scholarly journals Performance Evaluation of the Multiple Quantile Regression Model for Estimating Spatial Soil Moisture after Filtering Soil Moisture Outliers

2020 ◽  
Vol 12 (10) ◽  
pp. 1678
Author(s):  
Chunggil Jung ◽  
Yonggwan Lee ◽  
Jiwan Lee ◽  
Seongjoon Kim
Keyword(s):  
Soil Moisture ◽  
Quantile Regression ◽  
Outlier Detection ◽  
Land Surface ◽  
Removal Rate ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Soil Types ◽  
Coefficient Of Determination ◽  
Random Errors

The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data following outlier detection. This study obtained an average data removal rate of 20.1% at 58 stations. For various reasons, such as instrumentation, environment, and random errors, the original observed SM data contained approximately 20% uncertain data. After outlier detection, this study performed a regression analysis by estimating land surface temperature quantiles. The soil characteristics were considered through reclassification into four soil types (clay, loam, silt, and sand), and the five-day antecedent precipitation was considered in order to estimate the regression coefficient of the MQR model. For all soil types, the coefficient of determination (R2) and root mean square error (RMSE) values ranged from 0.25 to 0.77 and 1.86% to 12.21%, respectively. The MQR results showed a much better performance than that of the multiple linear regression (MLR) results, which yielded R2 and RMSE values of 0.20 to 0.66 and 1.08% to 7.23%, respectively. As a further illustration of improvement, the box plots of the MQR SM were closer to those of the observed SM than those of the MLR SM. This result indicates that the cumulative distribution functions (CDF) of MQR SM matched the CDF of the observed SM. Thus, the MQR algorithm with outlier detection can overcome the limitations of the MLR algorithm by reducing both the bias and variance.

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

2020 ◽  
Vol 21 (9) ◽  
pp. 2041-2060
Author(s):  
Peter J. Shellito ◽  
Sujay V. Kumar ◽  
Joseph A. Santanello ◽  
Patricia Lawston-Parker ◽  
John D. Bolten ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Soil Column ◽  
Soil Layer ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
L Band ◽  
The Impact

AbstractThe utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.

scholarly journals Impact of Soil Moisture Assimilation on Land Surface Model Spinup and Coupled Land–Atmosphere Prediction

2016 ◽  
Vol 17 (2) ◽  
pp. 517-540 ◽  
Author(s):  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Patricia M. Lawston
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Satellite Monitoring ◽  
Surface Model ◽  
The Impact

Abstract Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASA’s Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land–atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS–WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spinup can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux–PBL–ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

Evaluation of Various Soil Moisture Sensors in Four Different Soil Types

2018 ◽  
Vol 34 (6) ◽  
pp. 963-971 ◽  
Author(s):  
Tonny José Araújo da Silva ◽  
Edna Maria Bonfim-Silva ◽  
Adriano Bicioni Pacheco ◽  
Thiago Franco Duarte ◽  
Helon Hébano de Freitas Sousa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Samples ◽  
Soil Types ◽  
Coefficient Of Determination ◽  
Regression Analyses ◽  
Drying Method ◽  
Soil Moisture Sensors ◽  
Calibration Equations

Abstract.Accurate measurements of soil moisture content can contribute to resource conservation in irrigated systems. The objective of this study was to evaluate various soil moisture sensors (a porous cup tensiometer, Diviner 2000, PR2, XH300, PM100, and ML3; the mention of model names does not constitute an implied endorsement) used in four different soil types. The experiment was conducted inside a greenhouse using a specially constructed box that contained the soil samples. The soil samples were first saturated and subsequently drained before starting the measurements. The soil moisture content was determined by the oven-drying method. Using the standard deviation of the sensor readings, regression analyses were performed, resulting in calibration equations and coefficient of determination (R2) values for each sensor and soil type combination. The porous cup tensiometer, Diviner 2000, PR2, and ML3 measurements resulted in excellent R2 values that exceeded 0.95 for the four soils. However, measurements with the XH300 and PM100 sensors resulted in R2 values of 0.37 to 0.86 and 0.61 to 0.94, respectively, limiting their scientific applicability for the studied soils. Therefore, the porous cup tensiometer, Diviner 2000, PR2, and ML3 estimated the soil moisture content with greater confidence than did the other sensors and with an error of less than 5%. Keywords: Calibration, Tensiometer, Volumetric water content.

scholarly journals Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

2014 ◽  
Vol 18 (2) ◽  
pp. 673-689 ◽  
Author(s):  
M. Parrens ◽  
J.-F. Mahfouf ◽  
A. L. Barbu ◽  
J.-C. Calvet
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Soil Column ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Deep Soil ◽  
Background Error ◽  
Soil Layers ◽  
Diffusion Scheme

Abstract. Land surface models (LSM) have improved considerably in the last two decades. In this study, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) LSM soil diffusion scheme is used (with 11 soil layers represented). A simplified extended Kalman filter (SEKF) allows ground observations of surface soil moisture (SSM) to be assimilated in the multilayer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX (Surface Monitoring Of the Soil Reservoir Experiment) site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 15 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multilayer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly cumulative distribution function (CDF)-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF (diffusion scheme) with a local forcing, setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.

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

2020 ◽  
Vol 12 (3) ◽  
pp. 509 ◽  
Author(s):  
Ruodan Zhuang ◽  
Yijian Zeng ◽  
Salvatore Manfreda ◽  
Zhongbo Su
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
Root Zone ◽  
Cumulative Distribution ◽  
Analytical Relationship ◽  
The Tibetan Plateau ◽  
Climate Systems

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.

scholarly journals Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2011 ◽  
Vol 15 (2) ◽  
pp. 425-436 ◽  
Author(s):  
Y. Y. Liu ◽  
R. M. Parinussa ◽  
W. A. Dorigo ◽  
R. A. M. De Jeu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Degree Of Saturation ◽  
Combination Method ◽  
Surface Model ◽  
Volumetric Soil Water Content ◽  
Data Set ◽  
Spatial Coverage

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

scholarly journals Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

2020 ◽  
Vol 24 (2) ◽  
pp. 615-631 ◽  
Author(s):  
Yixin Mao ◽  
Wade T. Crow ◽  
Bart Nijssen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Red River ◽  
Future Research ◽  
Surface Model ◽  
Random Errors ◽  
Rainfall Estimates

Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.

scholarly journals USE OF BENDING TESTS AND VISUAL INSPECTION FOR MULTI-SCALE EXPERIMENTAL EVALUATION OF CHESTNUT TIMBER BEAMS STIFFNESS

2015 ◽  
Vol 22 (6) ◽  
pp. 728-738 ◽  
Author(s):  
Hélder S. SOUSA ◽  
Jorge M. BRANCO ◽  
Paulo B. LOURENÇO
Keyword(s):  
Experimental Evaluation ◽  
Visual Inspection ◽  
Castanea Sativa ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Coefficient Of Determination ◽  
Visual Grading ◽  
Bending Tests ◽  
Multi Scale ◽  
Bending Modulus

The assessment of existing timber structures requires the determination of the mechanical properties of the individual timber members, which is often obtained by visual grading combined with information of small clear wood specimens. The purpose of this work is to present the results obtained in a multi-scale experimental evaluation of 20 old chestnut (Castanea sativa Mill.) beams, aimed at defining the correlations between bending modulus of elasticity (MOE) in different scales of timber members in combination with visual grading. The results of bending tests, according to EN 408:2010 (2010), were statistically analyzed to obtain correlations of bending MOE between and within different scales size. The results of visual inspection according to UNI 11119:2004 (2004) regarding the presence and distribution of defects were considered for variation analysis of MOE, evidencing a significant variance between samples of differ­ent visual strength grades. Strong correlations within the same phase (coefficient of determination r2 from 0.82 to 0.89) and moderate to high correlations for different phases (r2 from 0.68 to 0.71) were found for bending MOE. Cumulative distribution functions for global MOE remained similar throughout the experimental phases. Moreover, percentage of segments attributed to a specific visual grade is given for each member and compared between scales.

scholarly journals SMAP retrieval assimilation improves soil moisture estimation across irrigated areas in South Asia

2021 ◽  
Author(s):  
Jawairia A. Ahmad ◽  
Barton A. Forman ◽  
Sujay V. Kumar
Keyword(s):  
Soil Moisture ◽  
South Asia ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Spatiotemporal Variability ◽  
Soil Conditions ◽  
Surface Model ◽  
Near Surface ◽  
Moisture Estimation

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 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 implemented to map the statistical moments of the SMAP soil moisture retrievals to the land surface model climatology. Comparison of assimilated and model-only soil moisture estimates with publicly available in-situ measurements highlight 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 soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. SMAP retrieval assimilation corrected biases associated with unmodeled hydrologic phenomenon (e.g., anthropogenic influences due to irrigation). The highest influence of assimilation was observed across croplands. Improvements in soil moisture translated into improved spatiotemporal patterns of modeled evapotranspiration, yet limited influence of assimilation was observed on states included 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.

scholarly journals Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2010 ◽  
Vol 7 (5) ◽  
pp. 6699-6724 ◽  
Author(s):  
Y. Y. Liu ◽  
R. M. Parinussa ◽  
W. A. Dorigo ◽  
R. A. M. de Jeu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Surface Model ◽  
Carbon Cycles ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Global Soil

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

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