Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network

Shi, Yajie; Ren, Chao; Yan, Zhiheng; Lai, Jianmin

doi:10.1080/01431161.2021.1988186

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…To boost the predictive accuracy and efficiency of coal mine gas generation patterns, Huice et al [ 34 ] proposed a method based on multi-source data fusion. Yajie et al [ 35 ] improved soil moisture measurement accuracy by integrating GNSS-IR technology with optical remote sensing, utilizing a multi-data fusion approach based on the Genetic Algorithm–Back-Propagation Neural Network (GAP-NN). In the industrial realm, aiming to minimize unexpected downtimes and extend the lifespan of crucial production machinery, Shreyas et al [ 36 ] applied an interpretable artificial intelligence methodology combined with multi-sensor data fusion, marking a significant advancement in intelligent manufacturing and predictive maintenance within Industry 4.0.…”

Section: Introductionmentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

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The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

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Section: Introductionmentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

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“…The L-band signals carried by Global Navigation Satellite Systems (GNSSs) are highly sensitive to soil moisture, making them particularly suitable for monitoring soil moisture variations [42]. By utilizing signal power or delay as attributes and actual soil moisture values as labels, inversion models based on navigation signals can be established through the combination of empirical dielectric constant models, Support Vector Machines (SVMs), Random Forest algorithms, and neural network methods such as BP and Deep Belief Networks [43][44][45]. Therefore, the introduction of soil moisture data retrieved from GNSS-R signals largely overcomes the limitations of traditional soil moisture measurement methods in terms of small effective measurement area and a lack of representativeness, meeting the demand for all-weather autonomous monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

Luo,

Yin,

Sun

et al. 2024

Water

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Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10−5 to 1.88 × 10−4, and the coefficient of determination (R2) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation.

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A GNSS-IR soil moisture retrieval method via multi-layer perceptron with consideration of precipitation and environmental factors

Xian,

Shen,

Guan

et al. 2024

GPS Solut

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Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network

Cited by 4 publications

References 50 publications

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

A GNSS-IR soil moisture retrieval method via multi-layer perceptron with consideration of precipitation and environmental factors

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