Cumulative Distribution Function Matching Research Articles

Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction

Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1–3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R2 = 0.782–0.829, RMSE = 3.240–3.685 MJ m−2 d−1, MAE = 2.465–2.799 MJ m−2 d−1, and NRMSE = 0.152–0.173.

Soil moisture estimation in South Asia via assimilation of SMAP retrievals

Abstract. A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

Improved subsurface soil moisture prediction from surface soil moisture through the integration of the (de)coupling effect

Soil moisture is a crucial component of the forest ecosystem because it primarily controls vegetation growth. However, the potential for predicting the subsurface from the surface soil moisture through (de)coupling in boreal larch forest (Larix gmenilii) in China has rarely been investigated. In the present study, the regression and residual as well as cross-correlation analyses were used to evaluate the relationship between the surface (5 cm) and subsurface (10, 20, and 40 cm as well as the profile soil moisture) soil moisture in the Larix gmenilii forest. The applicability of the exponential filter (ExpF) and cumulative distribution function matching (CDF) methods for estimating the subsurface using the surface soil moisture in the study area and the impacts of (de)coupling on the simulation accuracy were assessed. Our results shown that the coupling between surface and subsurface soil moisture decreased with soil depth increasing and decoupling is dominant in the range varying between 0.11 and 0.22 cm3cm−3. The CDF method exhibits superiority for estimating the subsurface soil moisture relative to the ExpF method in the study area. Furthermore, the surface soil moisture values in the coupling range capture the dynamics of the subsurface soil moisture, while decoupling significantly reduces the accuracy of simulating the subsurface soil moisture. Thus, we propose an approach involving the reconstruction of the coupling time series associated with the (de)coupling, which improves the accuracy of the subsurface soil moisture simulation, particularly at depth, where the coupling is weak. Thus, the (de)coupling effect should henceforth be incorporated in the future subsurface soil moisture simulation studies and is helpful to understand the hydrological process of soil, improve regional models and the associated parameters.

Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network

Spaceborne global navigation satellite system reflectometry (GNSS-R) has recently been applied for wind speed retrieval over oceans, where the wind speed is often retrieved using features extracted from delay-Doppler map (DDM) and empirical geophysical model functions (GMFs). However, it is challenging to utilize the other factors related to the GNSS-R process, such as the geometry and sea state, as the input in GMF given their complicated effects. The use of a fully connected network (FCN) has been recently proposed, but the overfitting occurs at high wind speed due to the non-uniform wind distribution, and some information is undesirably forfeited due to artificially extracting features from DDMs. To this end, we propose a deep learning-based end-to-end modified convolutional neural network (CNN) model, which applies the cumulative distribution function (CDF) matching. A multimodal approach is utilized where the convolutional layers extract effective DDM features at first. Then, they are fused with the auxiliary information, including the geolocation of the specular point, incidence angle, range corrected gain (RCG), uncertainty of bistatic radar cross section (BRCS), and significant wave height (SWH). Further, multiple fully connected layers compose the remainder of the network's layers. At the last step, CDF matching is applied to correct the system deviation of CNN winds. We found that the root mean square error (RMSE) and bias of wind speed retrievals are 1.53 m/s and − 0.097 m/s, respectively, within 0–25 m/s when the proposed method is used. Notably, the bias is reduced by 51%, compared with the FCN architecture, while the RMSE of the retrievals at 12–25 m/s is improved by 12%. Moreover, the RMSE and bias against the incidence angle differ no more than 0.35 m/s and 0.078 m/s, while those against RCG differ less than 0.24 m/s and 0.061 m/s, respectively. Time-series analyses indicated that the wind speed retrievals of the proposed model are in line with the referenced wind speeds. We identified a temporal increase of the retrieval bias, driven by the downward trend of DDM observation in the cyclone global navigation satellite system (CYGNSS) Version 2.1 inadequately calibrated products.

A Method for Downscaling Satellite Soil Moisture Based on Land Surface Temperature and Net Surface Shortwave Radiation

Due to the coarse spatial resolution of currently available microwave (mostly passive) soil moisture (SM) products, it is difficult to apply these SM data in watersheds or at local scales. To this end, a number of downscaling approaches have been developed to improve the spatial resolution of microwave SM products. Specifically, the optical-/thermal-based downscaling methods are most widely used in recent decades. However, such methods normally rely on instantaneous optical/thermal land surface parameters, which are commonly inapplicable under cloudy conditions. The purpose of this study is to develop a new downscaling method based on the temporal variation in geostationary satellite-derived land surface temperature and net surface shortwave radiation. The proposed method has a certain potential to improve data availability under cloudy conditions, because geostationary satellites are capable of providing land surface parameters at high temporal resolution. The proposed method was tested over the REMEDHUS network in Spain. The scaling strategy of cumulative distribution function matching was used to remove systematic differences in spatial mismatch between satellite pixels and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SM measurements. Results indicate that the downscaled SM agrees well with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements and has comparable accuracy with the original microwave SM product. The overall root mean square errors with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements for the original microwave SM and the downscaled SM are 0.054 and 0.057 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{3}/\text{m}^{3}$ </tex-math></inline-formula> , respectively. This method not only has a successful attempt to downscale microwave SM data using temporal information but also has the potential to avoid the failure of traditional instantaneous observations-based downscaling procedure due to clouds.

A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering

Fusing high-level passive microwave (PMW) LST and thermal infrared (TIR) LST products is a promising means of generating high-quality, all-weather land surface temperature (LST) data. In this paper, we propose a new fusion strategy for generating high-quality, all-weather LST data based on cumulative distribution function (CDF) matching and multiresolution Kalman filtering (MKF). CDF matching was employed to improve the quality of the high-resolution (0.01°) LST data downscaled with the retrieved coarse-resolution (0.1°) PMW LST from the Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (BT) data set. The MKF approach was employed to produce high-quality, all-weather LST data by fusing the retrieved coarse-resolution AMSR2 LST and high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) LST data sets filled with CDF-improved LST data. The spatial patterns in the high-resolution and coarse-resolution LST data were consistent after MKF fusion. The accuracy of coarse-resolution LST data was considerably improved. The biases, root mean square errors (RMSEs) and determination coefficients (R2) were 0.79–1.47 K, 3.0–3.82 K, 0.88–0.96, respectively. The RMSE of the fused coarse-resolution LST data decreased by 1.51 K/1.82 K and 1.81 K/1.39 K in daytime/nighttime in the Tibetan Plateau (TP) and Heihe River Basin (HRB) verification regions compared to the RMSE of the retrieved AMSR2 LST data. The accuracy of high-resolution LST data remained unchanged, but the spatial coverage was complete with exceptions at the orbital gaps. The bias, RMSE and R2 values of high-resolution LST data were − 0.91- -0.81 K, 3.02–3.32 K, and 0.93–0.97 under all-sky conditions in the TP and HRB verification regions, respectively. Compared to methods in the existing LST fusion studies, the proposed strategy has advantages in terms of LST data quality and validation accuracy. When both coarse-resolution and high-resolution LST data are missing, the spatial coverages of fused all-weather LST data are incomplete. We attempted to fill the vacancies in PMW LST data with the DINEOF method in advance and subsequently obtained spatially complete all-weather LST data.

Global assessments of two blended microwave soil moisture products CCI and SMOPS with in-situ measurements and reanalysis data

Multiple soil moisture (SM) products have been produced based on observations from microwave satellite sensors nowadays, allowing for the acquisition of global SM dynamics in a timely manner. Currently, only two blended microwave SM products, namely the Climate Change Initiative (CCI) from the European Space Agency and the Soil Moisture Operational Product System (SMOPS) from the National Oceanic and Atmospheric Administration, are available with either better temporal or better spatial coverage than those of other SM products derived from a single sensor. However, an assessment and especially a synchronous comparison of these two products are still lacking, making it difficult to determine a better alternative in actual applications. In the present study, a comprehensive assessment of the two blended products was conducted with reanalysis SM data from the European Centre for Medium-Range Weather Forecasts and in-situ measurements from the International Soil Moisture Network. The scaling strategy of cumulative distribution function matching was used to remove the systematic differences in spatial mismatch between the satellite pixels and ground in-situ observations. The results indicated that CCI reveals overall better accuracy than that of SMOPS with both in-situ measurements and reanalysis data under different climate patterns. Specifically, the overall root mean square error (RMSE) with the in-situ measurements were 0.042 m3/m3 and 0.046 m3/m3 for CCI and SMOPS, respectively. Further investigation also confirmed that SMOPS could be a potential alternative over the regions where CCI is not available, since SMOPS has better spatial coverage than CCI.

Evaluation of Radar-Gauge Merging Techniques to Be Used in Operational Flood Forecasting in Urban Watersheds

Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

Evaluating the Benefits of Merging Near-Real-Time Satellite Precipitation Products: A Case Study in the Kinu Basin Region, Japan

Abstract After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems.

Uncertainty-based design optimization approach based on cumulative distribution matching

Uncertainty-based design optimization (UBDO) is becoming increasingly important with great attention to critical system reliability and robustness. However, most traditional UBDO methods only consider low-order response moments and a limited number of aleatory uncertainties, which desiderates to be resolved. In this study, an efficient UBDO approach based on cumulative distribution matching is presented to address all response moments under mixed aleatory and epistemic uncertainties. The mathematical formulation of cumulative distribution function (CDF) matching is systematically derived including the distance metric definition and numerical computation process. Uncertainty analysis within the CDF matching optimization loop is also investigated, including the identification of active subspaces and the propagation of constructed surrogate model in reduced dimensions. Extended active subspaces are furthered discussed for mixed uncertainty analysis to obtain the bounds of CDF efficiently, instead of the time-consuming full-dimensional simulation. The proposed approach is illustrated by three typical optimization examples, i.e., response function matching, standard NASA test problem, and satellite system design in aerospace engineering. The UBDO results based on the CDF matching are discussed and compared with the probability density function matching, which exhibits higher efficiency and better convergence in tackling constrained optimization design problems subject to mixed uncertainties.

Assessment of remotely sensed soil moisture products and their quality improvement: A case study in South Korea

Soil moisture (SM) retrieved from satellite observations has become available at a global scale with relatively high spatial–temporal resolution, and the satellite-derived SM can be useful data sources where in-situ measurements are scarce or not available. In this study, the SM data from two different satellite sensors, the Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer 2 (AMSR2), are evaluated through the comparison with in-situ observation collected from twelve sites over a three-year period (2013–2015) in South Korea. The results reveal that the ASCAT descending overpass (09:30, the local equatorial crossing time) shows a better correlation with the in-situ observation than the ascending overpass (21:30, the local equatorial crossing time), while no significant difference in performance is found for AMSR2. Moreover, ASCAT SM retrieval shows a generally better agreement with in-situ observation. Considering the spatial mismatch and different measurement depths, a cumulative distribution function (CDF) matching method, as well as an exponential filter method, are employed to improve the applicability of satellite-derived SM. Specifically, the observation operators based on CDF matching are derived to find the optimal temporal period and tested by cross-validation. It is found that the CDF matching method split into two groups (i.e., growing and non-growing season) outperforms the other temporal groups. Additionally, considering different observation depths between the in-situ (>10 cm) and the satellite products (the top soil layer), the root-zone SM (RZSM) is derived from satellite surface SM by using the exponential filter method. For this study, a characteristic time length (T) at each observation depth is optimized by maximizing the r value between the SWI and the in-situ observation. Although the optimal T value generally increases with observation depth, it is clearly seen that T values are highly location-dependent. Given an encouraging improvement of the satellite SM estimation when scaling and filtering method applied, the results obtained in this study show that the satellite SM products have the useful potential for operational applications.

Evaluation and calibration of a high-resolution soil moisture product for wildfire prediction and management

Soil moisture deficit is a key variable used in operational fire prediction and management applications. In Australia, operational fire management practices use simple, empirical water balances models to estimate soil moisture deficit. The Bureau of Meteorology has recently developed a prototype, high-resolution, land surface modelling based, state-of-the-art soil moisture analyses for Australia. The present study examines this new product for use in operational fire prediction and management practices in Australia. The approach used is twofold. First, the new soil moisture product is evaluated against observations from ground based networks. Among the results, the mean Pearson’s correlation for surface soil moisture across the three in-situ networks is found to be between 0.78 and 0.85. Secondly, the study evaluate a few different calibration methods to facilitate the ready utilization of the new soil moisture product in the current operational fire prediction framework. The calibration approaches investigated here are: minimum-maximum matching, mean-variance matching and, cumulative distribution function matching. Validation of the calibrated products using extended triple collocation technique shows that the minimum-maximum method has the highest skill. Evaluation of the calibrated products against MODIS fire radiative power data highlights that large fires correspond to a drier soil in minimum-maximum outputs compared to other calibration results and the current operational method.

Impact of Bias-Correction Methods on Effectiveness of Assimilating SMAP Soil Moisture Data into NCEP Global Forecast System Using the Ensemble Kalman Filter

Improving numerical weather prediction was one of the main justifications for National Aeronautics and Space Administration's Soil Moisture Active/Passive (SMAP) Mission. The ensemble Kalman filter (EnKF) has been extensively applied to assimilate the SM observations into numerical weather predication models. Implementation of EnKF requires the observations and model simulations to be Gaussian distributed and not biased from each other. In this letter, we tested the impacts of three bias-correction methods on effectiveness of assimilating SMAP retrievals into the National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction Global Forecast System (GFS). They are: 1) global cumulative distribution function (CDF) matching with only one CDF for all grids and time series; 2) monthly CDF matching with one CDF for each grid; 3) the linear transformation technique that matches monthly mean and standard deviation of the SMAP retrievals and model simulations for each grid; and 4) assimilating SMAP SM data into GFS without any bias-correction procedure. With respect to the global land data assimilation (DA) system precipitation product, the results demonstrate that the effectiveness of assimilating SMAP retrievals into GFS is significantly impacted by the bias-correction methods. Relative to other DA cases, the monthly CDF matching produces the best precipitation forecast performance. Improvements of the three-hourly GFS precipitation prediction with SMAP assimilation using the monthly CDF matching can reach to 8% and 10% in sparsely and densely vegetated areas, respectively, and marginally positive in medium vegetation areas. Based on these results, assimilating SMAP retrievals into the GFS with the EnKF algorithm using the monthly CDF matching method is suggested for enhancing accuracy of the precipitation forecasts.

Deriving high-quality surface emissivity spectra from atmospheric infrared sounder data using cumulative distribution function matching and principal component analysis regression

The Atmospheric Infrared Sounder (AIRS) provides limited hyperspectral thermal infrared (TIR) emissivity data for the retrieval of critical land surface and climate parameters in environmental research. However, the AIRS land surface emissivity (LSE) data lack accuracy, resulting in low-quality data retrieval, particularly for the lower boundary layer. In this study, a practical and effective method is proposed to derive high-accuracy AIRS LSE data and continuous emissivity spectra in the TIR range of 8–14.5 μm. The AIRS LSE is first rescaled to the Moderate Resolution Imaging Spectroradiometer (MODIS) LSE using cumulative distribution function (CDF) matching, and then the emissivity spectra are recovered from the rescaled AIRS LSE using principal component analysis (PCA) regression. The results show that rescaling the AIRS LSE significantly reduced the bias and root mean square (RMS) error in the study area of Africa and the Arabian Peninsula, and PCA regression successfully recovered the emissivity spectra in the 8–14.5 μm range from the rescaled AIRS LSE. At two validation sites in the Namib and Kalahari deserts of southern Africa, the biases of the rescaled AIRS LSE at three hinge points are 0.62% and 0.61%, respectively, and the biases of the recovered AIRS LSE spectra in the 8–12 μm TIR range are 0.53% and 0.56%, respectively. Variations in land cover homogeneity and the accuracy of the MODIS LSE are the critical factors impacting the final accuracy of the rescaled AIRS LSE and the recovered emissivity spectra.

On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

Abstract Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve streamflow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the influence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity (namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03–0.15) and efficiency indices (10–45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve streamflow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required.

Improvement of AMSR2 Soil Moisture Products Over South Korea

Soil moisture (SM) is a critical parameter for interpreting the status of land surfaces and vegetation because it has an important role in the exchange of water and heat energy between the land surface and atmosphere. SM can be retrieved with satellite microwave sensors, which offer several advantages in that they are cost-effective, give rapid results, and provide data for inaccessible or isolated areas. In addition, these sensors cover large areas and perform periodic multi-channel observations. The advanced microwave scanning radiometer-2 (AMSR2), a successor of Advanced Microwave Scanning Radiometer for the Earth Observing System, provides regular global SM data products, which the Korea Meteorological Administration uses to calculate daily mean and seven-day moving window SM do not agree well with ground measurements in South Korea, and must be corrected for reliable drought monitoring. In this study, we used Global Land Data Assimilation System SM data to calibrate AMSR2 SM. In addition, we applied and combined two methods to correct AMSR2 SM: 1) linear regression with input variables, including insolation, daily precipitation, and the normalized difference vegetation index and 2) the cumulative distribution function (CDF) matching method. The results of the combined linear regression and CDF matching method were in better agreement with the in situ data than those of either method alone. Correcting AMSR2 SM by using the combined method improved the correlation coefficient (R) from 0.19 to 0.59 and the root-mean-square error from 0.23 to 0.09.

Combining the ensemble mean and bias correction approaches to reduce the uncertainty in hillslope-scale soil moisture simulation

The ROSETTA model has routinely been applied to predict the soil hydraulic properties for simulating the water flow at the hillslope scale. However, the uncertainties in water flow simulations are substantial due to the soil heterogeneity and ROSETTA model structure. In order to reduce these uncertainties, this study used the HYDRUS-2D and ensemble mean to simulate soil moisture based on the outputs of all candidate models. In addition, the bias correction techniques (including linear bias correction (LBC) and cumulative distribution function (CDF) matching) were also applied to improve the prediction of soil moisture. A total of 320days of observed soil moisture data at two depths (10 and 30cm) in the upper and lower slope positions were adopted to evaluate the performances of different bias correction methods results showed that the uncertainty in hillslope-scale soil moisture simulation due to the ROSETTA model structure was more important than that due to the soil heterogeneity. The CDF matching-based nonlinear bias correction approach was generally better than the LBC in reducing the uncertainty in soil moisture simulation. Combining the ensemble mean and CDF matching was a viable approach to improve the accuracy of the numerical model for simulating the hillslope-scale soil moisture variations.

The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products

In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of creating a homogenous soil moisture time series has been explored. The performances of 31 linear and nonlinear rescaling methods are evaluated by rescaling the Land Parameter Retrieval Model (LPRM) soil moisture datasets to station-based watershed average datasets obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds. The linear methods include first-order linear regression, multiple linear regression, and multivariate adaptive regression splines (MARS), whereas the nonlinear methods include cumulative distribution function matching (CDF), artificial neural networks (ANN), support vector machines (SVM), Genetic Programming (GEN), and copula methods. MARS, GEN, SVM, ANN, and the copula methods are also implemented to utilize lagged observations to rescale the datasets. The results of a total of 31 different methods show that the nonlinear methods improve the correlation and error statistics of the rescaled product compared to the linear methods. In general, the method that yielded the best results using training data improved the validation correlations, on average, by 0.063, whereas ELMAN ANN and GEN, using lagged observations methods, yielded correlation improvements of 0.052 and 0.048, respectively. The lagged observations improved the correlations when they were incorporated into rescaling equations in linear and nonlinear fashions, with the nonlinear methods (particularly SVM and GEN but not ANN and copula) benefitting from these lagged observations more than the linear methods. The overall results show that a large majority of the similarities between the LPRM and watershed average datasets are due to linear relations; however, nonlinear relations clearly exist, and the use of nonlinear rescaling methods clearly improves the accuracy of the rescaled product.

Scale and scaling in soils

Scale is recognized as a central concept in the description of the hierarchical organization of our world. Pressing environmental and societal problems require an understanding of how processes operate at different scales, and how they can be linked across scales. Soil science as many other disciplines obtain the bulk of their empirical information at fine scales, whereas results of environmental diagnostics, monitoring, and predictions are needed to make important policy decisions at much larger scales. It becomes imperative to relate the information that is available and produced at different scales. The objective of this work is to present an overview of concepts that are currently used to define and relate scales in soil studies. The paper is not intended to be a compendium, but rather should be viewed as material for discussion, reference, and critique. It discusses definitions and terminology, including general approaches of scale problems in environmental studies that are applicable to soils, including hierarchies, measurement metrics, similitude, non-geometric scale metrics, and notions of upscaling and downscaling. Concepts of general scaling methods and theories are dimensional analysis, power law scaling, space and time dependent scaling. A section on spatiotemporal patterns introduces scaling ideas that were used in soil studies such as empirical orthogonal functions, data assimilation, and cumulative distribution function matching. Reviewed scaling methods developed specifically to soil studies include geometric similitude of pore spaces, scaling with Richards equation, scale dependencies of water and solute flux model parameters, scaling based on temporal stability, overland flow and sediment transport as the scaling phenomenon, and the relevance of scaling to pedotransfer functions. An outlook for scaling research in soils is presented that shows the needs of additional research and the feasibility of using scaling to enrich and advance soil research to help face the grand challenges of modern times.