Estimation Of Soil Moisture Content Research Articles

Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index

The global agronomy community needs quick and frequent information on soil moisture variability and spatial trends in order to maximize crop production to meet growing food demands in a changing climate. However, in situ soil moisture measurement is expensive and labor intensive. Remote sensing based biophysical and predictive regression modeling approach have the potential for efficiently estimating soil moisture content over large areas. The study investigates the use of Moisture Stress Index (MSI) to estimate soil moisture variability in Alabama. In situ data were obtained from Soil Climate Analysis Network (SCAN) sites in Alabama and MSI developed from LANDSAT 8 OLI and LANDSAT 5 TM data. Pearson product moment correlation analysis showed that MSI strongly correlates with 16-day average growing season soil moisture measurements, with negative correlations of -0.519, -0.482 and -0.895 at 5, 10, and 20 cm soil depths respectively. The correlations of MSI and growing season moisture were low at sites where soil moisture was extremely low (<-0.3 at all depths). Simple linear regression model constructed for soil moisture at 20 cm depth (R²=0.79, p<0.05) correlated well with MSI values and was successfully used to estimate soil moisture percentage within a standard error of ± 3. Resulting MSI products were used to successfully produce the spatial distribution of soil moisture percentage at 20 cm depth. The study concludes that MSI is a good indicator of soil moisture conditions, and could be efficiently utilized in areas where in situ soil moisture data are unavailable.

Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity

Summary Neutron intensity measured by the aboveground cosmic-ray neutron intensity probe (CRP) allows estimating soil moisture content at the field scale. In this work, synthetic neutron intensities were used to remove the bias of simulated soil moisture content or update soil hydraulic properties (together with soil moisture) in the Community Land Model (CLM) using the Local Ensemble Transform Kalman Filter. The cosmic-ray forward model COSMIC was used as the non-linear measurement operator which maps between neutron intensity and soil moisture. The novel aspect of this work is that synthetically measured neutron intensity was used for real time updating of soil states and soil properties (or soil moisture bias) and posterior use for the real time scheduling of irrigation (data assimilation based real-time control approach). Uncertainty of model forcing and soil properties (sand fraction, clay fraction and organic matter density) were considered in the ensemble predictions of the soil moisture profiles. Horizontal and vertical weighting of soil moisture was introduced in the data assimilation in order to handle the scale mismatch between the cosmic-ray footprint and the CLM grid cell. The approach was illustrated in a synthetic study with the real-time irrigation scheduling of fields of citrus trees. After adjusting soil moisture content by assimilating neutron intensity, the irrigation requirements were calculated based on the water deficit method. Model bias was introduced by using coarser soil texture in the data assimilation experiments than in reality. A series of experiments was done with different combinations of state, parameter and bias estimation in combination with irrigation scheduling. Assimilation of CRP neutron intensity improved soil moisture characterization. Irrigation requirement was overestimated if biased soil properties were used. The soil moisture bias was reduced by 35% after data assimilation. The scenario of joint state-parameter estimation resulted in the best soil moisture characterization (50% decrease in root mean square error compared to open loop simulations), and the best estimate of needed irrigation amount (86% decrease in Hausdorff distance compared to open loop). The coarse scale synthetic CRP observation was proven to be useful for the fine scale soil moisture and soil properties estimation for the objective of irrigation scheduling.

Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels

Soil moisture content (SMC) plays an important role in different environmental. In this study, four different soil moisture indices, namely, SOMID, SOMID-FS, SOMID-FT, and CSOMID-FT, were introduced. In this work, the following parameters were used to estimate SMC at a depth of 5 cm: (a) the distance of pixels from the origin in the scatter-plot of near-infrared (NIR) and red bands (SNIR-R), (b) the fraction of soil cover in each pixel, and (c) the land surface temperature. It was concluded that the CSOMID-FT was the most accurate index for estimation of SMC (RMSE=0.045, R=0.92). This index divides the SNIR-R into three separate regions based on the pixels’ normalized difference vegetation index (NDVI) values and assigns a specific regression equation to each region. The results showed that as the NDVI values increase, the accuracy of the proposed indices decreases. Furthermore, the SOMID-FT and CSOMID-FT were used to estimate SMC at five different depths of 5, 10, 20, 50, and 100 cm. It was concluded that the satellite-estimated SMC was highly correlated with the field-measured data at 5-cm soil depth.

Soil Moisture Retrieval over a Semiarid Area by Means of PCA Dimensionality Reduction

The main objective of this study is to develop a multifeature soil moisture retrieval method based on the principal component analysis (PCA) dimensionality reduction technique. RADARSAT-2 data were used to compute the backscattering coefficients and polarimetric variables. The optimal input features for soil moisture retrieval were selected by means of PCA dimensionality reduction and least root mean square error (RMSE) criterion. The support vector regression (SVR) model was used to estimate soil moisture content. The results indicated that the optimal features extracted by the PCA dimensionality reduction showed high correlation with soil moisture content. The RMSE, R2 (determination coefficient) and mean relative error (MRE) were (1.4 vol.%, 0.73, 18.2%) and (1.6 vol.%, 0.66, 15.6%) over the low grass cover areas A and B, respectively. For the bare soil areas A and B, the statistic results were (1.3 vol.%, 0.76, 12.1%) and (1.6 vol.%, 0.72, 14.9%), respectively. This case study confirmed the potential of the developed approach to estimate soil moisture over the low grass cover and bare soil areas.

Relationship of Soil Moisture and Reflected GPS Signal Strength

Many agricultural fields across the country have a high degree of variability in soil type and water holding capacity that affects irrigation management. One way to overcome problems associated with the field variability for improving irrigation management is to utilize a site-specific irrigation system. This system applies water to match the needs of individual management zones within a field. A real-time continuous soil moisture measurement is essential for the success of site-specific irrigation systems. Recently the National Aeronautics and Space Administration (NASA) developed sensor technology that records the global positioning system (GPS) signal reflected from the surface of Earth, which estimates the dielectric properties of soil and can be used to estimate soil moisture contents. The overall objective of this study was to determine the feasibility of utilizing GPS-based technology developed by NASA for soil moisture measurements and to determine the influence of soil type, soil compaction, and ground cover on the measurements. The results showed strong positive correlations between soil moisture and reflected signals. Other factors (soil compaction and soil type), were not significantly related to reflectivity and did not significantly change the relationship between reflectivity and soil moisture contents. In addition, ground cover (rye crop) did not significantly reduce reflectivity. Therefore, this system could be used as a real-time and continuous nonintrusive soil moisture sensor for site-specific irrigation scheduling and watershed management.

The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.

Field Calibration of FDR Sensors-Limitations and Potentialities

Monitoring soil moisture content is an appropriate method to optimize agricultural irrigation. The use of capacitance sensors has increased over the last years because they have a convenient cost-benefit ratio when compared to neutron moisture gages or time domain reflectometers, which are the most accurate methods to estimate soil moisture content. This project is about the field calibration of a capacitance sensor in clay soils. Two calibration models were applied: a linear model and a quadratic one, which correlate the gravimetric content measured in field soil moisture samples with the excitation received by the sensor (mV) and with the measured dielectric permittivity (?b). The calibration equations were subsequently validated at two different depths at four field sampling sites. Despite the clay soil texture, high organic matter content and compaction of the soils, the equations correctly estimated soil moisture content with an estimation error of ± 0.02 m3m-3, a considerable improvement over the manufacturer's calibration equation, which has an average estimation error of ±0.12 m3m-3. A better understanding of sensor performance in the field should result in more precise water applications. A recommendation is made to perform tests in saline and different textured soils to ascertain the stability and sensitivity of these calibration equations.

Estimation of the moisture content of tropical soils using colour images and artificial neural networks

Information on soil moisture is important for various agricultural, environmental, and hydrological applications; thus, moisture must be determined with the greatest possible accuracy. In this study, based on the fact that soil changes colour as the moisture content changes, artificial neural networks (ANNs) were applied to estimate the moisture content of tropical soils from colour photographs taken with a digital camera. Three different soils were used to train and test the network, and data were collected from disturbed samples subjected to different water contents in the laboratory. MLP (multilayer perceptron) ANNs with one hidden neuron layer and three input variables (red, green, and blue) related to colour were used. To train the networks, various tests were performed by varying the number of hidden layer neurons and using input data of the three soils. The best performing ANN had a hidden layer with twelve neurons and used the tan-sigmoid transfer function. It was found that a single network could estimate the moisture content of all the soils studied from the photographs. The best ANN, which was trained with data of the three soils simultaneously, was also tested with individual soil data separately, and better results were obtained (RMSE ranging from 0.0321 to 0.0650g/g and r2 ranging from 0.6675 to 0.8231). Although the results are satisfactory, the simplicity of the experiment likely restricted a stronger characterisation of the pattern of soil colour variation due to the change in its moisture content, which thus reduced the performance of the method. However, the proposed method represents an advancement in the indirect estimation of soil moisture content because it has the advantages of being practical, rapid, and non-destructive; requiring relatively low cost; and automating the process in the field.

Validating the Modified Perpendicular Drought Index in the North China Region Using &lt;italic&gt;In Situ&lt;/italic&gt; Soil Moisture Measurement

Soil moisture content is one of the most important variables for monitoring and assessing the drought condition. In this letter, a modified perpendicular drought index (MPDI) derived from the Moderate Resolution Imaging Spectroradiometer satellite data was validated using in situ soil moisture measurements in Henan province of North China. The soil moisture at different depths of a soil layer and time lag on usefulness of the MPDI for estimating soil moisture content was analyzed; the effectiveness of the MPDI was evaluated with the observed soil moisture content and the comprehensive drought index K for different soil types. The results showed that the MPDI was significantly correlated with soil moisture content for the top soil layer with 10 cm depth. For a time lag ranging from 0 to 4 days, the correlation of MPDI to soil moisture was significant with no time lag in the depth of top 10 cm (r = -0.867). For the stations of the same soil texture type of loam, the correlation coefficient between MPDI and soil moisture is higher than that of all soil texture types. In a regional scale, the MPDI reflected an obvious spatial pattern of drought under different growing stages in the drought years over the study area.

Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains

The growth and yield of crops in the arid and semi-arid regions of the world is driven by the amount of soil moisture available to the crop through rainfall and irrigation. Various methods have been developed for quantifying the soil moisture status of agricultural crops. Recent technological advances in remote sensing have shown that soil moisture can be measured with a variety of remote sensing techniques, each with its own strengths and weaknesses. In this study, building on of the strengths of multispectral satellite imagery, a new approach is suggested for estimating soil moisture content. A soil moisture index, the Perpendicular Soil Moisture Index (PSMI), is proposed; it is evaluated using raw image digital count (DC) data in the red, near-infrared, and thermal infrared spectral bands. To test this approach, soil moisture was measured in 18 agricultural fields in the semi-arid Texas High Plains over two years and compared to corresponding PSMI values determined from Landsat image data. These results showed that PSMI was strongly correlated (R2 = 0.79) with observed soil moisture. It was further demonstrated that maps of PSMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region. While further study is needed to determine the exact relationship between PSMI and soil moisture in larger areas with different climates, this study suggests that PSMI is a good indicator of soil moisture and has potential for operationally monitoring soil moisture conditions at the field to regional scales.

Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.

Improvement of the trapezoid method using raw landsat image digital count data for soil moisture estimation in the Texas (USA) high plains.

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are ongoing. This study aims at developing a new index, the Thermal Ground cover Moisture Index (TGMI), for estimating soil moisture content. This index is based on empirical parameterization of the relationship between raw image digital count (DC) data in the thermal infrared spectral band and ground cover (determined from raw image digital count data in the red and near-infrared spectral bands).The index uses satellite-derived information only, and the potential for its operational application is therefore great. This study was conducted in 18 commercial agricultural fields near Lubbock, TX (USA). Soil moisture was measured in these fields over two years and statistically compared to corresponding values of TGMI determined from Landsat image data. Results indicate statistically significant correlations between TGMI and field measurements of soil moisture (R2 = 0.73, RMSE = 0.05, MBE = 0.17 and AAE = 0.049), suggesting that soil moisture can be estimated using this index. It was further demonstrated that maps of TGMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region.

Estimation of Soil Moisture Content in Corn Field Using Microwave Scatterometer Data

A ground-based microwave scatterometer has an advantage for monitoring soil moisture content using multi-polarization, multi-frequencies and various incidence angles. In this paper, ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was used to monitor the soil moisture conditions in a corn field over an entire growth cycle. Measurements of volumetric soil moisture were obtained and their relationships to the backscatter observations were examined. Time series of soil moisture content was not corresponding with backscattering coefficient pattern over the whole growth stage, although it increased until early July (Day Of Year, DOY 160). We examined the relationship between the backscattering coefficients from each band and soil moisture content of the field. Backscattering coefficients for all bands were not correlated with soil moisture content when considered over the entire stage (r≤0.48). However, L-band Horizontal transmit and Horizontal receive polarization (HH) had a good correlation with soil moisture (r=0.85**) when LAI was lower than 2. Prediction equations for soil moisture were developed using the L-HH data. Relation between L-HH and soil moisture shows linear pattern and related with soil moisture content (R²=0.77). Results from this study show that backscattering coefficients of microwave scatterometer appear to be effective to estimate soil moisture content in the field level.

Estimation of Soil Moisture and Surface Roughness From Single-Polarized Radar Data for Bare Soil Surface and Comparison With Dual- and Quad-Polarization Cases

A new inversion algorithm was developed to determine the surface roughness and soil moisture content of a bare soil surface from a temporal data set of single-polarized radar measurements. This inversion algorithm is simple as it is based on a well-known empirical scattering model, thereby avoiding time-consuming data training. For a temporal data set of single-polarized (VV-, HH-, or VH-polarization) radar measurements, possible combinations of surface root-mean-square (rms) height (hrms) and soil moisture contents (Mv) for each measurement were first computed using the scattering model, and all results were then juxtaposed in an hrms - Mv plane. Both the surface rms height and the soil moisture content were retrieved using their possibility distributions on the juxtaposed curves. The estimated soil moisture contents and surface rms heights using the juxtaposition/possibility method for single-polarized “time-series” data sets were compared with in situ field measurements and also the retrieval outputs of the inversion algorithms for dual- and quad-polarized “snapshot” data sets. The temporal VV-polarization data sets provided the greatest accuracy among single-polarization cases, with a correlation coefficient of 0.911 and an rmse of 0.034 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> .

流域貯留量推定のためのモデルパラメータの一般化に関する研究

The evaluation of soil moisture content is important in forecasting flood and landslide disasters. The authors previously proposed a method for estimating soil moisture content on the basin scale based on a tank model; however, determining model parameters from the observed values has been a problem to be solved in generalizing the proposed method. In this study, determination of parameters from geological information was attempted. First, the rainfall, snowmelt and evapotranspiration of the Ishikari River basin were estimated. Next, a relational expression between the parameters of the basins of tributaries and the geologic surface area ratio was obtained, and the discharge of the Ishikari River basin was simulated based on the relational expression. As a result of the simulation, the method for determining parameters based on the geologic surface area ratio was thought to be useful in generalizing the evaluation method for basin-scale soil moisture content.

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally &lt;0.04 m3/m3 and very low bias (i.e., &lt;0.01 m3/m3), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m3/m3 (≤0.058 m3/m3). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m3/m3. In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m3/m3 to 0.051 m3/m3 for comparison with ground measurements and SAR estimates, respectively.

Assimilation of microwave brightness temperature in a land data assimilation system with multi‐observation operators

AbstractA radiative transfer model (RTM) that provides a link between model states and satellite observations (e.g., brightness temperature) can act as an observation operator in land data assimilation to directly assimilate brightness temperatures. In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (The radiative transfer model for bare field (QH), land emissivity model (LandEM), and Community Microwave Emission Model (CMEM)) as its multi‐observation operators (LDAS‐MO). Assimilation experiments using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) satellite brightness temperature data from July 2005 to December 2008 were then conducted to investigate the impact of the RTMs on the assimilated results over China. It was found that the assimilated volumetric soil‐water content using each of the three observation operators improved the estimation of soil moisture content in the top soil layer (0–10 cm), with reduced root mean square errors (RMSEs), and increased correlation coefficients with field observations (OBS) as compared to a control run with no assimilation for the absence of frozen or snow‐covered conditions. The assimilated soil moisture for the QH model, which was more sensitive to dry soil than the other models, produced closer correlations with OBS in arid and semi‐arid regions while smaller RMSEs were observed for LandEM. CMEM agreed most closely with OBS over the middle and lower reaches of the Yangtze River due to its better simulation of the brightness temperature over densely vegetated areas. To improve assimilation accuracy, a Bayesian model averaging (BMA) scheme for the LDAS‐MO was developed. The BMA scheme was found to significantly enhance assimilation capability producing the soil moisture analysis, showing the lowest RMSEs and highest correlations with OBS over all areas. It was demonstrated that the BMA scheme with LDAS‐MO has the potential to estimate soil moisture with high accuracy.

Preliminary calibration of GPS signals and its effects on soil moisture estimation

In recent years, Global Navigation Satellite Systems Reflectometry (GNSS-R) is developed to estimate soil moisture content (SMC) as a new remote sensing tool. Signal error of Global Positioning System (GPS) bistatic radar is an important factor that affects the accuracy of SMC estimation. In this paper, two methods of GPS signal calibration involving both the direct and reflected signals are introduced, and a detailed explanation of the theoretical basis for such methods is given. An improved SMC estimation model utilizing calibrated GPS L-band signals is proposed, and the estimation accuracy is validated using the airborne GPS data from the Soil Moisture Experiment in 2002 (SMEX02). We choose 21 sites with soybean and corn in the Walnut Creek region of the US for validation. The sites are divided into three categories according to their vegetation cover: bare soil, mid-vegetation cover (Mid-Veg), and high-vegetation cover (High-Veg). The accuracy of SMC estimation is 11.17% for bare soil and 8.12% for Mid-Veg sites, much better than that of the traditional model. For High-Veg sites, the effect of signal attenuation due to vegetation cover is preliminarily taken into consideration and a linear model related to Normalized Difference Vegetation Indices (NDVI) is adopted to obtain a factor for rectifying the "over-calibration", and the error for High-Veg sites is finally reduced to 3.81%.

Research on Improved Thermal Inertia Model for Retrieving Soil Moisture

Soil moisture is one of the most important land environmental variables, relative to land surface climatology, hydrology, and ecology. A method to estimate soil moisture content from optical and thermal spectral in-formation of ASTER imagery based on thermal inertia is presented in this paper. Compared to models published previously, four improvements have been made: (1) as a key component of soil surface energy balance, the series two-layer is applied to solving soil latent and sensible heat flux in the better-covered vegetation area. And the Shuttleworth and Wallace (S-W) ET model is used to simulate soil latent flux; (2) because component temperature inversion is still an ill-posed problem, genetic inverse algorithm (GIA) is used to realize retrieval of component temperature; (3) in order to extend the scope of the thermal inertia model, B in the equation is derived from mechanism; (4) to eliminate partly atmospheric and the surface structure influence, the improved thermal inertia was normalized to fulfill the inversion of soil moisture. Taking YingKe green land in china for example, field experiment were carried out to validate the developed model. The method successfully estimated better-covered vegetation region surface soil moisture with an average error of 0.067. This model provides a new way of thinking about remote sensing thermal inertia methods to acquire regional-scale soil moisture.

SVAT modelling in support to flood risk assessment in Bulgaria

This study explores the benefit that can be drawn from incorporating the diagnosis of initial soil moisture of the top vegetation/soil layer and its anomalies as parameters in support of operational weather forecasting. For that purpose, a 1D vertical numerical land surface scheme, referred to as Soil Vegetation Transfer Model (‘SVAT_bg’) has been developed to simulate the soil–vegetation–atmosphere mass and energy transfer, accounting for local soil/climate features. The model is run daily for estimating soil moisture content and on this basis, a biogeophysical index designating Soil Moisture Availability Index (SMAI) to vegetation land cover is derived. SMAI is introduced as a measure of the proportion between the energy and water balances and their anomalies at different weather/climate conditions through a 6-level threshold scheme of land surface moistening. To facilitate the use of SMAI as a diagnostic tool for operational forecasting purposes, it is generated on a daily basis and visualised by colour-coded maps, covering the main administrative regions of Bulgaria in combination with a numerical part, which indicates the required flood-producing rainfall quantities (specific for each region). In case of overmoistening conditions, the numerical part denotes the rainfall excess above the soil saturation moisture content.The utility of this approach is illustrated in two case studies of severe weather produced by deep convection and a rapid cyclogenesis developed at initial ‘dry’/‘wet’ soil moisture anomalies, respectively. The thermodynamic conditions and space–time structure of the rainfall are analysed by NWP output fields and satellite information.The study contributes to a better definition of the role of vegetation–soil moistening in flood risk forecasting within strong synoptic scale forcing regimes. The utility of the results comes also from the recognition of soil moisture as a meteorological forcing factor, which may affect both severity and frequency of extreme weather events.