Using HJ - I satellite remote sensing data to surveying the Saline soil distribution in Yinchuan Plain of China

Zhang, Meimei; SuYanping,; Wu, Ping

doi:10.5897/ajar11.130

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Previous studies reported that the precision of soil salt content monitoring and mapping based on hyperspectral data was higher than that on multispectral data (Metternicht and Zinck, 2003;Fernandez-Buces et al, 2006;Ghosh et al, 2012;Mashimbye et al, 2012;Shamsi et al, 2013). The types and levels of soil salinity could be determined accurately by using hyperspectral data with high spectral resolution, which was a generally accepted monitoring method (Farifteh et al, 2008;Weng et al, 2010;Zhang M M et al, 2011;Zhang T et al, 2011;Ghosh et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Monitoring soil salt content using HJ-1A hyperspectral data: A case study of coastal areas in Rudong County, Eastern China

Liu

Zhu

et al. 2014

Chin. Geogr. Sci.

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Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, HuanJing-Hyper Spectral Imager (HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm (NDVI 705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index (NSSRI) was constructed from continuum-removed reflectance (CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area (NDVI 705 < 0.2). The soil adjusted salinity index (SAVI) was applied to predict the soil salt content in the vegetation-covered area (NDVI 705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping (R 2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.

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Section: Introductionmentioning

confidence: 99%

Monitoring soil salt content using HJ-1A hyperspectral data: A case study of coastal areas in Rudong County, Eastern China

Liu

Zhu

et al. 2014

Chin. Geogr. Sci.

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Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Gorji

Yıldırım

Sertel

et al. 2019

International Journal of Environment and Geoinformatics

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Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils.The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from . Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation.

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Using HJ - I satellite remote sensing data to surveying the Saline soil distribution in Yinchuan Plain of China

Cited by 2 publications

References 8 publications

Monitoring soil salt content using HJ-1A hyperspectral data: A case study of coastal areas in Rudong County, Eastern China

Monitoring soil salt content using HJ-1A hyperspectral data: A case study of coastal areas in Rudong County, Eastern China

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

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