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.     

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.     

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.     

Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

The Integral Equation Model (IEM) is the most widely-used, physically based radar backscatter model for sparsely vegetated landscapes. In general, IEM quantifies the magnitude of backscattering as a function of moisture content and surface roughness, which are unknown, and the known radar configurations. Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models. This study offers a solution strategy to this problem by the use of multi-angle radar images, and thus provides estimates of roughness and soil moisture without the use of ancillary field data. Results showed that radar images can provide estimates of surface soil moisture at the watershed scale with good accuracy. Results at the field scale were less accurate, likely due to the influence of image speckle. Results also showed that subsurface roughness caused by rock fragments in the study sites caused error in conventional applications of IEM based on field measurements, but was minimized by using the multi-angle approach.     

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。     

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.     

A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data

A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized.

Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1–7%.     

Monitoring leaf area of sugar beet using ERS-1 SAR data

This paper presents the results of field testing a radar model which relates leaf area index to radar backscatter for ERS-1 C-band VV polarization SAR data. Ground truth measurements of leaf area index and soil moisture content were made in selected sugar beet fields, with simultaneous acquisition of ERS-1 SAR image data. Radar backscatter coefficients were derived from the calibrated ERS-1 SAR data. The Leeuwen and Clevers expression of the water cloud model was fitted to determine the in situ relationship between radar back-scatter and leaf area index. The model can be inverted analytically to calculate leaf area index from radar backscatter. The results show considerable potential for the operational application of ERS-1 SAR data in crop monitoring.     

Topographically based spatially averaging of SAR data improves performance of soil moisture models

Spatial averaging schemes have often been used to improve empirical models that relate radar backscatter coefficient to soil moisture. However, reducing the noise in backscatter response not related to soil moisture often results in signal losses that are related to soil moisture. In this study we tested whether a spatial averaging scheme based on topographic features improved regressions relating backscatter coefficient and soil moisture on the low relief landscape of the Prairie Pothole Region of Canada. Soil moisture data were collected along hillslope transects within pothole drainage basins at intervals coincident with RADARSAT-1 satellite overpass. Spatial averaging schemes were designed at four scales: pixel, topographic feature (uplands, sideslopes, and lowlands), pothole drainage basin, and landscape (0.8 km × 1.6 km). The relationship between soil moisture and backscatter coefficient improved with increasing area of spatial averaging from a pixel (R² = 0.18, P < 0.005), to the pothole drainage basin (R² = 0.36, P < 0.005), to the landscape (R² = 0.66, P < 0.005). However, the strongest relationship (R² = 0.72, P < 0.005) was obtained by spatially averaging radar images based on topographic features. These findings indicate that topographically based spatial averaging of RADARSAT-1 imagery improves empirical models that are created to map the complex patterns of soil moisture in prairie pothole landscapes.     

Mapping near-surface soil moisture on regional scale using ERS-2 SAR data

Soil moisture is an important hydrologic variable of great consequence in both natural and agricultural ecosystems. Unfortunately, it is virtually impossible to accurately assess the spatial and temporal variability of surface soil moisture using conventional, point measurement techniques. Remote sensing has the potential to provide areal estimates of soil moisture at a variety of spatial scales. This investigation evaluates the use of European Remote Sensing Satellite (ERS-2) C-band, VV polarization, synthetic aperture radar (SAR) data for regional estimates of surface soil moisture. Radar data were acquired for three contiguous ERS-2 scenes in the Southern Great Plains (SGP) region of central Oklahoma from June 1999 to October 2000. Twelve test sites (each approximately 800?m×800?m) were sampled during the ERS-2 satellite overpasses in order to monitor changes in soil moisture and vegetation on the ground. An average radar backscattering coefficient was calculated for each test site. Landsat-5 and -7 Thematic Mapper (TM) scenes of the experimental sites close in time to the ERS-2 acquisition dates were also analysed. The TM scenes were used to monitor land cover changes and to calculate the Normalized Difference Vegetation Index (NDVI). Land cover and ground data were used to interpret the radar-derived soil moisture data. Linear relationships between soil moisture and the backscattering coefficient were established. Using these equations, soil moisture maps of the Little Washita and the El Reno test areas were produced.     

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.     

Radar estimates of soil moisture over the Konza Prairie

Abstract

Radar backscatter measurements were made as a part of the First International satellite land surface climatology project Field Experiment (FIFE) to estimate soil moisture for use by other investigators. The helicopter-mounted radar was flown along selected transects that coincided with soil moisture measurements. The radar operated at microwave frequencies of 5-3 and 9 6 GHz and at selected incidence angles between 0° and 60°. Vertical polarization was used for two days in June of 1987 and horizontal polarization was used for three days in July and October of 1987.

The scattering coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression, R², ranged between 0·9 and 0·5. The larger coefficients were for X -band measurements made at large antenna incidence angles, and the smaller coefficients were for C-band measurements made: at incidence angles near vertical.     

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.     

The implementation of a wateruse model to calculate radar backscatter with application to irrigation monitoring

This letter describes a coupled water use and radar backscatter model designed to assist irrigation monitoring and scheduling. The three components of the model (soil, plant, radar backscatter) are presented and simulations with the model explore its effectiveness in estimating soil and crop canopy moisture for potato crops by comparison with measurements obtained for test fields in Cambridgeshire, England, UK.     

基于SPOT-VGT可见光/短波红外波段数据对AMSR-E土壤湿度的降尺度研究

基于SPOT-VGT数据,由短波红外、红和蓝波段反射率计算了表征地表土壤湿度的可见光—短波红外干旱指数(VSDI),通过对1km空间分辨率的VSDI影像进行空间升尺度处理,采用多种函数建立了25km空间分辨率AMSR-E土壤湿度数据与VSDI指数的关系,发现二者关系最符合S型曲线模型,拟合残差在空间上呈现随机分布的特征。基于S曲线函数关系下的1km预测土壤湿度和残差值,对AMSR-E土壤湿度进行降尺度模拟,得到1km空间分辨率的土壤湿度。将原始AMSR-E土壤湿度和实测数据对降尺度结果分别比较验证后,表明基于该方法获得的土壤湿度模拟精度较高。     

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.     

Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data

This paper focuses on different methods for estimating soil moisture in a Sahelian environment by comparing ENVISAT/ASAR and ground data at the same spatial scale. The analysis is restricted to Wide Swath data in order to take advantage of their high temporal repetitivity (about 3-4 days) corresponding to a moderate spatial resolution (150 m). On the one hand, emphasis is put on the characterization of Surface Soil Moisture (SSM) at a spatial scale compatible with the derivation of the backscattering coefficients, and a transfer function is developed for up-scaling local measurements to the 1 km scale. On the other hand, three different approaches are used to normalize the angular variation of the observed backscattering coefficients. The results show a strong linear relationship between the HH normalized backscattering coefficients and SSM. The best result is obtained when restricting the ASAR data to low incidence angles and by taking into account vegetation effects using multi-angular radar data. For this case, the rms error of the SSM retrieval is 2.8%. These results highlight the capabilities of the ASAR instrument to monitor SSM in a semiarid environment.     

Initial results of land-reflected GPS bistatic radar measurements in SMEX02

To investigate scattering of Global Positioning System (GPS) signals from terrain and the potential for remotely sensing soil moisture with the L-band GPS bistatic radar concept, a prototype GPS bistatic radar participated in airborne measurements during the Soil Moisture Experiment 2002 (SMEX02). A 12-channel GPS navigation receiver, modified to perform bistatic radar measurements, was mounted on the NCAR C-130 aircraft to make co-located measurements with other instruments. Narrow pulse returns and comparison of the reflected GPS signal-to-noise ratio (SNR) measurements to digital imagery and cover maps indicated that the scattering was most likely quasi-specular, originating from a small footprint on the order of the first Fresnel zone (∼30 m). Temporal changes were observed in the measured signals and were expected to be proportional to varying soil moisture content. To investigate this effect, the bistatic signal measurements were interpolated to a spatial grid to produce daily maps of relative change of surface soil moisture over the study region. The maps of the study region showed a transition from dry surface soil moisture conditions to wet conditions following precipitation events occurring in the middle of the study period. Additionally, the maps showed the scattered power increased in areas with localized rainfall relative to areas without precipitation. Comparing the GPS-reflected SNR measurements with L-band brightness temperatures measured coincidently by the PALS radiometer showed good agreement in the trend measured by the two sensors. The scattered signal measurements were also compared with in situ soil moisture measurements and found to follow the general soil moisture trend as a function of time. These initial results from the first controlled experiment of GPS bistatic radar for soil moisture remote sensing indicate that the technique is sensitive to temporal and spatial variations in soil moisture.     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.