Principal component analysis and biophysical parameters in the assessment of soil salinity in the irrigated perimeter of Bahia, Brazil

Silva, Diego Castro da; Lopes, Pabrício Marcos Oliveira; Silva, Marcos Vinícius da; Moura, Geber Barbosa de Albuquerque; Nascimento, Cristina Rodrigues; Brito, José Ivaldo Barbosa de; Silva, Ênio Farias de França e; Rolim, Mário M.; Lima, Renato Paiva de

doi:10.1016/j.jsames.2021.103580

Cited by 4 publications

(5 citation statements)

References 40 publications

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“…Articles in the first category leverage evapotranspiration data from remote sensing to achieve their research goals. For example, Coelho et al [84] used the SEBAL model's evapotranspiration estimates to assess groundwater recharge in a semi-arid river basin, while Silva et al [84] employed the same approach to map soil salinity during the dry season in a Bahian experimental area. Other studies explored the impact of human activities on climate, microclimate, and evapotranspiration.…”

Section: Main Remote Sensing Methodologies For Estimating Evapotransp...mentioning

confidence: 99%

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Evapotranspiration Assessment by Remote Sensing in Brazil with Focus on Amazon Biome: Scientometric Analysis and Perspectives for Applications in Agro-Environmental Studies

Castagna,

Barbosa,

Martim

et al. 2024

Hydrology

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The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a valuable tool for measuring and estimating ET, particularly in the data-scarce Amazon region. A scientometric analysis was conducted to identify the most used RS-based ET product or model in Brazil and its potential application in the Amazon. Scientometrics allows for the quantitative analysis of scientific output; this study identified the most widely used RS product in the Amazon biome. Articles published in Web of Science, Scielo, and Scopus databases up to 2022 were searched using the keywords “Evapotranspiration”, “Remote Sensing”, and “Brazil”. After initial screening, 140 relevant articles were subjected to scientometric analysis using the Bibliometrix library in RStudio 2023.06.1+524. These articles, published between 2001 and 2022, reveal a collaborative research landscape involving 600 authors and co-authors from 245 institutions, with most studies originating from Brazil’s Southeast and North (Amazon) regions. Notably, within the 12 studies focusing on ET by RS in the Amazon biome, applications were diverse, encompassing river basins, climate change, El Niño, and deforestation, with the MOD16 product being the most frequently employed.

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Section: Main Remote Sensing Methodologies For Estimating Evapotransp...mentioning

confidence: 99%

“…Accurate estimation of evapotranspiration is essential for understanding plant water needs. Moreover, effective water management is crucial to prevent waste, especially during dry periods or in arid regions [83,84].…”

Section: Applications Of Remote Sensing Evapotranspiration In Brazilmentioning

confidence: 99%

Evapotranspiration Assessment by Remote Sensing in Brazil with Focus on Amazon Biome: Scientometric Analysis and Perspectives for Applications in Agro-Environmental Studies

Castagna,

Barbosa,

Martim

et al. 2024

Hydrology

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“…We reconstructed the spatiotemporal distribution of vegetation growth for each pixel-year using a standard remote sensing inversion method. To do so, we calculated the GDVI (see Equation ( 1) [42]), as this index has been shown to be superior in arid regions compared to other vegetation growth indicators such as the normalized difference vegetation index (NDVI) [42][43][44][45]. In addition, the GDVI is based on red and near-infrared bands and often contains more than 90% of the information relating to vegetation [46], and for Landsat data, its prediction of vegetation growth is the most accurate, with it being superior to other bands such as green, blue, and/or shortwave infrared bands [47].…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

A Landscape Restoration Initiative Reverses Desertification with High Spatiotemporal Variability in the Hinterland of Northwest China

Hao,

Liu,

Xie

et al. 2023

Land

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Although we are in an era of enormous global commitments to ecological restoration (the UN Decade on ER; the Bonn Challenge), little attention has been paid to the importance of sustained commitment to individual restoration initiatives and few resources have been dedicated to monitoring, especially the long-term and broad-scale evaluations that would allow us to understand how basin-scale restoration can result in complex spatiotemporal patterns. Remote sensing offers a powerful tool for evaluating restoration initiatives focused on water management in arid regions, where changes in vegetation growth can be tracked visually with measures like the generalized difference vegetation index (GDVI). In this paper, we evaluate the Comprehensive Treatment Program of the Shiyang River Basin (CTSRB), a landscape restoration initiative in China’s northwest, using a widely available remote sensing tool, showing how it can reveal the causes of fluctuating changes in restoration success. We focus on spatiotemporal variation, studying a time series from 2001 to 2020 using regression, trend, and stability analyses for six different divisions of the study region (the study area as a whole, the irrigated areas, the periphery of the irrigated regions, and the districts of Ba, Quanshan, and Hu) to evaluate the effects of the restoration initiative. The study period was divided into four equal-length phases based on the implementation timeline of the CTSRB, which includes one pre- and post-intervention interval and two stages of the CTSRB. We found that the CTSRB has played a positive role in the restoration of vegetation in the Minqin Basin, especially desert vegetation. However, the positive effects were not obvious in the first CTSRB period, which was characterized by a decline in vegetation growth likely caused by the strategy of “close the pumping-wells, transform the land”, which reversed a greening trend caused by the unsustainable irrigation of wasteland prior to the project’s initiation. During the second phase of the CTSRB, vegetation in the regions of “transform the land” gradually improved, and the growth of desert vegetation gradually improved and expanded as a result of more water flowing out of agricultural zones. The rate of vegetation recovery slowed down during the final phase of the CTSRB, and some areas even experienced a decline in the GDVI. Overall, our findings show that the CTSRB, by integrating water management and allowing for uninterrupted ecological restoration, drove complex regional changes in the GDVI, including successful restoration of desert vegetation. The spatiotemporal variable we observed underscores the importance of long-term commitment to arid land restoration initiatives and the importance of even longer-term monitoring using tools like remote sensing.

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“…It uses total variance in the real data, but the number of variables is reduced while the maximum variance is retained (Tripathi & Singal, 2019). PCA is used by several studies to assess the levels of heavy metal contamination in soil analysis, which involves data reduction and interpretation (Kardanpour et al, 2014;Park et al, 2006;da Silva et al, 2021;Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis of potentially toxic elements from selected mechanical workshops using the geo-accumulation index and principal component analysis in Omu-Aran Community, Nigeria

Elemile

Gana

Ejigboye

et al. 2023

Environ Monit Assess

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Bioaccumulation of potentially toxic elements in soil threatens public health and the ecosystem. This study aims to assess the concentration of potentially toxic elements (chromium (Cr), lead (Pb), iron (Fe), arsenic (As), and cadmium (Cd)) in selected automobile workshop premises in Omu-Aran, Nigeria. Forty-eight samples were collected at a depth (15 cm) in six locations, including a control point. Acid digestion was carried out to prepare the soil samples before assessing their concentration via an atomic absorption spectrophotometer. Geoaccumulation index (Igeo) was used to classify the level of contamination. Statistical analysis, which includes principal component analysis (PCA) and Pearson's correlation, was also determined. The difference in concentration was determined using ANOVA. In the study area, the lowest observed concentration values for Cr, Pb, Fe, As, and Cd, which are 0.246 ± 0.002 mg/kg, 0.178 ± 0.001 mg/ kg, 90.715 ± 0.038 mg/kg, 0.012 ± 0.004 mg/kg, and 0.078 ± 0.004 mg/kg, respectively, are relatively higher than observed for the control. The observed potentially toxic elements fall within three Igeo based on Muller's interpretation; heavily to extremely contaminated (Cd), moderately to heavily contaminated (Pb, Cr, and As), and uncontaminated to moderately contaminated (Fe). PCA shows that two principal components (PC) account for up to 91.052% of the original mean dataset variability. PC1 explains 67.723% of the total variance associated with Cd, Cr, Fe, Pb, and As, indicating anthropogenic is the primary source of these potentially toxic elements. The PC2 accounted for 23.329%, with Pb and As significant contributors. Cadmium contamination of soil was the most influential, with an Igeo value ranging from 4 to 5. Residents in the polluted region face considerable health risks from potentially toxic elements.

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Principal component analysis and biophysical parameters in the assessment of soil salinity in the irrigated perimeter of Bahia, Brazil

Cited by 4 publications

References 40 publications

Evapotranspiration Assessment by Remote Sensing in Brazil with Focus on Amazon Biome: Scientometric Analysis and Perspectives for Applications in Agro-Environmental Studies

Evapotranspiration Assessment by Remote Sensing in Brazil with Focus on Amazon Biome: Scientometric Analysis and Perspectives for Applications in Agro-Environmental Studies

A Landscape Restoration Initiative Reverses Desertification with High Spatiotemporal Variability in the Hinterland of Northwest China

Analysis of potentially toxic elements from selected mechanical workshops using the geo-accumulation index and principal component analysis in Omu-Aran Community, Nigeria

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