A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China

Jin, Rui; Li, Xin; Yan, Binbin; Li, Xiuhong; Luo, Wenjun; Ma, Ming; Guo, Jianwen; Kang, Jian; Zhu, Zhanyuan; Zhao, Shaojie

doi:10.1109/lgrs.2014.2319085

Cited by 108 publications

(31 citation statements)

References 14 publications

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“…Whether scale dependence or scale effects exist in multiscale estimations using those surplus two methods and whether they are caused by the spatial structure of the soil moisture data are ongoing topics of research. Moreover, we notice that the choice of theoretical semivariogram model is important for a semivariogram analysis, and, in our research, different spatial processes of soil moisture occurred on different acquisition dates [33]. We cannot stick to a unique theoretical semivariogram model.…”

Section: Lsmentioning

confidence: 88%

Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level

Zhang

Zuo

Shengli

et al. 2018

IJGI

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Upscaling in situ soil moisture observations (ISMO) to multiscale pixel estimations with kriging is a key step in the comprehensive usage of ISMO and remote sensing (RS) soil moisture data. Scale effects occur and introduce uncertainties during upscaling processes because of spatial heterogeneity and the kriging method. A nested hierarchical scale series was established at the field level, and upscaled estimations at each scale were obtained by block kriging (BK) to illustrate multiscale ISMO upscaling processes. Those uncertainties were described with the results of comparison analysis against RS data, statistical analysis, and spatial trend surface analysis on multiscale estimations and were explained from the spatial heterogeneity perspective with a semivariogram analysis on ISMO. The results show that uncertainties exist and vary in multiscale upscaling processes, and the range of the empirical semivariogram could indicate scale effects. When the target scale is shorter than the range, BK maintains similar scale effects and global trends during upscaling processes, and the direct pixel estimation by BK is relatively close to the average of nested pixel estimations. This has great implications for understanding the kriging method in similar works.

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Section: Lsmentioning

confidence: 88%

Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level

Zhang

Zuo

Shengli

et al. 2018

IJGI

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“…As the main crop in this area is maize, which has a great portion of roots distributed in 0~15 cm zone [29] and depends much on irrigation, monitoring of near-surface moisture is needed for the study of irrigation management. There were 48 WATERNET nodes deployed in the study area to measure soil moisture and soil temperature in two layers (4 cm, 10 cm), and the data were collected every 5 min [30]. In this study, we used the WSN-measured soil moisture at 10 cm depth for interpolation.…”

Section: Methodsmentioning

confidence: 99%

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Zhang

Yang

et al. 2017

Sensors

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In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation.

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“…Soil properties (bulk density, texture, and organic content) vary in the study area, and soil samples have been determined to be silt-loam with sand (9-36%), silt (56-81%), and clay (5-19%) [37]. agricultural area [33,34]. The possibility and the performance of vegetation suppression using the Forced Invariance Approach were discussed, and the results demonstrated that the accuracy of the determination of soil salinity at the depth of 10 cm had been improved.…”

Section: Study Area Of Zhangye Oasismentioning

confidence: 99%

“…Data was recorded from the Hydro Probe II sensors [34] every 10 min at two different depths: 4 cm and 10 cm. Recorded information included date and time of reading, soil temperature, soil moisture, electrical conductivity (EC, soil salinity) and soil conductivity.…”

Section: Eco-hydrological Wireless Sensor Network Datamentioning

confidence: 99%

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A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery

Liu

Buchroithner

2018

IJGI

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Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R 2 ). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods.

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A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China

Cited by 108 publications

References 14 publications

Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level

Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery

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