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2022 , Vol. 45 >Issue 4: 1155 - 1164

DOI: https://doi.org/10.12118/j.issn.1000-6060.2021.551

地球信息科学

MODIS和Landsat时空融合影像在土壤盐渍化监测中的适用性研究——以渭干河—库车河三角洲绿洲为例

  • 赵巧珍 ,
  • 丁建丽 ,
  • 韩礼敬 ,
  • 金晓叶 ,
  • 郝建平
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  • 1.新疆大学地理与遥感科学学院,智慧城市与环境建模自治区普通高校重点实验室,新疆 乌鲁木齐 830046
    2.新疆大学绿洲生态教育部重点实验室,新疆 乌鲁木齐 830046
    3.自然资源部中亚地理信息技术开发应用工程技术创新中心,新疆 乌鲁木齐 830046
    4.新疆阿克苏地区渭干河流域管理处,新疆 阿克苏 842000
赵巧珍（1991-）,女,硕士研究生,主要从事干旱区环境演变与遥感应用研究. E-mail: zhaoqz1026@163.com

收稿日期: 2021-11-22

  修回日期: 2022-01-19

  网络出版日期: 2022-08-11

基金资助

国家自然科学基金(41961059);新疆维吾尔自治区自然科学基金重点项目(2021D01D06)

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Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis

  • Qiaozhen ZHAO ,
  • Jianli DING ,
  • Lijing HAN ,
  • Xiaoye JIN ,
  • Jianping HAO
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  • 1. Key Laboratory of Smart City and Environmental Modeling of Autonomous Region Universities, School of Resources and Environmental Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
    2. Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi 830046, Xinjiang, China
    3. Ministry of Natural Resources, Central Asia Geographic Information Technology Development and Application Engineering Technology Innovation Center, Urumqi 830046, Xinjiang, China
    4. Ugan River Basin Management Office, Aksu Prefecture, Xinjiang, Aksu 842000, Xinjiang, China

Received date: 2021-11-22

  Revised date: 2022-01-19

  Online published: 2022-08-11

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摘要

土壤盐渍化的遥感监测依赖于高时空分辨率影像,但受经费预算、卫星回访周期及天气的影响,高时空分辨率的遥感影像较难获取,这就限制了根据采样时间来获取对应时期遥感影像进行土壤盐渍化监测反演的应用。为此,提出融合MODIS和Landsat影像生成高时空分辨率影像来提取土壤盐渍化信息,为时空影像进行土壤盐渍化监测研究提供数据参考。以渭干河—库车河绿洲为研究区,利用增强型时空自适应融合率反射模型（Enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM）和灵活的时空融合模型（Flexible spatiotemporal data fusion,FSDAF）,对MODIS和Landsat影像进行时空融合,并基于融合影像数据构建了关于土壤电导率（EC）的随机森林（RF）预测模型,对比分析时空融合影像应用于土壤盐渍化遥感监测的适用性。结果表明：ESTARFM融合影像的特征波段反射率与Landsat验证影像对应波段反射率一致性决定系数R2（Red）=0.8066、R2（SWIR2）=0.8444;FSDAF融合影像的特征波段与Landsat验证影像对应波段反射率一致性决定系数R2（Red）=0.6999、R2（SWIR2）=0.7493;基于ESTARFM融合影像构建的EC值预测模型精度最高,R2=0.9268,基于FSDAF融合影像构建的EC值预测模型精度良好,R2=0.8987,基于验证影像构建的EC值预测模型R2=0.9103; ESTARFM模型的融合精度高于FSDAF模型,并且基于融合影像构建的EC值预测模型效果良好。

关键词： 时空融合; 随机森林; 土壤电导率; 土壤盐渍化

本文引用格式

赵巧珍 , 丁建丽 , 韩礼敬 , 金晓叶 , 郝建平 . MODIS和Landsat时空融合影像在土壤盐渍化监测中的适用性研究——以渭干河—库车河三角洲绿洲为例[J]. 干旱区地理, 2022 , 45(4) : 1155 -1164 . DOI: 10.12118/j.issn.1000-6060.2021.551

Abstract

Remote sensing monitoring of soil salinization relies on high spatiotemporal resolution images, which are difficult to obtain due to high cost, revisit period of the satellite, and weather effects. The spatiotemporal fusion technology of remote sensing images has the advantages of low cost and good reliability and has been widely used in several studies, such as vegetation growth, flood monitoring, and temperature monitoring. This study investigates the effectiveness of spatiotemporal fusion images in soil salinization monitoring at Ugan River-Kuqa River Delta Oasis, south Xinjiang, China. An enhanced spatial and temporal adaptive reflectance fusion model and flexible spatiotemporal data fusion model were employed to fuse MODIS and Landsat images and generate high spatiotemporal resolution images. A random forest prediction model for soil conductivity (EC) was created using the fusion images. Furthermore, the precision of both models was evaluated. Results show that the fusion images made by the ESTARFM and FSDAF models have high spatial resolution and good uniformity compared with the verification image. Verification revealed the reliability of the fusion results from the ESTARFM algorithm, R2(Red)=0.8066 and R2(SWIR2)=0.8444, and the FSDAF algorithm, R2(Red)=0.6999 and R2(SWIR2)=0.7493. The EC prediction model created using the ESTARFM fusion images had the best accuracy with R2 of 0.9268. Meanwhile, the R2 of the EC prediction model constructed from FSDAF was 0.8987 and that constructed based on Landsat verification images was 0.9103. This finding showed that spatiotemporal fusion technology has good application in the remote sensing monitoring of soil salinization. Comparison of the salinity prediction results from the two spatiotemporal image fusion models revealed that the ESTARFM model is suitable for salinity prediction.

Key words： temporal and spatial fusion; random forest; electric conductivity; soil salinization

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