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Arid Land Geography >

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

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

Earth Information Sciences

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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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

Cite this article

Qiaozhen ZHAO , Jianli DING , Lijing HAN , Xiaoye JIN , Jianping HAO . 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 . DOI: 10.12118/j.issn.1000-6060.2021.551

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