Shaded area indicates where irrigations should occur. At the start of the season the soil is moist from winter and spring rains— the readings are less than 20 centibars (A). Gradually the soil dries and the readings increase, beginning with the sensor located at the one-foot depth followed by the deeper depths. When the soilmoisture reading dropped to near 80 in early May (B), irrigation water was applied and the centibar readings at all three depths went to below 20, indicating the soil profile had been refilled. The drying cycle resumed until a partial irrigation occurred in late May (D). A partial irrigation was needed to replenish enough soilmoisture to carry the crop through the harvest period without excessive soilmoisture depletion and crop stress. The first cutting occurred in early June [note point on graph when soilmoisture content was lowest; (E)]. Following cutting, irrigation resumed until the soilmoisture content at all three depths was restored (all readings below 20 centibars at point F).
Inclusion of the spatial distribution of soilmoisture in large- scale meteorological models (Dumenil and Todini, 1992; Wood et al., 1992) has improved the prediction of soilmoisture evolution by modelling the rainfallrunoff process (Blyth, 2002). The most readily used method of representing the heterogeneity of soilmoisture in a landscape is to assume the soilmoisture follows the Xinanxiang statistical relationship (Zhao et al., 1980) to describe the natural heterogeneity of the landscape, allowing some areas to be saturated while other areas remain dry. This assumption is used to calculate surface runoff generated during a period of rainfall in land surface models such as PDM (Probability Distributed Model, Moore, 1986) VIC (Variable Infiltration Capacity, Wood et al., 1992) and Arno (Dumenil and Todini, 1992). The methods have been used successfully to model the overall water balance of an area (which is what they were designed for) (Lohmann et al., 1998).
The GISS ModelE land surface model has been calibrated against measured evaporation fluxes from several FLUXNET sites representing different biomes, which has yielded re- gionally reduced biases in temperature, cloud cover, and precipitation fields relative to previous versions (Friend and Kiang, 2005). A version of the GISS ModelE that includes water isotope tracers showed good agreement with measured riverine water isotope ratios, suggesting that evaporation and runoff are being represented acceptably on the scale of large river basins (Aleinov and Schmidt, 2006). Compared to ob- servational time series of 20th Century soilmoisture, the per- formance of GISS ModelE was comparable to that of other general circulation models that have contributed to the Fourth Assessment Report of the Intergovernmental Panel on Cli- mate Change: the mean seasonal cycle of soilmoisture was generally well represented, but the decadal-scale increase in soilmoisture seen at sites in Russia and the Ukraine was not captured, perhaps because the model forcing fields underes- timate the magnitude of solar dimming due to aerosol pol- lution, which has regionally reduced evaporation (Li et al., 2007).
Gravimetric method: Gravimetric is the term related to analysis based on quantitative measurement of mass or solid. In gravimetric method of moisture measurement, the moisture content is expressed by ratio as weight of water to the weight of dry soil. This is very basic method of soilmoisture calculations. Different soil samples are taken from any depth from any farm or garden. The collected samples are put into an air tight container. The samples are weighed and then heated in oven at temperature 100-110ºC, more specifically at 105ºC. The heated samples are left for 24hours, so that no more content of water is present. Then dried soil samples are weighed and at the end the moisture content is calculated using formula
Within the framework of the MAP and RAPHAEL projects, airborne experimental campaigns were carried out by the IFAC group in 1999 and 2000, using a multifrequency microwave radiometer at L, C and X bands (1.4, 6.8 and 10 GHz). The aim of the experiments was to collect soilmoisture and vegetation biomass information on agricultural areas to give reliable inputs to the hydrological models. It is well known that microwave emission from soil, mainly at L-band (1.4 GHz), is very well correlated to its moisture content. Two experimental areas in Italy were selected for this project: one was the Toce Valley, Domodossola, in 1999, and the other, the agricultural area of Cerbaia, close to Florence, where flights were performed in 2000. Measurements were carried out on bare soils, corn and wheat fields at different growth stages and on meadows. Ground data of soilmoisture (SMC) were collected by other research teams involved in the experiments. From the analysis of the data sets, it has been confirmed that L-band is well related to the SMC of a rather deep soil layer, whereas C-band is sensitive to the surface SMC and is more affected by the presence of surface roughness and vegetation, especially at high incidence angles. An algorithm for the retrieval of soilmoisture, based on the sensitivity to moisture of the brightness temperature at C-band, has been tested using the collected data set. The results of the algorithm, which is able to correct for the effect of vegetation by means of the polarisation index at X-band, have been compared with soilmoisture measurements in the ground. Finally, the sensitivity of emission at different frequencies to the soilmoisture profile was investigated. Experimental data sets were interpreted by using the Integral Equation Model (IEM) and the outputs of the model were used to train an artificial neural network to reproduce the soilmoisture content at different depths.
The resulting root-soil water flow model was a coupled pair of partial differential equations that describe the macroscopic movement of water through a root-soil system. Rainfall, irrigation, and evaporation were treated as sources of potential soil surface flux, and transpiration is treated as a source of potential root-surface flux. Malik et al.,  developed model under crop condition utilizing either observed or generated root length densities incorporating factors which account for the decreased rate of water uptake by plant roots due to diminishing soilmoisture during the drying cycles and due to the decreasing root effectiveness during crop growth period. The soilmoisture contents simulated by the model using observed and generated root length densities were overestimated to the extent 6.0% and 9.6% on an overall basis, respectively in comparison to observed soilmoisture contents. These variations were due to assuming soil profile was homogeneous, neglecting hysteresis effect, assumption of multiplicative nature of soilmoisture dependent function and root effective function causing less water uptake by plant root. Gardner  developed a moving sink model to predict water uptake by roots. However, according to him, the moving sink does not explicitly explain the observed uptake patterns completely
Figure 1c illustrates the ensemble preparation. For the OSSE, a period is chosen which does not include extreme events (such as flooding or drought or strong precipitation). A pe- riod in spring was chosen, when not only soil texture but also vegetation and weather control the soilmoisture. Fur- thermore, in spring and summer soilmoisture impacts the development of convection in the atmosphere. This study starts on 5 May 1997 (day 125). The initial soil mois- ture of the CONTROL simulation is perturbed applying the 2-D-pseudorandom sampling method and algorithm (http: //enkf.nersc.no) of Evensen (2004) to obtain 100 ensemble members of initial soilmoisture fields, which include no step-functions within the 2-D-area. (See Evensen, 2004, for more details on this approach.) The soilmoisture of each grid cell is chosen to vary between +10% and − 40% of the CONTROL soilmoisture. This is to account for the typical underestimation of precipitation in NWP simulations in this area and to account for the fact that the precipitation might have been simulated in the wrong location within the catch- ment. The 2-D-pseudorandom fields vary up to d =± 1 and examples are shown for 2 ensemble members in Fig. 5. Ac- cording to the random number of each grid cell (i,j,k) of each ensemble member e, the soilmoisture η in the grid cell is perturbed to
The diagnostic soilmoisture equation could also be improved in future modeling efforts by considering overland and sub- surface flows, specifically in areas characterized by more complex topography. Currently, the model assumes that, in the absence of saturation, all rainfall will ultimately infil- trate, as the porosity parameter serves as an upper bound on soilmoisture levels. The diagnostic soilmoisture equation was designed originally as a daily model, and it is proba- bly rare that on any given day a significant fraction of pre- cipitation does not infiltrate. However, at the hourly scale it is quite possible that the water from an intense rainfall event will not make its way into the soil at the location of the sensor. To address this lateral transfer phenomenon, ad- ditional parameters can be introduced into the diagnostic soilmoisture equation that place an upper bound on the quan- tity of rainfall that can be infiltrated during any hour (or other interval) of the convolution calculation for any partic- ular soil type. Agricultural decision support includes traffi- cability when wet (Coopersmith et al., 2014) and irrigation support when dry. While overland flow is perhaps an un- needed component in water-limited catchments where irri- gation schemes represent the most significant soil-moisture- related decision, in wetter catchments, in which trafficability is a real concern, such an addition could improve the model. While this approach would require the fitting of additional parameters, it is likely that predictions would be improved. These additional parameters could also be considered in as- sessing cross-site edaphic similarity using the methods de- scribed above, although they may be highly correlated with existing parameters such as porosity, residual soilmoisture, and drainage.
soil exists mainly in the form of particles adsorbed by the solid phase. In this condition, water molecules are not as easy to polarize as in the free water condition. Therefore, the TDR soilmoisture measurement should underesti- mate the real values, as compared to the ther- mogravimetric method of the soilmoisture de- termination. This effect should be more acute for fine soils. Also the error coming from the hardware, which is constant, is more visible for low water contents, according to Eq. (19). The relative error of the TDR moisture values incre- ases practically to infinity when soilmoisture tends to go close to the zero value. Comparison of statistics for the relative errors of the TDR moisture calculated from models I and model III is presented in Table 6. For the soil moistures above 0.1, the standard deviation of Dq rel is about four times smaller than for the whole range of q.
also varied across sites and proxies within a range between 8.6 % LFM and 34 % LFM. Mean MAE values were 22.3 % for NDVI, 22.9 % for CWC, and 25.6 % for NDWI. Soilmoisture had smaller MAE values than the re- mote sensing proxies at all five Gambel oak sites and two big sagebrush sites except Mud Springs and Muskrat. Gambel oak had smaller averaged MAE values for remote sensing vari- ables than big sagebrush. Some soilmoisture values diverged from the general trends, for example in the big sagebrush sites Vernon (Figure 4d) and Muskrat (Figure 4i). Accord- ing to the historical weather and soilmoisture data, many abnormally high soilmoisture val- ues were observed following precipitation events. Soilmoisture was higher in the short- term, while LFM changed more slowly with a peak that lagged peak soilmoisture (Figure 2).
Abstract. Soilmoisture affects the partitioning of water and energy and is recognized as an essential climate variable. Soilmoisture estimates derived from passive microwave remote sensing can improve model estimates through data assimi- lation, but the relative effectiveness of microwave retrievals in different frequencies is unclear. Land Parameter Retrieval Model (LPRM) satellite soilmoisture derived from L-, C-, and X-band frequency remote sensing were assimilated in the Australian Water Resources Assessment landscape hydrol- ogy model (AWRA-L) using an ensemble Kalman filter ap- proach. Two sets of experiments were performed. First, each retrieval was assimilated individually for comparison. Sec- ond, each possible combination of two retrievals was assim- ilated jointly. Results were evaluated against field-measured top-layer and root-zone soilmoisture at 24 sites across Aus- tralia. Assimilation generally improved the coefficient of cor- relation (r) between modeled and field-measured soil mois- ture. L- and X-band retrievals were more informative than C-band retrievals, improving r by an average of 0.11 and 0.08 compared to 0.04, respectively. Although L-band re- trievals were more informative for top-layer soilmoisture in most cases, there were exceptions, and L- and X-band were equally informative for root-zone soilmoisture. The consis- tency between L- and X-band retrievals suggests that they can substitute for each other, for example when transitioning between sensors and missions. Furthermore, joint assimila-
So far, a broad range of studies from controlled experiments to observational studies without soilmoisture information have been conducted to quantify the effect of soilmoisture on SAR intensity and InSAR phase and coherence (Molan et al., 2018a, 2018b; Lu and Meyer, 2002; De Zan et al., 2014; Gabriel et al., 1989; Nolan et al., 2003; Zhang et al., 2008; Barrett et al., 2012; Hajnsek and Prats, 2008; Nesti et al., 1995, 1998; Rudant et al., 1996; Hensley et al., 2011; Morrison et al., 2011). The models can be divided into interferometric and intensity models. On one hand, a number of interferometric models have provided mathematical volume scattering models ranging from simple analytical expression (e.g. Zwieback et al., 2015) to more complicated numerical solutions to Maxwell’s equations to estimate soilmoisture induced InSAR phase artifacts (e.g. De Zan et al., 2014). Basically, the models can potentially be used or modified for different soil types as well as layered and/or depth-resolved observations. Yet, what all the interferometric models share in common is that the temporal change in volume soilmoisture has been purported to be the primary influential factor in the models. Hence, these interferometric models don’t consider the influence of soil’s structure, i.e. the size and
Abstract. Land surface hydrology is an important control of surface weather and climate. A valuable technique to inves- tigate this link is the prescription of soilmoisture in land surface models, which leads to a decoupling of the atmo- sphere and land processes. Diverse approaches to prescribe soilmoisture, as well as different prescribed soilmoisture conditions have been used in previous studies. Here, we com- pare and assess four methodologies to prescribe soil mois- ture and investigate the impact of two different estimates of the climatological seasonal cycle used to prescribe soil mois- ture. Our analysis shows that, though in appearance similar, the different approaches require substantially different long- term moisture inputs and lead to different temperature sig- nals. The smallest influence on temperature and the water balance is found when prescribing the median seasonal cy- cle of deep soil liquid water, whereas the strongest signal is found when prescribing soil liquid and soil ice using the mean seasonal cycle. These results indicate that induced net water-balance perturbations in experiments investigating soilmoisture–climate coupling are important contributors to the climate response, in addition to the intended impact of the de- coupling. These results help to guide the set-up of future ex- periments prescribing soilmoisture, as for instance planned within the Land Surface, Snow and SoilMoisture Model In- tercomparison Project (LS3MIP).
Figure 9 compares the prior soil map used as the initial guess in the DA (i.e. from the Harmonised World Soil Data Base) with the posterior soil map retrieved by DA. The pos- terior soil map shown is the soil map retrieved when forcing JULES with TAMSAT v3.0 rainfall. It can be seen that af- ter DA, the percentage clay is greatly reduced with increased percentages in silt and sand for the majority of grid cells. This change is reasonable for some grid cells, particularly in northern Ghana where soils are often much more sandy/silty in texture (Braimoh and Vlek, 2004). Comparing estimates of soil texture derived from CCI soilmoisture to in situ ob- servations is inevitably problematic due to issues of represen- tativity in the spatial domain. However, independent sources of verification are difficult to find over Ghana. We therefore compare our soil maps to in situ observations from the Africa Soil Profiles Database (Leenaars et al., 2014). This database is compiled by the International Soil Reference and Informa- tion Centre (ISRIC), with the quality of the data being rated from 1 (highest quality) to 4 (lowest quality); here we only compare our maps to observations with a quality flag of 1 or 2. In table 1 we show the root-mean-squared error (RMSE) for our soil maps when compared to 21 in situ observations of soil texture in the north of Ghana and 36 in situ observations in the south (locations shown as red dots in Fig. 9). For the north of Ghana where we have most confidence in our results we find a reduction in RMSE for both sand and clay (almost halving the RMSE in clay). However, the RMSE for silt is increased. In the south of Ghana we do not manage to re- cover a better estimate of soil texture after data assimilation, with an increase in RMSE for silt and clay but a decrease in RMSE for sand. The inability of the data assimilation to improve soil texture estimates at certain points is most likely due to issues of spatial representativity between the modelled soil map and the in situ data. It is also possibly impacted by errors in our pedo-transfer functions, which may perform better if they were specifically calibrated for Ghanaian soils (Patil and Singh, 2016).
intensity of precipitation at every point of the area. If the above conditions are met, possible differentiation in mois- ture at a constant depth may be due only to differentiation in those physical properties of the soil that affect the soil mois- ture. Hence the grounds for the above hypothesis. To verify the hypothesis, a field experiment was carried out on a grass- land object situated in the locality of Silna-Wrony in the Wielkopolskie Province. On the experimental plot 200 points were selected, located at the depth of 10 cm below the ground surface, distributed in a regular pattern within two squares with surface areas of 16 m 2 and 100 m 2 . At each of the points simultaneous determinations were made of the soilmoisture, dry bulk density, and infiltration rate. A site plan of the experimental plots, with the distribution of the measurement points shown, is presented in Fig. 1. Soilmoisture measurements were taken using the D-LOG/ms field apparatus (Malicki, 1990). Figure 2 presents the geo- metry of TDR probe placement in the soil profile so that the centre point of the rods is located at the depth of 10 cm beneath the soil surface. The infiltration rate determined on the basis of the field experiment was adopted as an index of the conditions of infiltration rate. At each measurement point a test pit was made, with a diameter of Æ 5 cm and a depth of 10 cm (Fig. 3) (Kowalski, 1998). The pit walls were strengthe- ned with PVC tube, inserted down to the depth of 10 cm from the soil surface. The bottom of each pit was covered with a 2 cm layer of gravel for protection against washing away and/or accumulation of mud. At the start of the experiment each pit was filled with water up to the level of the soil surface. Subsequently, water was added so as to keep the height of the water column constant. The experiment was con- ducted until stabilized conditions were achieved. The volu- me of water used for the topping up, referenced to time, provi- ded the rate of infiltration through the bottom of the test pits.
The final analysis in this research segregates predictor variables (i.e., decision system) based solely on ASM (e.g., wet, dry, normal). This requires performing a simple statistical analysis to determine the average and standard deviation of ASM data for the season and record of interest. Wet years are defined as those whose soilmoisture is 1.25 standard deviations ( σ ) above average, and similarly, dry years are defined as those whose soilmoisture is 1.25σ below the overall average. The 1.25σ (above and below) the overall average corresponds approximately to the 90th and 10th quartiles. Remaining years are considered normal years. After grouping the data into the appropriate categories using ASM conditions (wet, normal, dry), principle components stepwise linear regression was performed individually on the three sets of data (wet, normal, dry). This analysis results in three separate regression equations that are used to forecast streamflow based on ASM conditions.
Soilmoisture and climatic conditions are the two most important factors, which decide the agriculture productivity and its production. As India consumes 80% of total available water resources for irrigation purpose, there is an urgent need to reduce water consumption using advanced scientific techniques . The water is the biggest resource for the development of life on earth. Now days, it is scarce. So, we need to use it with utter care. During irrigation water wastage should be avoided. The plants or crop should be irrigated only when they need to be. When plants transpire more amount of water, the relative humidity of atmosphere increases . The presence of large amount of relative humidity increases the chances of disease attack . So, the status of soilmoisture in the field requires periodic inspection, from where one can come to know, when the next irrigation should be done and how much amount of water should be applied.
Understanding the physical behavior of soil water content started with Briggs and McLane in 1897, which was later carried forward by Buckingham, Gardener and Richard. These works provided a conceptual partitioning of soil water content, gravitational water, slender water, and hygroscopic water. The gravitational water depletes away because of the gravitational force, and the capillary action. Be that as it may, the hygroscopic water cannot drain due to both of these forces. The gravitational water empties out of soil inside 2– 3 days after rainfall. Along these lines, the capillary water and hygroscopic water are the two main components of the water content of regular soils. In the course of the only remaining century, scientists have been proceeding to create different procedures to measure soil water content. Be that as it may, planning a strong, minimal effort, dependable, and constant estimating soil dampness sensor is as yet a difficult assignment. There are a few procedures for estimating soilmoisture, thermogravimetric, soil-resistivity, capacitance, time-domain reflectometry, frequency-domain, reflectometry, and neutron scattering.Wireless Sensor Network (WSN) is the innovation, in which the data gathered from the field of intrigue is transmitted through wireless connection. WSN can be used in different fields, for example, monitoring, wireless measurements, controlling, and so on.
Crop yield is highly related to the availability of soilmoisture (SM) and it needs to be quantified precisely. SM varies in dry and wet climatic conditions, vegeta- tion cycles, and with soil depths. The continuous esti- mation of SM at a point scale is challenging, because it changes more dynamically in shallow soils than in subsoils (Penna et al. 2013). Therefore, monitoring of SM in the vertical profile is necessary for understand- ing the moisture dynamics in soil-plant relationship. Efforts have been made for a long time to determine variables that control the root zone SM, and many automation methods have been described for pre- cise estimation of SM (Stacheder et al. 2009). The indirect methods determine SM using soil dielectric or thermal properties. These include: tensiometers, resistance blocks (Chow et al. 2009), time domain reflectometry (TDR), and frequency domain reflec-