阿拉尔垦区土壤盐渍化遥感监测及时空特征分析

doi:10.12118/j.issn.1000-6060.2021.531

摘要/Abstract

摘要：

及时准确掌握区域土壤盐渍化信息对盐渍土治理和土地利用可持续发展有着重要意义。以阿拉尔垦区Landsat 7 ETM+/8 OLI影像为数据源,采用盐分指数（Salinity index,SI）和归一化植被指数（Normalized difference vegetation index,NDVI）构建遥感盐分监测指数（Salinization detection index,SDI）模型,对阿拉尔垦区土壤盐渍化进行反演,分析近10 a垦区土壤盐渍化空间分布特征。结果表明：SDI模型与土壤实测电导率拟合度R²=0.7579,该模型可反演阿拉尔垦区土壤盐量;2011—2021年非盐渍土和轻度盐渍土面积分别增加318.22 km²和0.80 km²,中度、重度土壤盐渍化面积减少229.87 km²,盐土面积增加68.61 km²;阿拉尔垦区北部和南部地区的土壤盐渍化程度得到明显转好,中部和东部地区土壤盐渍化程度加重,垦区土壤盐渍化总体得到较好改善。SDI模型能较好反演阿拉尔垦区土壤盐渍化时空特征,可为阿拉尔垦区经济与社会发展提供一定的参考依据。

关键词: 土壤盐渍化, 盐渍土, 遥感盐分监测指数（SDI）模型, SI-NDVI, 阿拉尔垦区

Abstract:

This paper analyzes the distribution and spatial variation of saline soil of different degrees in the Aral Reclamation Area, Xinjiang, China and provides an important reference for effectively controlling and preventing reclaimed saline soil in the reclamation area. Using Landsat ETM+ in 2011 and Landsat OLI in 2021, the remote salinization detection index (SDI) model of soil salinization was constructed using the salinity index and the normalized vegetation index to extract and analyze soil salinization information in the Aral Reclamation Area. Soil data were collected from 139 surface soil sample points (0-10 cm) in the Aral Reclamation Area, and three samples per point were collected using the triangular sampling method. Soil extract with water and soil (5:1) was prepared, and soil conductivity was measured in the laboratory. According to the soil conductivity value corresponding to the soil salinization grade standard of the American Saline Alkali Soil Laboratory, the soil salinization grade in the Aral Reclamation Area is divided into five categories. According to the natural discontinuity method, the SDI is divided into five levels to verify its classification accuracy and explore the temporal and spatial distribution characteristics of soil salinization in the study area in the recent 10 years. The fitting degree between the SDI model of soil salinization and the measured soil conductivity R²=0.7579. The farmland in the reclamation area comprises mainly nonsaline soil and light saline soil, the riverbed and grassland of the Tarim River are mainly moderately saline soil and heavy saline soil, and the land desertification and the area near the desert comprise mainly saline soil. From 2011 to 2021, the area of nonsaline soil and mild saline soil in the Aral Reclamation Area increased by 318.22 km² and 0.80 km², respectively; the area of moderate and severe soil salinization decreased by 229.87 km²; and the area of saline soil increased by 68.61 km². In the recent 10 years, the degree of soil salinization in the north and south of the Aral Reclamation Area has improved significantly, the degree of soil salinization in the middle and eastern parts has increased, and the overall soil salinization in the reclamation area has improved. The SDI has a certain reference value for the study of soil salinization and provides a scientific basis for the real-time monitoring and accurate treatment of soil salinization in reclamation areas.

Key words: soil salinization, saline soil, salinization detection index (SDI), salinity index-normalized difference vegetation index (SI-NDVI), Aral Reclamation Area

代云豪,管瑶,张钦凯,孙峻杰,贺兴宏. 阿拉尔垦区土壤盐渍化遥感监测及时空特征分析[J]. 干旱区地理, 2022, 45(4): 1176-1185.

DAI Yunhao,GUAN Yao,ZHANG Qinkai,SUN Junjie,HE Xinghong. Remote sensing monitoring and temporal and spatial characteristics of soil salinization in Aral Reclamation Area[J]. Arid Land Geography, 2022, 45(4): 1176-1185.

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参考文献 39

[1]	姜红, 玉素甫江·如素力, 热伊莱·卡得尔, 等. 基于神经网络模型的干旱区绿洲土壤盐渍化评价分析[J]. 地球信息科学学报, 2017, 19(7): 983-993. doi: 10.3724/SP.J.1047.2017.00983
	[ Jiang Hong, Rusuli Yusufujiang, Kadeer Reyilai, et al. Evaluation and analysis of soil salinization in the arid zones based on neural network model[J]. Journal of Geo-Information Science, 2017, 19(7): 983-993. ] doi: 10.3724/SP.J.1047.2017.00983
[2]	Yu T, Jiapaer G, Bao A, et al. Using synthetic remote sensing indicators to monitor the land degradation in a salinized area[J]. Remote Sensing, 2021, 13(15): 2851, doi: 10.3390/rs13152851. doi: 10.3390/rs13152851
[3]	曹肖奕, 丁建丽, 葛翔宇, 等. 基于不同卫星光谱模拟的土壤电导率估算研究[J]. 干旱区地理, 2020, 43(1): 172-181.
	[ Cao Xiaoyi, Ding Jianli, Ge Xiangyu, et al. Estimation of soil conductivity based on spectral simulation of different satellites[J]. Arid Land Geography, 2020, 43(1): 172-181. ]
[4]	冯娟, 丁建丽, 杨爱霞, 等. 干旱区土壤盐渍化信息遥感建模[J]. 干旱地区农业研究, 2018, 36(1): 266-273.
	[ Feng Juan, Ding Jianli, Yang Aixia, et al. Remote sensing modeling of soil salinization information in arid areas[J]. Agricultural Research in the Arid Areas, 2018, 36(1): 266-273. ]
[5]	Mohamed E S, Saleh A M, Belal A B, et al. Application of near-infrared reflectance for quantitative assessment of soil properties[J]. The Egyptian Journal of Remote Sensing and Space Science, 2018, 21(1): 1-14. doi: 10.1016/j.ejrs.2017.02.001
[6]	贾萍萍, 尚天浩, 张俊华, 等. 利用多源光谱信息反演宁夏银北地区干湿季土壤含盐量[J]. 农业工程学报, 2020, 36(17): 125-134.
	[ Jia Pingping, Shang Tianhao, Zhang Junhua, et al. Inversion of soil salinity in dry and wet seasons based on multi-source spectral data in Yinbei area of Ningxia, China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(17): 125-134. ]
[7]	张素铭, 赵庚星, 王卓然, 等. 滨海盐渍区土壤盐分遥感反演及动态监测[J]. 农业资源与环境学报, 2018, 35(4): 349-358.
	[ Zhang Suming, Zhao Gengxing, Wang Zhuoran, et al. Remote sensing inversion and dynamic monitoring of soil salt in coastal saline area[J]. Journal of Agricultural Resources and Environment, 2018, 35(4): 349-358. ]
[8]	Dwivedi R S, Rao B R M. The selection of the best possible landsat TM band combination for delineating salt-affected soils[J]. International Journal of Remote Sensing, 1992, 13(11): 2051-2058. doi: 10.1080/01431169208904252
[9]	Sidike A, Zhao S, Wen Y. Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 26: 156-175. doi: 10.1016/j.jag.2013.06.002
[10]	El-Horiny M M. Mapping and monitoring of soil salinization using remote sensing and regression techniques: A case study in the Bahariya depression, Western Desert, Egypt[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 1-4.
[11]	孙亚楠, 李仙岳, 史海滨, 等. 基于多源数据融合的盐分遥感反演与季节差异性研究[J]. 农业机械学报, 2020, 51(6): 169-180.
	[ Sun Ya’nan, Li Xianyue, Shi Haibin, et al. Remote sensing inversion of soil salinity and seasonal difference analysis based on multi-source data fusion[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(6): 169-180. ]
[12]	黄权中, 徐旭, 吕玲娇, 等. 基于遥感反演河套灌区土壤盐分分布及对作物生长的影响[J]. 农业工程学报, 2018, 34(1): 102-109.
	[ Huang Quanzhong, Lü Lingjiao, et al. Soil salinity distribution based on remote sensing and its effect on crop growth in Hetao irrigation district[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(1): 102-109. ]
[13]	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.
	[ Chen Hongyan, Zhao Gengxing, Chen Jingchun, et al. Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 107-114. ]
[14]	刘恩, 王军涛, 常步辉, 等. 小开河引黄灌区土壤盐渍化定量遥感反演[J]. 中国农村水利水电, 2019(12): 20-24.
	[ Liu En, Wang Juntao, Chang Buhui, et al. Quantitative remote sensing inversion of soil salinization in Xiaokaihe Yellow River irrigation district[J]. China Rural Water and Hydropower, 2019(12): 20-24. ]
[15]	边玲玲, 王卷乐, 郭兵, 等. 基于特征空间的黄河三角洲垦利县土壤盐分遥感提取[J]. 遥感技术与应用, 2020, 35(1): 211-218.
	[ Bian Lingling, Wang Juanle, Guo Bing, et al. Remote sensing extraction of soil salinity in Yellow River delta Kenli County based on feature space[J]. Remote Sensing Technology and Application, 2020, 35(1): 211-218. ]
[16]	丁建丽, 瞿娟, 孙永猛, 等. 基于MSAVI-WI特征空间的新疆渭干河-库车河流域绿洲土壤盐渍化研究[J]. 地理研究, 2013, 32(2): 223-232.
	[ Ding Jianli, Qu Juan, Sun Yongmeng, et al. The retrieval model of soil salinization information in arid region based on MSAVI-WI feature space case study of the delta oasis in Weigan-Kuqa watershed[J]. Geographical Research, 2013, 32(2): 223-232. ]
[17]	陈实, 高超, 徐斌, 等. 新疆石河子农区土壤含盐量定量反演及其空间格局分析[J]. 地理研究, 2014, 33(11): 2135-2144. doi: 10.11821/dlyj201411013
	[ Chen Shi, Gao Chao, Xu Bin, et al. Quantitative inversion of soil salinity and analysis of its spatial pattern in agricultural area in Shihezi of Xinjiang[J]. Geographical Research, 2014, 33(11): 2135-2144. ] doi: 10.11821/dlyj201411013
[18]	王爽, 丁建丽, 王璐, 等. 基于地表光谱建模的区域土壤盐渍化遥感监测研究[J]. 干旱区地理, 2016, 39(1): 190-198.
	[ Wang Shuang, Ding Jianli, Wang Lu, et al. Remote sensing monitoring of soil salinization based on surface spectral modeling[J]. Arid Land Geography, 2016, 39(1): 190-198. ]
[19]	王丹丹, 于志同, 程猛, 等. 渭干河绿洲不同土地利用类型土壤盐分的变化特征分析[J]. 干旱区地理, 2018, 41(2): 349-357.
	[ Wang Dandan, Yu Zhitong, Cheng Meng, et al. Characteristics of soil salinity under different land use types in Weigan River Oasis[J]. Arid Land Geography, 2018, 41(2): 349-357. ]
[20]	麦麦提吐尔逊·艾则孜. 绿洲土壤盐渍化及水盐调控[M]. 北京: 北京理工大学出版社, 2016.
	[ Azezi Maimaitituerxun. Assessment and monitoring of soil salinization using remote sensing in arid area[M]. Beijing: Beijing Institute of Technology Press, 2016. ]
[21]	张添佑, 王玲, 曾攀丽, 等. 基于MSAVI-SI特征空间的玛纳斯河流域灌区土壤盐渍化研究[J]. 干旱区研究, 2016, 33(3): 499-505.
	[ Zhang Tianyou, Wang Ling, Zeng Panli, et al. Soil salinization in the irrigated area of the Manas River Basin based on MSAVI-SI feature space[J]. Arid Zone Research, 2016, 33(3): 499-505. ]
[22]	Liu J, Zhang L, Dong T, et al. The applicability of remote sensing models of soil salinization based on feature space[J]. Sustainability, 2021, 13(24): 13711, doi: 10.3390/su132413711. doi: 10.3390/su132413711
[23]	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.
	[ Wang Fei, Ding Jianli, Wei Yang, 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, China[J]. Acta Ecologica Sinica, 2017, 37(15): 5007-5022. ]
[24]	卢晶, 张绪教, 叶培盛, 等. 基于 SI-MSAVI 特征空间的河套灌区盐碱化遥感监测研究[J]. 国土资源遥感, 2020, 32(1): 169-175.
	[ Lu Jing, Zhang Xujiao, Ye Peisheng, et al. Remote sensing monitoring of salinization in Hetao irrigation district based on SI-MSAVI feature space[J]. Remote Sensing for Land & Resources, 2020, 32(1): 169-175. ]
[25]	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): 168-173.
	[ Wang Fei, Ding Jianli, Wu Manchun. Remote sensing monitoring models of soil salinization based in NDVI-SI feature space[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8): 168-173. ]
[26]	史晓艳, 李维弟, 余露, 等. 玛纳斯河流域农灌区土壤盐渍化遥感定量评价[J]. 灌溉排水学报, 2018, 37(11): 69-75, 83.
	[ Shi Xiaoyan, Li Weidi, Yu Lu, et al. Using remote sensing to evaluate soil salinization distribution over the irrigation areas in the Manas River Basin[J]. Journal of Irrigation and Drainage, 2018, 37(11): 69-75, 83. ]
[27]	王雪梅, 周晓红. 渭干河-库车河三角洲绿洲棉田土壤盐分估算及遥感反演[J]. 干旱地区农业研究, 2018, 36(6): 250-254, 262.
	[ Wang Xuemei, Zhou Xiaohong. Estimation and inversion modeling of salinity of cotton field soil using remote sensing in the Delta Oasis of Weigan and Kuqa Rivers[J]. Agricultural Research in the Arid Areas, 2018, 36(6): 250-254, 262. ]
[28]	杨小虎, 罗艳琴, 杨海昌, 等. 玛纳斯河流域绿洲农田土壤盐分反演及空间分布特征[J]. 干旱区资源与环境, 2021, 35(2): 156-161.
	[ Yang Xiaohu, Luo Yanqin, Yang Haichang, et al. Soil salinity retrieval and spatial distribution of oasis farmland in Manasi River Basin[J]. Journal of Arid Land Resources and Environment, 2021, 35(2): 156-161. ]
[29]	吴家林. 阿拉尔垦区棉田土壤盐渍化的遥感监测与植棉效益分析[D]. 阿拉尔: 塔里木大学, 2021.
	[ Wu Jialin. Remote sensing monitoring and cotton plantation benefit analysis of cotton field soil salinization in Aral Reclamation Area[D]. Aral: Tarim University, 2021. ]
[30]	宋奇, 冯春晖, 高琪, 等. 阿拉尔垦区近30年耕地变化及其驱动因子分析[J]. 国土资源遥感, 2021, 33(2): 202-212.
	[ Song Qi, Feng Chunhui, Gao Qi, et al. 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. ]
[31]	新疆生产建设兵团年鉴编辑委员会. 兵团年鉴[M]. 乌鲁木齐: 新疆生产建设兵团年鉴社, 2020.
	[Editorial Committee of Xinjiang Production and Construction Corps Yearbook. Production and Construction Corps yearbook[M]. Urumqi: Xinjiang Production and Construction Corps Yearbook Society, 2020. ]
[32]	卢晶. 基于3S技术的土壤盐碱化时空分布规律及影响因素研究[D]. 北京: 中国地质大学, 2019.
	[ Lu Jing. Study on temporal-spatial distribution and impact factors of soil salinization based on 3S technology[D]. Beijing: China University of Geosciences, 2019. ]
[33]	陈实, 徐斌, 金云翔, 等. 北疆农区土壤盐渍化遥感监测及其时空特征分析[J]. 地理科学, 2015, 35(12): 1607-1615. doi: 10.13249/j.cnki.sgs.2015.012.1607
	[ Chen Shi, Xu Bin, Jin Yunxiang, et al. Remote sensing monitoring and spatial-temporal characteristics analysis of soil salinization in agricultural area of northern Xinjiang[J]. Scientia Geographica Sinica, 2015, 35(12): 1607-1615. ] doi: 10.13249/j.cnki.sgs.2015.012.1607
[34]	Khan N M, Sato Y. Monitoring hydro-salinity status and its impact in irrigated semi-arid areas using IRS-1B LISS-II data[J]. Asian Journal of Geoinform, 2001, 1(3): 63-73.
[35]	李艳菊, 丁建丽, 米热古力·艾尼瓦尔. 渭-库绿洲土壤剖面盐分分布特征及驱动因子分析[J]. 灌溉排水学报, 2019, 38(6): 58-65.
	[ Li Yanju, Ding Jianli, Ainiwaer Mireguli. Soil salt distribution and the factors affect it in Ogan Kucha River Oasis[J]. Journal of Irrigation and Drainage, 2019, 38(6): 58-65. ]
[36]	王遵亲, 祝寿泉, 尤文瑞, 等. 中国盐渍土[M]. 北京: 科学出版社, 1993.
	[ Wang Zunqin, Zhu Shouquan, You Wenrui, et al. Saline soil in China[M]. Beijing: Science Press, 1993. ]
[37]	Staff U S S L. Diagnosis and improvement of saline and alkali soils[J]. Agriculture Handbook, 1954, 60: 83-100.
[38]	王旭, 田长彦, 赵振勇, 等. 滴灌条件下盐地碱蓬 (Suaeda salsa) 种植年限对盐碱地土壤盐分离子分布的影响[J]. 干旱区地理, 2020, 43(1): 211-217.
	[ Wang Xu, Tian Changyan, Zhao Zhenyong, et al. Effects of different planting years of Suaeda salsa on the soil ions distribution in saline-sodic soil under drip irrigation[J]. Arid Land Geography, 2020, 43(1): 211-217. ]
[39]	王世明, 范敬龙, 赵英, 等. 咸水灌溉条件下塔里木河下游沙漠土壤水盐运移数值模拟[J]. 干旱区地理, 2021, 44(4): 1104-1113.
	[ Wang Shiming, Fan Jinglong, Zhao Ying, et al. Numerical simulation of water and salt migration in desert soil in the lower reaches of Tarim River under salt-water irrigation[J]. Arid Land Geography, 2021, 44(4): 1104-1113. ]

模型	公式	拟合度R²
线性模型	y=0.2863-0.2832x	0.8677
二次模型	y=0.3163-0.4948x+0.2251x²	0.8867
指数模型	y=0.3488×0.1816^x-0.0239	0.8881
几何模型	y=-0.3565x^0.4846+0.3842	0.8877
双曲模型	y=1.0/(2.5226+12.022x)	0.8609
对数平方模型	y=0.0334-0.3386logx-0.1013logx²	0.8886

指标	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐土
土壤电导率/mS·cm^-1	0~2	2~4	4~8	8~16	>16
SDI模型	0.000~0.3119	0.3119~0.3877	0.3877~0.5393	0.5393~0.8425	>0.8425

年份	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐土
2011	972.98	251.09	405.55	872.51	810.60
2021	1291.20	251.89	328.71	719.48	879.21

土壤盐渍化等级		2011年
土壤盐渍化等级		非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐土
2021年	非盐渍土	758.50	107.67	155.89	183.29	51.11
	轻度盐渍土	87.83	39.14	60.40	51.07	8.65
	中度盐渍土	58.94	40.45	99.71	105.19	15.31
	重度盐渍土	43.58	20.56	70.42	392.91	140.02
	盐土	19.10	3.35	7.12	94.15	506.45