Effective soil moisture sampling depth of L-band radiometry: A case study

The aim of this study is to analyze the influence of the soil moisture sampling depth in the parameterization of soil emission in microwave radiometry at L-band. The analysis is based on brightness temperature, soil moisture and temperature measurements acquired over a bare soil during the SMOSREX experiment. A more detailed profile of surface soil moisture was obtained with a soil heat and water flows mechanistic model. It was found that (1) the soil moisture sampling depth depends on soil moisture conditions, (2) the effective soil moisture sampling depth is shallower than provided by widely used field moisture sensors, and (3) the soil moisture sampling depth has an impact on the calibration of soil roughness model parameters. These conclusions are crucial for the calibration and validation of remote sensing data at L-band.     

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.     

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.     

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.     

Influence of forest cover fraction on L-band soil moisture retrievals from heterogeneous pixels using multi-angular observations

Airborne L-band data from the Australian National Airborne Field Experiment 2005 (NAFE '05) field campaign were used to investigate the influence of fractional forest cover on soil moisture retrievals from heterogeneous (grass/forest) pixels. This study is, to our knowledge, the first to use experimental data on this subject and was done in view of the SMOS mission, in order to contribute to calibration/validation studies and the analysis of heterogeneous surfaces. Because the multi-angle observations were contained in swaths, swaths were used instead of pixels as the basic surface unit in this study. Simultaneous retrievals of soil moisture (SM) and vegetation optical depth (τ_NAD) were undertaken by inversion of the L-MEB zero-order radiative transfer model. This was done for two different retrieval configurations, the first consisting of swath-effective values of SM and τ_NAD and the second consisting of values of SM and τ_NAD for the non-forested (i.e. grass) fraction of the swath, with forest emission known from forward modelling. Model inputs for non-retrieved parameters were either default values taken from the literature or site- and time-specific values obtained from observations of nearby homogeneous swaths gathered during the same flight. The main focus of this study was on retrieval behaviour for various soil moisture conditions and forest fractions. Area-averaged retrieval results were generally very reasonable for both retrieval configurations. When retrieving swath-effective values of SM and τ_NAD, τ_NAD showed an increased overestimation with increased forest fraction. Highest retrieved values of SM were found at intermediate values of forest fraction. The results show the difficulty in flagging upper limits of pixel forest fraction during soil moisture retrievals, besides the fact that erroneous parameter values can lead to high errors in retrieved SM, especially in wet conditions. This study is the first to give a realistic idea of the errors and uncertainties involved in soil moisture retrievals from partly forested swaths, and as such will contribute to a better understanding of SMOS calibration/validation issues.     

SMEX02: Field scale variability, time stability and similarity of soil moisture

Evaluation of air- or space-borne remote sensors measuring soil moisture requires strategic ground-based sampling. As part of the Soil Moisture Experiment 2002 (SMEX02), daily surface soil moisture sampling at 90-140 locations were conducted in four fields in Walnut Creek watershed, Iowa. Various combinations of soils, vegetation, and topography characterize the fields. Depending on the field's characteristics and soil moisture content, 3-32 independent measurements were necessary to capture the field mean volumetric soil moisture with a ±2% bias and 95% confidence interval. Validation of the retrieved soil moisture products from the aircraft microwave instruments using the average of 14 samples per field is more appropriate for dry (<10% volumetric soil moisture) or wet (>25% volumetric soil moisture) range than for intermediate soil moisture range. Time stability analysis showed that an appropriately selected single sampling point could provide similar accuracy across a range of soil moisture conditions. Analyses based on landscape position (depression, hilltop, steep slope, and mild slope) showed that locations with mild slopes consistently exhibit time stable features. Hilltop and steep slope locations consistently underestimated mean field soil moisture. Soils parameters could not be used to identify time stable features as sampling locations with relatively high sand content consistently underestimated the field mean while those locations with relatively high clay content consistently overestimated the field mean. However, the slope position characterization of time stable features was enhanced using soils properties. The mild slope locations having the best time-stable features are those with moderate to moderately high clay content as compare to the field average (28-30% clay).     

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soil information using simple parameter estimation with few but appropriately timed remote sensing retrievals.     

A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, based on an aperture synthesis L-band radiometer was successfully launched in November 2009. In the context of a validation campaign for the SMOS mission, intensive airborne and in situ observations were performed in southwestern France for the SMOS CAL/VAL, from April to May 2009 and from April to July 2010. The CAROLS (Cooperative Airborne Radiometer for Ocean and Land Studies) bi-angular (34°-0°) and dual-polarized (V and H) L-band radiometer was designed, built and installed on board the French ATR-42 research aircraft. During springs of 2009 and 2010, soil moisture observations from the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France were complemented by airborne observations of the CAROLS L-band radiometer, following an Atlantic-Mediterranean transect in southwestern France. Additionally to the 12 stations of the SMOSMANIA soil moisture network, in situ measurements were collected in three specific sites within an area representative of a SMOS pixel. Microwave radiometer observations, acquired over southwestern France by the CAROLS instrument were analyzed in order to assess their sensitivity to surface soil moisture (w_g). A combination of microwave brightness temperature (T_b) at either two polarizations or two contrasting incidence angles was used to retrieve w_g through regressed empirical logarithmic equations with good results, depending on the chosen configuration. The regressions derived from the CAROLS measurements were applied to the SMOS T_b and their retrieval performance was evaluated. The retrievals of w_g showed significant correlation (p-value < 0.05) with surface measurements for most of the SMOSMANIA stations (8 of 12 stations) and with additional field measurements at two specific sites, also. Root mean square errors varied from 0.03 to 0.09 m³ m^− 3 (0.06 m³ m^− 3 on average).     

A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.     

Temporal dynamics of PSR-based soil moisture across spatial scales in an agricultural landscape during SMEX02: A wavelet approach

In this study, we examined the characteristics of soil moisture dynamics of wet and dry fields across hierarchical spatial scales within the region of Soil Moisture Experiment 2002 (SMEX02) hydrology campaign in Iowa. The Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface (∼ 0-5 cm) soil moisture at 800 m × 800 m resolution was used in this study. Wavelet-based multiresolution technique decomposed the soil moisture into large-scale mean soil moisture fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. Results suggested linearity in the log-log dependency of the variance of soil moisture up to a resolution of 6400 m × 6400 m on PSR sampling dates during SMEX02. The wet fields (with high soil moisture) show almost similar variance for all the resolutions signifying the strong spatial correlation. Analysis of the dry fields (with low soil moisture) indicated a log-log linearity of moments with various scales, and the slopes of these relationships exhibit a concave functional form with the order of moments, typically representing a multiscaling process. The scaling exponent of soil moisture during dry-down suggests a transition from simple scaling (in wet fields) to multiscaling (in dry fields) behavior. The fluctuation components of multiresolution analysis in the horizontal, diagonal, and vertical directions for dry and wet fields exhibited self-similarity. Another important finding of this study is the increase of subpixel soil moisture variability with increasing resolution, especially for the wet fields. These findings will help develop appropriate up-and down-scaling schemes of remotely sensed soil moisture data for various hydrologic and environmental modeling applications.     

Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case

Water and energy fluxes at the interface between the land surface and atmosphere are strongly depending on the surface soil moisture content which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite borne mission concepts, as e.g. the SMOS mission, are dedicated to provide global soil moisture information with a temporal frequency of 1-3 days to capture it's high temporal dynamics. Passive satellite microwave sensors have spatial resolutions in the order of tens of kilometres. The retrieved soil moisture fields from that sensors therefore represent surface information which is integrated over large areas. It has been shown that the heterogeneity within an image pixel might have considerable impact on the accuracy of soil moisture retrievals from passive microwave data.The paper investigates the impact of land surface heterogeneity on soil moisture retrievals from L-band passive microwave data at different spatial scales between 1 km and 40 km. The impact of sensor noise and quality of ancillary information is explicitly considered. A synthetic study is conducted where brightness temperature observations are generated using simulated land surface conditions. Soil moisture information is retrieved from these simulated observations using an iterative approach based on multiangular observations of brightness temperature. The soil moisture retrieval uncertainties resulting from the heterogeneity within the image pixels as well as the uncertainties in the a priori knowledge of surface temperature data and due to sensor noise, is investigated at different spatial scales. The investigations are made for a heterogeneous hydrological catchment in Southern Germany (Upper Danube) which is dedicated to serve as a calibration and validation site for the SMOS mission.     

A field experiment on microwave forest radiometry: L-band signal behaviour for varying conditions of surface wetness

From July-December 2004 the experimental campaign ‘Bray 2004’ was conducted in the coniferous forest of Les Landes near Bordeaux, France, using a multi-angle L-band (1.4 GHz) radiometer to measure from above the forest at horizontal polarization. At the same time, ground measurements were taken of soil and litter moisture content, while precipitation was also permanently monitored. This experiment was done in the context of the upcoming SMOS mission in order to improve our understanding of the behaviour of the L-band signal from forested areas for different wetness conditions and viewing angles. This is especially relevant for solving the problem of heterogeneity since a large fraction of SMOS pixels (∼ 30 × 30 km²) is partially covered by forest.This paper describes the objectives and the overall set-up of the Bray-2004 experiment and shows some first results. The greater part of the horizontally polarized L-band signal is found to be dominated by the influence of physical temperature. Variations in soil and/or litter moisture content are visible in the angular signal and in the above-canopy microwave emission, although the dynamic range of this last effect is very small. This, together with the fact that emissivity values are very high, is possibly due to the presence of a substantial litter layer. However, decoupling of soil and litter effects is difficult because of the strong correlation found between soil and litter moisture.     

A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface-atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150 km² study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.     

基于驻波比原理的鲜玉米含水量快速检测方法研究

以鲜玉米为研究对象,研究了一种基于驻波比原理的玉米含水量快速测量方法,该方法采用100 MHz正弦波作为测试信号,长10 mm,直径0.7 mm,间距8 mm的平行双针探头,传输线长37 cm。将重复实验测得的电压差与该组对应的玉米实际水分做拟合,得到较好的线性曲线,其相关系数为0.9062,非线性误差为±1.519%,再对所得的线性模型的稳定性做进一步的实验验证。结果表明:该线性模型的平均误差为-1.15%,最大误差不超过±3%。由此得出,线性度和误差均在合理范围之内,说明利用驻波比法快速检测玉米含水量的方法是可行的。     

Capability representation model for heterogeneous remote sensing sensors: Case study on soil moisture monitoring

Sensor capability information can be used as a basis for the integrated management of vast and heterogeneous remote sensing sensors in Global Earth Observation System of Systems environment. However, the existing representation of this information shows an inconsistent pattern, incomplete capability aspects, and casual expression forms, resulting in information silos among different systems. A sensor capability representation model (SCRM) is proposed in this study. Based on the Meta Object Facility architecture, a five-tuple hierarchical SCRM framework is formulated. Five specific representation element collections for typical remote sensing sensor types are developed to satisfy the requirements of detailed capability expression. The Open Geospatial Consortium Sensor Model Language is used as the expression form of the proposed SCRM. A prototype system is developed and a case study is conducted for a soil moisture monitoring application in Baoxie Town. The SCRM can also be extensively utilised for other environmental monitoring and modelling situations.     

基于微波和可见光遥感的冬小麦土壤墒情反演

土壤墒情是水文学、气象学及农业科学研究领域中的一个重要指标参数,对气候、农业、旱情监测都具有极为重要的意义。以河南省广利灌区冬小麦为研究对象,以水云模型为基础,利用Landsat-8和Sentinel-1 A数据计算双层衰减因子、植被和土壤的后向散射系数。采用RBF神经网络拟合土壤的后向散射系数与土壤含水量的关系,通过实测监测点对反演的结果进行精度验证,结果表明:相关系数的平方为0.754 5,均方根误差为0.022,结果良好,可为灌区冬小麦土壤墒情的评估提供参考。     

Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination

A new index was proposed to estimate soil and vegetation moisture based on NIR (858 nm) and SWIR (1240 and 1640 nm) MODIS bands. The Shortwave Angle Slope Index (SASI) parameterizes the general shape of the NIR-SWIR part of the spectrum. We expand on the novel approach used to develop SASI by proposing another index, the Angle at NIR (ANIR). We use laboratory and simulation datasets to validate the ability of SASI to estimate soil and vegetation moisture, and evaluate the advantage for using both SASI and ANIR as land-cover discrimination tools. Our results demonstrate that SASI is a good indicator of soil and vegetation moisture. ANIR shows promise in discriminating dry plant matter from soil and quantifying the amount of dry matter irrespective of changes in soil moisture or green vegetation. When SASI is combined with ANIR, discrimination between soil, dry plant matter, and low to high amounts of green vegetation is significantly improved. Angle indexes are an exciting new way to engineer indexes that contain valuable environmental information not accessible from other indexes.     

Assessing spatial and temporal variations in surface soil moisture in fire-disturbed black spruce forests in Interior Alaska using spaceborne synthetic aperture radar imagery — Implications for post-fire tree recruitment

Recent studies [Bourgeau-Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S.M., Nolan, M., Hyer, E.J., Slawski, J.J., Medvecz, M., Walters, T., and Ames, S. (in press). Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. Int. J. Rem. Sens.] demonstrated that ERS SAR imagery can be used to estimate surface soil moisture in recently burned black spruce forests in interior Alaska. We used this relationship to analyze the intra- and inter-annual variations surface soil moisture in two burned black spruce forests in Alaska. The results of this study showed distinct seasonal and longer-term trends in soil moisture in the two sites, with the site that burned in 1994 having higher soil moisture than the site that burned in 1999. The differences in soil moisture between the sites were related to landscape-scale variations in soil drainage and seasonal permafrost thawing. Finally, we found that the 1999 site had dramatically lower levels of tree recruitment (both aspen and black spruce) than the 1994 site as a result of the lower soil moisture levels. These results show that the ERS SAR and similar systems can be used to monitor a site characteristic that is important to understanding changes in the ecosystem community structure that result from variations in climate and the fire regime in the boreal region.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.