A derivation of roughness correlation length for parameterizing radar backscatter models

Surface roughness is a key parameter of radar backscatter models designed to retrieve surface soil moisture (θ_S) information from radar images. This work offers a theory‐based approach for estimating a key roughness parameter, termed the roughness correlation length (L _c). The L _c is the length in centimetres from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other. The approach is based on the relation between L _c and h _RMS as theorized by the Integral Equation Model (IEM). The h _RMS is another roughness parameter, which is the root mean squared height variation of a rough surface. The relation is calibrated for a given site based on the radar backscatter of the site under dry soil conditions. When this relation is supplemented with the site specific measurements of h _RMS, it is possible to produce estimates of L _c. The approach was validated with several radar images of the Walnut Gulch Experimental Watershed in southeast Arizona, USA. Results showed that the IEM performed well in reproducing satellite‐based radar backscatter when this new derivation of L _c was used as input. This was a substantial improvement over the use of field measurements of L _c. This new approach also has advantages over empirical formulations for the estimation of L _c because it does not require field measurements of θ_S for iterative calibration and it accounts for the very complex relation between L _c and h _RMS found in heterogeneous landscapes. Finally, this new approach opens up the possibility of determining both roughness parameters without ancillary data based on the radar backscatter difference measured for two different incident angles.     

Statistical properties of soil moisture images derived from Radarsat-1 SAR data

In this article, we report on the assessment of the spatial variability of soil moisture using synthetic aperture radar (SAR) data. The imagery was acquired during five different periods over the Roseau River watershed in southern Manitoba, Canada. For validation purposes, ground measurements were carried out at 62 locations simultaneous with the satellite data acquisitions. The first step in this analysis was to assess the performance of the Integral Equation Model (IEM) in simulating backscatter coefficients for selected bare soils. In order to reduce the surface roughness effect on radar backscatter, a semi-empirical calibration technique was implemented. This calibrated model was then implemented in a simplex inversion routine in order to estimate and map soil moisture. Derived spatial patterns of near-surface moisture content were then examined using scale analyses. It has been confirmed that the variance of radar-based soil moisture images follows power law decay versus the observation scale. Also, more explicit analysis of the same soil moisture maps shows a ln–ln linear spatial scale with statistical moments. Concave shape dependency of the corresponding slopes with the moment order was observed during all radar acquisition periods. The latter indicates the presence of multifractal effects.     

Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements

Estimating surface parameters by radar-image inversion requires the use of well-calibrated backscattering models. None of the existing models is capable of correctly simulating scatterometer or satellite radar data. We propose a semi-empirical calibration of the Integral Equation Model (IEM) backscattering model in order to better reproduce the radar backscattering coefficient over bare agricultural soils. As correlation length is not only the least accurate but also the most difficult to measure of the parameters required in the models, we propose that it be replaced by a calibration parameter that would be estimated empirically from experimental databases of radar images and field measurements. This calibration was carried out using a number of radar configurations with different incidence angles, polarization configurations, and radar frequencies. Using several databases, the relationship between the calibration parameter and the surface roughness was determined for each radar configuration. In addition, the effect of the correlation function shape on IEM performance was studied using the three correlation functions (exponential, fractal, and Gaussian). The calibrated version of the IEM was then validated using another independent set of experimental data. The results show good agreement between the backscattering coefficient provided by the radar systems and that simulated by the calibrated version of the IEM. This calibrated version of the IEM can be used in inversion procedures to retrieve surface roughness and/or moisture values from radar images.     

Remote sensing of bare surface soil moisture using EMAC/ESAR data

In this paper we present first results of bare surface soil moisture retrieval using data from the European Multisensor Airborne Campaign/ Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on 9 April 1994 in the Zwalm catchment, Belgium. Data from EMAC Reflective Optics System Imaging Spectrometer (ROSIS) collected on 12 July 1994 over the same catchment were used to develop land use maps. Concurrent to the EMAC/ESAR overflights field data were collected in two subcatchments of the Zwalm catchment. The paper first presents the data processing procedures used for the radar images. Then we apply a theoretical backscattering model to investigate the sensitivity of EMAC/ESAR backscattering coefficients to surface parameters (topography, surface roughness, vegetation and soil moisture). By comparing the predicted backscattering coefficients to the observed ones, we can conclude that classical measurement techniques for surface roughness parameters in remote sensing campaigns are not accurate enough for retrieving soil moisture using theoretical models. A method based on simultaneous retrieval of surface roughness parameters and soil moisture using multiple ESAR measurements is hence proposed. Promising results for retrieved soil moisture confirm the validity of the proposed method.     

The SIR-C/X-SAR experiment on Montespertoli: Sensitivity to hydrological parameters

Multi-frequency and multi-temporal polarimetric SAR measurements, carried out during SIR-C/X-SAR missions over the Montespertoli area have been analysed and compared with data collected at the same frequency and polarization, but at different dates, with the NASA/JPL AIRSAR. This paper presents an analysis of the achieved results aiming at evaluating the contribution of SAR data for estimating some geophysical parameters which play a significant role in hydrological processes and in particular soil moisture and roughness. The study has pointed out that in the scale of surface roughness typical of agricultural areas, a co-polar L-bandsensor gives the highest information content for estimating soil moisture and surface roughness. The sensitivity to soil moisture and surface roughness for individual fields is rather low since both parameters affect the radar signal. However, considering data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture is significant, since the effects of spatial roughness variations are smoothed. On the other hand the sensitivity to surface roughness is better manifested at a spatial scale, integrating on time to reduce the effects of moisture variation. The retrieval of both soil moisture and surface roughness has been performed with good results by means of a semi-empirical model.     

Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

The objective of this investigation is to analyze the sensitivity of ASAR (Advanced Synthetic Aperture Radar) data to soil surface parameters (surface roughness and soil moisture) over bare fields, at various polarizations (HH, HV, and VV) and incidence angles (20°-43°). The relationships between backscattering coefficients and soil parameters were examined by means of 16 ASAR images and several field campaigns. We have found that HH and HV polarizations are more sensitive than VV polarization to surface roughness. The results also show that the radar signal is more sensitive to surface roughness at high incidence angle (43°). However, the dynamics of the radar signal as a function of soil roughness are weak for root mean square (rms) surface heights between 0.5 cm and 3.56 cm (only 3 dB for HH polarization and 43° incidence angle). The estimation of soil moisture is optimal at low and medium incidence angles (20°-37°). The backscattering coefficient is more sensitive to volumetric soil moisture in HH polarization than in HV polarization. In fact, the results show that the depolarization ratio σ_HH⁰/σ_HV⁰ is weakly dependent on the roughness condition, whatever the radar incidence. On the other hand, we observe a linear relationship between the ratio σ_HH⁰/σ_HV⁰ and the soil moisture. The backscattering coefficient ratio between a low and a high incidence angle decreases with the rms surface height, and minimizes the effect of the soil moisture.     

Appropriate scale of soil moisture retrieval from high resolution radar imagery for bare and minimally vegetated soils

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m² sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5 × 5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

Investigating the potential for soil moisture and surface roughness monitoring in drylands using ERS SAR data

Multitemporal ERS-1 and ERS-2 SAR data were acquired for northern Jordan between 1995 and 1997 to investigate changes in the backscatter coefficients of a range of typical desert land surfaces. The changes in backscatter found were ascribed to variations in surface soil moisture, and changes in surface roughness caused by a range of natural and anthropogenic factors. Data collected from monitored sites were input into the Integral Equation Model (IEM). The model outputs were strongly correlated with observed backscatter coefficients (r ²=0.84). The results show that the successful monitoring of soil moisture in these environments is strongly dependent on the surface roughness. On surfaces with RMS height 0.5 cm, the sensitivity of the backscatter coefficient to changes in surface microtopography did not allow accurate soil moisture estimation. Microtopographic change on rougher surfaces has less influence on the backscatter coefficient, and the probability of soil moisture estimation from SAR imagery is greater. These results indicate that knowledge of the surface conditions (both in terms of surface roughness and geomorphology) is essential for accurate soil moisture monitoring, whether in a research or operational context. The potential benefits of these findings are discussed in the context of the Jordan Badia Research and Development Project.     

利用多时相ASAR数据反演黑河流域中游地表土壤水分

土壤水分是地表能、水循环过程中的重要变量之一,利用主动微波遥感,特别是合成孔径雷达（SAR）进行土壤水分的反演已经越来越受到人们的关注。地表与微波相互作用机理非常复杂,受到粗糙度的强烈影响,成为制约土壤水分准确反演的一个重要因素。利用3景时序接近的ASAR影像对黑河中游临泽草地试验区地表参数进行了多通道的反演,获得了像元尺度上的粗糙度分布状况,从而不需要借助粗糙度的地面测量辅助信息,节省了工作量。土壤水分反演取得了较为满意的结果（均方根误差< 6%）。      

The field row direction relative to the radar azimuth considered as an apparent surface roughness for smooth bare soils

The row direction of fields relative to the radar view direction, as well as the natural random roughness of soil, both influence the backscattering coefficient of bare soils. On radar images, row directions are random and generally unknown, making the use of two-scale roughness radar models difficult or impossible. However it is shown here that, in given conditions, a cultivated field with eroded rows behaves like an isotropical surface. One-scale models therefore apply, and the periodic roughness of the soil can be considered together with the natural random roughness as an ‘equivalent’ roughness variable dependent on the row direction. If this observation can be generalized, variations of apparent roughness values can therefore be surveyed on temporal series of radar images without a priori knowledge of the field rows.     

Passive microwave sensing of soil moisture content: The effects of soil bulk density and surface roughness

Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature af soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.     

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.     

基于Sentinel-1及 Landsat 8数据的黑河中游农田土壤水分估算

土壤水分是陆地表层系统中的关键变量。利用主动微波遥感,特别是合成孔径雷达(Synthetic Aperture Radar,SAR)的观测,在监测和估计表层土壤水分时空分布方面已开展了诸多研究。然而,SAR土壤水分反演仍存在诸多挑战,特别是地表粗糙度和植被的影响。因此,本文提出了一种结合主动微波和光学遥感的优化估计方案,旨在同步反演植被含水量、地表粗糙度和土壤水分。反演算法首先在水云模型的框架下对模型中的植被透过率因子(与植被含水量密切相关)采用3种不同的光学遥感指数——修正的土壤调节植被指数(Modified Soil Adjusted Vegetation Index,MSAVI)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)和归一化水体指数(Normalized Difference Water Index,NDWI)进行参数化估计,用于校正植被层的散射贡献。在此基础上,构造基于SAR观测和Oh模型的代价函数,利用复型洗牌全局优化算法进行土壤水分和地表粗糙度的联合反演。采用Sentinel-1 SAR和Landsat 8多光谱数据在黑河中游开展了反演试验,并利用相应的地面观测数据对结果进行了验证。结果表明反演结果与地面观测具有良好的一致性,其中基于NDWI的植被含水量反演效果最佳,与地面观测比较,土壤水分决定系数(R 2)在0.7以上,均方根误差(RMSE)为0.073 m^ 3/m^ 3;植被含水量R 2大于0.9,RMSE为0.885 kg/m 2,表明该方法能够较准确地估计土壤水分。同时发现植被含水量的估计结果,以及植被透过率的参数化方案对土壤水分的反演精度有一定的影响,在未来的研究中需要进一步探索。     

空间遥感现状及未来发展

从第三十五届国际宇航联合会的空同遥感专业小组会议上可以看出,目前空间遥感的现状及未来发展前景。今后空间遥感将从具有单一遥感能力向具有综合遥感能力方面发展,不仅能对陆地,而且对海     

SAR imagery to estimate roughness parameters when modelling runoff risk

The influence of the roughness of agricultural soil on runoff and erosion is a proven fact. Synthetic aperture radar (SAR) sensors should enable discrimination between plots with different cropping patterns. A study of Mediterranean vineyards in southern France was made, with the aim of obtaining a better understanding of the potential for using radar satellite data from ERS-1 when estimating roughness parameters. Roughness measurements enabled modelling of the backscattering coefficient (sigma0) of known surfaces, using the electromagnetic Integral Equation Model (IEM). The good correlation between ERS-1 and IEM data indicated the feasibility of extracting roughness parameters by means of remote sensing methods. Seven ERS-1 images were examined, corresponding to different stages in the development of vegetation and roughness. Two images were finally selected as they offered the possibility of discriminating between two factors: (1) the orientation of mechanical labour, which can be related toa periodic and stable roughness over time, and (2) cropping practices, corresponding to a random roughness pattern that changes with season. Both roughness parameters derived from SAR satellite data contribute additional data to runoff models a preferred runoff direction as defined by furrow direction, as well as the intensity of this runoff under the influence of random roughness. A rule for the behaviour of sigma0 in terms of furrow orientation is presented.     

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

The main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm³ cm^?3 to 0.079 cm³ cm^?3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust

Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5 GHz) data acquired in 2009 and 2010. We analyzed the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (> 25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°).The high spatial resolution of the TerraSAR-X data (1 m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust.