自然资源遥感, 2023, 35(1): 205-212 doi: 10.6046/zrzyyg.2021427

技术应用

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 Yunhao,1, GUAN Yao1, FENG Chunyong2, JIANG Min1, HE Xinghong,1

1. College of Water Conservancy and Architecture Engineering, Tarim University, Alar 843300, China

2. Department of Geographical Sciences, Beijing Normal University, Beijing 100088, China

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。

责任编辑: 李瑜

收稿日期: 2021-12-6   修回日期: 2022-04-4  

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)

Received: 2021-12-6   Revised: 2022-04-4  

作者简介 About authors

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。

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。

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.

Keywords： spectral index; Alar reclamation area; soil salinization; remote sensing salinity detection index model

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本文引用格式

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 doi:10.6046/zrzyyg.2021427

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 Land & Resources, 2023, 35(1): 205-212 doi:10.6046/zrzyyg.2021427

0 引言

土壤盐渍化是干旱、半干旱区农业的突出问题,严重阻碍了地区的农业发展,是致使土地退化和土地荒漠化的主要成因[1-2]。南疆位于干旱区,土壤盐渍化问题严重,节水灌溉技术的应用有效降低了农田用水量,但土壤次生盐渍化现象却日益增加。有效提取土壤盐渍化信息是干旱区土地可持续发展和农业科学管理的关键[3-4]。

传统的土壤盐渍化监测需土壤采集和实验室测定,耗时费力。遥感技术的发展弥补了传统手段的缺陷,遥感技术凭借其独特优势被广泛应用于土壤盐渍化监测[5-6]。最便捷的土壤盐渍化反演多是采用相关的遥感光谱指数或构建遥感盐分监测指数模型对其评价,常用光谱指数有盐分指数(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)、土壤亮度指数(brightness Index,BI)等[7⇓⇓⇓⇓-12]。新疆干旱区土壤盐渍化研究的热点区域主要集中在流域[13⇓-15]和垦区[16-17]等地,研究方法为光谱指数构建遥感盐分监测指数(salinization detection index,SDI)模型,NDVI和SI构建遥感盐分监测指数模型能有效的反演其塔里木南缘于田绿洲土壤盐渍化[18]; MSAVI和SI构建的遥感盐分监测指数模型可作为玛纳斯河流域灌区土壤盐渍化信息提取指标[19]; Albedo与MSAVI构建遥感盐分监测指数模型能够较好地反映渭库绿洲土壤盐渍化情况[20]。因此,通过构建遥感盐分监测指数模型能较好反演干旱区土壤盐渍化信息。

以阿拉尔垦区Landsat8 OLI遥感影像数据和野外实测数据为基础,基于NDVI,Albedo,MSAVI和SI构建4类SDI1(SI-NDVI),SDI2(SI-MSAVI),SDI3(SI-Albedo)和SDI4(Albedo-MSAVI)遥感盐分监测指数模型,探究反演阿拉尔垦区土壤盐渍化最优遥感盐分监测指数模型,为垦区精准治理土壤盐渍化提供科学依据。

1 材料与研究方法

1.1 研究区概况

阿拉尔垦区是新疆维吾尔自治区直辖县级市(E80°30'~81°58',N40°22'~40°57'),属暖温带极端大陆性干旱荒漠气候,生态环境十分脆弱,傍依塔里木河,总面积4 197.58 km2[21](图1)。垦区内有胜利、上游、多浪水库,年均降水量40.1~82.5 mm,年均气温10~12 ℃,年均蒸发量1 876.6~2 558.9 mm,其蒸发量远大于降水量[22]。垦区土壤的有机质在表层集聚现象明显,土壤氮、磷含量偏低,土壤钾素含量较高[23]。野生木本植物以胡杨、红柳为主,主要经济农作物包括棉花、水稻、红枣、核桃等。

图1

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图1   阿拉尔垦区概况及采样点分布

Fig.1   General situation and sampling point distribution of Alar reclamation area


1.2 数据来源

遥感影像数据来源于地理空间数据云(http://www.gscloud.cn),选取云量小于10%的2021年9月8日Landsat8 OLI数据,影像空间分辨率为30 m,行列号为146/32。室外土壤样品采集时间为2021年3月、2021年8月底—9月初,采样点随机均匀分布在阿拉尔垦区。样点根据三角形采样法[24]对每个样点采集3份0~10 cm表层土壤样品,并利用GPS测定坐标(图1),室外采样点共139个,样品数量共计417个。

1.3 数据处理

遥感影像数据经过几何纠正、辐射定标、大气校正等预处理后裁剪得到研究区影像图,为避免水体和无植被覆盖的沙地对提取精度造成干扰,故利用归一化土壤湿度指数(normalized difference moisture index,NDMI)和SWIR波段对研究区水体和沙地掩模去除[25]。

将土壤样品按标准规定在烘箱105 ℃烘干8 h,将烘干后同一样点的3份样品磨碎均匀混合为1份,剔除土壤中的植物根系、石子等后过2 mm筛,取蒸馏水100 ml,按水土比例5∶1配置溶液,充分均匀搅动后静止放置5~6 h过滤,用上海科佑电导仪DDS-11A测定土壤电导率。

1.4 光谱指数及模型

植被长势受土壤盐渍化影响,土壤盐渍化程度越重,植被长势将受到抑制,通过植被指数可作为间接反映土壤盐渍化程度的依据[25]。地表反射率受植被覆盖和土壤水分影响,随着土壤盐渍化加重,植被长势受到抑制,地表反照率也会发生变化[20]。研究发现蓝色波段、红色波段、短红外波对土壤盐渍化信息有较好响应[26-27]。光谱指数公式及遥感盐分监测指数模型公式见表1。

表1   遥感光谱指数及模型

Tab.1  Remote sensing spectral index and model

光谱指数公式①参考文献
NDVINDVI=NIR-RNIR+R[18]
AlbedoAlbedo=0.356B+0.130R+0.373NIR+0.085SWIR1+0.072SWIR2-0.0018[20]
MSAVIMSAVI=(2NIR+1)-(2NIR-1)2-8(NIR-R)2[20]
SISI=B·R[18]
SDI1模型(SI-NDVI)SDI1=(NDVI-1)2+SI2[18]
SDI2模型(SI-MSAVI)SDI2=(MSAVI-1)2+SI2[19]
SDI3模型(SI-Albedo)SDI3=Albedo2+SI2[28]
SDI4模型(Albedo-MSAVI)SDI4=(1-Albedo)2+MSAVI2[20⇓⇓⇓⇓⇓⇓⇓][28]

①式中: B,R,NIR,SWIR1,SWIR2分别为Landsat8 OLI影像中的蓝波段、红波段、近红外波段、短红外波段1、短红外波段2的反射率。

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1.5 归一化处理

由于每个指数的量纲不同,为了消除不同变量的数据之间单位和数量级差异性带来的影响,在其建立遥感盐分监测指数模型之前对各指数归一化处理[24],公式为:

Xi=X-XminXmax-Xmin

式中: Xi为各指数归一化后的值; X为各光谱指数; Xmax和Xmin分别为各指数最大值和最小值。

2 结果与分析

2.1 二维散点图

为直观了解各光谱指数之间存在的相关性,以归一化后的SI作为坐标横轴,归一化后的NDVI,MSAVI及Albedo分别作为坐标纵轴建立 SI-NDVI,SI-MSAVI和SI-Albedo二维散点图; 以归一化Albedo作为坐标横轴,归一化MSAVI作为坐标纵轴建立Albedo-MSAVI二维散点图(图2)。利用ENVI5.3中新版2D散点图插件计算各光谱指数构建的二维散点拟合度(见表2)。

图2

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图2   二维散点图

Fig.2   Two dimensional scatter diagram


表2   二维散点图模型拟合性

Tab.2  Goodness of fit of two-dimensional scatter diagram model

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

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由图2、表2可知,归一化植被指数、地表反照率、修改型土壤调节植被指数、盐分指数构建的SI-NDVI,SI-MSAVI,SI-Albedo及MSAVI-Albedo二维散点拟合度较好的分别为0.929 6,0.914 3,0.336 1及0.050 5,初步表明盐分指数和植被指数更适合构建遥感盐分监测指数模型,具体4类遥感盐分监测指数模型对阿拉尔垦区土壤盐渍化反演则需要进一步精度验证。

2.2 盐分反演

利用表1公式分别计算遥感盐分监测指数模型SDI1,SDI2,SDI3和SDI4,并采用Jenk自然间断点分级法将上述模型值域区间划分为5个盐渍土等级[28](表3),得到4种模型监测的土壤盐渍化分布(图3)。

表3   阿拉尔垦区土壤盐渍化分类

Tab.3  Classification of soil salinization in Alar reclamation area

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

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图3

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图3   不同模型下的阿拉尔垦区土壤盐渍化等级分布

Fig.3   Distribution of soil salinization grade in Alar Reclamation Area under different models


由图3可知,遥感盐分监测指数模型SDI1和SDI2反演阿拉尔垦区土壤盐分以轻度盐渍土、非盐渍土为主,中度盐渍土、重度盐渍土和盐土主要分布在垦区的东北、东南地区。遥感盐分监测指数模型SDI3反演阿拉尔垦区土壤盐分则以轻度盐渍土和中度盐渍土为主,重度盐渍土少量分布在垦区的东北、东南地区,盐土在垦区几乎无分布。遥感盐分监测指数模型SDI4反演阿拉尔垦区土壤盐分以非盐渍土为主,中度盐渍土和重度盐渍土主要分布在垦区的东北、东南地区,较SDI2模型差异在于重度盐渍土和盐土的分布较少4类遥感盐分监测指数模型在一定程度上都有所差异,因此对模型进行精度验证,选取反演精度较高的遥感盐分监测指数模型是十分必要的。

2.3 精度评价

查阅南疆地区遥感盐渍土相关研究,参考王遵亲等[29]的干旱区土壤盐渍化分级指标,按土壤盐渍化等级标准[30]所对应的土壤电导率值进行盐渍化等级划分[31]: 非盐渍土含盐量<2 mS·cm-1; 轻度盐渍土含盐量[2,4) mS·cm-1; 中度盐渍土含盐量[4,8) mS·cm-1; 重度盐渍土含盐量[8,16) mS·cm-1; 盐土含盐量≥16 mS·cm-1。按此标准测定土壤采样数据的电导率并进盐渍化分级,判定遥感盐分监测指数模型SDI1,SDI2,SDI3和SDI4精度,结果如表4、图4所示。

表4   模型样点精度验证

Tab.4  Accuracy verification of model sample points

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
SDI11161026322383.45
SDI2976616684269.78
SDI37435314676553.23
SDI41003618663971.94

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图4

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图4   模型与实测电导率拟合

Fig.4   Fitting between model and measured conductivity


由表4可知,利用遥感盐分监测指数模型反演阿拉尔垦区土壤盐分,SDI1模型样点分类正确个数较多,SDI3模型样点分类正确个数较少,SDI1模型、SDI2模型、SDI3模型、SDI4模型总体精度分别为83.45%,69.78%,53.23%和71.94%。样点分类错误中,SDI1模型样点分类错误主要为非盐渍土,SDI2模型样点分类错误主要为中度盐渍土,SDI3模型样点分类错误主要为非盐渍土,SDI4模型样点分类错误主要为中度盐渍土。

由遥感盐分监测指数模型与实测电导率拟合效果可知(图4),SDI模型、SDI2模型、SDI3模型、SDI4模型的拟合度分别为0.757 9,0.822 7,0.503 4和0.740 0,SDI模型、SDI2模型、SDI3模型与实测电导率呈正相关,SDI4模型与实测电导率呈负相关。

综上,SI和NDVI光谱指数构建遥感盐分监测指数模型SDI1其分类总体精度达83.45%,模型与实测电导率拟合R2=0.757 9,综合对比选择SDI1模型对阿拉尔垦区土壤盐渍化信息提取较好,更适合反演垦区土壤盐渍化程度。

2.4 阿拉尔垦区土壤盐渍化时空分布

采用SDI1模型对2011年、2021年垦区影像数据进行土壤盐渍化等级划分(图5),统计去除水体和沙地面积后的阿拉尔垦区不同程度土壤盐渍化面积(表5)。

图5

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图5   2011、2021年阿拉尔垦区土壤盐渍化等级分布

Fig.5   Distribution of soil salinization grade in Alar reclamation area in 2011 and 2021


表5   2011年、2021年阿拉尔垦区土壤盐渍化面积统计

Tab.5  Statistics of soil salinization area in Alar reclamation area in 2011 and 2021(km2)

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906.31524.37519.46538.39787.72
2021年1116.39606.86410.34506.88830.03

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由图5分布状况并结合奥维地图分析,非盐渍土主要分布在垦区农田,轻度盐渍土呈零散形分布于垦区内,中度盐渍土、重度盐渍土主要分布在阿拉尔市中心、垦区南部和垦区东北地区,盐土主要分布在垦区东北和东南地区。

2011—2021年,垦区土壤盐渍化面积呈现出非盐渍土、轻度盐渍土面积增加,中度、重度盐渍土面积下降,盐土面积增加。其中,非盐渍土面积增加210.08 km2,增加的区域主要分布在垦区北部、南部和东部地区,由中度盐渍土和重度盐渍土转入为主; 轻度盐渍土面积增加82.49 km2,增加的区域以零散化分布在垦区; 中度盐渍土、重度盐渍土面积分别减少109.12 km2和31.51 km2,减少的区域主要在垦区南部和东部地区; 盐土面积增加42.31 km2,增加的区域则主要位于垦区东北和东南地区。近10 a间,垦区的北部、南部和东部地区非盐渍土明显增加,土壤盐渍化程度有所改善。

3 讨论

1)模型选择。常规土壤盐分监测多由野外数据采集结合实验室测定得来,多光谱遥感数据获取简单、成本低、易操作,直接通过光谱指数构建遥感盐分监测指数模型,能快速对其研究区土壤盐分进行测定。选择模型时,考虑不同研究区所处地理位置的地形和气候条件存在差异性,不同光谱指数构建的遥感盐分监测指数模型精度结果不一致,直接选取一种模型反演垦区盐分,不能确保为最优精度。边玲玲等[28]对黄河三角洲垦利县土壤盐渍化反演中认为遥感盐分监测指数模型SDI3(SI-Albedo)效果最佳,冯娟等[20]认为SDI4(Albedo-MSAVI)反演新疆渭库绿洲土壤盐分精度更高,故选择不同光谱指数构建常用4类遥感盐分监测指数模型进行对比分析。

2)模型精度。对4类遥感盐分监测指数模型总体精度进行验证,盐分指数和归一化植被指数构建的遥感盐分监测指数模型对阿拉尔垦区土壤盐渍化反演效果更好。由总体精度对比可知,植被指数参与构建的遥感盐分监测指数模型SDI1,SDI4和SDI2高于无植被指数参与构建的遥感盐分监测指数模型SDI3,其原因可能是遥感影像选择为植被覆盖旺盛时期,此时植被指数能反映出植被长势状况,植被长势受不同盐分含量影响,故而更能间接反映出垦区土壤盐渍化状况。同时,SDI2,SDI4模型对阿拉尔垦区非盐渍土和轻度盐渍土反演效果较好,对中度盐渍土、重度盐渍土和盐土反演效果较差,说明SDI2,SDI4模型对垦区土壤盐渍化监测具有一定参考意义,但适宜性还需进一步验证。总之,不同研究区应根据自身特性合理选择光谱指数构建遥感盐分监测指数模型,以便能精确反演土壤盐分信息。

3)垦区土壤盐分分布。通过遥感盐分监测指数模型SDI1对阿拉尔垦区土壤盐渍化进行反演,反演结果中农田分布状况同吴家林[27]基于高光谱研究垦区棉田土壤盐渍化结果基本一致。土壤盐渍化分布受自然因素和人为因素影响[10],土壤水分对土壤盐分影响较大,土壤中的盐分会随着水分的运移而迁移[1]。垦区位于新疆南部干旱区,光照强,蒸发量较大,土壤水分蒸发会携带盐分一同上移,农田多在春季或冬季采用漫灌洗盐,采用膜下滴灌控制水量和土壤盐分,极大降低了农田中表层土壤的盐分含量,故垦区农田主要以非盐渍土和轻度盐渍土为主。通过结合奥维地图分析,垦区重度盐渍土和盐土主要是裸地和植被覆盖稀少的沙地,日照时间长、降水量和灌水量低是导致土壤盐分多量汇集在土壤表层形成盐斑的原因之一。

土壤盐渍化反演精度一定程度上受遥感影像数据分辨率影响,本研究选取Landsat8卫星数据,空间分辨率30 m,故分类精度只能是一个近似区域,若进一步提高土壤盐渍化反演精度,可采用高分辨率的遥感影像数据,如国产高分系列、WorldView系列、GeoEye系列、IKONOS系列等。

4 结论

研究利用Landsat8 OLI遥感影像,通过盐分指数(SI)、归一化植被指数(NDVI)、地表反照率(Albedo)、修改型土壤调节植被指数(MSAVI)构建遥感盐分监测指数模型SDI1,SDI2,SDI3及SDI4对阿拉尔垦区土壤盐渍化进行信息提取,结合实测电导率进行精度评价,得出以下结论:

1)遥感盐分监测指数模型SDI1,SDI2,SDI3及SDI4与实测电导率的拟合度分别为0.757 9,0.822 7,0.503 4和0.740 0,样点总体分类精度为83.45%,69.78%,53.23%和71.94%,综合比较,阿拉尔垦区土壤盐渍化信息提取的模型精度依次是SDI1>SDI2>SDI4>SDI3。

2)SDI2和SDI4总体反演精度近70%,表明在一定程度上对阿拉尔垦区土壤盐渍化监测有一定的参考意义。

3)利用遥感盐分监测指数模型SDI1对阿拉尔垦区土壤盐渍化信息提取,阿拉尔垦区以非盐渍土和轻度盐渍土为主,非盐渍土分布在垦区西部、北部和东部地区,轻度盐渍土则零散分布于垦区。中度盐渍土、重度盐渍土和盐土主要分布在垦区南部、东北和东南地区。

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