scholarly journals Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

2020 ◽  
Vol 12 (21) ◽  
pp. 3503 ◽  
Author(s):  
Volkan Senyurek ◽  
Fangni Lei ◽  
Dylan Boyd ◽  
Ali Cafer Gurbuz ◽  
Mehmet Kurum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Temporal Resolution ◽  
Mean Square ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Mean Square Errors

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

scholarly journals Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

2019 ◽  
Vol 11 (16) ◽  
pp. 1875 ◽  
Author(s):  
Burak Bulut ◽  
M. Tugrul Yilmaz ◽  
Mehdi H. Afshar ◽  
A. Ünal Şorman ◽  
İsmail Yücel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Soil Type ◽  
Vegetation Cover ◽  
Remotely Sensed ◽  
Mean Square ◽  
Climate Regime ◽  
Model Based ◽  
In Situ Observations

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

scholarly journals Development of observation-based global multilayer soil moisture products for 1970 to 2016

2021 ◽  
Vol 13 (9) ◽  
pp. 4385-4405
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Layer ◽  
Added Value ◽  
Surface Model ◽  
Mean Square ◽  
Wide Range ◽  
Mean Square Errors

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals Evaluation of Sentinel-3A Wave Height Observations Near the Coast of Southwest England

2019 ◽  
Vol 11 (24) ◽  
pp. 2998 ◽  
Author(s):  
Francesco Nencioli ◽  
Graham D. Quartly
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Significant Wave Height ◽  
Mean Square ◽  
Significant Wave ◽  
In Situ Observations ◽  
Regression Slopes

Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters.

Quantifying connectivity uncertainty arising from circulation modelling inaccuracy.

2020 ◽  
Author(s):  
Elise Vissenaekens ◽  
Katell Guizien
Keyword(s):  
Root Mean Square ◽  
Flow Speed ◽  
Population Connectivity ◽  
Mean Square ◽  
Statistical Parameters ◽  
Ocean Modelling ◽  
Gulf Of Lion ◽  
Study Population ◽  
In Situ Observations

<p>Ocean modelling has become an increasingly important tool to study population connectivity and is our only tool to anticipate changes in dispersal routes in future climates. To estimate the uncertainties in model predictions, a comparison was made between the simulated currents and in situ observations in the Gulf of Lion over the period of 2009-2013. The uncertainties in Eulerian current values were described using several statistical parameters, like the bias, the root mean square (RMSE), the naturalised root mean square (NRMSE), the Hannah and Heinold parameter (HH) and the correlation. Another parameter that was introduced was the correctness, which states the percentage of time the model was deemed “correct”, based on low HH values (<75%) and high correlation (>0.25). So far, the model simulated the flow speed correctly 60-70% of the time and the relative deviation between observed and simulated flow speed was about 10%. Furthermore, ensembles of Lagrangian tracks were simulated accounting for uncertainties in Eulerian flow speed. These uncertainties were either correlated to speed values or chosen according to their statistical distribution. The Lagrangian tracks were analysed to construct connectivity matrices with and without these Eulerian uncertainties. Resulting deviation in retention and larval transfer arising from flow speed uncertainty were quantified.</p>

scholarly journals Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

2020 ◽  
Vol 92 (6) ◽  
pp. 7-16
Author(s):  
AKSHANSH MISHRA
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Regression Model ◽  
Decision Trees ◽  
Root Mean Square ◽  
Welded Joints ◽  
Mean Square ◽  
Friction Stir ◽  
Mean Square Errors

In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e. Artificial Neural Network (ANN) and Decision Trees regression model are selected for the purpose. The input variables are Tool Rotational Speed (RPM), Tool Traverse Speed (mm/min) and Axial Force (KN) while the output variable is Ultimate Tensile Strength (MPa). It is observed that in case of the Artificial Neural Networks the Root Mean Square Errors for training and testing sets are 0.842 and 0.808 respectively while in case of Decision Trees regression model, the training and testing sets result Root Mean Square Errors of 11.72 and 14.61. So, it can be concluded that ANN algorithm gives better and accurate result than Decision Tree regression algorithm.

scholarly journals Assessment of open source digital elevation models (SRTM-30, ASTER, ALOS) for erosion processes modeling

2019 ◽  
Vol 28 (1) ◽  
pp. 95-105 ◽  
Author(s):  
I. P. Kovalchuk ◽  
K. A. Lukianchuk ◽  
V. A. Bogdanets
Keyword(s):  
Open Source ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Digital Elevation Models ◽  
Mean Square ◽  
Erosion Processes ◽  
Aster Gdem ◽  
Digital Elevation ◽  
Vertical Accuracy ◽  
Mean Square Errors

The relief has a major impact on the landscape`s hydrological, geomorphological and biological processes. Many geographic information systems used elevation data as the primary data for analysis, modeling, etc. A digital elevation model (DEM) is a modern representation of the continuous variations of relief over space in digital form. Digital Elevation Models (DEMs) are important source for prediction of soil erosion parameters. The potential of global open source DEMs (SRTM, ASTER, ALOS) and their suitability for using in modeling of erosion processes are assessed in this study. Shumsky district of Ternopil region, which is located in the Western part of Ukraine, is the area of our study. The soils of Shumsky district are adverselyaffected by erosion processes. The analysis was performed on the basis of the characteristics of the hydrological network and relief. The reference DEM was generated from the hypsographic data(contours) on the 1:50000 topographical map series compiled by production units of the Main Department of Geodesy and Cartography under the Council of Ministers. The differences between the reference DEM and open source DEMs (SRTM, ASTER and ALOS) are examined. Methods of visual detection of DEM defects, profiling, correlation, and statistics were used in the comparative analysis. This research included the analysis oferrors that occurred during the generation of DEM. The vertical accuracy of these DEMs, root mean square error (RMSE), absolute and relative errors, maximum deviation, and correlation coefficient have been calculated. Vertical accuracy of DEMs has been assessed using actual heights of the sample points. The analysis shows that SRTM and ALOS DEMs are more reliable and accurate than ASTER GDEM. The results indicate that vertical accuracy of DEMs is 7,02m, 7,12 m, 7,60 mand 8,71 m for ALOS, SRTM 30, SRTM 90 and ASTER DEMs respectively. ASTER GDEM had the highest absolute, relative and root mean square errors, the highest maximum positive and negative deviation, a large difference with reference heights, and the lowest correlation coefficient. Therefore, ASTER GDEM is the least acceptable for studying the intensity and development of erosion processes. The use of global open source DEMs, compared with the vectorization of topographic maps,greatly simplifies and accelerates the modeling of erosion processes and the assessment of the erosion risk in the administrative district.

scholarly journals Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7970
Author(s):  
Abdel-Rahman Hedar ◽  
Majid Almaraashi ◽  
Alaa E. Abdel-Hakim ◽  
Mahmoud Abdulrahim
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Root Mean Square ◽  
Prediction Models ◽  
Weather Prediction ◽  
Hybrid Models ◽  
Mean Square ◽  
Learning Models ◽  
Mean Square Errors ◽  
Machine Learning Models

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to 5.6% and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.

scholarly journals Using Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture

2020 ◽  
Vol 86 (2) ◽  
pp. 91-98
Author(s):  
Ju Hyoung Lee
Keyword(s):  
Soil Moisture ◽  
Root Mean Square ◽  
Tropical Rainfall Measuring Mission ◽  
Stochastic Approach ◽  
Skill Score ◽  
Mean Square ◽  
Ocean Salinity ◽  
Satellite Retrievals ◽  
Republic Of Benin ◽  
Mean Square Errors

To mitigate instantaneously evolving biases in satellite retrievals, a stochastic approach is applied over West Africa. This stochastic approach independently self-corrects Soil Moisture Ocean Salinity (<small>SMOS</small>) wet biases, unlike the cumulative density function (<small>CDF</small>) matching that rescales satellite retrievals with respect to several years of reference data. Ranked probability skill score (<small>RPSS</small>) is used as nonlocal root-mean-square errors (<small>RMSEs</small>) to assess stochastic retrievals. Stochastic method successfully decreases <small>RMSEs</small> from 0.146 m3/m3 to 0.056 m3/m3 in the Republic of Benin and from 0.080 m3/m3 to 0.038 m3/m3 in Niger, while the <small>CDF</small> matching method exacerbates the original <small>SMOS</small> biases up to 0.141 m3/m3 in Niger, and 0.120 m3/m3 in Benin. Unlike the <small>CDF</small> matching or European Centre for Medium-Range Weather Forecasts (<small>ECMWF</small>) Re-Analysis (<small>ERA</small>))–interim soil moisture, only a stochastic retrieval responds to Tropical Rainfall Measuring Mission rainfall. Based on the effects of bias correction, RPSS is suggested as a nonlocal verification without needing local measurements.

scholarly journals In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

2018 ◽  
Vol 11 (05) ◽  
pp. 1850027 ◽  
Author(s):  
Hongxia Huang ◽  
Haibin Qu
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Nir Spectroscopy ◽  
In Situ Monitoring ◽  
Mean Square ◽  
Model Errors ◽  
Precipitation Processes ◽  
Mean Square Errors

As unsafe components in herbal medicine (HM), saccharides can affect not only the drug appearance and stabilization, but also the drug efficacy and safety. The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using near-infrared (NIR) spectroscopy. NIR spectra in the 4000–10,000-cm[Formula: see text] wavelength range are acquired in situ using a transflectance probe. These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation. Calibration models based on partial least squares (PLS) regression have been developed for the three saccharide impurities, namely glucose, fructose, and sucrose. Model errors are estimated as the root-mean-square errors of cross-validation (RMSECVs) of internal validation and root-mean-square errors of prediction (RMSEPs) of external validation. The RMSECV values of glucose, fructose, and sucrose were 1.150, 1.535, and 3.067[Formula: see text]mg[Formula: see text]mL[Formula: see text], and the RMSEP values were 0.711, 1.547, and 3.740[Formula: see text][Formula: see text], respectively. The correlation coefficients [Formula: see text] between the NIR predictive and the reference measurement values were all above 0.94. Furthermore, NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation. The results demonstrate that, as an alternative process analytical technology (PAT) tool for monitoring batch alcohol precipitation processes, NIR spectroscopy is advantageous for both efficient determination of quality characteristics (fast, in situ, and requiring no toxic reagents) and process stability, and evaluating the repeatability.

scholarly journals Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2451 ◽  
Author(s):  
Emanuele Santi ◽  
Mohammed Dabboor ◽  
Simone Pettinato ◽  
Simonetta Paloscia
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Correlation Coefficients ◽  
Direct Measurements ◽  
Linearly Polarized ◽  
In Situ Observations ◽  
Artificial Neural Network Ann ◽  
Agricultural Areas ◽  
Better Than

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (σ°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than σ°, with correlation coefficients up to R ≃ 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of σ° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized σ°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9.

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