基于光谱指数建模的沙井子灌区土壤盐分反演

doi:10.12118/j.issn.1000-6060.2023.584

Abstract

Abstract:

This study aims to rapidly and accurately obtain surface soil salinity information in arid regions. To achieve this, Shajingzi irrigation district, Aksu Prefecture, Xinjiang, China was taken as the research area, ground-collected soil salinity data at depths of 0-10 cm and 10-20 cm were used along with corresponding Landsat 9 OLI remote-sensing images to extract band reflectance values. This led to the creation of two- and three-band spectral indices, which resulted in the development of four remote-sensing salt detection indices (SDI1, SDI2, SDI3, and SDI4) under conditions of low vegetation cover. These four indices were then evaluated for their accuracy in estimating soil salinity at different depths. The results showed that: (1) The classification accuracies of the four indices were 73.56%, 66.35%, 43.75%, and 74.52%, respectively, for a soil depth of 0-10 cm and 61.06%, 62.50%, 66.35%, and 64.42%, respectively, for a soil depth of 10-20 cm. These findings suggest that the optimal inversion depth for soil layers in the irrigation district is 0-10 cm. (2) Among the four indices, SDI4, which was constructed with a three-band spectrum, outperformed the others, which are two-band spectrum-based. SDI4 effectively estimates the degree of soil salinization in the Shajingzi irrigation district, providing valuable technical references for managing and preventing soil salinization in irrigated areas.

Key words: soil salinization, remote sensing monitoring, spectral index, Shajingzi irrigation district

XIE Junbo, WANG Xingpeng, HE Shuai, LIU Yang, ZHONG Zhibo, LI Yan, HONG Guojun. Soil salinity inversion in the Shajingzi irrigation district based on spectral index modeling[J].Arid Land Geography, 2024, 47(7): 1199-1209.

Figures/Tables 11

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References 35

[1]	黄晶, 孔亚丽, 徐青山, 等. 盐渍土壤特征及改良措施研究进展[J]. 土壤, 2022, 54(1): 18-23.
	[Huang Jing, Kong Yali, Xu Qingshan, et al. Progresses for characteristics and amelioration measures of saline soil[J]. Soils, 2022, 54(1): 18-23.]
[2]	Li J G, Pu L J, Han M F, et al. Soil salinization research in China: Advances and prospects[J]. Journal of Geographical Sciences, 2014, 24(5): 943-960.
[3]	杨劲松, 姚荣江, 王相平, 等. 中国盐渍土研究: 历程、现状与展望[J]. 土壤学报, 2022, 59(1): 10-27.
	[Yang Jinsong, Yao Rongjiang, Wang Xiangping, et al. Research on salt-affected soils in China: History, status quo and prospect[J]. Acta Pedologica Sinica, 2022, 59(1): 10-27.]
[4]	Pang G J, Wang T, Liao J, et al. Quantitative model based on field-derived spectral characteristics to estimate soil salinity in Minqin County, China[J]. Soil Science Society of America Journal, 2014, 78(2): 546-555.
[5]	Li P Y, Wu J H, Qian H. Regulation of secondary soil salinization in semi-arid regions: A simulation research in the Nanshantaizi area along the Silk Road, northwest China[J]. Environmental Earth Sciences, 2016, 75: 1-12.
[6]	Li H Y, Liu X L, Hu B F, et al. Field-scale characterization of spatio-temporal variability of soil salinity in three dimensions[J]. Remote Sensing, 2020: 12(24): 4043, doi: 10.3390/rs12244043.
[7]	张子璇, 宋雨桐, 张惠中, 等. 水文气候影响下黄河三角洲土壤盐分时空动态[J]. 应用生态学报, 2021, 32(4): 1393-1405. doi: 10.13287/j.1001-9332.202104.012
	[Zhang Zixuan, Song Yutong, Zhang Huizhong, et al. Spatiotemporal dynamics of soil salinity in the Yellow River Delta under the impacts of hydrology and climate[J]. Chinese Journal of Applied Ecology, 2021, 32(4): 1393-1405.] doi: 10.13287/j.1001-9332.202104.012
[8]	石聪, 陈礼瀚, 张怡菲, 等. 新疆小海子灌区耕地土壤盐渍化特征研究[J]. 干旱区地理, 2023, 46(2): 321-329.
	[Shi Cong, Chen Lihan, Zhang Yifei, et al. Soil salinization characteristics of cultivated land in Xiaohaizi irrigation area of Xinjiang[J]. Arid Land Geography, 2023, 46(2): 321-329.]
[9]	Stavi I, Thevs N, Priori S. Soil salinity and sodicity in drylands: A review of causes, effects, monitoring, and restoration measures[J]. Frontiers in Environmental Science, 2021, 9: 712831, doi: 10.3389/fenvs.2021.712831.
[10]	Tan J, Ding J L, Han L J, et al. Exploring planet scope satellite capabilities for soil salinity estimation and mapping in arid regions oases[J]. Remote Sensing, 2023, 15(4): 1066, doi: 10.3390/rs15041066.
[11]	Zhao W J, Zhou C, Zhou C Q, et al. Soil salinity inversion model of oasis in arid area based on UAV multispectral remote sensing[J]. Remote Sensing, 2022, 14(8): 1804, doi: 10.3390/rs14081804.
[12]	Zhou X H, Zhang F, Liu C J, et al. Soil salinity inversion based on novel spectral index[J]. Environmental Earth Sciences, 2021, 80: 1-13.
[13]	Wu T S, Fu H P, Feng F, et al. A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland[J]. International Journal of Remote Sensing, 2019, 40(23): 9050-9063.
[14]	Matsushita B, Yang W, Chen J, et al. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest[J]. Sensors, 2007, 7(11): 2636-2651. doi: 10.3390/s7112636 pmid: 28903251
[15]	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.
[16]	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 on NDVI-SI feature space[J]. Transactions of the CSAE, 2010, 26(8): 168-173.]
[17]	Cheng T T, Zhang J H, Zhang S, et al. Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization[J]. Environmental Science Pollution Research, 2022, 29: 24269-24285.
[18]	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.]
[19]	冯娟, 丁建丽, 魏雯瑜. 基于Albedo-MSAVI特征空间的渭库绿洲土壤盐渍化研究[J]. 中国农村水利水电, 2018, 2: 147-152.
	[Feng Juan, Ding Jianli, Wei Wenyu. A study of soil salinization in Weigan and Kuqa Rivers oasis based on Albedo-MSAVI feature space[J]. China Rural Water and Hydropower, 2018, 2: 147-152.]
[20]	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.]
[21]	杨宁, 崔文轩, 张智韬, 等. 无人机多光谱遥感反演不同深度土壤盐分[J]. 农业工程学报, 2020, 36(22): 13-21.
	[Yang Ning, Cui Wenxuan, Zhang Zhitao, et al. Soil salinity inversion at different depths using improved spectral index with UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(22): 13-21.]
[22]	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.]
[23]	Fan X W, Liu Y B, Tao J M, et al. Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression[J]. Remote Sensing, 2015, 7(1): 488-511.
[24]	Wang J Z, Ding J L, Abulimiti A, et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake wetland, northwest China[J]. PeerJ, 2018, 6: e4703, doi: 10.7717/peerj.4703.
[25]	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 Geoinformatics, 2001, 1(3): 63-73.
[26]	Pinty B, Lavergne T, Dickinson R E, et al. Simplifying the interaction of land surfaces with radiation for relating remote sensing products to climate models[J]. Journal of Geophysical Research: Atmospheres, 2006, 111: D02116, doi: 10.1029/2005JD005952.
[27]	代云豪, 管瑶, 冯春涌, 等. 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, 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.]
[28]	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. doi: 10.11873/j.issn.1004-0323.2020.1.0211
	[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.]
[29]	Chen Y W, Du Y Y, Yin H Y, et al. Radar remote sensing-based inversion model of soil salt content at different depths under vegetation[J]. PeerJ, 2022, 10: e13306, doi: 10.7717/peerj.13306.
[30]	谭丞轩, 张智韬, 许崇豪, 等. 无人机多光谱遥感反演各生育期玉米根域土壤含水率[J]. 农业工程学报, 2020, 36(10): 63-74.
	[Tan Chengxuan, Zhang Zhitao, Xu Chonghao, et al. Soil water content inversion model in field maize root zone based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(10): 63-74.]
[31]	Hu J, Peng J, Zhou Y, et al. Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images[J]. Remote Sensing, 2019, 11(7): 736, doi: 10.3390/rs11070736.
[32]	Zhang J R, Zhang Z T, Chen J Y, et al. Estimating soil salinity with different fractional vegetation cover using remote sensing[J]. Land Degradation & Development, 2021, 32(2): 597-612.
[33]	Chen B L, Zheng H W, Luo G P, et al. Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization[J]. International Journal of Remote Sensing, 2022, 43(3): 778-811.
[34]	Wang L Y, Hu P, Zheng H W, et al. Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images[J]. Geoderma, 2023, 430: 116321, doi: 10.1016/j.geoderma.2022.116321.
[35]	Xu H T, Chen C B, Zheng H W, et al. AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring[J]. International Journal of Remote Sensing, 2020, 41(12): 4470-4495.

波段组合类型	光谱指数	公式
两波段光谱指数（2DVI）	盐分指数（SI）	$B × R$
	差值环境植被指数（DVI）	$N I R - R$
	土壤调解植被指数（SAVI）	$N I R - R × 1.5 N I R$
	修改型土壤调节植被指数（MSAVI）	$2 × N I R - 1 + 2 × N I R + 1 2 - 8 × N I R - R 2 2$
三波段光谱指数（3DVI）	绿蓝波段归一化差值植被指数（GBNDVI）	$N I R - R - B N I R + R + B$

二维散点图	光谱指数拟合公式	拟合度（R²）
SI-GBNDVI	线性：y= -1.0585x+0.2001	0.7984
	二次：y= -2.7603x²-0.7608x+0.1787	0.9352
	指数：y=0.1915e^3.724^x	0.9118
	对数：y= -0.3572 $-$ 0.2747logx+0.1978logx²	0.7676
	几何：y= -2.772x^0.0776+2.3768	0.7488
SI-SAVI	线性：y= -2.9893x+0.5704	0.7967
	二次：y=14.767x²-5.7517x+0.6786	0.8372
	指数：y=0.69e^-10.31^x	0.8270
	对数：y= -0.039-0.1694logx-0.0016logx²	0.5624
	几何：y= -0.4402x^0.2033+0.3955	0.5598
SI-DVI	线性：y= -3.5664x+0.5468	0.4582
	二次：y=35.913x²-9.1993x-0.7483	0.5471
	指数：y=0.609e^-11.76^x	0.5253
	对数：y= -0.0489-0.2178logx-0.0885logx²	0.5205
	几何：y= -0.1537x^0.9426+0.0924	0.5285
SI-MSAVI	线性：y= -3.2091x-2.636	0.7372
	二次：y=18.488x²+30.464x+12.675	0.7888
	指数：y=0.316e^-10.99^x	0.7767
	对数：y= -1.041-0.1793logx-0.0228logx²	0.5473
	几何：y= -0.328x^0.2764-0.717	0.5462

盐渍化监测模型	公式
SDI1模型	$S I 2 + M S A V I - 1 2$
SDI2模型	$S I 2 + D V I - 1 2$
SDI3模型	$S I 2 + S A V I - 1 2$
SDI4模型	$S I 2 + G B N D V I - 1 2$

盐渍化监测模型	4个模型的值域区间
盐渍化监测模型	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐渍土
SDI1模型	1.71~1.90	1.90~1.95	1.95~2.02	2.02~2.12	2.12~2.26
SDI2模型	0.78~0.96	0.96~1.03	1.03~1.13	1.13~1.25	1.25~1.44
SDI3模型	0.70~0.92	0.92~0.97	0.97~1.06	1.06~1.21	1.21~1.42
SDI4模型	0.38~0.90	0.90~1.09	1.09~1.20	1.20~1.37	1.37~1.65

Soil salinity inversion in the Shajingzi irrigation district based on spectral index modeling

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盐渍化监测模型	正确分类样本点个数	错误分类样本点个数						总体精度/%
盐渍化监测模型	正确分类样本点个数	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐渍土	共计	总体精度/%
SDI1	144	13	33	5	5	8	64	73.56
SDI2	138	9	40	6	3	12	70	66.35
SDI3	91	0	76	16	22	0	117	43.75
SDI4	155	13	25	5	4	6	53	74.52

[1]	MENG Huimin, ZHAN Chesheng, HU Shi, LIN Zhonghui. Research progress on simulation of soil water-salt transport in large-scale irrigation districts [J]. Arid Land Geography, 2024, 47(9): 1566-1576.
[2]	SHI Cong, CHEN Lihan, ZHANG Yifei, HE Shuai, XIE Haixia. Soil salinization characteristics of cultivated land in Xiaohaizi Irrigation Area of Xinjiang [J]. Arid Land Geography, 2023, 46(8): 1314-1323.
[3]	LI Ke, DING Jianli, HAN Lijing, GE Xiangyu, GU Yongsheng, ZHOU Qian, LYU Yangxia. Digital mapping of soil salinization in a typical oasis based on PlanetScope images [J]. Arid Land Geography, 2023, 46(8): 1291-1302.
[4]	JIANG Lei, LIU Xiaolong, GUO Shuai, HE Liang, XING Jianlei, GUO Junjie. Evaluation of soil salinization susceptibility based on Logistic regression analysis: A case of Tarim irrigation area in southern Xinjiang [J]. Arid Land Geography, 2023, 46(11): 1858-1867.
[5]	DING Yuekai, LIU Rui, ZHANG Cuilan, TONG Liyuan, DONG Jun. Remote sensing monitoring of glacier and glacial lake changes in Yairu Zangbo Basin, Himalayas [J]. Arid Land Geography, 2022, 45(6): 1870-1880.
[6]	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.
[7]	ZHAO Qiaozhen,DING Jianli,HAN Lijing,JIN Xiaoye,HAO Jianping. Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis [J]. Arid Land Geography, 2022, 45(4): 1155-1164.
[8]	CAO Xiao-yi, DING Jian-li, GE Xiang-yu, LIANG Jing, CHEN Wen-qian, CHEN Xiang-yue, TANG Pu-en. Estimation of soil conductivity based on spectral simulation of different satellites [J]. Arid Land Geography, 2020, 43(1): 172-181.
[9]	LIU Yang, ZHANG Nan-nan, GUO Hong-yan, ZHUANG Yang, DONG Wen-tong, ZOU Li-qun, ZHOU Hong-ying. Spatial and temporal characteristics of environmental elements evolution in oil field based on time series of remote sensing: Acase of Karamay City [J]. 干旱区地理, 2018, 41(3): 616-624.
[10]	WANG Shuang, DING Jian-li, WANG lu, NIU Zeng-yi. Remote sensing monitoring of soil salinization based on surface spectral modeling [J]. , 2016, 39(1): 190-198.
[11]	NIU Zeng-yi, DING Jian-li, LI Yan-hua, WANG Shuang, WANG Lu, MA Cheng-xia. Soil salinization information extraction method based on GF-1 image [J]. , 2016, 39(1): 171-181.
[12]	HE Jian-cun, BAI Yun-gang, ZHANG Yan-jun. Soil drought characteristics in Xinjiang with remote sensing data [J]. , 2015, 38(4): 735-742.
[13]	. Desertification monitoring with remote sensing in the Central Asia：a case of Turkmenistan [J]. , 2013, 36(4): 724-730.