Using hyperspectral vegetation indices as a proxy to monitor soil salinity

Zhang, Tingting

doi:10.1364/orse.2010.otub4

Cited by 18 publications

(34 citation statements)

References 5 publications

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“…Previous studies reported the same pattern found in the near infrared reflectance when salt-tolerant species were irrigated with moderately saline water (Poss et al, 2006, Tilley et al, 2007. Similarly, Zhang et al (2011) observed a rise in near infrared reflectance for salt-tolerant species growing on moderately saline soils in a wetland environment.…”

Section: Resultssupporting

confidence: 68%

“…The general pattern of near infrared reflectance reduction results was adequate for describing the effect of salt stress on salt-sensitive species, but not good enough for salt-tolerant species that grow better on moderately saline soils than on non-saline and highly saline soils (Läuchli, 2002;Zhang et al, 2011). Another spectral change associated with higher salinity conditions that was observed was the narrowing of the red absorption band and a shift of the red edge to shorter wavelengths (Horler et al, 1983;Peñuelas et al, 1994).…”

Section: Resultsmentioning

confidence: 99%

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Spectral indices for the detection of salinity effects in melon plants

Hernández

Melendez-Pastor

Pedreño

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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). Spectral data of leaves were transformed into vegetation indices indicative of the physiological status of the plants. The results showed differences for N (p < 0.05), K and Na content (p < 0.01) due to salinity suggesting different degrees of salt stress on the plants. Specific leaf area increased with salinity levels (p < 0.001). The capabilities of VNIR radiometry to assess the influence of soil salinity on melon physiology using a non-destructive method were demonstrated. A normalized difference vegetation index , and the ratio between water index (WI) and normalized difference vegetation index (WI/NDVI 750-705 ) showed significant relationships (p < 0.01) with the salinity. Therefore, this method could be used for in-situ early detection of salinity stress effects.

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Section: Resultssupporting

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Spectral indices for the detection of salinity effects in melon plants

Hernández

Melendez-Pastor

Pedreño

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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“…This technique has been successfully used in remote sensing applications to estimate the level of salinity in soils, using numerous indices to assess the concentration of salt according to different wavelengths of reflectance (Poss et al, 2006;Hamzeh et al, 2013). It has been used also in the remote sensing assessment of salt stress effects in many different crops and plants: sugarcane (Hamzeh et al, 2013), reed, cogon grass, cotton, saltcedar, corn, suaeda or aeluropus (Zhang et al, 2011a(Zhang et al, , 2011b. However, the more precise imaging and analyses of reflectance spectra at the plant level are required to provide desired information useful in phenomics systems.…”

Section: Abstract a R T I C L E I N F Omentioning

confidence: 99%

“…The hyperspectral records of the root-zone of the soil and plant material of halophyte species with typical salt-sensitive character were used to study the vegetation spectra and soil salinity relationships. It was found that using hyperspectral vegetation indices are promising to screen soil salinity with a hyperspectral profile of salt-sensitive and halophyte plants (Zhang et al, 2011a(Zhang et al, , 2011b.…”

Section: Other Applications Of Hyperspectral Image Analysis To Identimentioning

confidence: 99%

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Sytar

Brestič

Živčák

et al. 2017

Science of The Total Environment

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• Salinity represents an abiotic stress constraint affecting growth and productivity of plants • Better solutions is to improve the level of salt resistance using natural genetic variability within crop species • Phenomic methodology employing different non-invasive imaging systems for detecting quantitative and qualitative changes caused by salt stress at the whole plant and canopy level. Hyperspectral imaging techniques provide unique opportunities for fast and reliable evaluation of numerous characteristics associated both with various structural, biochemical and physiological traits • Salt-soil-plant interaction and sustainable coastal agriculture need powerful phenotyping tools Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical Science of the Total Environment 578 (2017) 90-99 ⁎ Corresponding authors at:

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“…Spectral collinearity analyses have been used by remote sensing scientists to retrieve biogeochemical activity from spectral measures using in situ, airborne or orbital hyperspectral sensors. Remote sensing scientists have been investigating the use of spectral collinearity analyses to identify and map several biogeochemical components, such as aquatic chlorophyll-a (chl-a) [4], phycocyanin (PC) [5], soil salinity [6], soil types [7], leaf nitrogen, and leaf chl-a concentration [8].…”

Section: Introductionmentioning

confidence: 99%

Interactive Correlation Environment (ICE) — A Statistical Web Tool for Data Collinearity Analysis

Ogashawara

Curtarelli

Souza³

et al. 2014

Remote Sensing

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Web tools for statistical investigation with an interactive and friendly interface enable users without programming knowledge to conduct their analyses. We develop an Interactive Correlation Environment (ICE) in an open access platform to perform spectral collinearity analysis for biogeochemical activity retrieval. We evaluate its performance on different browsers and applied it to retrieve chlorophyll-a (chl-a) concentration in a tropical reservoir. The use of ICE to retrieve water chl-a concentration got a Root Mean Square Error (RMSE) lower than 7% for seasonal datasets, enhancing ICE's ability to adapt it within season. An RMSE of 17% was found for the mixed dataset with a large range of chl-a concentrations. We conclude that the use of ICE is recommended, due to its quick response, easily manipulation, high accuracy, and empirical adaptation to seasonal variability. Its use is enhanced by the development of hyperspectral sensors, which allow the identification of several biogeochemical components, such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf nitrogen, and leaf chl-a concentration.

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Using hyperspectral vegetation indices as a proxy to monitor soil salinity

Abstract: For monitoring soil salinity, the potential of various hyperspectral vegetation indices were investigated. Furthermore, the most sensitive band combinations to salt-stress were identified and developed into a satisfied and specific salinity index for heterogeneous vegetation.

Cited by 18 publications

References 5 publications

Spectral indices for the detection of salinity effects in melon plants

Spectral indices for the detection of salinity effects in melon plants

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Interactive Correlation Environment (ICE) — A Statistical Web Tool for Data Collinearity Analysis

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