Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

Hamzeh, Saeid; Naseri, Abd Ali; Alavipanah, Seyed Kazem; Mojaradi, Barat; Bartholomeus, Harm; Clevers, J.G.P.W.; Behzad, M.

doi:10.1016/j.jag.2012.07.002

Cited by 80 publications

(59 citation statements)

References 44 publications

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“…Image enhancement is data processing that aims to increase the overall visual quality of an image or to enhance the visibility and interpretability of certain features of interest in it [53]. Several studies have shown that image enhancement techniques consisting of spectral indices (e.g., NDVI, SI, NDSI, TNDVI) have a great potential in enhancing and delineating soil salinity detail in an image [29,31,[54][55][56][57][58][59]. For example, Tripathi et al [49] found and emphasized that identifying salt-affected soils based on the image enhancement method, represented by the salinity index, yields better results than individual bands, due to its ability to enhance the saline patches by suppressing the vegetation.…”

Section: The Developed Regressions Modelsmentioning

confidence: 99%

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

2014

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Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R 2 = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on OPEN ACCESSRemote Sens. 2014, 6 1138 regression analysis and remote sensing data is a promising approach, as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken in the early stages to prevent soil salinity from becoming prevalent, sustaining agricultural lands and natural ecosystems.

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Section: The Developed Regressions Modelsmentioning

confidence: 99%

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

2014

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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%

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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“…This spectral unmixing technique helps in mapping of fractions of various salt classes in each pixel. Hamzeh, et al (2012) investigated the ability of hyperspectral Hyperion data for mapping salinity stress in sugarcane fields. They used different classifications such as Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) with different band combinations and classified soil salinity into three classes (low, moderate and high salinity).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

K¹,

Kumar²

2015

IJARSG

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Hyperspectral remote sensing is widely used for analyzing and estimating the severity of soil salinity in arid and semi-arid regions, throughout the world. The present study is an attempt to map the various soil salinity severity classes using different hyperspectral indices generated using EO-1 Hyperion data and Support Vector Machine (SVM) method, in the Mathura region of Indo-Gangetic plain of India. Various hyperspectral indices such as Soil Adjusted Vegetation Index (SAVI), Desertification Soil Index (DSI), Salinity Index (SI) and Normalized Difference Water Index (NDWI) were chosen, generated and effectively used for characterizing and mapping soil salinity severity. Salt infestation in the study area was categorized into four classes of normal, slight, moderate, high soil salinity. Hyperspectral indices helped in identification of various features like vegetation, waterlogged area and soil areas under various classes of soil salinity. The salinity index and desertification soil indices were found to respond well to varying degrees of soil salinity. The SVM technique generated soil salinity map with overall classification accuracy of 78.13 percent, with a kappa statistic of 0.71. The results indicated highest accuracy in high soil salinity class in comparison to other classes, attaining producers and users accuracies of 85.71% and 90.0% respectively. Slight saline class showed poor producers and users accuracy. The result showed high accuracy for mapping soil salinity severity with machine learning classifier like SVM using various indices generated from hyperspectral remote sensing data. These generated images can be effectively used in planning of various management practices and effective reclamation measures of salt affected soils.

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Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

Cited by 80 publications

References 44 publications

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

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

Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique - A Case Study of Alluvial Plains

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