Obstacles to long‐term soil moisture monitoring with heated distributed temperature sensing

The field deployment of a heated distributed temperature sensor (DTS) for over three years has revealed two obstacles to estimating soil moisture (θ) that may hamper subsurface DTS applications as well as use of other subsurface thermal probes. The first observed obstacle was a hysteretic response of the DTS sensor. The relationship between θ and the temperature response (?T) within the cable was not only dependent on θ of the soil, but also on the previous wetting and drying cycles leading to that state. The second observed obstacle was soil structure healing. Soil structure healing causes the relationship between ?T and θ to evolve through time; this calibration curve becomes flatter, or less sensitive, as the surrounding soil makes better contact with the cable. Effects of the hysteretic response of the instrument and soil structure healing are largely the result of small gaps between the cable and soil. These small gaps can be approximated by a contact resistance between the cable and soil. In this article we characterize the occurrence of hysteretic and soil structure healing effects from field data and parameterize contact resistance by simulating heat transfer using a numerical modelling approach Copyright © 2015 John Wiley & Sons, Ltd.     

Fiber Optic Measurements of Soil Moisture in a Waste Rock Pile

The design and construction of a waste rock pile influences water infiltration and may promote the production of contaminated mine drainage. The objective of this project is to evaluate the use of an active fiber optic distributed temperature sensing (aFO-DTS) protocol to measure infiltration and soil moisture within a flow control layer capping an experimental waste rock pile. Five hundred meters of fiber optic cable were installed in a waste rock pile that is 70 m long, 10 m wide, and was covered with 0.60 m of fine compacted sand and 0.25 m of non-reactive crushed waste rock. Volumetric water content was assessed by heating the fiber optic cable with 15-min heat pulses at 15 W/m every 30 min. To test the aFO-DTS system 14 mm of recharge was applied to the top surface of the waste rock pile over 4 h, simulating a major rain event. The average volumetric water content in the FCL increased from 0.10 to 0.24 over the duration of the test. The volumetric water content measured with aFO-DTS in the FCL and waste rock was within ±0.06 and ±0.03, respectively, compared with values measured using 96 dielectric soil moisture probes over the same time period. Additional results illustrate how water can be confined within the FCL and monitored through an aFO-DTS protocol serving as a practical means to measure soil moisture at an industrial capacity.     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating <Emphasis Type="Italic">in situ</Emphasis> observations and SSM/I data

The active layer of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables. When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96°C on average relative to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m³·m⁻³. When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter decreased by 0.76°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m³·m⁻³.     

Evaluation of seasonal patterns of hydraulic redistribution in a humid subtropical area,East China

Antecedent soil moisture or soil moisture status has a great impact on hydrological processes. Hydraulic redistribution (HR), a widely observed phenomenon, is defined as water distributed (typically at night) from moist soil to drier soil via plant roots, which plays an important role in soil moisture replenishment. Knowledge on seasonal patterns of HR and on the relationship between HR and soil water use is not fully understood. We investigated temporal variations in HR and total daily water use (Δθ) at stands of camphor and peach by monitoring soil moisture content in a humid region in eastern China. HR at the three locations reached its maximum values in summer (0.68 mm d⁻¹ to 1.15 mm d⁻¹) at depths of 15 cm and 35 cm. Redistributed water replenished 41% of water depleted in the soil at a 5–45-cm depth. Interestingly, normalized HR (i.e., HR/Δθ) showed a constant pattern during the growing season implying it is independent of seasonal climate alterations. This also indicated that HR had a stable effect on the replenishment of daily water use. Positive linear relationships between HR and Δθ were found at three measuring locations (camphor: R² = .35, p < .01; peach1: R² = .57, p < .01; peach2: R² = .63, p < .01), suggesting a relatively stable inherent linkage between HR and Δθ. This study suggested that hydrological processes involving soil moisture status or antecedent soil moisture, needs to take the HR effect into account across timescales from intraday to seasonal.     

Improvement of multi-layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Soil moisture prediction is of great importance in crop yield forecasting and drought monitoring. In this study, the multi-layer root zone soil moisture (0-5, 0-10, 10-40 and 40-100 cm) prediction is conducted over an agriculture dominant basin, namely the Xiang River Basin, in southern China. The support vector machines (SVM) coupled with dual ensemble Kalman filter (EnKF) technique (SVM-EnKF) is compared with SVM for its potential capability to improve the efficiency of soil moisture prediction. Three remote sensing soil moisture products, namely SMAP, ASCAT and AMSR2, are evaluated for their performance in multi-layer soil moisture prediction with SVM and SVM-EnKF, respectively. Multiple cases are designed to investigate the performance of SVM, the effectiveness of coupling dual EnKF technique and the applicability of the remote sensing products in soil moisture prediction. The main results are as follows: (a) The efficiency of soil moisture prediction with SVM using meteorological variables as inputs is satisfactory for the surface layers (0-5 and 0-10 cm), while poor for the root zone layers (10-40 and 40-100 cm). Adding SMAP as input to SVM can improve its performance in soil moisture prediction, with more than 47% increase in the R-value and at least 11% reduction in RMSE for all layers. However, adding ASCAT or AMSR2 has no improvement for its performance. (b) Coupling dual EnKF can significantly improve the performance of SVM in the soil moisture prediction of both surface and the root zone layers. The increase in R-value is above 80%, while the reduction in BIAS and RMSE is respectively more than 90% and 63%. However, adding remote sensing soil moisture products as inputs can no further improve the performance of SVM-EnKF. (c) The SVM-EnKF can eliminate the influence of remote sensing soil moisture extreme values in soil moisture prediction, therefore, improve its accuracy.     

A particle batch smoother for soil moisture estimation using soil temperature observations

In this study, we present a particle batch smoother (PBS) to determine soil moisture profiles by assimilating soil temperatures at two depths (4 and 8 cm). The PBS can be considered as an extension of the standard particle filter (PF) in which soil moisture is updated within a window of fixed length using all observed soil temperatures in that window. This approach was developed with a view to assimilating temperature observations from distributed temperature sensing (DTS) observations, a technique which can provide temperature observations every meter or less along cables up to kilometers in length. Here, the PBS approach is tested using soil moisture and temperature, and meteorological data from an experimental site in Citra, Florida. Results demonstrate that the PBS provides a statistically significant improvement in estimated soil moisture compared to the PF, with only a marginal increase in computational expense ( < 3% of CPU time). This confirms that assimilating a sequence of temperature observations yields a better soil moisture estimate compared to sequential assimilation of individual temperature observations. The impact of observation interval was investigated for both PF and PBS, and the optimal window length was determined for the PBS. While increasing the observation interval is essential to maintain the spread of particle values in the PF, the PBS performance is best when all available observations are assimilated.     

Soil moisture variability of root zone profiles within SMEX02 remote sensing footprints

Remote sensing of soil moisture effectively provides soil moisture at a large scale, but does not explain highly heterogeneous soil moisture characteristics within remote sensing footprints. In this study, field scale spatio-temporal variability of root zone soil moisture was analyzed. During the Soil Moisture Experiment 2002 (SMEX02), daily soil moisture profiles (i.e., 0–6, 5–11, 15–21, and 25–31 cm) were measured in two fields in Walnut Creek watershed, Ames, Iowa, USA. Theta probe measurements of the volumetric soil moisture profile data were used to analyze statistical moments and time stability and to validate soil moisture predicted by a simple physical model simulation. For all depths, the coefficient of variation of soil moisture is well explained by the mean soil moisture using an exponential relationship. The simple model simulated very similar variability patterns as those observed.As soil depth increases, soil moisture distributions shift from skewed to normal patterns. At the surface depth, the soil moisture during dry down is log-normally distributed, while the soil moisture is normally distributed after rainfall. At all depths below the surface, the normal distribution captures the soil moisture variability for all conditions. Time stability analyses show that spatial patterns of sampling points are preserved for all depths and that time stability of surface measurements is a good indicator of subsurface time stability. The most time stable sampling sites estimate the field average root zone soil moisture value within ±2.1% volumetric soil moisture.     

A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Furthermore, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polarization) show that the root mean square error (RMSE) of soil moisture in the top layer (0–10 cm) by assimilation is 0.03355 m³ · m⁻³, which is reduced by 33.6% compared with that by simulation (0.05052 m³ · m⁻³). The mean RMSE by assimilation for the deeper layers (10–50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.     

TDR pressure cell for monitoring water content retention and bulk electrical conductivity curves in undisturbed soil samples

The water retention curve (θ(ψ)), which defines the relationship between soil volumetric water content (θ) and matric potential (ψ), is of paramount importance in characterizing the hydraulic behaviour of soils. However, few methods are so far available for estimating θ(ψ) in undisturbed soil samples. We present a new design of TDR‐pressure cell (TDR‐Cell) for estimating θ(ψ) in undisturbed soil samples. The TDR‐Cell consists of a 50‐mm‐long and 50‐mm internal diameter stainless steel cylinder (which constitutes the outer frame of a coaxial line) attached to a porous ceramic disc and closed at the ends with two aluminium lids. A 49‐mm‐long and 3‐mm‐diameter stainless steel rod, which runs longitudinally through the centre of the cylinder, constitutes the inner rod of a coaxial TDR probe. The TDR‐Cell was used to determine the θ(ψ) curves of a packed sand and seven undisturbed soil samples from three profiles of agricultural soils. These θ(ψ) curves were subsequently compared to those obtained from the corresponding 2‐mm sieved soils using the pressure plate method. Measurements of bulk electrical conductivity, σ_a, as a function of the water content, σ_a(θ), of the undisturbed soil samples were also performed. An excellent correlation (R² = 0·988) was found between the θ values measured by TDR on the different undisturbed soils and the corresponding θ obtained from the soil gravimetric water content. A typical bimodal θ(ψ) function was found for most of the undisturbed soil samples. Comparison between the θ(ψ) curves measured with the TDR‐Cell and those obtained from the 2‐mm sieved soils showed that the pressure plate method overestimates θ at low ψ values. The σ_a(θ) relationship was well described by a simple power expression (R² > 0·95), in which the power factor, defined as tortuosity, ranged between 1·18 and 3·75. Copyright © 2011 John Wiley & Sons, Ltd.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Applicability of cosmic-ray neutron sensor for measuring soil moisture at the agricultural-pastoral ecotone in northwest China

Accurate monitoring of soil moisture is crucial in hydrological and ecological studies. Cosmic-ray neutron sensors (CRNS) measure area-average soil moisture at field scale, filling a spatial scale gap between in-situ observations and remote sensing measurements. However, its applicability has not been assessed in the agricultural-pastoral ecotone, a data scarce semi-arid and arid region in Northwest China (APENC). In this study, we calibrated and assessed the CRNS (the standard N₀ method) estimates of soil moisture. Results show that Pearson correlation coefficient, R_P, and the root mean square error (RMSE) between the CRNS soil moisture and the gravimetric soil moisture are 0.904 and less than 0.016 m³ m⁻³, respectively, indicating that the CRNS is able to estimate the area-average soil moisture well at our study site. Compared with the in-situ sensor network measurements (ECH2O sensors), the CRNS is more sensitive to the changes in moisture in its footprint, which overestimates and underestimates the soil moisture under precipitation and dry conditions, respectively. The three shape parameters a₀, a₁, a₂ in the standard calibration equation (N₀ method) are not well suited to the study area. The calibrated parameters improved the accuracy of the CRNS soil moisture estimates. Due to the lack of low gravimetric soil moisture data, performance of the calibrated N₀ function is still poor in the extremely dry conditions. Moreover, aboveground biomass including vegetation biomass, canopy interception and widely developed biological soil crusts adds to the uncertainty of the CRNS soil moisture estimates. Such biomass impacts need to be taken into consideration to further improve the accuracy of soil moisture estimation by the CRNS in the data scarce areas such as agricultural-pastoral ecotone in Northwest China.

     

Field trial of seismic recording using distributed acoustic sensing with broadside sensitive fibre‐optic cables

Distributed acoustic sensing is an emerging technology using fibre‐optic cables to detect acoustic disturbances such as flow noise and seismic signals. The technology has been applied successfully in hydraulic fracture monitoring and vertical seismic profiling. One of the limitations of distributed acoustic sensing for seismic recording is that the conventional straight fibres do not have broadside sensitivity and therefore cannot be used in configurations where the raypaths are essentially orthogonal to the fibre‐optic cable, such as seismic reflection methods from the surface. The helically wound cable was designed to have broadside sensitivity. In this paper, a field trial is described to validate in a qualitative sense the theoretically predicted angle‐dependent response of a helically wound cable. P‐waves were measured with a helically wound cable as a function of the angle of incidence in a shallow horizontal borehole and compared with measurements with a co‐located streamer. The results show a similar behaviour as a function of the angle of incidence as the theory. This demonstrates the possibility of using distributed acoustic sensing with a helically wound cable as a seismic detection system with a horizontal cable near the surface. The helically wound cable does not have any active parts and can be made as a slim cable with a diameter of a few centimetres. For that reason, distributed acoustic sensing with a helically wound cable is a potential low‐cost option for permanent seismic monitoring on land.     

Estimation of soil hydraulic properties and their uncertainty: comparison between laboratory and field experiment

Often the soil hydraulic parameters are obtained by the inversion of measured data (e.g. soil moisture, pressure head, and cumulative infiltration, etc.). However, the inverse problem in unsaturated zone is ill‐posed due to various reasons, and hence the parameters become non‐unique. The presence of multiple soil layers brings the additional complexities in the inverse modelling. The generalized likelihood uncertainty estimate (GLUE) is a useful approach to estimate the parameters and their uncertainty when dealing with soil moisture dynamics which is a highly non‐linear problem. Because the estimated parameters depend on the modelling scale, inverse modelling carried out on laboratory data and field data may provide independent estimates. The objective of this paper is to compare the parameters and their uncertainty estimated through experiments in the laboratory and in the field and to assess which of the soil hydraulic parameters are independent of the experiment. The first two layers in the field site are characterized by Loamy sand and Loamy. The mean soil moisture and pressure head at three depths are measured with an interval of half hour for a period of 1 week using the evaporation method for the laboratory experiment, whereas soil moisture at three different depths (60, 110, and 200 cm) is measured with an interval of 1 h for 2 years for the field experiment. A one‐dimensional soil moisture model on the basis of the finite difference method was used. The calibration and validation are approximately for 1 year each. The model performance was found to be good with root mean square error (RMSE) varying from 2 to 4 cm³ cm^?3. It is found from the two experiments that mean and uncertainty in the saturated soil moisture (θ_s) and shape parameter (n) of van Genuchten equations are similar for both the soil types. Copyright © 2010 John Wiley & Sons, Ltd.     

Evapotranspiration models of different complexity for multiple land cover types

A comparison between half‐hourly and daily measured and computed evapotranspiration (ET) using three models of different complexity, namely, the Priestley–Taylor (P‐T), the reference Penman–Monteith (P‐M) and the Common Land Model (CLM), was conducted using three AmeriFlux sites under different land cover and climate conditions (i.e. arid grassland, temperate forest and subhumid cropland). Using the reference P‐M model with a semiempirical soil moisture function to adjust for water‐limiting conditions yielded ET estimates in reasonable agreement with the observations [root mean square error (RMSE) of 64–87 W m^?2 for half‐hourly and RMSE of 0.5–1.9 mm day^?1 for daily] and similar to the complex Common Land Model (RMSE of 60–94 W m^?2 for half‐hourly and RMSE of 0.4–2.1 mm day^?1 for daily) at the grassland and cropland sites. However, the semiempirical soil moisture function was not applicable particularly for the P‐T model at the forest site, suggesting that adjustments to key model variables may be required when applied to diverse land covers. On the other hand, under certain land cover/environmental conditions, the use of microwave‐derived soil moisture information was found to be a reliable metric of regional moisture conditions to adjust simple ET models for water‐limited cases. Further studies are needed to evaluate the utility of the simplified methods for different landscapes. Copyright © 2011 John Wiley & Sons, Ltd.     

Research on the method for retrieving soil moisture using thermal inertia model

1 Introduction Thermal inertia is a bulk property that shows the re- sistance of a material to an input or output of heat. This plays a very important role in certain geological and hydrological studies, and climate modeling. In the 1970s, a simple thermal inertia model was proposed by Watson et al.[1―3]. Pratt (1979)[4] improved the thermal inertia model based on application tests where more factors were considered such as solar ra- diance, thermal conductivity effect, average humidity of g…     

基于光学与被动微波遥感的青藏高原地区土壤水分反演

青藏高原地区高精度的土壤水分反演对高原能水循环、全球大气循环研究有着极大的影响.因此,获取青藏高原土壤水分时空布信息是一个迫切需要解决的问题.温度植被干旱指数(TVDI),是基于光学与热红外遥感通道数据反演土壤水分的重要方法,但在研究区域较大、地表覆盖格局差异显著时,TVDI模型反演精度会受到地表温度(Ts)等因素的影响.被动微波AMSR-E数据精确记录了像元内的土壤水分信息,但空间分辨率低.本文利用同时期的MODIS与被动微波数据,发展了针对青藏高原地区高精度土壤水分反演算法.首先,在TVDI模型中,利用修正型土壤调整植被指数(MSAVI)代替归一化植被指数(NDVI),以改进NDVI易饱和的缺点;其次,利用ASTER GDEM数据,对地形高程和纬度差异引起的地表温度变化进行了校正;然后,通过神经网络训练建立基于TVDI、被动微波以及辅助气象数据的土壤水分反演模型,并应用该模型反演了青藏高原地区三个观测网(CAMP/Tibet、玛曲和那曲)的土壤水分;最后,利用实测土壤水分数据对反演结果进行验证,结果表明该模型的精度均方根误差(RMSE)数值可达到0.031~0.041 m~3·m~(-3).本文还应用该算法反演了青藏高原连续的土壤水分的空间分布,并比较了土壤水分的变化趋势与实测降水变化趋势,结果表明二者变化量的正负关系一致.     

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m³/m³. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m³/m³ when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m³/m³ was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m³/m³ was possible.     

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region,USA

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km² Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R² value of 0.72 and average root mean square error (RMSE) of 0.042.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.