Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

Shrestha, Rajendra P.

doi:10.1002/ldr.752

Cited by 94 publications

(55 citation statements)

References 12 publications

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“…This is in part due to the high spatial resolution of the IKONOS images. The selected model in this study showed superiority in the prediction power (R 2 = 0.65) of soil salinity over those reported by Shrestha [27] and Landsat, are economically priced or free, more accessible and typically offer broader spatial coverage than more expensive high spatial resolution imagery. Nonetheless, differences in spatial resolution can have a high impact on predicting soil salinity.…”

Section: The Developed Regressions Modelsmentioning

confidence: 72%

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: 72%

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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“…Nevertheless, these indices are generally weakly or moderately correlated with soil salinity. In most cases, the correlation coefficient may be less than 0.50 [25,26,30]. This paper incorporated all spectral bands for statistical analysis, and demonstrated the usefulness of the PLSR model for soil salinity retrieval with the ALI-convolved field spectra ( Figure 6).…”

Section: Soil Salinity Retrieval From Multi-spectral Sensor Data Basementioning

confidence: 79%

“…Instead of all spectral bands, a majority of existing studies perform statistical analysis based on selected bands [25] and/or indices generated from band combinations. The commonly used indices can be generated from green/red [26], red/NIR [28], NIR/SWIR [29] and SWIR/SWIR bands [30].…”

Section: Soil Salinity Retrieval From Multi-spectral Sensor Data Basementioning

confidence: 99%

Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression

Fan

Liu

Tao³

et al. 2015

Remote Sensing

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Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In situ field spectra and soil salinity data were collected in the Yellow River Delta, China. Statistical analysis demonstrated the importance of ALI blue and near infrared (NIR) bands for soil salinity. A partial least square regression (PLSR) model was established between soil salinity and ALI-convolved field spectra. The model estimated soil salinity with a R 2 (coefficient of determination), RPD (ratio of prediction to deviation), bias, standard deviation (SD) and root mean square error (RMSE) of 0.749, 3.584, 0.036 g·kg . The model was then applied to atmospherically corrected ALI data. Soil salinity was underestimated for moderately (soil salinity within 2-4 g·kg −1) and highly saline (soil salinity >4 g·kg sensing pixels was probably the major source responsible for the underestimation. Our study demonstrates the effectiveness of PLSR model in retrieving soil salinity from new-generation multi-spectral sensors. This is very valuable for achieving worldwide soil salinity mapping with low cost and considerable accuracy.

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“…The EC values in the greenhouse soil are higher than those in the open-field soil, which can be attributed to the fact that there was no soil leaching due to rainfall that occurred under the greenhouse conditions, and the evaporation of the soil water resulted in salt accumulation (Shrestha, 2006).…”

Section: Discussionmentioning

confidence: 92%

Simple heat profiles and biogeochemical patterns for analysis the influence on soil microbial community of plastic-greenhouse and open field condition

et al. 2018

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The diversity of the soil microbial community is remarkably sensitive to different cropping techniques. To examine the effects of cultivation in a greenhouse on the soil microbial activity and diversity, we collected soil samples from vegetable crops continuously grown in a plastic greenhouse (G) and an open field (N). Heat profiling was obtained using microcalorimeter in the presence of different carbon sources. Power time curves were recorded after glucose and ammonium sulphate supplementation. The total heat release QT (Jg-1), maximum heat flow Pmax (μW) and growth rate constant k (h-1) are higher in G treatment group than the in N treatment group. On the other hand, the time (tmax) to reach the peak heat flow is lower in G treatment group compared to N treatment group, as is cell specific heat rate JQ/N (J cell-1). Sample G1 has the highest Pmax (1246.07 μW) and the lowest tmax (11.86 h); in contrast, sample N5 has the lowest Pmax (411.03 μW) and the highest tmax (21.78 h). The microbial community in the soil was estimated from phospholipid fatty acid (PLFA) biomarkers analysis and a viable count, followed by a subsequent determination of the physicochemical parameters. The total average PLFA concentration range is found as 129.76 - 142.54 nmol g-1. Both thermodynamics properties and PLFA concentration was are significantly greater in G1, G2 and G3 treatment than in the other two (N5 & N6) management systems (P < 0.05). Combination of both thermodynamic and PLFA profiling determines that the microbial population is higher in the soil from the greenhouse fertilization than from the open field cultivation. This novel approach could be applicable to a better understanding of (i) the changes in the soil microbial community in green house and open field cultivation practices, and (ii) the relationship between physicochemical properties and soil microbial community’s diversity. Moreover, this would be a notable technique for the decision maker to choose the potential cultivation method for sustainable green-food development program.

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Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

Cited by 94 publications

References 12 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

Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression

Simple heat profiles and biogeochemical patterns for analysis the influence on soil microbial community of plastic-greenhouse and open field condition

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