Semi-empirical Water Cloud Model Research Articles

Soil moisture retrieval at high spatial resolution over alpine ecosystems on Nagqu-Tibetan plateau: A comparative study on semiempirical and machine learning approaches

Soil moisture (SM) is an essential climate variable that directly and indirectly affects vegetation growth and survival through land‒atmosphere interactions. Alpine vegetation on the Tibetan Plateau is part of a unique ecosystem that is vulnerable to changes in environmental factors such as SM; consequently, this makes this ecosystem extremely sensitive to climate change. This study investigated the potential of synthetic aperture radar (SAR) vegetation indices based on Sentinel-1 data for retrieving SM at high spatial resolution (10 m) over an alpine grassland ecosystem in the Nagqu region. Several SAR vegetation indices, including the dual polarization SAR vegetation index (DPSVI), modified dual polarization SAR vegetation index (mDPSVI), dual polarimetric radar vegetation index (DpRVI), polarimetric radar vegetation index (PRVI), and radar vegetation index (RVI), were used in the semiempirical water cloud model (WCM) to determine which indices provide better SM retrievals in this alpine ecosystem. In addition, the potential of the distributed random forest (DRF) machine learning algorithm was explored using the same variables as the WCM together with several ecohydrological parameters from different data sources. The recursive feature elimination algorithm was used to establish the optimized DRF model. Among the vegetation indices based on SAR data, DPSVI, DpRVI, and PRVI showed similar results, with DPSVI performing slightly better than the other SAR indices, with a correlation coefficient (R2) of 0.70 and root mean squared error (RMSE) of 0.04 m3m-3. A comparison of the optimized DRF with the best fitted WCM reveals that the DRF algorithm outperformed the WCM, including having more predictors (10 variables) in the model. The results show that the overall accuracies in terms of the R2 values and the RMSEs of both the WCMs and the DRF models were 0.52–0.75 and 0.08 m3 m−3 to 0.04 m3 m−3, respectively, which was validated over in situ SM measurements in the Nagqu region.

Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

A multi-year cross-validation experiment for estimating rice plant area index (PAI) over the JECAM-India test site from simulated RADARSAT constellation mission (RCM) compact polarimetric SAR data

ABSTRACT Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiments provide an opportunity for the potential implementation of cross-validation strategies for biophysical parameters retrieval utilizing the next-generation compact polarimetric (CP) modes available from the RADARSAT Constellation Mission (RCM). This work first uses the conventional semi-empirical Water Cloud Model (WCM) modified by exploiting the scattering power decompositions of CP measurements to estimate the Plant Area Index (PAI) for rice. The modified WCM (MWCM) is then inverted using the scattering power components from the decomposition. We compare the PAI estimates using MWCM- between the estimates obtained from (1) the conventional WCM using the RH and RV backscatter intensities and (2) MWCM- decomposition scattering powers. We exploit a time series of simulated compact-pol SAR data over the JECAM test site in Vijayawada, India, throughout 2018 and 2019. We use the C-band RADARSAT-2 full-pol data to simulate the RADARSAT Constellation Mission (RCM) compact-pol mode data. Utilizing the advantage of systematically collected multi-year SAR data and in-situ measurements, the present research also assesses the calibrated model transferability performances to another data set and cross-validation of a model in a multi-year experiment setting. The comparative analysis indicates potential improvements in PAI estimation with MWCM- scattering powers. A high range of correlation coefficient () between the estimated and observed PAI is observed with good Root Mean Square Error (RMSE) of m2 m−2, and Mean Absolute Error (MAE) of m2 m−2.

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data.

The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.

Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel - 1A images

ABSTRACTThe sensible heat flux component calculation in the Surface Energy Balance Algorithm for Land (SEBAL) completely relies upon the anchor pixel locations. The fragmented agriculture regions with diverse crops make the selection process more challenging with less probability to correlate with ground realities. The conventional methods for anchor pixel selection have limitations in capturing the soil moisture variations beneath the canopy as they utilize only the thermal remote sensing images and ancillary datasets like land cover and crop type maps. The current research attempts to reduce the existing gap between the ground realities and simulated results by adding soil moisture as a supplementary parameter. The prospects of semi-empirical Water Cloud Model (WCM) to estimate the soil moisture content was tested in a fragmented agricultural region for different time periods. The concurrent satellite data obtained from Sentinel - 1A and Landsat 8 were utilized to supply the inputs for inversion modelling based on the Levenberg-Marquardt approach. The current research introduced ‘virtual Normalized Difference Vegetation Index’ concept to refine the WCM and yielded reliable soil moisture output for supplementing the anchor pixel selection process. The robustness of the approach was justified by considering the available energy at anchor pixel locations. The research outcomes revealed that the anchor pixel selection with and without soil moisture criterion has a significant impact on actual evapotranspiration estimation. The research also explores the scope of the synergetic use of optical and Synthetic Aperture Radar (SAR) inputs in SEBAL.

Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data

Retrieval of the Plant Area Index (PAI) and wet biomass from polarimetric SAR (PolSAR) data is of paramount importance for in-season monitoring of crop growth. Notably, the joint estimation of biophysical parameters might be effective instead of an individual parameter due to their inherent relationships (possibly nonlinear). The semi-empirical water cloud model (WCM) can be suitably utilized to estimate biophysical parameters from PolSAR data. Nevertheless, instability problems could occur during the model inversion process using traditional inversion approaches. Iterative optimization (IO) can have difficulty in finding the global minima while look up table (LUT) searches have a lower generalization capability. These challenges reduce the transferability of IO and LUT search inversions in computational efficiency and seldom account for the inter-correlation among the parameters. Alternatively, a machine learning regression technique with a regularization routine may provide a stable and optimum solution for ill-posed problems related to the inversion of the WCM. In the present work, the crop biophysical parameters viz. PAI and wet biomass are estimated simultaneously using the multi-target Random Forest Regression (MTRFR) technique. The accuracy of the retrieval method is analyzed using the in-situ measurements and quad-pol RADARSAT-2 data acquired during the SMAPVEX16 campaign over Manitoba, Canada. The inversion process is tested with different polarization combinations of SAR data for wheat and soybean. The validation used ground measured biophysical parameters for various crops, indicating promising results with a correlation coefficient (r) in the range of 0.6–0.8. In addition, the relationship between PAI and wet biomass using the multi-target and single output model is also assessed based on in-situ measurements. The results confirm that the inter-correlation between biophysical parameters is well preserved in the MTRFR based joint inversion technique for both wheat and soybean.

Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data

The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2 = 0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal0 (σT_RHo, σT_RVo) and σveg0 (σV_RHo, σV_RVo) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg0. The present study is a first step towards retrieving crop parameters from hybrid-polarized data and thus possesses the potential to serve as a reference for further research initiatives.

IMPROVED PARAMETERIZATION OF WATER CLOUD MODEL FOR HYBRID-POLARIZED BACKSCATTER SIMULATION USING INTERACTION FACTOR

Abstract. The prime aim of this study was to assess the potential of semi-empirical water cloud model (WCM) in simulating hybrid-polarized SAR backscatter signatures (RH and RV) retrieved from RISAT-1 data and integrate the results into a graphical user interface (GUI) to facilitate easy comprehension and interpretation. A predominant agricultural wheat growing area was selected in Mathura and Bharatpur districts located in the Indian states of Uttar Pradesh and Rajasthan respectively to carry out the study. The three-date datasets were acquired covering the crucial growth stages of the wheat crop. In synchrony, the fieldwork was organized to measure crop/soil parameters. The RH and RV backscattering coefficient images were extracted from the SAR data for all the three dates. The effect of four combinations of vegetation descriptors (V1 and V2) viz., LAI-LAI, LAI-Plant water content (PWC), Leaf water area index (LWAI)-LWAI, and LAI-Interaction factor (IF) on the total RH and RV backscatter was analyzed. The results revealed that WCM calibrated with LAI and IF as the two vegetation descriptors simulated the total RH and RV backscatter values with highest R2 of 0.90 and 0.85 while the RMSE was lowest among the other tested models (1.18 and 1.25 dB, respectively). The theoretical considerations and interpretations have been discussed and examined in the paper. The novelty of this work emanates from the fact that it is a first step towards the modeling of hybrid-polarized backscatter data using an accurately parameterized semi-empirical approach.

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

In order to monitor crop growth along the season with synthetic aperture radar (SAR) images, radiative transfer models were developed to retrieve key biophysical parameters, such as the Leaf Area Index (LAI). The semi-empirical water cloud model (WCM) can be used to estimate LAI values from SAR data and surface soil moisture information. Nevertheless, instability problems can occur during the model calibration, which subsequently reduce its transferability in both time and space. To avoid these ill-posed cases, three calibration methodologies are benchmarked in the present study. The accuracy of the retrieved LAI values for each methodology was analyzed, as well as the sensitivity of the signal to LAI for different soil moisture values. The sensitivity of the cross-polarization was highlighted especially for high LAI. The VV polarization was found sensitive for LAI values inferior to 2 m 2 /m 2 . Given the differential sensitivity of the C-band backscatter to maize canopies in each polarization, a Bayesian fusion of the LAI estimates in linear polarizations was developed. This fusion gives lower weights to estimates with a high uncertainty. This method systematically reduces the error and its associated variance. When considering all polarizations, the RMSE on LAI estimation decreased by 0.32 m 2 /m 2 , i.e., one fourth of the error value, as compared to the best estimation from a single polarization, and the associated uncertainty was reduced by a factor of two. Focusing on the two most sensitive polarizations to maize canopies (VV-HV), the error diminished by a third. This fusion framework shows thus a great potential to improve the accuracy and reliability of LAI retrieval of C-band quad-polarized data, as well as dual-polarized data, such as Sentinel-1.

Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery

We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination ${{\bf R}^{\bf 2}}$ of 0.70, while the external validation indicates an RMSE of 13 t/ha with ${{\bf R}^{\bf 2}}$ of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study ( $ {>} {\bf 100}\;{\bf t/ha}$ ) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.

Monitoring Soil Moisture in an Agricultural Test Site Using SAR Data: Design and Test of a Pre-Operational Procedure

An algorithm for pre-operational high resolution soil moisture mapping using Synthetic Aperture Radar (SAR) data is presented. It has been conceived to be inserted in the operational weather alert system of the Italian Department of Civil Protection. The Maximum A Posteriori (MAP) probability criterion is applied to retrieve soil moisture by inverting a forward backscattering model, and ancillary data such as optical images and land cover maps are also used to identify areas in which the retrieval can be carried out. The well-established semiempirical water cloud model is adopted to correct for the effect of vegetation on SAR data. In anticipation of the use of the algorithm in an operational system, in which the SAR-derived high resolution soil moisture product can be assimilated within weather prediction models or hydrological ones, an uncertainty index is associated to each estimate. The algorithm has been tested on a dataset consisting of ground data gathered for seven years (2003-2010) on an agricultural test site in Northern Italy and radar data provided by the C-band ENVISAT/ASAR instrument. A comparison, performed at field scale, between estimated and in situ soil moisture data has shown that, by discarding the estimates with the largest uncertainty, the correlation coefficient can exceed 0.80 and the root mean square estimation error is less than 0.05 m3/m3. Moreover, the uncertainty index has turned out to be fairly correlated to the actual estimation error.

Vegetation effects on soil moisture estimation from ERS-2 SAR images

The aim of this study was to map soil moisture from ERS-2 SAR images by minimizing the effect of vegetation on the backscatter coefficient. Detailed analysis was carried out to identify the prominent crop descriptor (i.e. crop height, h; leaf area index, LAI; and plant water content, PWC), and to minimize its effect on soil moisture estimation. A semi-empirical water cloud model was used to eliminate the vegetation effects on the backscatter coefficient. Our results showed that the water cloud model based on LAI as the canopy descriptor was able to estimate the crop-covered backscatter coefficient more accurately than the models based on either of the other two crop descriptors. Once the crop-covered backscatter coefficient was determined, a nonlinear least square method (LSM) was implemented to estimate the volumetric soil moisture. A significantly high correlation (R2 ≈ 0.94) between the estimated soil moisture and the corresponding observed soil moisture for barren land, as well as crop-covered surfaces, was obtained. Subsequently, individual soil moisture maps were generated from the three ERS-2 SAR images to depict the spatial distribution of soil moisture during the three seasons. Editor Z.W. Kundzewicz; Associate editor J. Simunek Citation Said, S., Kothyari, U.C. and Arora, M.K., 2012. Vegetation effects on soil moisture estimation from ERS-2 SAR images. Hydrological Sciences Journal, 57 (3), 517–534.

On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters

The synthetic aperture radar (SAR)-based soil moisture retrieval of agricultural fields is often hampered by vegetation effects on the backscattered signal. The semiempirical water cloud model (WCM) allows for estimating the backscatter of a vegetated surface, accounting for both the contributions of the vegetation and the underlying soil. The latter is often described through the integral equation model (IEM). Unfortunately, the IEM requires an accurate parameterization of the surface roughness which is very difficult to achieve. Therefore, this letter extends the WCM with a bare soil contribution that is based on the IEM, which, however, relies on calibrated or effective roughness parameters. Furthermore, this letter compares a number of vegetation indicators for their use in the WCM. Based on a series of L-band SAR observations, it is shown that effective roughness parameters are a promising tool for soil moisture retrieval under a wheat canopy and that the use of a leaf area index may be recommended above other vegetation indicators, as it leads to the lowest root-mean-square errors of about 5.5 vol%. These results prove the operational potential of L-band SAR data for soil moisture inferred under a wheat canopy throughout the entire crop growth cycle.

Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

The aim of this study was to estimate soil moisture from RADARSAT-2 Synthetic Aperture Radar (SAR) images acquired over agricultural fields. The adopted approach is based on the combination of semi-empirical backscattering models, four RADARSAT-2 images and coincident ground measurements (soil moisture, soil surface roughness and vegetation characteristics) obtained near Saskatoon, Saskatchewan, Canada during the summer of 2008. The depolarization ratio (χ v ), the co-polarized correlation coefficient ( ρ vvhh ) and the ratio of the absolute value of cross polarization to crop height (Λ vh ) derived from RADARSAT-2 data were analyzed with respect to changes in soil surface roughness, crop height, soil moisture and vegetation water content. This sensitivity analysis allowed us to develop empirical relationships for soil surface roughness, crop height and crop water content estimation regardless of crop type. The latter were then used to correct the semi-empirical Water–Cloud model for soil surface roughness and vegetation effects in order to retrieve soil moisture data. The soil moisture retrieved algorithm is evaluated over mature crop fields (wheat, pea, lentil, and canola) using ground measurements. Results show average relative errors of 19%, 10%, 25.5% and 32% respectively for the retrieval of crop height, soil surface roughness, crop water content and soil moisture.

Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval

This paper discusses the effects of vegetation on C- (4.75 GHz) and L- (1.6 GHz) band backscattering ( σ o) measured throughout a growth cycle at incidence angles of 15, 35 and 55°. The utilized σ o data set was collected by a truck mounted scatterometer over a corn field and is supported by a comprehensive set of ground measurements, including soil moisture and vegetation biomass. Comparison of σ o measurement against simulations by the Integral Equation Method (IEM) surface scattering model (Fung et al., 1992) shows that the σ o measurements are dominated either by an attenuated soil return or by scattering from vegetation depending on the antenna configuration and growth stage. Further, the measured σ o is found to be sensitive to soil moisture even at peak biomass and large incidence angles, which is attributed to scattering along the soil-vegetation pathway. For the simulation of C-band σ o and the retrieval of soil moisture two methods have been applied, which are the semi-empirical water cloud model (Attema & Ulaby, 1978) and a novel method. This alternative method uses the empirical relationships between the vegetation water content ( W) and the ratio of the bare soil and the measured σ o to correct for vegetation. It is found that this alternative method is superior in reproducing the measured σ o as well as retrieving soil moisture. The highest retrieval accuracies are obtained at a 35° incidence angle leading to RMSD's of 0.044 and 0.037 m 3 m − 3 for the HH and VV-polarization, respectively. In addition, the sensitivity of these soil moisture retrievals to W and surface roughness parameter uncertainties is investigated.

Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis

The goal of this study was to extract from dual-frequency satellite SAR signatures consistent information about moisture in soils and about various features of plants for analyzing crop growth conditions in any agricultural region. The study was carried out on Polish agricultural regions but it is hoped that it will be applicable anywhere on the planet. During a satellite overpass on a particular date, the ground-based measurements required such as soil moisture (SM), Leaf Area Index (LAI), and biomass were collected from 10 to14 May 1998. The backscattering coefficients at various frequencies were collected from ERS-2.SAR (C-VV) on May 10, 1998 and from JERS-SAR (L-HH) on May 14, 1998. The applicability of three different vegetation descriptors to the semi-empirical water-cloud model was investigated. The contribution to the backscatter values of vegetation features such as leaf area expressed in the Leaf Area Index and the dielectric properties of leaf surface expressed in the Leaf Water Area Index (LWAI) and the Vegetation Water Mass (VWM) was examined in order to reveal the best fit of the model. It was found that in C-band, which had an incidence angle of 23°, the soil moisture contribution to the sigma value was predominant over the vegetation contribution. When the canopy cover increases, the sensitivity of a radar signal to dry soil conditions ( SM < 0.1) decreased. The sigma value was the most sensitive to vegetation descriptor VWM which described the amount of water in vegetation. Attenuation of soil signal by the canopy was found in all three vegetation descriptors types; the strongest attenuation effect was observed in the case of VWM. In L-band (where the incidence angle was 35°), the dominant signal to total σ o value comes from volume scattering of vegetation for LAI > 3. When LAI < 3 the vegetation contribution to total σ o value appeared in two-way attenuation. The results gave us the possibility of comparing the modeled with the measured soil and vegetation parameters.

Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation

The present paper focuses on the ability of currently available RADARSAT-1 data to estimate surface soil moisture over an agricultural catchment using the theoretical integral equation model (IEM). Five RADARSAT-1 scenes acquired over Navarre (north of Spain) between February 27, 2003 and April 2, 2003 have been processed. Soil moisture was measured at different fields within the catchment. Roughness measurements were collected in order to obtain representative roughness parameters for the different tillage classes. The influence of the cereal crop that covered most of the fields was taken into account using the semiempirical water cloud model. The IEM was run in forward and inverse mode using vegetation corrected RADARSAT-1 data and surface roughness observations. Results showed a great dispersion between IEM simulations and observations at the field scale, leading to inaccurate estimations. As the surface correlation length is the most difficult parameter to measure, different approaches for its estimation have been tested. This analysis revealed that the spatial variability in the surface roughness parameters seems to be the reason for the dispersion observed rather than a deficient measurement of the correlation length. At the catchment scale, IEM simulations were in good agreement with observations. The error values obtained in the inverse simulations were in the range of in situ soil moisture measuring methods (0.04 cm/sup 3//spl middot/cm/sup -3/). Taking into account the small size of the catchment studied, these results are encouraging from a hydrological point of view.

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.

Extracting biophysical parameters from remotely sensed radar data: a review of the water cloud model

The semi-empirical water cloud model can be implemented to derive spatial values of biophysical parameters from remotely sensed radar data for use throughout different disciplines of physical geography. The water cloud model formulation and development are detailed in this review paper, incorporating the major advances and publications related to the model.