A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations

2012 ◽  
Vol 30 ◽  
pp. 139-142 ◽  
Author(s):  
Guojie Wang ◽  
Damien Garcia ◽  
Yi Liu ◽  
Richard de Jeu ◽  
A. Johannes Dolman
Keyword(s):  
Soil Moisture ◽  
Three Dimensional ◽  
Gap Filling ◽  
Filling Method

Performance of a Physically Based Gap-Filling Technique of in-situ Soil Moisture, in Comparison with Machine Learning

2020 ◽  
Author(s):  
Seulchan Lee ◽  
Hyunho Jeon ◽  
Jongmin Park ◽  
Minha Choi
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Low Voltage ◽  
Time Domain Reflectometry ◽  
Limited Information ◽  
Gap Filling ◽  
Flux Tower ◽  
Physically Based ◽  
Filling Method

<p>As the importance of Soil Moisture (SM) has been recognized in various fields, including agricultural practices, natural hazards, and climate predictions, ground-based SM sensors such as Frequency Domain Reflectometry (FDR), Time Domain Reflectometry (TDR) are being widely used. However, gaps in in-situ SM data are still unavoidable due not only to sensor failure or low voltage supply, but to environmental conditions. Since it is essential to acquire accurate and continuous SM data for its application purpose, the gaps in the data should be handled properly. In this study, we propose a physically based gap-filling method in a mountainous region, in which in-situ SM measurements and flux tower are located. This method is developed only with in-situ SM and precipitation data, by considering variation characteristics of SM: increases rapidly with precipitation and decreases asymptotically afterward. SM data from the past is used to build Look-Up-Tables (LUTs) that contains the amount and speed of increment and decrement of SM, with and without precipitation, respectively. Based on the developed LUTs, the gaps are filled successively from where the gaps started. At the same time, we also introduce a machine learning-based gap-filling framework for the comparison. Ancillary data from the flux tower (e.g. net radiation, relative humidity) was used as input for training, with the same period as in the physically based method. The trained models are then used to fill the gaps. We found that both proposed methods are able to fill the gaps of in-situ SM reasonably, with capabilities to capture the characteristics of SM variation. Results from the comparison indicate that the physically based gap-filling method is very accurate and efficient when there’s limited information, and also suitable to be used for prediction purposes.</p>

scholarly journals Three-dimensional thermomechanical converting of CTMP substrates: effect of bio-based strengthening agents and new mineral filling concept

Cellulose ◽  
2021 ◽  
Vol 28 (15) ◽  
pp. 9751-9768
Author(s):  
Teija Laukala ◽  
Sami-Seppo Ovaska ◽  
Ninja Kerttula ◽  
Kaj Backfolk
Keyword(s):  
Tensile Strength ◽  
Elastic Modulus ◽  
Three Dimensional ◽  
Microfibrillated Cellulose ◽  
New Mineral ◽  
Thermomechanical Pulp ◽  
Cationic Starch ◽  
Precipitated Calcium Carbonate ◽  
Filling Method ◽  
Positive Effect

AbstractThe effects of bio-based strengthening agents and mineral filling procedure on the 3D elongation of chemi-thermomechanical pulp (CTMP) handsheets with and without mineral (PCC) filling have been investigated. The 3D elongation was measured using a press-forming machine equipped with a special converting tool. The strength of the handsheets was altered using either cationic starch or microfibrillated cellulose. Precipitated calcium carbonate (PCC) was added to the furnish either as a slurry or by precipitation of nano-sized PCC onto and into the CTMP fibre. The 3D elongation of unfilled sheets was increased by the dry-strengthening agents, but no evidence on the theorised positive effect of mineral fill on 3D elongation was seen in either filling method. The performance of the strengthening agent depended on whether the PCC was as slurry or as a precipitated PCC-CTMP. The starch was more effective with PCC-CTMP than when the PCC was added directly as a slurry to the furnish, whereas the opposite was observed with microfibrillated cellulose. The 3D elongation correlated positively with the tensile strength, bursting strength, tensile stiffness, elastic modulus and bending stiffness, even when the sheet composition was varied, but neither the strengthening agent nor the method of PCC addition affected the 3D elongation beyond what was expectable based on the tensile strength of the sheets. Finally, mechanisms affecting the properties that correlated with the 3D elongation are discussed.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

2021 ◽  
Vol 258 ◽  
pp. 112377
Author(s):  
Laura Almendra-Martín ◽  
José Martínez-Fernández ◽  
María Piles ◽  
Ángel González-Zamora
Keyword(s):  
Soil Moisture ◽  
Southern Europe ◽  
Gap Filling

chapter 2 A Statistical Gap-Filling Method to Interpolate Global Monthly Surface Ocean Carbon Dioxide Data

2017 ◽  
pp. 15-62
Author(s):  
STEVE D. JONES ◽  
CORINNE LE QUÉRÉ ◽  
CHRISTIAN RÖDENBECK ◽  
ANDREW C. MANNING ◽  
ARE OLSEN
Keyword(s):  
Carbon Dioxide ◽  
Gap Filling ◽  
Surface Ocean ◽  
Filling Method ◽  
Ocean Carbon

scholarly journals New techniques for gap-filling and partitioning of H<sub>2</sub>O and CO<sub>2</sub> eddy fluxes measured over forests in complex mountainous terrain

2017 ◽  
Author(s):  
Minseok Kang ◽  
Joon Kim ◽  
Bindu Malla Thakuri ◽  
Junghwa Chun ◽  
Chunho Cho
Keyword(s):  
Environmental Changes ◽  
Water Cycle ◽  
Co2 Flux ◽  
Test Method ◽  
Co2 Fluxes ◽  
Gap Filling ◽  
New Techniques ◽  
Diameter Increment ◽  
Filling Method ◽  
Rain Events

Abstract. The continuous measurement of H2O and CO2 fluxes using the eddy covariance (EC) technique is still challenging for forests in complex terrain because of large amounts of wet canopy evaporation (EWC), which occur during and following rain events when the EC systems rarely work correctly, and the horizontal advection of CO2 generated at night. We propose new techniques for gap-filling and partitioning of the H2O and CO2 fluxes: (1) a model-stats hybrid method (MSH) and (2) a modified moving point test method (MPTm). The former enables the recovery of the missing EWC in the traditional gap-filling method and the partitioning of the evapotranspiration (ET) into transpiration and (wet canopy) evaporation. The latter determines the friction velocity (u*) threshold based on an iterative approach using moving windows for both time and u*, thereby allowing not only the nighttime CO2 flux correction and partitioning but also the assessment of the significance of the CO2 drainage. We tested and validated these new methods using the datasets from two flux towers, which are located at forests in hilly and complex terrains. The MSH reasonably recovered the missing EWC of 16 ~ 41 mm year−1 and separated it from the ET (14 ~ 23 % of the annual ET). The MPTm produced consistent carbon budgets using those from the previous research and diameter increment, while it has improved applicability. Additionally, we illustrated certain advantages of the proposed techniques, which enables us to understand better how ET responses to environmental changes and how the water cycle is connected to the carbon cycle in a forest ecosystem.

scholarly journals Rational size and stability analysis of horizontal isolated pillars in deep mining from caving to filling method

2021 ◽  
Vol 303 ◽  
pp. 01040
Author(s):  
Fan Feng ◽  
Xibing Li ◽  
Shaojie Chen ◽  
Dingxiao Peng ◽  
Zhuang Bian
Keyword(s):  
Numerical Simulation ◽  
Three Dimensional ◽  
Mining Area ◽  
Middle Section ◽  
Deep Mining ◽  
Metal Mines ◽  
Lead Zinc ◽  
Filling Method ◽  
Zinc Mine ◽  
Cut And Fill

For mining using the caving and filling methods in metal mines, determining a suitable size for the isolated pillars—the connecting part of the extension from shallow to deep—is crucial for ensuring safety and efficiency. Considering actual cases involving deep caving and cut-and-fill mining in the Chifeng Hongling lead-zinc mine in Inner Mongolia, China, the reserved thickness range of the horizontal isolation layer is obtained via theoretical analysis. On this basis, the pre-processing software HyperMesh is used to build a high-precision hexahedral grid model of the mining area, and the three-dimensional geological model of the mining area is imported into the finite-difference software FLAC3D. The stress field, displacement field, and plastic area evolution law of pillars (horizontally isolated pillars and adjacent rib pillars) in the stope of the ninth middle section after excavation are analyzed via numerical simulation inversion of the selected scheme of horizontal isolated pillars. The numerical simulation results show that the scheme employed to retain the upper horizontal isolated pillars in the ninth middle section involves reserving thicknesses of 8 m and 32 m at average ore body thicknesses of 15 m and 35 m, respectively. These results can provide theoretical guidance and a basis for safe and efficient mining of deep metal mines.

An Improved Olympic Hole-Filling Method for Ultrasound Volume Reconstruction of Human Spine

2012 ◽  
pp. 259-271 ◽  
Author(s):  
D. E. O. Dewi ◽  
T. L. R. Mengko ◽  
I. K. E. Purnama ◽  
A. G. Veldhuizen ◽  
M. H. F. Wilkinson
Keyword(s):  
Three Dimensional ◽  
3D Ultrasound ◽  
Hole Filling ◽  
Filling Process ◽  
Volume Reconstruction ◽  
Acquisition Process ◽  
Human Spine ◽  
Filling Method ◽  
Averaging Filter

Hole-filling in ultrasound volume reconstruction using freehand three-dimensional ultrasound estimates the values for empty voxels from the unallocated voxels in the Bin-filling process due to inadequate sampling in the acquisition process. Olympic operator, as a neighbourhood averaging filter, can be used to estimate the empty voxel. However, this method needs improvement to generate a closer estimation of the empty voxels. In this paper, the authors propose an improved Olympic operator for the Hole-filling algorithm, and apply it to generate the volume in a 3D ultrasound reconstruction of the spine. The conventional Olympic operator defines the empty voxels by sorting the neighbouring voxels, removing the n% of the upper and lower values, and averaging them to attain the value to fill the empty voxels. The empty voxel estimation can be improved by thresholding the range width of its neighbouring voxels and adjusting it to the average values. The method is tested on a hole-manipulated volume derived from a cropped 3D ultrasound volume of a part of the spine. The MAE calculation on the proposed technique shows improved result compared to all tested existing methods.

scholarly journals Gap-filling method for suppressing grating lobes in ultrasound imaging: Theory and simulation results

2019 ◽  
Vol 145 (3) ◽  
pp. EL236-EL242
Author(s):  
Bae-Hyung Kim ◽  
Viksit Kumar ◽  
Azra Alizad ◽  
Mostafa Fatemi
Keyword(s):  
Ultrasound Imaging ◽  
Gap Filling ◽  
Filling Method ◽  
Imaging Theory ◽  
Simulation Results

An Improved Olympic Hole-Filling Method for Ultrasound Volume Reconstruction of Human Spine

2010 ◽  
Vol 1 (3) ◽  
pp. 28-40 ◽  
Author(s):  
D. E. O. Dewi ◽  
T. L. R. Mengko ◽  
I. K. E. Purnama ◽  
A. G. Veldhuizen ◽  
M. H. F. Wilkinson
Keyword(s):  
Three Dimensional ◽  
3D Ultrasound ◽  
Hole Filling ◽  
Filling Process ◽  
Volume Reconstruction ◽  
Acquisition Process ◽  
Human Spine ◽  
Filling Method ◽  
Averaging Filter

Hole-filling in ultrasound volume reconstruction using freehand three-dimensional ultrasound estimates the values for empty voxels from the unallocated voxels in the Bin-filling process due to inadequate sampling in the acquisition process. Olympic operator, as a neighbourhood averaging filter, can be used to estimate the empty voxel. However, this method needs improvement to generate a closer estimation of the empty voxels. In this paper, the authors propose an improved Olympic operator for the Hole-filling algorithm, and apply it to generate the volume in a 3D ultrasound reconstruction of the spine. The conventional Olympic operator defines the empty voxels by sorting the neighbouring voxels, removing the n% of the upper and lower values, and averaging them to attain the value to fill the empty voxels. The empty voxel estimation can be improved by thresholding the range width of its neighbouring voxels and adjusting it to the average values. The method is tested on a hole-manipulated volume derived from a cropped 3D ultrasound volume of a part of the spine. The MAE calculation on the proposed technique shows improved result compared to all tested existing methods.

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