Please wait a minute...
 
Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 124-133     DOI: 10.6046/gtzyyg.2020210
|
An applicability analysis of salinization evaluation index based on multispectral remote sensing to soil salinity prediction in Yinbei irrigation area of Ningxia
WU Xia(), WANG Zhangjun, FAN Liqin, LI Lei
Institute of Agricultural Resources and Environment, Ningxia Academy of Agriculture and Forestry Science, Yinchuan 750002, China
Download: PDF(1927 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Soil salinization is one of the important factors that affect the soil health in the arid area, so it is very important to obtain the information of soil salinity and monitor the change of soil salinity for the rational use of land resources and soil restoration in the arid area. Based on 52 soil samples collected in the field and Landsat 8 OLI remote sensing images obtained at the same time, the correlation and curve regression analysis were used to quantitatively analyze the correlation and fitting degree between the soil salinization evaluation index based on multispectral remote sensing data and the measured soil Electrical Conductivity (EC). The results are as follows: ① The soil salinity in the study area is relatively light, and the total proportion of non-salinized and slightly salinized soil samples is 82.68%; ② The correlation between salinity index and soil EC is higher than that of vegetation index. The correlation between salinity index S3 (S3), salinity index S5 (S5), salinity index S6 (salinity index, S6) and salinity index Si (salinity index, SI) is above 0.50; ③ Salinity indexes S2 (S2), S3, S5 and Si have the highest fitting degree with soil EC in the whole sample, among which S5 has the best performance (R2 = 0.41). The fitting degree of index and soil EC increases significantly with the increase of soil salinity under different salinity levels. The highest fitting degree of salinity index and soil EC is S1 (R2 = 0.73) and S2 (R2 = 0.72); ④ In the fitting model, the evaluation index and soil EC calculated based on cubic model, quadratic model and S model has a high fitting degree. This study has analyzed the applicability of various soil salinization evaluation indexes in soil salinity monitoring of Yinbei irrigation area, and the preliminary conclusions can provide reference for remote sensing monitoring of soil salinity in Yinbei irrigation area of Ningxia.

Keywords soil salinization      salinity index      vegetation index      curve fitting      Landsat     
ZTFLH:  TP79S153  
Issue Date: 21 July 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xia WU
Zhangjun WANG
Liqin FAN
Lei LI
Cite this article:   
Xia WU,Zhangjun WANG,Liqin FAN, et al. An applicability analysis of salinization evaluation index based on multispectral remote sensing to soil salinity prediction in Yinbei irrigation area of Ningxia[J]. Remote Sensing for Land & Resources, 2021, 33(2): 124-133.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020210     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/124
Fig.1  Study area image and sampling points
Fig.1  Study area image and sampling points
指数 公式 参考文献
归一化盐分指数 NDSI=(R-NIR)/(R+NIR) [14]
盐分指数S1 S1=B/R [14]
盐分指数S2 S2=(B-R)/(B+R) [14]
盐分指数S3 S3=(B×R)/B [14]
盐分指数S5 S5=(B×R)/G [14]
盐分指数S6 S6=(R×NIR)/G [14]
盐分指数SI SI= ( B × R ) [14]
盐分指数SI1(salt index1,SI1) SI1= ( G × R ) [14]
盐分指数SI2(salt index 2,SI2) SI2= G 2 + R 2 + NI R 2 [14]
盐分指数SI3(salt index3,SI3) SI3= R 2 + G 2 [14]
强度指数1(intensity index 1,Int1) Int1=(G+R)/2 [9]
强度指数2(intensity index 2,Int1) Int2=(G+R+NIR)/2 [9]
归一化植被指数 NDVI=(NIR-R)/(NIR+R) [25]
扩展的归一化植被指数(enhanced NDVI,ENDVI) ENDVI= ( NIR + SWI R 2 - R ) ( NIR + SWI R 2 + R ) [16]
增强型植被指数(enhanced vegetation index,EVI) EVI=G(NIR-R)/(NIR+6R+7.5B+1) [16]
指数 公式 参考文献
扩展的增强型植被指数(extented enhanced vegetation index,EEVI) EEVI=2.5(NIR+SWIR1-R)/(NIR+SWIR1+6R-7.5B+1) [16]
冠层盐分响应指数 CRSI= NIR × R - G × B NIR × R + G × B [17]
联合光谱指数(combined spectral response index,
CORSI) 		CORSI= ( B + G ) ( R + NIR ) ×NDVI [17]
广义植被指数(generalized difference vegetation index,GDVI) GDVI=(NIR-G)/(NIR+G) [26]
盐渍化遥感监测指数模型 SDI= ( NDVI - 1 ) 2 - S I 2 [27]
Tab.1  Ealuation index of soil salinization
指数 公式 参考文献
归一化盐分指数 NDSI=(R-NIR)/(R+NIR) [14]
盐分指数S1 S1=B/R [14]
盐分指数S2 S2=(B-R)/(B+R) [14]
盐分指数S3 S3=(B×R)/B [14]
盐分指数S5 S5=(B×R)/G [14]
盐分指数S6 S6=(R×NIR)/G [14]
盐分指数SI SI= ( B × R ) [14]
盐分指数SI1(salt index1,SI1) SI1= ( G × R ) [14]
盐分指数SI2(salt index 2,SI2) SI2= G 2 + R 2 + NI R 2 [14]
盐分指数SI3(salt index3,SI3) SI3= R 2 + G 2 [14]
强度指数1(intensity index 1,Int1) Int1=(G+R)/2 [9]
强度指数2(intensity index 2,Int1) Int2=(G+R+NIR)/2 [9]
归一化植被指数 NDVI=(NIR-R)/(NIR+R) [25]
扩展的归一化植被指数(enhanced NDVI,ENDVI) ENDVI= ( NIR + SWI R 2 - R ) ( NIR + SWI R 2 + R ) [16]
增强型植被指数(enhanced vegetation index,EVI) EVI=G(NIR-R)/(NIR+6R+7.5B+1) [16]
指数 公式 参考文献
扩展的增强型植被指数(extented enhanced vegetation index,EEVI) EEVI=2.5(NIR+SWIR1-R)/(NIR+SWIR1+6R-7.5B+1) [16]
冠层盐分响应指数 CRSI= NIR × R - G × B NIR × R + G × B [17]
联合光谱指数(combined spectral response index,
CORSI) 		CORSI= ( B + G ) ( R + NIR ) ×NDVI [17]
广义植被指数(generalized difference vegetation index,GDVI) GDVI=(NIR-G)/(NIR+G) [26]
盐渍化遥感监测指数模型 SDI= ( NDVI - 1 ) 2 - S I 2 [27]
Tab.1  Ealuation index of soil salinization
曲线模型 公式
线性函数(Linear) Y=b0+b1t
对数函数(Logarithmic) Y=b0+b1lnt
逆模型(Inverse) Y=b0+b1/t
二次函数(Quadratic) Y=b0+b1t+b2t2
三次函数(Cubic) Y=b0+b1t+b2t2+b3t3
复合函数(Compound) Y=b0 b 1 t
幂函数(Power) Y=b0 t b 1
S函数 Y= e ( b 0 + b 1 / t )
增长函数(Growth) Y= e ( b 0 + b 1 t )
指数函数(Exponential) Y=b0 e b 1 t
逻辑函数(Logistic) Y= ( 1 / u + b 0 b 1 t ) - 1
Tab.2  Curve regression model and its mathematical expression
曲线模型 公式
线性函数(Linear) Y=b0+b1t
对数函数(Logarithmic) Y=b0+b1lnt
逆模型(Inverse) Y=b0+b1/t
二次函数(Quadratic) Y=b0+b1t+b2t2
三次函数(Cubic) Y=b0+b1t+b2t2+b3t3
复合函数(Compound) Y=b0 b 1 t
幂函数(Power) Y=b0 t b 1
S函数 Y= e ( b 0 + b 1 / t )
增长函数(Growth) Y= e ( b 0 + b 1 t )
指数函数(Exponential) Y=b0 e b 1 t
逻辑函数(Logistic) Y= ( 1 / u + b 0 b 1 t ) - 1
Tab.2  Curve regression model and its mathematical expression
统计特征 样本数 最小值/
(mS·cm-1) 		最大值/
(mS·cm-1) 		均值/
(mS·cm-1) 		标准差/
(mS·cm-1) 		变异系数
全样本 52 0.04 7.52 0.84 1.50 1.78
非盐渍化 20 0.04 0.27 0.19 0.08 0.40
轻度盐渍化 23 0.29 0.99 0.47 0.20 0.42
中重度盐渍化 9 1.08 7.52 3.24 2.51 0.77
Tab.3  Statistical characteristics of soil EC in the study area
统计特征 样本数 最小值/
(mS·cm-1) 		最大值/
(mS·cm-1) 		均值/
(mS·cm-1) 		标准差/
(mS·cm-1) 		变异系数
全样本 52 0.04 7.52 0.84 1.50 1.78
非盐渍化 20 0.04 0.27 0.19 0.08 0.40
轻度盐渍化 23 0.29 0.99 0.47 0.20 0.42
中重度盐渍化 9 1.08 7.52 3.24 2.51 0.77
Tab.3  Statistical characteristics of soil EC in the study area
Fig.2  Correlation between soil EC and salinization evaluation index
Fig.2  Correlation between soil EC and salinization evaluation index
模型 NDSI S1 S2 S3 S5 S6 SI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.036 0.729 0.215 0.657 0.199 0.664 0.335 0.605 0.272 0.633 0.257 0.640 0.268 0.635
对数函数 0.195 1.055 0.215 1.541 0.216 1.547 0.172 1.389 0.216 1.545
逆模型 0.025 0.733 0.175 0.674 0.298 0.622 0.089 0.708 0.161 0.680 0.082 0.711 0.163 0.679
二次函数 0.036 0.729 0.293 0.624 0.278 0.631 0.367 0.591 0.346 0.600 0.290 0.625 0.337 0.604
三次函数 0.036 0.729 0.304 0.619 0.327 0.609 0.395 0.577 0.406 0.572 0.319 0.612 0.382 0.583
复合函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
幂函数 0.121 0.728 0.261 0.694 0.264 0.691 0.211 0.718 0.262 0.693
S函数 0.063 0.765 0.112 0.734 0.168 0.670 0.156 0.747 0.225 0.744 0.138 0.750 0.225 0.719
增长函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
指数函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
逻辑函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
模型 SI1 SI2 SI3 Int1 Int2 NDVI ENDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.218 0.656 0.180 0.672 0.210 0.659 0.214 0.658 0.193 0.667 0.036 0.729 0.135 0.690
对数函数 0.181 1.400 0.153 1.028 0.175 1.188 0.178 1.389 0.163 1.130 0.032 0.788 0.150 1.433
模型 SI1 SI2 SI3 Int1 Int2 NDVI ENDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
逆模型 0.142 0.687 0.125 0.694 0.138 0.689 0.140 0.688 0.133 0.691 0.025 0.733 0.165 0.678
二次函数 0.269 0.635 0.233 0.650 0.259 0.639 0.264 0.637 0.252 0.642 0.036 0.728 0.205 0.662
三次函数 0.292 0.625 0.257 0.640 0.281 0.629 0.286 0.627 0.277 0.631 0.036 0.728 0.205 0.662
复合函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.761 0.161 0.730
幂函数 0.235 0.711 0.178 0.729 0.231 0.713 0.233 0.712 0.196 0.723 0.082 0.762 0.171 0.722
S函数 0.207 0.728 0.159 0.738 0.204 0.729 0.205 0.729 0.175 0.735 0.063 0.765 0.182 0.714
增长函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
指数函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
逻辑函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
模型 EVI EEVI COSRI CRSI GDVI SDI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.110 0.699 0.131 0.692 0.009 0.739 0.199 0.664 0.095 0.706 0.055 0.722
对数函数 0.100 1.611 0.119 1.142 0.006 0.770 0.221 0.895 0.114 0.912 0.053 0.753
逆模型 0.070 0.716 0.108 0.701 0.003 0.741 0.245 0.645 0.140 0.688 0.051 0.723
二次函数 0.110 0.699 0.207 0.661 0.014 0.737 0.284 0.628 0.114 0.699 0.058 0.720
三次函数 0.210 0.660 0.213 0.658 0.015 0.769 0.284 0.628 0.186 0.670 0.059 0.720
复合函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
幂函数 0.050 0.758 0.076 0.751 0.044 0.770 0.176 0.698 0.136 0.737 0.118 0.755
S函数 0.040 0.761 0.073 0.752 0.034 0.772 0.183 0.684 0.138 0.728 0.117 0.756
增长函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
指数函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
逻辑函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
Tab.4  Curve fitting results between salinization evaluation index and soil EC(Full sample)
模型 NDSI S1 S2 S3 S5 S6 SI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.036 0.729 0.215 0.657 0.199 0.664 0.335 0.605 0.272 0.633 0.257 0.640 0.268 0.635
对数函数 0.195 1.055 0.215 1.541 0.216 1.547 0.172 1.389 0.216 1.545
逆模型 0.025 0.733 0.175 0.674 0.298 0.622 0.089 0.708 0.161 0.680 0.082 0.711 0.163 0.679
二次函数 0.036 0.729 0.293 0.624 0.278 0.631 0.367 0.591 0.346 0.600 0.290 0.625 0.337 0.604
三次函数 0.036 0.729 0.304 0.619 0.327 0.609 0.395 0.577 0.406 0.572 0.319 0.612 0.382 0.583
复合函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
幂函数 0.121 0.728 0.261 0.694 0.264 0.691 0.211 0.718 0.262 0.693
S函数 0.063 0.765 0.112 0.734 0.168 0.670 0.156 0.747 0.225 0.744 0.138 0.750 0.225 0.719
增长函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
指数函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
逻辑函数 0.095 0.761 0.129 0.720 0.122 0.726 0.293 0.610 0.292 0.652 0.246 0.668 0.288 0.658
模型 SI1 SI2 SI3 Int1 Int2 NDVI ENDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.218 0.656 0.180 0.672 0.210 0.659 0.214 0.658 0.193 0.667 0.036 0.729 0.135 0.690
对数函数 0.181 1.400 0.153 1.028 0.175 1.188 0.178 1.389 0.163 1.130 0.032 0.788 0.150 1.433
模型 SI1 SI2 SI3 Int1 Int2 NDVI ENDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
逆模型 0.142 0.687 0.125 0.694 0.138 0.689 0.140 0.688 0.133 0.691 0.025 0.733 0.165 0.678
二次函数 0.269 0.635 0.233 0.650 0.259 0.639 0.264 0.637 0.252 0.642 0.036 0.728 0.205 0.662
三次函数 0.292 0.625 0.257 0.640 0.281 0.629 0.286 0.627 0.277 0.631 0.036 0.728 0.205 0.662
复合函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.761 0.161 0.730
幂函数 0.235 0.711 0.178 0.729 0.231 0.713 0.233 0.712 0.196 0.723 0.082 0.762 0.171 0.722
S函数 0.207 0.728 0.159 0.738 0.204 0.729 0.205 0.729 0.175 0.735 0.063 0.765 0.182 0.714
增长函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
指数函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
逻辑函数 0.255 0.690 0.192 0.718 0.250 0.694 0.252 0.692 0.212 0.710 0.095 0.710 0.161 0.730
模型 EVI EEVI COSRI CRSI GDVI SDI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.110 0.699 0.131 0.692 0.009 0.739 0.199 0.664 0.095 0.706 0.055 0.722
对数函数 0.100 1.611 0.119 1.142 0.006 0.770 0.221 0.895 0.114 0.912 0.053 0.753
逆模型 0.070 0.716 0.108 0.701 0.003 0.741 0.245 0.645 0.140 0.688 0.051 0.723
二次函数 0.110 0.699 0.207 0.661 0.014 0.737 0.284 0.628 0.114 0.699 0.058 0.720
三次函数 0.210 0.660 0.213 0.658 0.015 0.769 0.284 0.628 0.186 0.670 0.059 0.720
复合函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
幂函数 0.050 0.758 0.076 0.751 0.044 0.770 0.176 0.698 0.136 0.737 0.118 0.755
S函数 0.040 0.761 0.073 0.752 0.034 0.772 0.183 0.684 0.138 0.728 0.117 0.756
增长函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
指数函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
逻辑函数 0.050 0.757 0.078 0.749 0.051 0.769 0.169 0.709 0.135 0.744 0.118 0.754
Tab.4  Curve fitting results between salinization evaluation index and soil EC(Full sample)
模型 S3 S5 SI SI2 Int2 CRSI GDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.320 0.080 0.279 0.082 0.270 0.082 0.302 0.081 0.293 0.081
对数函数 0.224 0.226 0.26 0.227 0.225 0.226 0.267 0.177 0.259 0.191
逆模型 0.176 0.088 0.227 0.085 0.220 0.085
二次函数 0.333 0.079 0.360 0.077 0.338 0.079 0.359 0.077 0.344 0.078 0.329 0.079
三次函数 0.362 0.077 0.341 0.078 0.356 0.077 0.342 0.078 0.329 0.079 0.419 0.074
复合函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
幂函数 0.198 0.085 0.193 0.085 0.197 0.085 0.249 0.083 0.239 0.083
S函数 0.211 0.085 0.201 0.086
增长函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
指数函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
逻辑函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
Tab.5  Curve fitting results between salinization evaluation index and soil EC(Slight salinization)
模型 S3 S5 SI SI2 Int2 CRSI GDVI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.320 0.080 0.279 0.082 0.270 0.082 0.302 0.081 0.293 0.081
对数函数 0.224 0.226 0.26 0.227 0.225 0.226 0.267 0.177 0.259 0.191
逆模型 0.176 0.088 0.227 0.085 0.220 0.085
二次函数 0.333 0.079 0.360 0.077 0.338 0.079 0.359 0.077 0.344 0.078 0.329 0.079
三次函数 0.362 0.077 0.341 0.078 0.356 0.077 0.342 0.078 0.329 0.079 0.419 0.074
复合函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
幂函数 0.198 0.085 0.193 0.085 0.197 0.085 0.249 0.083 0.239 0.083
S函数 0.211 0.085 0.201 0.086
增长函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
指数函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
逻辑函数 0.292 0.080 0.245 0.082 0.241 0.082 0.283 0.081 0.272 0.081
Tab.5  Curve fitting results between salinization evaluation index and soil EC(Slight salinization)
模型 S1 S2 S3 S5 S6 SI SI1
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.578 0.767 0.604 0.744
对数函数 0.610 1.881
逆模型 0.638 0.711
二次函数 0.722 0.623 0.717 0.628
三次函数 0.724 0.621 0.711 0.635
复合函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
幂函数 0.699 0.913 0.528 0.895 0.508 0.913 0.501 0.902 0.523 0.931 0.491 0.913
模型 S1 S2 S3 S5 S6 SI SI1
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
S函数 0.730 0.872 0.498 1.063 0.463 0.925 0.476 0.912
增长函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
指数函数 0.664 0.949 0.692 0.921 0.463 0.983 0.508 0.939 0.493 0.984 0.523 0.931 0.502 0.934
逻辑函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
模型 SI2 SI3 Int1 Int2 EEVI CRSI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.580 0.766
对数函数 0.553 3.000
逆模型 0.520 0.819
二次函数
三次函数
复合函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.946
幂函数 0.487 0.912 0.481 0.919 0.486 0.916 0.495 0.906 0.588 0.780 0.496 0.961
S函数 0.548 0.803 0.477 0.974
增长函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
指数函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
逻辑函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
Tab.6  Curve fitting results between salinization evaluation index and soil EC(Moderate and severe salinization)
模型 S1 S2 S3 S5 S6 SI SI1
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.578 0.767 0.604 0.744
对数函数 0.610 1.881
逆模型 0.638 0.711
二次函数 0.722 0.623 0.717 0.628
三次函数 0.724 0.621 0.711 0.635
复合函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
幂函数 0.699 0.913 0.528 0.895 0.508 0.913 0.501 0.902 0.523 0.931 0.491 0.913
模型 S1 S2 S3 S5 S6 SI SI1
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
S函数 0.730 0.872 0.498 1.063 0.463 0.925 0.476 0.912
增长函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
指数函数 0.664 0.949 0.692 0.921 0.463 0.983 0.508 0.939 0.493 0.984 0.523 0.931 0.502 0.934
逻辑函数 0.664 0.949 0.692 0.921 0.463 0.983 0.507 0.939 0.493 0.984 0.523 0.931 0.502 0.934
模型 SI2 SI3 Int1 Int2 EEVI CRSI
R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1) 		R2 RMSE/
(mS·cm-1)
线性函数 0.580 0.766
对数函数 0.553 3.000
逆模型 0.520 0.819
二次函数
三次函数
复合函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.946
幂函数 0.487 0.912 0.481 0.919 0.486 0.916 0.495 0.906 0.588 0.780 0.496 0.961
S函数 0.548 0.803 0.477 0.974
增长函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
指数函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
逻辑函数 0.511 0.915 0.493 0.937 0.498 0.935 0.517 0.915 0.623 0.770 0.515 0.983
Tab.6  Curve fitting results between salinization evaluation index and soil EC(Moderate and severe salinization)
Fig.3  Scatter plot of relationship between predicted and measured values of EC
Fig.3  Scatter plot of relationship between predicted and measured values of EC
Fig.4  Distribution map of soil EC in the study area
Fig.4  Distribution map of soil EC in the study area
[1] 张雅莉, 尔达克·克里木. 基于Landsat8 OLI影像光谱的土壤盐分估算模型研究[J]. 国土资源遥感, 2018, 30(1):87-94.doi: 10.6046/gtzyyg.2018.01.12.
doi: 10.6046/gtzyyg.2018.01.12
[1] Zhang Y L, Kelimu A. Estimation model of soil salinization based on Landsat8 OLI image spectrum[J]. Remote Sensing for Land and Resources, 2018, 30(1):87-94.doi: 10.6046/gtzyyg.2018.01.12.
doi: 10.6046/gtzyyg.2018.01.12
[2] 厉彦玲. 黄河三角洲土壤质量退化对土地利用/覆被变化的响应研究[D]. 泰安:山东农业大学, 2018.
[2] Li Y L. Response of soil quality degradation to land use/cover changes in the Yellow River delta[D]. Taian:Shandong Agricultural University, 2018.
[3] 吴加敏, 姚建华, 张永庭, 等. 银川平原土壤盐渍化与中低产田遥感应用研究[J]. 遥感学报, 2007, 11(3):414-419.
[3] Wu J M, Yao J H, Zhang Y T, et al. Remote sensing application study on the soil salinity and the medium & low yield croplands in Yinchuan Plain(YCP)[J]. Journal of Remote Sensing, 2007, 11(3):414-419.
[4] 李剑萍, 马力文, 苏占胜, 等. 宁夏银北地区盐碱地遥感调查分析[C]//周光召.加入WTO和中国科技与可持续发展——挑战与机遇、责任和对策(下册). 北京: 中国科学技术出版社, 2002:136-139.
[4] Li J P, Ma L W, Su Z S, et al. Remote sensing investigation and analysis of saline alkali land in Yinbei area of Ningxia[C]//Zhou G Z.China’s accession to WTO and China’s science and technology and sustainable development:Challenges and opportunities, responsibilities and Countermeasures (Volume II). Beijing: China Science and Technology Press, 2002:136-139.
[5] 李重阳, 宋豫秦. 宁夏银北灌区土壤盐渍化时空分布特征[J]. 人民黄河, 2014, 36(8):134-136,140.
[5] Li C Y, Song Y Q. Spatial-temporal distribution characteristics of soil salinization in northern Yinchuan plain of Ningxia[J]. Yellow River, 2014, 36(8):134-136,140.
[6] 吴亚坤, 刘广明, 苏里坦, 等. 多源数据的区域土壤盐渍化精确评估[J]. 光谱学与光谱分析, 2018, 38(11):3528-3533.
[6] Wu Y K, Liu G M, Su L T, et al. Accurate evaluation of regional soil salinization using multi-source data[J]. Spectroscopy and Spectral Analysis, 2018, 38(11):3528-3533.
[7] 吐尔逊·艾山, 海米提·司马义. 土壤盐渍化遥感监测研究进展综述[J]. 新疆大学学报(自然科学版), 2008, 109(1):1-7.
[7] Hasan T, Ishmael H. Research progress and summary of remote sensing monitoring of soil salinization[J]. Journal of Xinjiang University(Natural Science Edition), 2008, 109(1):1-7.
[8] 胡婕. 基于多源遥感的干旱地区土壤盐分反演研究[D]. 杭州:浙江大学, 2019.
[8] Hu J. Estimation of soil salinity in arid area based on multi-source remote sensing[D]. Hangzhou:Zhejiang University, 2019.
[9] 梁静, 丁建丽, 王敬哲, 等. 基于反射光谱与Landsat 8 OLI多光谱数据的艾比湖湿地土壤盐分估算[J]. 土壤学报, 2019, 56(2):320-330.
[9] Liang J, Ding J L, Wang J Z, et al. Quantitative estimation and mapping of soil salinity in the Ebinur Lake wetland based on Vis-NIR reflectance and Landsat 8 OLI data[J]. Acta Pedologica Sinica, 2019, 56(2):320-330.
[10] 樊彦国, 侯春玲, 朱浩, 等. 基于BP神经网络的盐渍土盐分遥感反演模型研究[J]. 地理与地理信息科学, 2010, 26(6):24-27,115.
[10] Fan Y G, Hou C L, Zhu H, et al. Research on salinity inversion from remote sensing of saline soil based on BP neural network[J]. Geography and Geo-Information Science, 2010, 26(6):24-27,115.
[11] 缪琛. 基于时空序列重构的物候与土壤盐渍化关系研究[D]. 乌鲁木齐:新疆大学, 2017.
[11] Miu C. Study on the relationship between phenology and soil salinization based on spatial and temporal sequence reconstruction: A case study of Ugan-Kugqa oases[D]. Urumchi:Xinjiang University, 2017.
[12] Metternicht G I, Zinck J A. Remote sensing of soil salinity:Potentials and constraints[J]. Remote Sensing of Environment, 2003, 85(1):1-20.
doi: 10.1016/S0034-4257(02)00188-8 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425702001888
[13] 扶卿华, 倪绍祥, 王世新, 等. 土壤盐分含量的遥感反演研究[J]. 农业工程学报, 2007(23):48-54.
[13] Fu Q H, Ni S X, Wang S X, et al. Retrieval of soil salt content based on remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2007(23):48-54.
[14] Allbed A, Kumar L, Aldakheel Y Y, Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region[J]. Geoderma, 2014, 230-231:1-8.
doi: 10.1016/j.geoderma.2014.03.025 url: https://linkinghub.elsevier.com/retrieve/pii/S0016706114001372
[15] Elia S, Todd H S, Dennis L C. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance[J]. Remote Sensing of Environment, 2015, 169(9):335-343.
doi: 10.1016/j.rse.2015.08.026 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425715301127
[16] 陈红艳, 赵庚星, 陈敬春, 等. 基于改进植被指数的黄河口区盐渍土盐分遥感反演[J]. 农业工程学报, 2015, 31(5):107-114.
[16] Chen H Y, Zhao G X, Chen J C, et al. Remote sensing inversion of saline soil salinity based on modifiedvegetation index in estuary arca of Yellow River[J]. Transactions of the Chinese Socicty of Agricultural Engineering, 2015, 31(5):107-114.
[17] 王飞, 丁建丽, 魏阳, 等. 基于Landsat系列数据的盐分指数和植被指数对土壤盐度变异性的响应分析——以新疆天山南北典型绿洲为例[J]. 生态学报, 2017, 37(15):5007-5022.
[17] Wang F, Ding J L, Wei Y, et al. Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images:Applications in different oases in Xinjiang[J]. Acta Ecologica Sinica, 2017. 37(15):5007-5022.
[18] 于文婧. 基于环境小卫星和GIS的灌区土壤盐渍化研究[D]. 北京:中国农业大学, 2016.
[18] Yu W J. Study of the soil salinization in irrigated area based on HJ satellite and GIS[D]. Beijing:China Agricultural University, 2016.
[19] 孙玉芳. 基于遥感监测指数模型的银川平原土壤盐渍化动态研究[J]. 地下水, 2019, 41(5):80-82.
[19] Sun Y F. Study on soil salinization dynamics in Yinchuan Plain based on remote sensing monitoring index model[J]. Ground Water, 2019, 41(5):80-82.
[20] 阿也提古丽·斯迪克. 宁夏平罗土壤含盐量遥感估算方法研究[D]. 南京:南京大学, 2011.
[20] Sidike A. Estimation model of salt content in soils by remote sensing:A case study of Pingluo County,Ningxia Province,China[D]. Nanjing:Nanjing University, 2011.
[21] 鲁如坤. 土壤农业化学分析方法[M]. 北京: 中国农业科技出版社, 2000.
[21] Lu R K. Soil agrochemical analysis methods[M]. Beijing: China Agricultural Science and Technology Press, 2000.
[22] Rai A K, Mandal N, Singh A, et al. Landsat 8 OLI satellite image classification using convolutional neural network[J]. Procedia Computer Science, 2020, 167:987-993.
doi: 10.1016/j.procs.2020.03.398 url: https://linkinghub.elsevier.com/retrieve/pii/S1877050920308644
[23] 蒲智, 于瑞德, 尹昌应, 等. 干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究[J]. 水土保持通报, 2012, 32(6):129-133.
[23] Pu Z, Yu R D, Yin C Y, et al. Optimal hyperspectral indices for soil salt content estimation on typical saline soil in arid areas[J]. Bulletin of Soil and Water Conservation, 2012, 32(6):129-133.
[24] 张智韬. 基于遥感技术的灌区动态信息监测[D]. 杨陵:西北农林科技大学, 2005.
[24] Zhang Z T. Dynamic information monitoring in irrigation distract base on remote sensing[D]. Yangling:Northwest A & F University, 2005.
[25] 王飞, 丁建丽. 基于土壤植被光谱协同分析的土壤盐度推理模型构建研究[J]. 光谱学与光谱分析, 2016, 36(6):1848-1853.
[25] Wang F, Ding J L. Soil salinity modelling study with salinity inference model based on the integration of soil and vegetation spectrum in arid land[J]. Spectroscopy and Spectral Analysis, 2016, 36(6):1848-1853.
[26] Wu W C. The generalized difference vegetation index (GDVI) for dryland characterization[J]. Remote Sensing, 2014, 6(2):1211-1233.
doi: 10.3390/rs6021211 url: http://www.mdpi.com/2072-4292/6/2/1211
[27] 王飞, 丁建丽, 伍漫春. 基于NDVI-SI特征空间的土壤盐渍化遥感模型[J]. 农业工程学报, 2010, 26(8):8,168-173.
[27] Wang F, Ding J L, Wu M C. Remote sensing monitoring models of soil salinization based on NDVI-SI feature space[J]. Transactions of the Chinese Socicty of Agricultural Engineering, 2010, 26(8):8,168-173.
[28] 尹德光. 农业统计学[M]. 北京: 中国统计出版社, 1995.
[28] Yin D G. Agricultural statistics[M]. Beijing: China Statistics Press, 1995.
[29] 曹雷, 丁建丽, 玉米提·哈力克,等.基于国产高分一号卫星数据的区域土壤盐渍化信息提取与建模[J]. 土壤学报, 2016, 53(6):1399-1409.
[29] Cao L, Ding J L, Halik U, et al. Extraction and modeling of regional soil salinization based on data from GF-1 satellite[J]. Acta Pedologica Sinica, 2016, 53(6):1399-1409.
[30] 关红, 贾科利, 张至楠, 等. 盐渍化土壤光谱特征分析与建模[J]. 国土资源遥感, 2015, 27(2):100-104.doi: 10.6046/gtzyyg.2015.02.16.
doi: 10.6046/gtzyyg.2015.02.16
[30] Guan H, Jia K L, Zhang Z N, et al. Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis[J]. Remote Sensing for Land and Resources, 2015, 27(2):100-104.doi: 10.6046/gtzyyg.2015.02.16.
doi: 10.6046/gtzyyg.2015.02.16
[31] 买买提·沙吾提, 丁建丽, 等. 基于GIS的干旱区土壤盐渍化敏感性评价——以渭干河-库车河三角洲绿洲为例[J]. 资源科学, 2012, 34(2):353-358.
[31] Sawut M, Ding J L, et al. A GIS-based assessment on sensitivity of soil salinization in arid areas:A case study of the Ugan-Kuqa River delta[J]. Resources Science, 2012, 34(2):353-358.
[32] 彭杰, 刘焕军, 史舟, 等. 盐渍化土壤光谱特征的区域异质性及盐分反演[J]. 农业工程学报, 2014, 30(17):167-174.
[32] Peng J, Liu H J, Shi Z, et al. Regional heterogeneity of hyperspectral characteristics of salt-affected soil and salinity inversion[J]. Transactions of the Chinese Socicty of Agricultural Engineering, 2014, 30(17):167-174.
[33] 江红南. 新疆不同区域土壤盐渍化光学遥感定量监测研究[D]. 武汉:武汉大学, 2018.
[33] Jiang H N. Quantitative monitoring on soil salinization utilizing optical remote sensing in different regions of Xinjiang[J]. Wuhan:Wuhan University, 2018.
[34] 赵群, 吴加敏. 基于遥感技术的银川土地盐渍化动态监测研究[J]. 现代农业科技, 2009, 517(23):312-314,317.
[34] Zhao Q, Wu J M, Dynamic monitoring of land salinization in Yinchuan based on remote sensing[J]. Modern Agricultural Science and Technology, 2009, 517(23):312-314,317.
[35] 阿尔达克·克里木, 尼格拉·塔什甫拉提, 等. 艾比湖自然保护区荒漠-盐生植被VIS-NIR光谱反射特征研究[J]. 草地学报, 2016, 24(2):453-458.
[35] Kelimu A, Tashpolat N, et al. Study on hyperspectral reflectance characteristics of typical vegetation in Ebinur Lake wetland[J]. Acta Agrestia Sinica, 2016, 24(2):453-458.
[1] LI Na, GAN Fuping, DONG Xinfeng, LI Juan, ZHANG Shifan, LI Tongtong. Investigation and applications of rocky desertification based on GF-5 hyperspectral data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 230-235.
[2] YU Sen, JIA Mingming, CHEN Gao, LU Yingying, LI Yi, ZHANG Bochun, LU Chunyan, LI Huiying. A study of the disturbance to mangrove forests in Dongzhaigang, Hainan based on LandTrendr[J]. Remote Sensing for Natural Resources, 2023, 35(2): 42-49.
[3] DAI Yunhao, GUAN Yao, FENG Chunyong, JIANG Min, HE Xinghong. 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.
[4] CHEN Huixin, CHEN Chao, ZHANG Zili, WANG Liyan, LIANG Jintao. A remote sensing information extraction method for intertidal zones based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(4): 60-67.
[5] LI Yi, CHENG Lina, LU Yingying, ZHANG Bochun, YU Sen, JIA Mingming. A study on the changes in coastal tidal flats in the Laizhou Bay based on MSIC and OTSU[J]. Remote Sensing for Natural Resources, 2022, 34(4): 68-75.
[6] QIN Le, HE Peng, MA Yuzhong, LIU Jianqiang, YANG Bin. Change detection of satellite time series images based on spatial-temporal-spectral features[J]. Remote Sensing for Natural Resources, 2022, 34(4): 105-112.
[7] LI Xingyou, ZHANG Fei, WANG Zheng. Present situation and development trend in building remote sensing monitoring models of soil salinization[J]. Remote Sensing for Natural Resources, 2022, 34(4): 11-21.
[8] DONG Shuangfa, FAN Xiao, SHI Haigang, XU Liping, ZHANG Xinyi. Study on distribution of thermal discharge in Fuqing nuclear power plant based on Landsat8 and UAV[J]. Remote Sensing for Natural Resources, 2022, 34(3): 112-120.
[9] WANG Siyao, ZHAO Chunlei, CHEN Xia, LIU Dan. Remote sensing-based green space evolution in Tangshan and its influence on heat island effect[J]. Remote Sensing for Natural Resources, 2022, 34(2): 168-175.
[10] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[11] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[12] ZHANG Aizhu, WANG Wei, ZHENG Xiongwei, YAO Yanjuan, SUN Genyun, XIN Lei, WANG Ning, HU Guang. A hierarchical spatial-temporal fusion model[J]. Remote Sensing for Natural Resources, 2021, 33(3): 18-26.
[13] SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[14] SONG Chengyun, HU Guangcheng, WANG Yanli, TANG Chao. Downscaling FY-3B soil moisture based on apparent thermal inertia and temperature vegetation index[J]. Remote Sensing for Land & Resources, 2021, 33(2): 20-26.
[15] SONG Qi, FENG Chunhui, GAO Qi, WANG Mingyue, WU Jialin, PENG Jie. Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years[J]. Remote Sensing for Land & Resources, 2021, 33(2): 202-212.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech