Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements. Estimation of soil parameters by inverse modelling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise for heterogeneous layered soils. Although extensive soil data is becoming more and more available at various scales in the form of digital soil maps there is still a large gap between this available information and the input parameters needed for hydrological models. Inverse modeling has been extensively used but the spatial variability of the parameters and insufficient data sets restrict its applicability at the catchment scale. Use of remote sensed soil moisture data to estimate soil properties using the inverse modeling approach received attention in recent years but yielded only an estimate of the surface soil properties. However, in multilayered and heterogeneous soil systems the estimation of soil properties of different layers yielded poor results due to uncertainties in simulating root zone soil moisture from remote sensed surface soil moisture. Surface soil properties can be estimated by inverse approach using surface soil moisture data retrieved from remote sensing data. Since soil moisture retrieved from remote sensing is representative of the top 5 cm only, inversion of models using surface soil moisture cannot give good estimates of soil properties of deeper layers. Crop variables like biomass and leaf area index are sensitive to the deeper layer soil properties. The main focus of this study is to develop a methodology of estimation of root zone soil hydraulic properties in heterogeneous soils by crop model based inversion techniques. Further the usefulness of the radar soil moisture and leaf area index in retrieving soil hydraulic properties using the develop approach is be tested in different soil and crop combinations. A brief introduction about the soil hydraulic properties and their importance in agro-hydrological model is discussed in Chapter 1. Soil water retention parameters are explained in detail in this chapter. A detailed review of the literature is presented in chapter 2 to establish the state of art on the following: (i) estimation of soil hydraulic properties, (ii) role of crop models in estimating soil hydraulic properties, (iii) retrieval of surface soil moisture using water cloud model from SAR data, (iv) retrieval of leaf area index from SAR (synthetic aperture radar) data and (v) modeling of root zone soil moisture and potential recharge. The thesis proposes a methodology for estimating the root zone soil hydraulic properties viz. field capacity, wilting point and soil thickness. To test the methodology developed in this thesis for estimating the soil hydraulic properties and their uncertainty, three synthetic experiments were conducted by inversion of STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) model for maize crop using the GLUE (Generalized Likelihood Uncertainty Estimation) approach. The estimability of soil hydraulic properties in a layer-wise heterogeneous soil was examined with several sets of likelihood combinations, using leaf area index, surface soil moisture and above ground biomass. The robustness of the approach is tested with parameter estimation (model inversion) in two different meteorological conditions. The details of the numerical experiments and the several likelihood and meteorological cases examined are given in Chapter 3. The likelihood combination of leaf area index and surface soil moisture provided consistently good estimates of soil hydraulic properties for all soil types and different meteorological cases. Relatively wet year provided better estimates of soil hydraulic properties as compared with a dry year. To validate the approach of estimating root zone soil properties and to test the applicability of the approach in several crops and soil types, field measurements were carried out in the Berambadi experimental watershed located in the Kabini river basin in south India. The profile soil measurements were made for every 10 cm upto 1 m depth. Maize, Marigold, Sunflower, Sorghum and Turmeric crops were monitored during the four year period from 2010 to 2013. Crop growth parameters viz. leaf area index, above ground biomass, yield, phenological stages and crop management activities were measured/monitored at 10 day frequency for all the five crops in the study area. The details of the field experiments performed, the data collected and the results of the model inversion using the ground measured data are given in Chapter 4. The likelihood combination of leaf area index and surface soil moisture provided consistently lower root mean square error (1.45 to 2.63 g/g) and uncertainty in the estimation of soil hydraulic properties for all soil crop and meteorological cases. The uncertainty in the estimation of soil hydraulic properties was lower in the likelihood combination of leaf area index and soil moisture. Estimability of depth of root zone showed sensitivity to the rooting depth. Estimating root zone soil properties at field plot scale using SAR data (incidence angle 24o, wave length 5.3 GHz) of RADARSAT-2 is presented in the Chapter 5. In the first step, an approach of estimating leaf area index from radar vegetation index using the parametric growth curve of leaf area index and the retrieval of soil moisture using water cloud model are given in Chapter 5. The parameters of the growth curve and the leaf area index are generated using a time series of RADARSAT-2 for two years 2010-2011 and 2011-12 for the crops (maize, marigold, sunflower, sorghum and turmeric) considered in this study. The surface soil moisture is retrieved using the water cloud model, which is calibrated using the ground measured values of leaf area index and surface soil moisture for different soils and crops in the study area. The calibration and validation of LAI and water cloud models are discussed in this Chapter. Eventually, the retrieved leaf area index and surface soil moisture from RADARSAT-2 data were used to estimate the soil hydraulic properties and their uncertainty in a similar manner as discussed in Chapter 4 for various crop and soil plots and the results are presented in Chapter 5. The mean and uncertainty in the estimation of soil hydraulic properties using inversion of remote sensing data provided results similar to the estimates from inversion of ground data. The estimates of soil hydraulic properties compared well (R2 of 0.7 to 0.80 and RMSE of 2.1 to 3.16 g/g) with the physically measured vales of the parameters. In Chapter 6, root zone soil moisture and potential recharge are modelled using the STICS model and the soil hydraulic parameters estimated using the RADARSAT-2 data. The potential recharge is highly sensitive to the water holding capacity of rooting zone. Variability in the root zone soil moisture for wet and dry years for different soil types on irrigated and non-irrigated crops were investigated. Potential recharge from different crop and soil types were compared. The uncertainty in the estimation of potential recharge due to uncertainty in the estimation of field capacity is quantified. The root zone soil moisture modeled by STICS showed good agreement with the measured root zone soil moisture in all crop and soil cases. This was tested for both dry and wet year and provides similar results. The temporal variability of root zone soil moisture was also modeled well by the STICS model; the model also predicted well the intra-soil variability of soil moisture of root zone. The results of the modeling of root zone soil moisture and potential recharge are presented in Chapter 6. At the end, in Chapter 7, the major conclusions drawn from the various chapters are summarized.