Soil salinity of urban turf areas irrigated with saline waterI. Spatial variability

Miyamoto, S.; Chacon, Arturo; Hossain, Manwar; Martinez, I.

doi:10.1016/s0169-2046(04)00078-7

Cited by 24 publications

(24 citation statements)

References 6 publications

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“…The salt accumulated at the aeolian sandy soil consists of mainly Na + , Cl − and SO 4 2− , which is in good agreement with the ion composition of the irrigation water. (2) The salt accumulation at the soil surface is influenced by air temperature, wind speed, mineralization of irrigation water, sand burial thickness, soil texture, and litter content. High temperature, high wind speed, and high mineralization of irrigation water will promote salt accumulation.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The soil water and soil salt dynamics have been extensively researched in relation to irrigation method, irrigation water quality, soil types, and many other factors [1][2][3][4][5][6][7][8][9][10] . Nevertheless, the results were mainly on salt accumulation at plough horizon after irrigation, and the research on the dynamic salt accumulation at soil surface is relatively scarce.…”

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confidence: 99%

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The salt accumulation at the shifting aeolian sandy soil surface with high salinity groundwater drip irrigation in the hinterland of the Taklimakan Desert

Jian-guo

Lei

et al. 2008

Sci. Bull.

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Section: Conclusion and Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

The salt accumulation at the shifting aeolian sandy soil surface with high salinity groundwater drip irrigation in the hinterland of the Taklimakan Desert

Jian-guo

Lei

et al. 2008

Sci. Bull.

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“…Recently, several methods have been developed to determine the NRS for a specific area, including spatial-temporal variability analysis [18,23,[26][27][28][29][30], geostatistical [31][32][33], stratified sampling [34,35], and bootstrap methods [20,[36][37][38][39]. The spatial-temporal variability analysis usually uses some statistics, such as relative difference or correlation coefficient, to determine the NRS and the representative sites [18,23,26,27,30,40].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this method needs the information of the semivariance. Although stratified sampling method has been used in many research areas, the effectiveness of this method highly depends on the information, which divides the population into internally homogeneous strata [35]. Recently, the bootstrap method has been used to determine the NRS to estimate the population mean.…”

Section: Introductionmentioning

confidence: 99%

Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe

Xiao

Wen

et al. 2015

Remote Sensing

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Abstract:To evaluate and improve the quality of land surface albedo products, validation with ground measurements of albedo is crucial over the spatially and temporally heterogeneous land surface. One of the essential steps for satellite albedo product validation is coarse scale observation technique development with long time ground-based measurements. In this paper, the optimal nodes were selected from the wireless sensor network (WSN) to perform observation at large scale and in longer time series for validation of albedo products. The relative difference is used to analyze the spatiotemporal representativeness of each node. The random combination method is used to assess the number of required sites (NRS) and then to identify the most representative combination (MRC). On this basis, an upscaling OPEN ACCESS Remote Sens. 2015, 7 14758 transform function with different weights for each node in the MRC, which are calculated with the ordinary least squares (OLS) linear regression method, is used to upscale WSN node albedo from point scale to the field scale. This method is illustrated by selecting the optimal nodes and upscaling surface albedo from point observation to the field scale in the Heihe River basin, China. Primary findings are: (a) The method of reducing the number of observations without significant loss of information about surface albedo at field scale is feasible and effective; (b) When only few sensors are available, the most representative locations in time and space should be the first priority; when a number of sensors are available in the heterogeneous land surface, it is preferable to install them in different land surface, rather than the most representative locations; (c) The most representative combination (MRC) combined with the upscaling weight coefficients can give a robust estimate of the field mean surface albedo. These efforts based on ground albedo observations promote the chance to use point information for validation of coarse scale albedo products. Moreover, a preliminary validation of the MODIS (Moderate Resolution Imaging Spectroradiometer) albedo product was performed as the tentative application for upscaling predictions.

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“…Although the geostatistical method considers the spatial correlations of soil properties and has been proved to be more efficient at estimating the NSS, information about the semivariance is a prerequisite for this method. The stratified sampling method is limited by the need to divide the target population into homogeneous strata based on the available information (Miyamoto et al 2005). The bootstrap method is limited in its application by the fluctuation of the confidence interval with the sample size (Kamgar et al 1993).…”

Section: Introductionmentioning

confidence: 99%

The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau, China

Gao

Shao

2011

J Soils Sediments

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Purpose Knowledge of the changes in interpolation accuracy with changing sampling scales is important when designing an appropriate sampling strategy. The objectives of this study were (1) to analyze the changes in interpolation accuracy with changing sampling scales for seven soil properties and (2) to find a suitable index that could predict the interpolation accuracy well. Materials and methods Nine hundred sixty-one samples were collected from a 30×30-m area. Seven soil properties were measured for each sample. Using a re-sampling analysis method, we grouped the samples under 16 subscales. Then, we divided the 16 subscales into two subsets, the first consisting of eight scales used as training sets and the second having the other eight scales as validation sets. Using the training sets, the interpolation accuracy and the contribution rate (CR) for the seven soil properties were compared and the relations of the interpolation accuracy to the coefficient of variation (CV), or to the ratio of sampling spacing to correlated range (S/R), or to the extent and spacing (E & S) were determined, the accuracy of prediction of which were then tested using the validation sets. Results and discussionThe results showed that the mean interpolation accuracies varied greatly for different soil properties, with mean G values of training sets ranging from 2.4% for soil organic carbon, to 62.1% for sand content. With increasing sampling spacing or decreasing sampling extent, the interpolation accuracy decreased for all soil properties. The scales with the largest CR were not consistent with those with the highest interpolation accuracies. The interpolation accuracy was predicted better by E & S than by CV or by S/R. Conclusions The measurement and analysis gave insight into the changes of interpolation accuracy and CR at various sampling scales. Predicting interpolation accuracy based on the scale parameters of sampling spacing and sampling extent was feasible, which provided a useful means by which to determine appropriate sample size and sampling strategy.

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Soil salinity of urban turf areas irrigated with saline waterI. Spatial variability

Cited by 24 publications

References 6 publications

The salt accumulation at the shifting aeolian sandy soil surface with high salinity groundwater drip irrigation in the hinterland of the Taklimakan Desert

The salt accumulation at the shifting aeolian sandy soil surface with high salinity groundwater drip irrigation in the hinterland of the Taklimakan Desert

Optimal Nodes Selectiveness from WSN to Fit Field Scale Albedo Observation and Validation in Long Time Series in the Foci Experiment Areas, Heihe

The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau, China

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