Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment

ZHANG Siyuan; YUE Chu; YUAN Guoli; YUAN Shuai; PANG Wenqiang; LI Jun

doi:10.6046/zrzyyg.2021349

2022 Vol. 34, No. 4

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Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 136-143

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ZHANG Siyuan, YUE Chu, YUAN Guoli, YUAN Shuai, PANG Wenqiang, LI Jun. 2022. Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment. Remote Sensing for Natural Resources, 34(4): 136-143. doi: 10.6046/zrzyyg.2021349

Citation:

ZHANG Siyuan, YUE Chu, YUAN Guoli, YUAN Shuai, PANG Wenqiang, LI Jun. 2022. Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment. Remote Sensing for Natural Resources, 34(4): 136-143. doi: 10.6046/zrzyyg.2021349

Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment

1. Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 010010, China；
2. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China；
3. Bayannur City Modern Agriculture and Animal Husbandry Development Center, Bayannur 015000, China

Received Date: 21 October 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

Abstract

Soil salinization is the most severe environmental risk in arid and semi-arid areas. The remote sensing method that constructs a characteristic space based on characteristic parameters provides an effective and economical tool and technique for the timely monitoring and inversion of soil salinization. Presently, the normalized difference vegetation index (NDVI) and the salinity index (SI) are mainly selected as the characteristic parameters for salinization inversion, while refined analysis and regional applicability are lacking. This study investigated Urad Front Banner in Inner Mongolia based on the Landsat8 OLI data. The ENDVI-SI3 characteristic space was constructed using the enhanced normalized difference vegetation index (ENDVI) that introduced the shortwave infrared band and the salinity index 3 (SI3) with the best inversion effect for semi-arid areas. Accordingly, the improved salinization monitoring index (ISMI) model was built. The results show that the correlation coefficient between ISMI and soil salt content was up to 0.82, and the inversion precision of the ISMI model was higher than that of NDVI, EDNVI, and SI3 (-0.66, -0.70, and 0.75, respectively). Based on the ISMI, this study achieved the quantitative inversion analysis and risk assessment of soil salinization in Urad Front Banner. This study provides an approach for selecting the optimal characteristic parameters of the characteristic space in the salinization inversion of semi-arid areas.
- salinization /
- remote sensing model /
- ENDVI /
- SI3 /
- feature space /
- risk assessment

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References

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