Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

This paper presents an original methodology to retrieve surface (<5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellites. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. As a result, a mixed target model is proposed. The WSC backscattered signal may be represented as a combination of the vegetation and bare soil contributions weighted by their respective fractional covers. Over our temperate regions and time periods of interest, the vegetation signal is assumed to be principally due to forests backscattered signal. Then, thanks to the high spatial resolution of the SAR instrument, the forest contribution may be quantified from the analysis of the SAR image, and then removed from the total WSC signal in order to estimate the soil contribution. Finally, the Integral Equation Model (IEM, [IEEE Transactions on Geoscience and Remote Sensing, 30 (2), (1992) 356]) is used to estimate the effect of surface roughness and to retrieve surface soil moisture from the WSC multi-angular measurements. This methodology has been developed and applied on ERS data acquired over three different Seine river watersheds in France, and for a 3-year time period. The soil moisture estimations are compared with in situ ground measurements. High correlations (R² greater than 0.8) are observed for the three study watersheds with a root mean square (rms) error smaller than 4%.     

Application of the Sahebi model using ALOS/PALSAR and 66.3 cm long surface profile data

Soil moisture is important information for agricultural fields in which erosion of upper soil layers depends upon the soil moisture, and in which the yield depends on soil water content during sowing, growing and harvest periods. Although sensitivity of microwave backscatters to soil moisture is well understood, several factors, such as surface roughness and incidence angle, can interfere with the estimation of soil moisture using Synthetic Aperture Radar (SAR) data. In this letter, we evaluate the influence of these variables using Advanced Land Observing Satellite (ALOS)/Phased Array type L-Band Synthetic Aperture Radar (PALSAR) data and 66.3 cm long surface profile data using the Sahebi model (Sahebi et al. 2003 Sahebi, M. R., Bonn, F. and Gwyn, Q. H.J. 2003. Estimation of the moisture content of bare soil from RADASAT-1 SAR using simple empirical models. International Journal of Remote Sensing, 24: 2575–2583. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Estimation of the moisture content of bare soil from RADASAT-1 SAR using simple empirical models. International Journal of Remote Sensing, 24, pp. 2575–2583). The model applied in this study has a root mean square error (RMSE) of only 1.34 dB, which suggests that 66.3 cm long surface profile data are effective for characterization of surface roughness effects on backscattering coefficients.     

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.     

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.     

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.     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.     

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.     

The effect of changing environmental conditions on microwave signatures of forest ecosystems: preliminary results of the March 1988 Alaskan aircraft SAR experiment

Abstract

In preparation for the first European Space Agency (ESA) Remote Sensing(ERS-I) mission,a series of multitemporal, multifrequency, multipolarization aircraft synthetic aperture radar (SAR) data sets were acquired over the Bonanza Creek Experimental Forest near Fairbanks, Alaska in March, 1988. P-, L- and C-band data were acquired with the NASA/JPL Airborne SAR on five differentdays over a period of two weeks. The airborne data were augmented with intensiveground calibration data as well as detailed, simultaneous in situ measurements of the geometric, dielectric and moisture properties of the snow and forest canopy. During the time period over which the SAR data were collected, the environmental conditions changed significantly; temperatures ranged from unseasonably warm (I to 9°C) to well below freezing (-8 to - 15°C), and the moisture content of the snow and trees changed from a liquid to a frozenstate. The SAR data clearly indicate the radar return is sensitive to these changing environmental factors and preliminary analysis of the L-band SAR data shows a 0·4 to 5·8dB increase (depending on polarization and canopy type) in the radar cross section of the forest stands under the warm conditions relative to the cold. These SAR observations are consistent with predictions from a theoretical scattering model. These preliminary results are presented to illustrate the opportunity afforded by the ERS-l SAR to monitor temporal changes in forest ecosystems.     

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.     

A test of detecting spring leaf flush within the Alaskan boreal forest using ERS-2 and Radarsat SAR data

This study tests whether ERS-2 and Radarsat Standard Beam SAR data could be used to detect spring green-up of broadleaf forests on floodplains in interior Alaska. SAR data were acquired for pre- and post-budburst dates during the spring of 1997, 1998 and 1999. The images were radiometrically calibrated to dB and then geometrically co-registered to a colour infrared Landsat TM image. Ten broadleaf and ten conifer floodplain forest polygons were then delineated from the Landsat TM image for the extent of each SAR image. The mean dB from each broadleaf and conifer stand was computed from each SAR image to test whether there was a consistent change in mean dB associated with spring leaf-out of broadleaf trees. There was no evidence that the 1997 or 1998 ERS-2 dB SAR data were useful for detecting spring leaf flush. The 1998 Radarsat broadleaf stands all increased in dB after budburst, but this was attributed to an increase in surface moisture from rain, rather than due to spring leaf flush. The 1999 Radarsat stands all decreased in SAR dB after budburst, while control conifer stands increased and decreased in dB during the same time period. Confounding factors such as changing surface moisture, and possibly freezing/ thawing effects may mask any change in ERS-2 or Radarsat SAR dB due to spring leaf flush.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/Sounder (CMIS), planned for launch in the 2009–2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (～25?km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the ‘Universal Triangle’ approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (～1?km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high-resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June–July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0–5?cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.     

Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data

Soil moisture retrieval is often confounded by the influence of vegetation and surface roughness on the backscattered radar signal in vegetated areas. In this study, a semi-empirical methodology is proposed to retrieve soil moisture in prairie areas. The effect of vegetation is eliminated by the ratio vegetation method and water cloud model (WCM), respectively. The conditions of vegetation are characterized by leaf area index (LAI), vegetation water content (VWC), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI), respectively. To remove the dependence on surface roughness, the dielectric constant is explicitly expressed as the function of co-polarization backscattering coefficients and sensor parameters based on the Dubois model. The ground measurements and satellite data collected from the Ruoergai and Wutumeiren prairies of China allow for validating the feasibility and effectiveness of the proposed methodology. From the perspective of soil moisture retrieval accuracy, the ratio vegetation method performs better than WCM. In the Ruoergai prairie, the best soil moisture retrieval result is obtained when EVI is used, with correlation coefficient (r) and root mean square error (RMSE) of 0.87 and 3.50 vol.%, respectively. While in the Wutumeiren prairie, the lowest retrieval error is obtained when LAI is used, with r and RMSE values of 0.79 and 5.73 vol.%, respectively. These results demonstrate that the Dubois model has a potential for enhancing soil moisture retrieval in prairie areas using synthetic aperture radar (SAR) and optical data.     

Mapping fire scars in global boreal forests using imaging radar data

This study is an extension of earlier research which demonstrated the utility of ERS SAR data for detection and monitoring of fire-disturbed boreal forests of Alaska. Fire scars were mappable in Alaska due to the ecological changes that occur post-burn including increased soil moisture. High soil moisture caused a characteristic enhanced backscatter signal to be received by the ERS sensor from burned forests. Since regional ecological differences in the global boreal biome may have an effect on post-fire ecosystem changes, it may also affect how fire scars appear in C-band SAR imagery. In the current study we evaluate the use of C-band SAR data to detect, map and monitor boreal fire scars globally. Study sites include four regions of Canada and an area in central Russia. Fire boundaries were mapped from SAR data without a priori knowledge of fire scar locations. SAR-derived maps were validated with fire service records and field checks. Based on results from test areas in Northwest Territories, Ontario, southeastern Quebec, and central Russia, C-band SAR data have high potential for use in detecting and mapping fire scars globally.     

Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter

Ensemble Kalman filter is a new sequential data assimilation algorithm which was originally developed for atmospheric and oceanographic data assimilation. It can be applied to calculate error covariance matrix through Monte-Carlo simulation. This approach is able to resolve the nonlinearity and discontinuity existed within model operator and observation operator. When observation data are assimilated at each time step, error covariances are estimated from the phase-space distribution of an ensemble of model states. The error statistics is then used to calculate Kalman gain matrix and analysis increments. In this study, we develop a one-dimensional soil moisture data assimilation system based on ensemble Kalman filter, the Simple Biosphere Model (SiB2) and microwave radiation transfer model (AIEM, advanced integration equation model). We conduct numerical experiments to assimilate in situ soil surface moisture measurements and low-frequency passive microwave remote sensing data into a land surface model, respectively. The results indicate that data assimilation can significantly improve the soil surface moisture estimation. The improvement in root zone is related to the model bias errors at surface layer and root zone. The soil moisture does not vary significantly in deep layer. Additionally, the ensemble Kalman filter is predominant in dealing with the nonlinearity of model operator and observation operator. It is practical and effective for assimilating observations in situ and remotely sensed data into land surface models.     

Detecting changes in surface soil moisture content using differential SAR interferometry

The feasibility of measuring changes in surface soil moisture content with differential interferometric synthetic aperture radar (DInSAR) has received little attention in comparison with other active microwave techniques. In this study, multi-polarization C- and L-band DInSAR is explored as a potential tool for the measurement of changes in surface soil moisture in agricultural areas. Using 10 ascending phased array L-band SAR (PALSAR) scenes acquired by the Japanese Advanced Land Observing Satellite (ALOS) and 12 descending advanced SAR (ASAR) scenes acquired by the European ENVISAT satellite between July 2007 and November 2009, a series of 27 differential interferograms covering a common study area over southern Ireland were generated to investigate whether small-scale changes in phase are linked to measured soil moisture changes. Comparisons of observed mean surface displacement and in situ mean soil moisture change show that C-band cross-polarization pairs displayed the highest correlation coefficients over both the barley (correlation coefficient, r = 0.51, p = 0.04)- and potato crop (r = 0.81, p = 0.003)-covered fields. Current results support the hypothesis that a soil moisture phase contribution exists within differential interferograms covering agricultural areas.     

微波遥感监测土壤水分的研究初探

在GPS定位的基础上，同步测量土攘水分、土壤后向散射系数，和同步获取的X波段、HH机化SAR图像进行了土攘水分监N.]的徽波遥感试验研究。结果表明，X波段SAR图像的灰度与表层土壤(0~10cm)水分有较好的相关性，35OHH极化的土峨后向散射系数与SAR图像灰度和土攘水分也有较好的相关性，由SAR图像及土攘的后向散射系数估算的土峨水分精度相近，相对误差均为12%左右，因而利用X波段、HH极化的机载SAR图像监浏土壤水分是可行的。雷达图像的穿透力一般在10cm以内，因此探讨了由表层土壤水分推求剖面土壤水分的可能性，并提出以土攘水分计法在浏童精度和速度上改进传统土壤水分测量的方法。     

Assessment of polarimetric SAR data for discrimination between wet versus dry soil moisture conditions

Radarsat-2 imagery from extreme dry versus wet conditions are compared in an effort to determine the value of using polarimetric synthetic aperture radar (SAR) data for improving estimation of fuel moisture in a chronosequence of Alaskan boreal black spruce ecosystems (recent burns, regenerating forests dominated by shrubs, open canopied forests, moderately dense forest cover). Results show strong distinction between wet and dry conditions for C-HH and C-LR polarized backscatter, and Freeman–Durden and van Zyl surface bounce decomposition parameters (35–65% change for all but the dense spruce site). These four SAR variables have high potential for evaluation of within site surface soil moisture, as well as for relative distinction between wet and dry conditions across sites for lower biomass and sparse canopy forested sites. However, for any given test site except the shrubby regrowth site, van Zyl volume, surface, and double bounce scattering all result in similar percentage increases from dry to wet soil condition. This indicates that for most of these test sites/cases moisture enhances the magnitude of the return for all scattering mechanisms evaluated. Thus, differences in scattering from the interaction of biomass, surface roughness, and moisture condition across sites remains an issue and backscatter due to surface roughness or biomass cannot be uniquely estimated. In contrast, the Cloude–Pottier C-band decomposition variables appear invariant to soil moisture, but may instead account for variations in ecosystem structure and biomass. Further investigation is needed, as results warrant future research focused on evaluation of multiple polarimetric parameters in algorithm development.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.     

Effects of sub-pixel heterogeneity on the retrieval of soil moisture from passive microwave radiometry

A simple formulation relating the L-band microwave brightness temperature detected by a passive microwave radiometer to the near surface soil moisture was developed using MICRO-SWEAT, a coupled microwave emission model and soil-vegetation-atmosphere-transfer (SVAT) scheme. This simple model provides an ideal tool with which to explore the impact of sub-pixel heterogeneity on the retrieval of soil moisture from microwave brightness temperatures. In the case of a bare soil pixel, the relationship between apparent emissivity and surface soil moisture is approximately linear, with the clay content of the soil influencing just the intercept of this relationship. It is shown that there are no errors in the retrieved soil moisture from a bare soil pixel that is heterogeneous in soil moisture and texture. However, in the case of a vegetated pixel, the slope of the relationship between apparent emissivity and surface soil moisture decreases with increasing vegetation. Therefore for a pixel that is heterogeneous in vegetation and soil moisture, errors can be introduced into the retrieved soil moisture. Generally, under moderate conditions, the retrieved soil moisture is within 3% of the actual soil moisture. Examples illustrating this discussion use data collected during the Southern Great Plains '97 Experiment (SGP97).