: Y=0.942 6-3.063X
二次: Y=1.165 1-6.570X+9.859 7X2
几何: Y=-2.918X0.2594+2.201 5
双曲: Y=1.0/(0.425 2+14.226X)
对数: Y=-0.670 2-1.565lgX-0.297 7lg(X)2		 R2=0.867 7
R2=0.929 6
R2=0.915 3
R2=0.872 4

R2=0.916 5
SI-
MSAVI		 线性: Y=1.053 5-3.087X
二次: Y=1.198 9-5.384X+6.475 4X2
几何: —
双曲: Y=1.0/(0.550 6+9.570 0X)
对数: Y=-0.834 3-2.241lgX-0.677 6lgX2
R2=0.888 6
R2=0.914 3
—
R2=0.859 0
R2=0.909 7
SI-
Albedo		 线性: Y=0.282 8+0.185 9X
二次: Y=0.363 2-1.085X+3.583 5X2
几何: —
双曲: Y=1.0/(3.556 1-2.169X)
对数: —		 R2=0.097 5
R2=0.336 1
—
R2=0.115 3
—
Albedo-
MSAVI		 线性: Y=0.677 6-0.237 9X
二次: Y=-0.800 5+9.844 8X-16.65X2
几何: Y=0.254 5X-0.064 5+0.3284
双曲: Y=1.0/(1.500 1+0.504 1X)
对数: Y=-1.022-6.013lgX-5.416lgX2
R2=0.001 9
R2=0.050 5
R2=0.000 2
R2=0.001 5
R2=0.033 3
Tab.2  Goodness of fit of two-dimensional scatter diagram model
模型 非盐渍土 轻度盐渍土 中度盐渍土 重度盐渍土 盐土
SDI1 <0.27 [0.27,0.46) [0.46,0.67) [0.67,0.85) ≥0.85
SDI2 <0.21 [0.27,0.40) [0.46,0.62) [0.62,0.80) ≥0.80
SDI3 <0.13 [0.13,0.18) [0.18,0.24) [0.24,0.30) ≥0.30
SDI4 >1.09 [0.97,1.09) [0.85,0.97) [0.74,0.85) ≤0.74
Tab.3  Classification of soil salinization in Alar reclamation area
Fig.3  Distribution of soil salinization grade in Alar Reclamation Area under different models
模型 样点分
类正确		 样点分类错误 总体精
度/%
非盐 轻度 中度 重度 盐土 共计
SDI1 116 10 2 6 3 2 23 83.45
SDI2 97 6 6 16 6 8 42 69.78
SDI3 74 35 3 14 6 7 65 53.23
SDI4 100 3 6 18 6 6 39 71.94
Tab.4  Accuracy verification of model sample points
Fig.4  Fitting between model and measured conductivity
Fig.5  Distribution of soil salinization grade in Alar reclamation area in 2011 and 2021
年份 非盐渍土 轻度盐渍土 中度盐渍土 重度盐渍土 盐土
2011年 906.31 524.37 519.46 538.39 787.72
2021年 1116.39 606.86 410.34 506.88 830.03
Tab.5  Statistics of soil salinization area in Alar reclamation area in 2011 and 2021(km2)
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