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自然资源遥感  2023, Vol. 35 Issue (1): 205-212    DOI: 10.6046/zrzyyg.2021427
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基于光谱指数建模的阿拉尔垦区土壤盐渍化信息提取与分析
代云豪1(), 管瑶1, 冯春涌2, 蒋敏1, 贺兴宏1()
1.塔里木大学水利与建筑工程学院,阿拉尔 843300
2.北京师范大学地理科学学部,北京 100088
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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摘要 

为了探究反演新疆维吾尔自治区阿拉尔垦区土壤盐渍化最优遥感盐分监测指数模型,以Landsat8 OLI遥感影像和野外实测数据为基础,通过盐分指数(salinity index,SI)、归一化植被指数(normalized difference vegetation index,NDVI)、修改型土壤调节植被指数(modified soil - adjusted vegetation index,MSAVI)、地表反照率(Albedo)构建遥感盐分监测指数模型(salinization detection index,SDI),提取阿拉尔垦区土壤盐渍化信息并验证模型精度,对比分析得出最优遥感盐分监测指数模型。结果表明: 4类遥感盐分监测指数模型中SDI1(SI-NDVI),SDI2(SI-MSAVI),SDI3(SI-Albedo)和SDI4(Albedo-MSAVI)总体分类精度为83.45%,69.78%,53.23%和71.94%; SDI1模型最适合反演阿拉尔垦区土壤盐渍化程度,SDI2和SDI4模型对阿拉尔垦区土壤盐渍化监测有一定参考意义; 利用SDI1模型反演阿拉尔垦区土壤盐渍化分布,垦区以非盐渍土和轻度盐渍土为主,重度盐渍土和盐土主要分布在垦区的东北和东南地区。由SI和NDVI构建SDI1对阿拉尔垦区土壤盐渍化信息提取精度较高,可作为反演垦区土壤盐渍化的遥感盐分监测指数模型,可为垦区土壤盐渍化治理和防治提供有效的技术参考。
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代云豪
管瑶
冯春涌
蒋敏
贺兴宏
关键词 ： 光谱指数,  阿拉尔垦区,  土壤盐渍化,  遥感盐分监测指数模型    
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.
Key words： spectral index    Alar reclamation area    soil salinization    remote sensing salinity detection index model
收稿日期: 2021-12-06      出版日期: 2023-03-20
ZTFLH:  TP79  
  S153  
基金资助:农业部作物需水与调控重点实验室开放课题“南疆极端干旱区不同土质点源滴灌入渗及水盐运移规律研究”(FIRI2019-03-0202);兵团重大项目子课题“新疆空中水资源利用研究与示范”(2017AA002);兵团重点产业项目“南疆生态农田水资源多维调控模式研究”(2021DB017);中国农业大学塔里木大学联合基金“南疆极端干旱区残余盐土滴灌水盐运移原理及水分高效利用研究”(2019TC157);塔里木大学研究生科研创新项目“基于3S技术对塔里木灌区的土壤盐渍化时空动态预测分析研究”(TDGRI202042)
通讯作者: 贺兴宏(1981-),男,教授,主要研究方向为水资源高效利用。Email: hexinghong0611@163.com。
作者简介: 代云豪(1996-),男,硕士研究生,主要研究方向为3S技术在农业工程中的应用。Email: 917473073@qq.com。
引用本文:   
代云豪, 管瑶, 冯春涌, 蒋敏, 贺兴宏. 基于光谱指数建模的阿拉尔垦区土壤盐渍化信息提取与分析[J]. 自然资源遥感, 2023, 35(1): 205-212.
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. Remote Sensing for Natural Resources, 2023, 35(1): 205-212.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021427      或      https://www.gtzyyg.com/CN/Y2023/V35/I1/205
Fig.1  阿拉尔垦区概况及采样点分布
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  遥感光谱指数及模型
Fig.2  二维散点图
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  二维散点图模型拟合性
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  阿拉尔垦区土壤盐渍化分类
Fig.3  不同模型下的阿拉尔垦区土壤盐渍化等级分布
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  模型样点精度验证
Fig.4  模型与实测电导率拟合
Fig.5  2011、2021年阿拉尔垦区土壤盐渍化等级分布
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  2011年、2021年阿拉尔垦区土壤盐渍化面积统计
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