Larger Root Mean Square Error Research Articles

Performance of cone-beam computed tomography imaging during megavoltage beam irradiation under phase-gated conditions

PurposeTarget positions should be acquired during beam delivery for accurate lung stereotactic body radiotherapy. We aimed to perform kilovoltage (kV) imaging during beam irradiation (intra-irradiation imaging) under phase-gated conditions and evaluate its performance. MethodsCatphan 504 and QUASAR respiratory motion phantoms were used to evaluate image quality and target detectability, respectively. TrueBeam STx linac and the Developer Mode was used. The imaging parameters were 125 kVp and 1.2 mAs/projection. Flattened megavoltage (MV) X-ray beam energies 6, 10 and 15 MV and un-flattened beam energies 6 and 10 MV were used with field sizes of 5 × 5 and 15 × 15 cm2 and various frame rates for intra-irradiation imaging. In addition, using a QUASAR phantom, intra-irradiation imaging was performed during intensity-modulated plan delivery. The root-mean-square error (RMSE) of the CT-number for the inserted rods, image noise, visual assessment, and contrast-to-noise ratio (CNR) were evaluated. ResultsThe RMSEs of intra-irradiation cone-beam computed tomography (CBCT) images under gated conditions were 50–230 Hounsfield Unit (HU) (static < 30 HU). The noise of the intra-irradiation CBCT images under gated conditions was 15–35 HU, whereas that of the standard CBCT images was 8.8–27.2 HU. Lower frame rates exhibited large RMSEs and noise; however, the iterative reconstruction algorithm (IR) was effective at improving these values. Approximately 7 fps with the IR showed an equivalent CNR of 15 fps without the IR. The target was visible on all the gated intra-irradiation CBCT images. ConclusionSeveral image quality improvements are required; however, intra-irradiated CBCT images showed good visual target detection.

Construction of a meteorological application system based on BDS ground-based augmentation network and water vapor products validation

The national Beidou Navigation Satellite System (BDS) ground-based augmentation network (BGAN) of China is constructed with the existing GNSS observation resources of industrial sectors and local governments, based on the concept of joint building and sharing with sustainable development. This study provides a detailed introduction to the design, construction and operation of a meteorological application system based on BGAN, and validation of its water vapor products. BDS and GPS real-time observation of atmospheric water vapor is achieved nationwide in China and multi-GNSS applications. Through the application of multi-GNSS data and validation of the water vapor products from 2018 to 2020, the accuracy of precipitable water vapor (PWV) derived from BDS only is equivalent to that from GPS only. The root mean square error (RMSE) between them is about 2 mm with high correlation coefficient. Based on radiosonde data, the validation is conducted with the products of BDS-PWV, GPS-PWV, and Combined-PWV derived with multi-GNSS of BDS and GPS. The error characteristics of the three products show a consistent trend over the months. The bias is relatively small. The RMSE of the three products is in the range of 2.18–2.73 mm. The BDS-PWV has the largest RMSE, followed by GPS-PWV, and Combined-PWV has the smallest RMSE.

Modeling energy partition patterns of growing pigs fed diets with different net energy levels based on machine learning.

The objectives of this study were to evaluate the energy partition patterns of growing pigs fed diets with different net energy (NE) levels based on machine learning methods, and to develop prediction models for the NE requirement of growing pigs. Twenty-four Duroc × Landrace × Yorkshire crossbred barrows with an initial body weight of 24.90 ± 0.46kg were randomly assigned to 3 dietary treatments, including the low NE group (2,325 kcal/kg), the medium NE group (2,475 kcal/kg), and the high NE group (2,625 kcal/kg). The total feces and urine produced from each pig during each period were collected, to calculate the NE intake, NE retained as protein (NEp), and NE retained as lipid (NEl). A total of 240 sets of data on the energy partition patterns of each pig were collected, 75% of the data in the dataset was randomly selected as the training dataset, and the remaining 25% was set as the testing dataset. Prediction models for the NE requirement of growing pigs were developed using algorithms including multiple linear regression (MR), artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF), and the prediction performance of these models was compared on the testing dataset. The results showed pigs in the low NE group showed a lower average daily gain, lower average daily feed intake, lower NE intake, but greater feed conversion ratio compared to pigs in the high NE group in most growth stages. In addition, pigs in the 3 treatment groups did not show a significant difference in NEp in all growth stages, while pigs in the medium and high NE groups showed greater NEl compared to pig in the low NE group in growth stages from 25 to 55kg (P < 0.05). Among the developed prediction models for NE intake, NEp, and NEl, the ANN models demonstrated the most optimal prediction performance with the smallest root mean square error (RMSE) and the largest R2, while the RF models had the worst prediction performance with the largest RMSE and the smallest R2. In conclusion, diets with varied NE concentrations within a certain range did not affect the NEp of growing pigs, and the models developed with the ANN algorithm could accurately achieve the NE requirement prediction in growing pigs.

UPDATING ABOVEGROUND BIOMASS AT A PAN-EUROPEAN SCALE THROUGH SATELLITE DATA AND ARTIFICIAL INTELLIGENCE

Abstract. In this work, an ensemble of machine learning algorithms was trained using stratified sampling from an existing European-scale biomass map from 2018 to predict an updated version for 2020. The objective of stratification is to make sure that the full range of biomass values is represented. The sampled biomass values from 2018 were filtered to remove areas that did were subject to forest disturbances between 2018 and 2020. This information was available from forest cover/loss/gain maps derived from satellite imagery. We train using a total of 49 features derived from the following sources: bioclimatic data, maps of land-cover, tree cover, tree height, annual composites of vegetation indices per pixel (EVI and NDVI) obtained from Sentinel-2, radar backscatter median annual values from Sentinel-1 and ALOS-2, and the ALOS DSM (3D) elevation grid. A model was created dividing Europe into 19 tiles to limit variability due to very different bioclimatic zones. The result is a raster with 100 m × 100 m resolution and an estimated value of biomass (Mg ha−1) at each node. Overall results on validation data over Europe report a root mean square error (RMSE) of 32.4 Mg ha−1 and a mean absolute error (MAE) of 21.5 Mg ha−1; when considering single tiles, the largest RMSE was 54.7 Mg ha−1 in tile D2, which can be explained by the very high variance of climate, environment, terrain topography and biomass values as the tile enclosed the Alpine region and the western part of Eastern Europe.

Impact of convection scheme on ENSO prediction of SINTEX-F2

A spectral cumulus parameterization (spectral scheme) is implemented in Scale Interaction Experiment Frontier version 2 (SINTEX-F2) seasonal prediction system, and the impact on the El Niño Southern Oscillation (ENSO) prediction is examined. By conducting hindcast experiments using the original convection scheme (Tiedtke scheme) and the spectral scheme, and comparing the ENSO prediction skill, the impact of the spectral scheme is analyzed in detail. It was found that prediction skill in terms of ENSO phase and the sea surface temperature (SST) persistence were improved by using the spectral scheme, but the root-mean-square error (RMSE) increased. The ENSO feedback was also changed by changing the convection scheme. The original scheme failed to predict the zonal wind stress anomaly toward the Niño 3.4 region, whereas the spectral scheme simulated it over the equatorial eastern Pacific with narrowing the meridional width, indicating that the spectral scheme strengthened the ENSO feedback. The spectral scheme also improved zonal-vertical atmospheric response to the Niño 3.4 index due to its advantageous features. Analysis of the ENSO feedback terms revealed that strengthened forcing in the eastern Pacific improved the thermocline feedback of ENSO, as its reversed timing of positive and negative tendencies for the mixed layer temperature matched that estimated from the reanalysis data. In conclusion, the spectral scheme can improve ENSO prediction through the atmospheric forcing and mean state in the eastern Pacific which impacted the ocean properties. It improved the phase error by improving thermocline feedback, but did not improve the RMSE. Tuning of the original scheme to obtain additional improvements to ENSO prediction would be difficult, since it requires modification of detailed convective cloud properties to correct the phase error. The spectral scheme tends to overestimate the ENSO amplitude, i.e., large RMSE, but this drawback can be mitigated by tuning the convection scheme so that it suppresses the warm SST climate drift, and this is considered the more promising method to further improve ENSO prediction.

Time Series Prediction of Lung Cancer Death Rates on the Basis of SEER Data.

The purpose of this study was to apply different time series analytical techniques to SEER US lung cancer death rate data to develop a best fit model. Three models for yearly time series predictions were built: autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt's double expansional smoothing (HDES) models. The three models were built using Python 3.9, on the basis of Anaconda 2022.10. This study used SEER data from 1975 to 2018 and included 545,486 patients with lung cancer. The best parameters for ARIMA are ARIMA (p, d, q) = (0, 2, 2). In addition, the best parameter for SES was α = .995, whereas the best parameters for HDES were α = .4 and β = .9. The HDES was the model that best fit the lung cancer death rate data, with a root mean square error (RMSE) of 132.91. Including monthly diagnoses, death rates, and years in SEER data increases the number of observations for training and test sets, enhancing the performance of time series models. The reliability of the RMSE was based on the mean lung cancer mortality rate. Owing to the high mean lung cancer death rate of 8,405 patients per year, it is acceptable for reliable models to have large RMSEs.

Development and validation of a mathematical model to simulate human cardiovascular and respiratory responses to battlefield trauma.

Mathematical models of human cardiovascular and respiratory systems provide a viable alternative to generate synthetic data to train artificial intelligence (AI) clinical decision-support systems and assess closed-loop control technologies, for military medical applications. However, existing models are either complex, standalone systems that lack the interface to other applications or fail to capture the essential features of the physiological responses to the major causes of battlefield trauma (i.e., hemorrhage and airway compromise). To address these limitations, we developed the cardio-respiratory (CR) model by expanding and integrating two previously published models of the cardiovascular and respiratory systems. We compared the vital signs predicted by the CR model with those from three models, using experimental data from 27 subjects in five studies, involving hemorrhage, fluid resuscitation, and respiratory perturbations. Overall, the CR model yielded relatively small root mean square errors (RMSEs) for mean arterial pressure (MAP; 20.88 mm Hg), end-tidal CO2 (ETCO2 ; 3.50 mm Hg), O2 saturation (SpO2 ; 3.40%), and arterial O2 pressure (PaO2 ; 10.06 mm Hg), but a relatively large RMSE for heart rate (HR; 70.23 beats/min). In addition, the RMSEs for the CR model were 3% to 10% smaller than the three other models for HR, 11% to 15% for ETCO2 , 0% to 33% for SpO2 , and 10% to 64% for PaO2 , while they were similar for MAP. In conclusion, the CR model balances simplicity and accuracy, while qualitatively and quantitatively capturing human physiological responses to battlefield trauma, supporting its use to train and assess emerging AI and control systems.

Comparison of Spatial Interpolation Methods for Estimating the Annual Rainfall in the Wadi Al-Mujib Basin in Jordan

Accurate rainfall data are essential for environmental applications in the actual assessment of the geographical distribution of rainfall. Interpolation methods are usually applied to monitor the spatial distribution of the rainfall data. There are many spatial interpolation methods, but none of them can achieve in all cases the best results. In this study, three different interpolation methods were investigated with regard to their suitability for producing a spatial rainfall distribution. Rainfall data from 14 meteorological stations were spatially interpolated using three common interpolation techniques: inverse distance weighting (IDW), ordinary kriging (OK), and kernel smoothing (KS) were compared and assessed against station rainfall data and modeled rainfall. Cross-validation was applied to evaluate the accuracy of interpolation methods in terms of the root-mean-square error (RMSE). The best results were obtained by the lowest RMSE for interpolating the precipitation (RMSE) = 100.86542, while the inverse distance weighting (IDW) performed the worst, and are least efficient with the largest RMSE=103.43; in addition, the kernel smoothing with the least minimum (-) and maximum (+) error is -92.38 mm and 313.33 mm.

Validation of CHIRPS Estimation Rainfall Data Using Numerical Accuracy Test with Precitipation Observation Data

Rainfall is the dominant and most studied weather and climate factor in Indonesia. The use of global satellite estimation data is a solution to overcome the constraints of limited observational rainfall data. Although the global data can be extracted according to the desired location, the data still has to be validated with observational data. This study was conducted to validate the CHIRPS rainfall estimation data with observation rainfall data at the Central MKG Region I Medan. Estimation data used is CHIRPS daily estimation data version 2.0 with a spatial resolution of 0.05°, and validator data is daily observation data of Central MKG Region I Medan for the period 2017- 2019. Validation was done by calculating Pearson correlation, accuracy, bias, mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) values, and using simple linear regression to see overestimated or underestimated estimation data on observation data. Results of this study indicated that the CHIRPS data has a low correlation with the observation data. Large MAE and RMSE values of CHIRPS indicated that the average error of CHIRPS is quite large. Estimation of CHIRPS was underestimated to the observation data so that it is not suitable for daily rainfall forecasting in Medan City. High accuracy value of CHIRPS indicated that CHIRPS was able to detect rain events based on a threshold of 1 mm.

Validation of a 3D CT imaging method for quantifying implant migration following anatomic total shoulder arthroplasty.

Glenoid component loosening remains a common complication following anatomic total shoulder arthroplasty (TSA); however, plain radiographs are unable to accurately detect early implant migration. The purpose of this study was to validate the accuracy of a method of postoperative, three-dimensional (3D) computed tomography (CT) imaging with metal artifact reduction (MAR) to detect glenoid component migration following anatomic TSA. Tantalum bead markers were inserted into polyethylene glenoid components for implant detection on 3D CT. In-vitro validation was performed using a glenoid component placed into a scapula sawbone and incrementally translated and rotated, with MAR 3D CT acquired at each test position. Accuracy was evaluated by root mean square error (RMSE). In-vivo validation was performed on six patients who underwent anatomic TSA, with two postoperative CT scans acquired in each patient and marker-based radiostereometric analysis (RSA) performed on the same days. Glenoid component migration was calculated relative to a scapular coordinate system for both MAR 3D CT and RSA. Accuracy was evaluated by RMSE and paired Student's t-tests. The largest RMSE on in-vitro testing was 0.24 mm in translation and 0.11° in rotation, and on in-vivo testing was 0.47 mm in translation and 1.04° in rotation. There were no significant differences between MAR 3D CT and RSA measurement methods. MAR 3D CT imaging is capable of quantifying glenoid component migration with a high level of accuracy. MAR 3D CT imaging is advantageous over RSA because it is readily available clinically and can also be used to evaluate the implant-bone interface.

A global eddy-resolving ocean forecast system in China – LICOM Forecast System (LFS)

ABSTRACT A global eddy-resolving forecast system LFS is developed based on a primitive ocean general circulation model. The system's configuration, forecast experiments, and a preliminary evaluation of the preoperational system are shown. In the preoperational stage, since the full data assimilation has not yet been set up for LFS, the initial state is obtained by nudging the ocean temperature and salinity from an ocean analysis dataset. Despite this, the LFS demonstrates a generally good performance in short-term oceanography forecasting, except for sea level anomaly (SLA). The median values of the 1-day forecast leading root mean square error (RMSE) for the sea surface temperature (SST), SLA, upper 2000 m temperature, and salinity are approximately 0.52°C, 0.10 m, 0.57°C and 0.13 psu, respectively. Although there are slight warm biases in the forecasted SST, the forecasts of temperature and salinity in the thermocline by the LFS are comparable with the results of operational oceanography systems under the framework of the Intercomparison and Validation Task Team. However, the forecast SLA has a relatively large RMSE related to the absence of direct observational constraints in the initial state. Further investigations are needed to improve the performance of LFS.

Characterizing the Great Lakes Coastal Wetlands with InSAR Observations from X-, C-, and L-Band Sensors

We investigated the potential of using Synthetic Aperture Radar (SAR) imagery from three different frequencies: X-, C-, and L-band, to characterize coastal wetlands in the Great Lakes. Three sets of SAR data acquired over the Bay of Quinte, Ontario, Canada between 2016 and 2018 from Radarsat-2, 2016 from TerraSAR-X, and 2018 from ALOS-2 satellites were processed using small baseline subset (SBAS) Interferometric SAR (InSAR) techniques to provide maps of surface changes in marshes and swamps. Results showed that SAR backscatter and coherence were sensitive to sensor characteristics (frequency, polarization, incidence angle, acquisition interval), changes in water level, and phenology. InSAR time series observations were evaluated using measurements from water level loggers based on correlation and root mean square error (RMSE) from a linear regression model. Correlation between InSAR measurements and water level changes in the field varied from −1 to 1 depending on the site, type of wetland vegetation, and incidence angle. Although results from some sensor modes provided good correlation (0.77–1) at a few locations, the low fringe rate and large RMSE between 4 and 64 cm indicated that InSAR observations of water level changes in the dynamic wetland environment were generally underestimated.

Precipitable Water Vapor Retrieval from Shipborne GNSS Observations on the Korean Research Vessel ISABU

We estimate precipitable water vapor (PWV) from data collected by the low-cost Global Navigation Satellite System (GNSS) receiver at a vessel. The dual-frequency GNSS receiver that the vessel ISABU is equipped with that is operated by the Korea Institute of Ocean Science and Technology. The ISABU served in the Pacific Ocean for scientific research during a period from August 30 to September 21, 2018. It also performs radiosonde observations to obtain a vertical profile of troposphere on the vessel’s path. The GNSS-derived PWV is compared to radiosonde observations and the Atmospheric Infrared Sounder (AIRS) on NASA’s Aqua satellite output. A bias and root-mean-square (RMS) error between shipborne GNSS-PWV and radiosonde-PWV were −1.48 and 5.22 mm, respectively. When compared to the ground GNSS-PWV, shipborne GNSS-PWV has a relatively large RMS error in comparison with radiosonde-PWV. However, the GNSS observations on the vessel are still in good agreement with radiosonde observations. On the other hand, the GNSS-PWV is not well linearly correlated with AIRS-PWV. The RMS error between the two observations was approximately 8.97 mm. In addition, we showed that the vessel on the sea surface has significantly larger carrier phase multipath error compared to the ground-based GNSS observations. This also can result in reducing the accuracy of shipborne GNSS-PWV. However, we suggest that the shipborne GNSS has sufficient potential to derive PWV with the kinematic precise point positioning (PPP) solution on the vessel.

Newly collected data across Alaska reveal remarkable biases in solar radiation products

AbstractData on surface solar radiation are scarce in high‐latitude regions, and few studies have evaluated the performance of reanalysis products in estimating solar radiation in those regions. Here, an extensive solar radiation dataset is compiled from 98 stations across Alaska to evaluate 11 different surface solar radiation products (seven reanalysis and four observation‐derived). No product can capture all aspects of the ground‐based observations, and there is ample room for improvement; root mean square errors (RMSEs) of daily, monthly, and annual average comparisons of the products against observations are 38–65, 19–39, and 11–17 W⋅m−2, respectively. ERA5, MERRA2, and ERA‐Interim performed the best in Alaska. Daily records from all products show large RMSEs of 60–108 W⋅m−2 during May–July, equivalent to 30–55% of the observed solar radiation during this season. The sparseness of Alaskan observations, cloud cover, and algorithm issues may be potential sources of bias.

Comparison of Methods Accounting for Subgrid-Scale Model Error in Convective-Scale Data Assimilation

Abstract Different approaches for representing model error due to unresolved scales and processes are compared in convective-scale data assimilation, including the physically based stochastic perturbation (PSP) scheme for turbulence, an advanced warm bubble approach that automatically detects and triggers absent convective cells, and additive noise based on model truncation error. The analysis of kinetic energy spectrum guides the understanding of differences in precipitation forecasts. It is found that the PSP scheme results in more ensemble spread in assimilation cycles, but its effects on the root-mean-square error (RMSE) are neutral. This leads to positive impacts on precipitation forecasts that last up to three hours. The warm bubble technique does not create more spread, but is effective in reducing the RMSE, and improving precipitation forecasts for up to 3 h. The additive noise approach contributes greatly to ensemble spread, but it results in a larger RMSE during assimilation cycles. Nevertheless, it considerably improves the skill of precipitation forecasts up to 6 h. Combining the additive noise with either the PSP scheme or the warm bubble technique reduces the RMSE within cycles and improves the skill of the precipitation forecasts, with the latter being more beneficial.

Evaluation of Precipitation in Multi-Generation Reanalyses with Land Observations over Zhejiang Province

Based on various statistical indices, the abilities of multi-generation reanalyses, namely the NCEP / NCAR Reanalysis 1 (R1), the NCEP-DOE Reanalysis 2 (R2) and the NCEP Climate Forecast System Reanalysis (CFSR), to reproduce the spatiotemporal characteristics of precipitation over Zhejiang Province are comprehensively compared. The mean absolute bias percentages for three reanalyses are 20% (R1), 10% (R2) and 37% (CFSR). R2 (R1) gives the best (worst) general depiction of the spatial characteristics of the observed precipitation climatology, whereas a significant wet bias is noticed in the CFSR. All reanalyses reasonably reproduce the interannual variability with the correlation coefficients of 0.72 (R1), 0.72 (R2) and 0.84 (CFSR). All reanalyses well represent the first two modes of the observed precipitation through Empirical Orthogonal Function analysis, with CFSR giving the best capture of the principal components. The root-mean-square error (RMSE) is the largest (smallest) in the CFSR (R2). The large RMSE of CFSR in summer (especially in June) contributes mostly to its systematic wet bias. After 2001, the wet bias of CFSR substantially weakens, probably attributed to increasing observations assimilated in the CFSR. On a monthly basis, the percentage of neutral bias cases are similar for all reanalyses, while the ratio of positive (negative) bias cases for CFSR is distinctly larger (smaller) than that of R1 and R2. The proportions of negative bias cases for R1 and R2 begin to increase after 2001 while keeping stable for CFSR. On a daily basis, all reanalyses give good performances of reproducing light rain; however, the reflection of moderate rain and heavier rain by the CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, the CRSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.

An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China

Soil moisture is an important factor in land-atmosphere interactions and other land processes. Improved estimates from climate models have, in the last two decades, become an important alternate source of information. In this study, we extend the evaluation of soil moisture anomalies of different generations of three families of model datasets (the European Center for Medium-Range Weather Forecasts’ (ECMWF) reanalysis, the Modern Era Retrospective Analysis for Research and Applications of NASA, and the Global Land Data Assimilation System of theNational Oceanic and Atmospheric Administration (NOAA)) in recent studies to the People’s Republic of China. Two validation techniques, namely, root-mean-square error (RMSE) from triple collocation analysis (TCA) and correlations (R) with ground observations, were used. The study confirmed the results of previous studies that focused on other regions and showed that the newer generations of each modeling family generally had better skill than the older generations with higher correlations and lower RMSEs. A cross-validation of the results from the two techniques for the newer products showed that the higher correlations and lower RMSEs from the TCA were found over regions with moderate vegetation cover, while regions with less vegetation cover had lower correlations and larger RMSEs (ECMWF (R: −0.93), NASA (R: −0.73), and NOAA (R: −0.61)), indicating that these two techniques complement each other to fairly validate the products.

Effects of sampling strategies and estimation algorithms on total nitrogen load determination in a small agricultural headwater watershed

Monitoring data collected from rivers are used to support assessments of the quality of the river environment and help decision makes formulate appropriate management plans. Therefore, accurate and precise estimates of constituent fluxes in streams and rivers are important. However, seasonal errors have not received enough attention and the published results have rarely been verified by other methods. In this study, the error associated with sampling frequencies (3 daily, weekly, 2 weekly, 4 weekly, 6 weekly, 8 weekly) and seven load estimation algorithms were examined. Seasonal and annual total N loads were estimated and wavelet coherence was used as a reference. In order to reduce the contingency of the results, three years data were used and total N (with 17.53% particulate N, 58.70% NO3−-N and 23.77% other dissolved N fractions) was chosen to reflect water quality parameters. Results indicated that 2 weekly sampling frequency and algorithm F (linear interpolation) are sufficient for evaluating the characters of annual total N load in Fengyu River Watershed with 5% RMSE (root-mean-square error). There was a large RMSE (3.97–44.66%) in summer with a high CV (coefficient variation) (35.2%) for total N concentration. These results can serve as a useful reference for decision makes who need to establish monitoring programmes in watersheds with similar characteristics.

Validation of Satellite-Derived Sensible Heat Flux for TERRA/MODIS Images Over Three Different Landscapes Using Large Aperture Scintillometer and Eddy Covariance Measurements

Sensible heat flux (H) is the key component of the earth surface energy balance, which plays a vital role in the evolution of regional climate. The regional and global scale estimation of H is critical and challenging due to the validation of satellite-based estimation of surface energy balance components. This paper focused on the validation and performance evaluation of the satellite-derived H based on a single source remote sensing model in regional scale over three distinct landscapes in northwest China during May to September 2015 using data from TERRA-MODIS and meteorological observations. The results indicated that the remote sensing model with MODIS scenes performed well for the purpose of H estimation, but the influence of different landscapes was noticeable. The satellite-derived H (MODIS_H) was compared with the large aperture scintillometer (LAS)-measured H (LAS_H) and eddy covariance (EC)-measured H (EC_H) over the three different land surfaces. The root-mean-square errors (RMSE) of MODIS_H with respect to LAS_H were 31.63 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over alpine grassland, 44.07 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over croplands, and 112.98 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over mixed forests. The aggregated values (from May to September) of mean and standard deviation showed that the MODIS_H was moderately overestimated with fewer fluctuation over alpine grasslands, slightly overestimated with moderate fluctuation over croplands, and highly overestimated with higher fluctuation over mixed forests. The larger RMSE and over estimations could be explained by the vegetation heterogeneity, the wind speed profiles, and the complicated thermodynamic states.

The anatomy of uncertainty for soil pH measurements and predictions: Implications for modellers and practitioners

SummaryStatistical validation of spatial predictions of soil properties requires assessment of errors against measured values. The objective of this study was to assess the size of errors in the measurement of soil pH from different sources in the United States of America national databases and implications of the size of errors for prediction, validation and management decision making under uncertain conditions. Error sources included measurement methods, laboratory conditions, pedotransfer functions, database manipulations, location accuracy, and spatial and polygon methods of interpolation. The databases consisted of measured soil pH values from the US National Cooperative Soil Survey Characterization Database (NCSS–SCDB) and estimated values from US Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO2) databases. The degree of agreement between measurement methods ranged from poor to substantial, with Lin's concordance correlation coefficients (ρc) varying from 0.83 (pH 1:1W against 1:5CaCl2) to 0.95 (pH 1:1W against pH 1:5W) and root mean square error (RMSE) varying from 0.27 to 0.43. The degree of agreement between pH 1:1W, 1:2CaCl2 and mid‐infrared spectroscopy (MIR) ranged from poor to moderate. The RMSE for MIR was 0.40 for pH 1:1W and 0.32 for soil pH 1:2CaCl2. The RMSE for between‐laboratory reproducibility varied from 0.50 (pH 1:1W) to 0.68 (pH 1:2CaCl2) and was greater than within‐laboratory reproducibility (pH 1:1W, 0.34; pH 1:2 CaCl2, 0.22) and repeatability (pH 1:1W, 0.19; pH 1:2CaCl2, 0.04). The RMSE for the relations for profile depth slicing (weighted mean against equal‐area spline) was 0.36. The RMSE for the relation between soil pH 1:1W for the Global Positioning System and Public Land Survey System was 0.57. Predictions based on polygon or spatial interpolation had the largest RMSEs, 0.78 and 0.62, respectively. Soil liming recommendations based on 0.1 pH increments do not reflect error measurements or the uncertainty of spatial prediction. Although it was not possible to establish consistent trends in the size of error (progressively increasing from measurement to aggregation), its assessment can improve modelling and management at various scales.Highlights We assessed sources of errors and uncertainty for measured and spatial predictions of soil pH. The smallest error was reported for measured pH (0.06). Polygon or spatial interpolation resulted in the largest error (0.68). Differences in error size influenced rates of liming and cost.