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

干旱区地理 ›› 2024, Vol. 47 ›› Issue (7): 1199-1209.doi: 10.12118/j.issn.1000-6060.2023.584

基于光谱指数建模的沙井子灌区土壤盐分反演

谢俊博¹(), 王兴鹏¹^,², 何帅²^,³^,⁴(), 刘洋¹, 忠智博²^,³^,⁴, 李妍⁵, 洪国军⁶

1.塔里木大学水利与建筑工程学院，新疆阿拉尔 843300
2.农业农村部西北绿洲节水农业重点实验室，新疆石河子 832000
3.新疆农垦科学院农田水利与土壤肥料研究所，新疆石河子 832000
4.水肥资源高效利用兵团重点实验室，新疆石河子 832000
5.塔里木大学农学院资源利用与植物保护专业，新疆阿拉尔 843300
6.江西科技学院区域发展研究院，江西南昌 330200

收稿日期:2023-10-18 修回日期:2023-12-04 出版日期:2024-07-25 发布日期:2024-07-30
通讯作者: 何帅（1976-），男，硕士，副研究员，主要从事节水农业和土壤改良等方面的研究. E-mail: xjshzhs@163.com
作者简介:谢俊博（1998-），男，硕士研究生，主要从事遥感技术在节水农业及土壤改良中的应用研究. E-mail: junboxie123@gmail.com
基金资助:
兵团财政科技计划资助(2021AB009);国家重点研发计划项目(2021YFD1900805)

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

XIE Junbo¹(), WANG Xingpeng¹^,², HE Shuai²^,³^,⁴(), LIU Yang¹, ZHONG Zhibo²^,³^,⁴, LI Yan⁵, HONG Guojun⁶

1. College of Water Hydraulic and Architectural Engineering, Tarim University, Aral 843300, Xinjiang, China
2. Northwest Oasis Water-Saving Agriculture Key Laboratory, Ministry of Agriculture and Rural Affairs, Shihezi 832000, Xinjiang, China
3. Institute of Water Conservancy and Soil Fertilizer, Xinjiang Academy of Agricultural Sciences, Shihezi 832000, Xinjiang, China
4. Xinjiang Production & Construction Corps Key Laboratory of Efficient Utilization of Water and Fertilizer, Shihezi 832000, Xinjiang, China
5. Department of Resource Utilization and Plant Protection, College of Agriculture, Tarim University, Aral 843300, Xinjiang, China
6. Institute of Regional Development, Jiangxi University of Science and Technology, Nanchang 330200, Jiangxi, China

Received:2023-10-18 Revised:2023-12-04 Published:2024-07-25 Online:2024-07-30

摘要/Abstract

摘要：

为了快速准确地获取干旱地区表层土壤盐分信息，以沙井子灌区为研究区，利用地面采集的0~10 cm和10~20 cm深度的土壤盐分数据，以及同步获取的Landsat 9 OLI遥感影像上相应点位的波段反射率值，组合两波段和三波段光谱指数，建立低植被度覆盖下盐渍化监测SDI1、SDI2、SDI3、SDI4模型，并检验4类模型对不同土层深度土壤盐分的反演精度。结果表明：（1）当土层深度为0~10 cm时，4类盐渍化监测模型对土壤盐渍化等级分类精度分别为73.56%、66.35%、43.75%和74.52%；而当土层深度为10~20 cm时，相应的分类精度分别为61.06%、62.50%、66.35%和64.42%，说明灌区内土层最佳反演深度为0~10 cm。（2）三波段光谱指数构建的SDI4模型优于双波段光谱指数构建的其余3种模型，能够有效反演沙井子灌区土壤盐渍化程度。研究结果可为灌区土壤盐渍化治理和防治提供有效的技术参考。

关键词: 土壤盐渍化, 遥感监测, 光谱指数, 沙井子灌区

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

谢俊博, 王兴鹏, 何帅, 刘洋, 忠智博, 李妍, 洪国军. 基于光谱指数建模的沙井子灌区土壤盐分反演[J]. 干旱区地理, 2024, 47(7): 1199-1209.

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.

图/表 11

图1

表1

波段组合与光谱指数"

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）	$N I R - R - B N I R + R + B$

表1

图2

表2

光谱指数二维散点拟合性"

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

表2

图3

表3

盐渍化监测模型"

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	$S I 2 + G B N D V I - 1 2$

表3

表4

图4

图5

表5

表6

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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

基于光谱指数建模的沙井子灌区土壤盐分反演

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

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