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干旱区地理 >

2024 , Vol. 47 >Issue 7: 1199 - 1209

DOI: https://doi.org/10.12118/j.issn.1000-6060.2023.584

生物与土壤

基于光谱指数建模的沙井子灌区土壤盐分反演

  • 谢俊博 ,
  • 王兴鹏 ,
  • 何帅 ,
  • 刘洋 ,
  • 忠智博 ,
  • 李妍 ,
  • 洪国军
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  • 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
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
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

收稿日期: 2023-10-18

  修回日期: 2023-12-04

  网络出版日期: 2024-07-30

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)

收起

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

  • XIE Junbo ,
  • WANG Xingpeng ,
  • HE Shuai ,
  • LIU Yang ,
  • ZHONG Zhibo ,
  • LI Yan ,
  • HONG Guojun
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  • 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 date: 2023-10-18

  Revised date: 2023-12-04

  Online published: 2024-07-30

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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

本文引用格式

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 . DOI: 10.12118/j.issn.1000-6060.2023.584

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

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