Applicability of cosmic-ray neutron sensor for measuring soil moisture at the agricultural-pastoral ecotone in northwest China

Tan, Xian-Zheng; Zhang, Lanhui; He, Chansheng; Zhu, Yuzuo; Han, Zhibo; Li, Xuliang

doi:10.1007/s11430-020-9650-2

Cited by 16 publications

(29 citation statements)

References 44 publications

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“…Based on this estimation, statistical evaluation of these models is tabulated in Based on this comparison, it can be observed that different models are useful for different application requirements. For instance, in terms of accuracy as observed from figure 5, DBN RBM [2], CRNS [3], SMAP RF DN [19], GOFCHS [27], TDR [28], and P Band & L Band [34] models outperform other models, thus, they can be used for highly accurate moisture detection applications. Similarly, cost of deployment & computational complexity is visualized from figure 6, wherein it is observed that HPCM [6], HF RFID TFS [9], PWM [10], PMMA [15], FFCSM [16], MHPS [21], ECT [24], PQCWC [25], and HSAAA [32] require lowest deployment cost, while HPCM [6], PHS [17], ECT [24], and PQCWC [25] have lower computational complexity when compared with other models.…”

Section: Discussionmentioning

confidence: 92%

“…[45] These models are highly useful, when moderate accuracy of sensing is needed, but the cost & complexity of sensing are needed to be minimized. In terms of speed of sensing, figure 7 recommends that CRNS [3], FBG [4], FoS [20], MHPS [21], and FTO [35] have the fastest response, thus must be used for low delay, and high throughput applications. These models utilize high-speed sensors, which might be costly, but provide quicker results when compared with other sensing models.…”

Section: Discussionmentioning

confidence: 99%

“…This performance can be improved via use of low complexity interfaces that use highly accurate sensing devices. Such an interface is discussed in [3], wherein cosmic-ray Neutron sensor (CRNS) is deployed for low-complexity measurements. The sensor was deployed using standard 𝑁 0 method, which assisted in low-complexity & highly accurate measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

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Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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“…To achieve a better calibration result against the observations of the reference sensors in the near-field, we adjusted the shape of the transfer function by tuning the parameters a 0 , a 1 and a 2 . Tuning all shape-defining parameters was done in previous studies and resulted in a better goodness-of-fit between CRNS-derived soil moisture values and reference measurements (e.g., Rivera Villarreyes et al, 2011;Lv et al, 2014;Heidbüchel et al, 2016;Tan et al, 2020). In this study, we adjust parameters N 0 , a 0 , a 1 and a 2 using a Monte-Carlo based approach by testing 10,000 quasi-random combinations of the parameters and selecting the parameter set producing the highest statistical goodness-of-fit in terms of the KGE.…”

Section: Improving the Crns-derived Soil Moisture Estimationmentioning

confidence: 99%

Towards disentangling heterogeneous soil moisture patterns in Cosmic-Ray Neutron Sensor footprints

Rasche

Köhli

Schrön

et al. 2021

Preprint

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Abstract. Cosmic-Ray Neutron Sensing (CRNS) allows for non-invasive soil moisture measurements at the field scale. The derivation of soil moisture generally relies on secondary cosmic-ray neutrons in the epithermal-to-fast energy range. Most approaches and processing techniques for observed neutron intensities are based on the assumption of homogeneous site conditions within the measurement footprint of the neutron detector. In this study we investigated how a non-uniform soil moisture distribution within the footprint impacts the CRNS soil moisture estimation and how the combined use of epithermal and thermal neutrons can be advantageous in this case. Thermal neutrons have lower energies and a substantially smaller measurement footprint around the sensor than epithermal neutrons. Analyses using URANOS neutron Monte-Carlo simulations to investigate measurement footprint dynamics at a study site in north-eastern Germany revealed that the thermal footprint mainly covers mineral soils in the near-field to the sensor while the epithermal footprint also covers large areas with organic soils. We found that either combining the observed thermal and epithermal neutron intensities by a rescaling method developed in this study, or adjusting all parameters of the transfer function leads to an improved calibration against reference soil moisture measurements in the near field compared to the standard approach and using epithermal neutrons alone. We also found that the relationship between thermal and epithermal neutrons provided an indicator for footprint heterogeneity. We therefore suggest that the combined use of thermal and epithermal neutrons offers the potential of a spatial discretization of the measurement footprint in terms of near and far field soil moisture dynamics.

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“…Cosmic ray sensors have a very large measurement footprint of around 260-600 m radius [113] which maybe suited to broadscale cropping on uniform soils but are inoperable with the growing trend toward precision agriculture and variable rate irrigation [115,116]. Additional limitations with cosmic ray sensors include their high cost, very large and imprecise measured soil volume, long measurement durations which can be in excess of 4 h, variable depth of measurement which ranges from around 15 cm in wet soils, to approximately 70 cm in dry soils, and difficulty deriving precise calibrations [3,114,117,118].…”

Section: Cosmic Ray Sensorsmentioning

confidence: 99%

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

Hardie

2020

Sensors

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The measurement of soil moisture in agriculture is currently dominated by a small number of sensors, the use of which is greatly limited by their small sampling volume, high cost, need for close soil–sensor contact, and poor performance in saline, vertic and stony soils. This review was undertaken to explore the plethora of novel and emerging soil moisture sensors, and evaluate their potential use in agriculture. The review found that improvements to existing techniques over the last two decades are limited, and largely restricted to frequency domain reflectometry approaches. However, a broad range of new, novel and emerging means of measuring soil moisture were identified including, actively heated fiber optics (AHFO), high capacity tensiometers, paired acoustic / radio / seismic transceiver approaches, microwave-based approaches, radio frequency identification (RFID), hydrogels and seismoelectric approaches. Excitement over this range of potential new technologies is however tempered by the observation that most of these technologies are at early stages of development, and that few of these techniques have been adequately evaluated in situ agricultural soils.

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Applicability of cosmic-ray neutron sensor for measuring soil moisture at the agricultural-pastoral ecotone in northwest China

Cited by 16 publications

References 44 publications

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Towards disentangling heterogeneous soil moisture patterns in Cosmic-Ray Neutron Sensor footprints

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

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