Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

Singh, Aviral; Gaurav, Kumar; Meena, Ganesh Kumar; Kumar, Shashi

doi:10.3390/rs12142266

Cited by 39 publications

(18 citation statements)

References 72 publications

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“…Most studies employed Refined Lee filter [26], [105]- [110]. A small number of studies employed Mean filter [111], [112] and Lee filter [113], [114]. Some studies did not filter the backscattering coefficients images of Sentinel-1 [115], [116] or did not explain the filtering methods [117]- [120].…”

Section: A Optimal Filtering Methods For Estimating Sm By Sentinel-1mentioning

confidence: 99%

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

Liu

Qian

Yue

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: A Optimal Filtering Methods For Estimating Sm By Sentinel-1mentioning

confidence: 99%

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

Liu

Qian

Yue

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Depending on the soil type and moisture conditions, at this frequency, the SAR signals can penetrate up to 5 cm of the topsoil surface [54,55]. Sentinel-1 satellites have a temporal resolution of 12 days, that jointly (1A and 1B) result in a 6-day repeat pass over the equator [56,57]. Sentinel-1 acquires images in four different modes: Stripmap (SM), Interferometric Wide swath (IW), Extra-Wide swath (EW), and Wave (WV).…”

Section: Satellite Datamentioning

confidence: 99%

Machine Learning to Estimate Surface Roughness from Satellite Images

et al. 2021

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We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

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“…Wireless sensor networks are made of a finite number of spatially distributed sensors and are self-configurable, requiring no pre-installed infrastructure for their effective operations. A wireless sensor network (WSN) can be installed on-the-fly in almost no time; as a result, they have applications in nearly every walk of life, such as in health, environmental monitoring, infrastructure monitoring, telecommunication, Internet of Things (IoT), precision agriculture, intrusion detection, disaster management, military surveillance, reconnaissance and so on [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. The basic building blocks of these networks are sensors.…”

Section: Introductionmentioning

confidence: 99%

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Kotiyal

Singh

Sharma

et al. 2021

Sensors

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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

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Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

Cited by 39 publications

References 72 publications

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

Machine Learning to Estimate Surface Roughness from Satellite Images

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

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