Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-II Imagery by Using a Deep Belief Network

Wang, Wenwen; Zhang, Chengming; Li, Feng; Song, J.; Li, Peiqi; Zhang, Yuhua

doi:10.1007/s13351-020-9191-x

Cited by 12 publications

(11 citation statements)

References 53 publications

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“…The medium resolution spectral images from 2018 to 2019 were selected as the dataset. The experimental results show that the root mean square error of the overall accuracy of SM-DBN model is 0.032, which is better than the test results of linear regression and back-propagation neural network [15]. Samadi et al (2019) proposed a change detection algorithm for synthetic aperture radar images based on DBN.…”

Section: Dbnmentioning

confidence: 92%

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

Long

Liu

2021

J Supercomput

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In order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

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Section: Dbnmentioning

confidence: 92%

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

Long

Liu

2021

J Supercomput

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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%

“…Figure 2. Deployment of RBM based DBN for moisture estimation [2] 4th International Conference on Intelligent Circuits and Systems Journal of Physics: Conference Series 2327 (2022) 012026…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Wang et al [61] used DBN to extract features from Fengyun-3D (FY-3D) Medium Resolution Spectral Imager-II (MERSI-II) imagery to estimate soil moisture in the Ningxia Hui Autonomous Region of China. The developed model, called SM-DBN, has outperformed the other conventional models of linear regression (LR) and ANN, based on accuracy performance in correlation with the actual ground measurement data.…”

Section: Soil Moisture Estimationmentioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-II Imagery by Using a Deep Belief Network

Cited by 12 publications

References 53 publications

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

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

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

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