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Temporal monitoring of soil moisture using ERS-1 SAR data
Abstract
Multi-temporal synthetic aperture radar (SAR) imagery from the European Remote Sensing Satellite (ERS-1) was evaluated for monitoring soil moisture at the Romney Marsh test site as part of the UK SAR Calibration and Crop Backscatter Experiment. A total of 18 C-band (5.3 GHz) ERS-1 SAR images were acquired during the three day orbit and co-registered. Accurate calibration of the backscatter measurements was achieved using calibration constants derived from an analysis of corner reflector target responses. Mean backscatter measurements were recorded for each field and compared with field data on soil moisture, surface roughness and rainfall patterns. A comparison of daily and hourly rainfall and soil moisture measurements with backscatter for different cover types showed that the observed trends in backscatter are dominated by moisture effects. A high positive correlation between volumetric soil moisture in the range 10–40% was observed for bare soil fields. A much weaker positive relationship between soil moisture and backscatter was observed for grassland fields.
... The confidence interval widths were plotted versus the number of pixels used in the calculation which translates directly to ground area. Confidence intervals have been used to establish the suitability of backscatter estimates for soil moisture retrieval (Griffiths & Wooding, 1996; Mattia et al., 2003) but have not been used in the inverse sense to determine the spatial scale over variable surfaces required for an estimate of known quality. ...
... In a practical sense, either the confidence level for parameter estimate must be lowered, or very large areas that cross field boundaries must be included thus dramatically increasing the minimum resolution for good parameter estimates. This finding may explain the high degree of variation reported in the literature when observers regress soil moisture on backscatter (Griffiths & Wooding, 1996; Hutchinson, 2003; Shoshany et al., 2000). It is likely, but difficult to ascertain from these studies that the strength of the relationship was indirectly related to the variation in surface roughness or other varying surface properties. ...
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.
... Considering the sensitivity to SSM, several retrieval algorithms based on C-band data have been developed on bare soils. They were empirically based taking advantage of the linear relationships between σ 0 and SSM (Amazirh et al., 2018;Griffiths and Wooding, 1996;Moran et al., 1997;Sano et al., 1998;Zribi et al., 2003;Zribi and Dechambre, 2002) or they relied on the inversion of a soil backscattering model Bertuzzi et al., 1992;Bindlish and Barros, 2000;Ezzahar et al., 2020;Walker et al., 2004). For vegetated surface, the signal at C-band is a complex mix of a soil contribution attenuated by the canopy, of the volume scattering within the canopy and of the interactions between soil and vegetation (Ulaby et al., 1986). ...
Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients σ0 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence ρ are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that ρ and PR are tightly related to the canopy development. ρ is also sensitive to soil preparation. By contrast, σ0 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (<1.5 m2/m2). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band σ0, in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed σ0 yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between ρVV and the AGB (R and RMSE of 0.82, 0.05 m3/m3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and σ0. The method based on the WCM and the ρVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m3/m3 and bias = 0.01 m3/m3 over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = −0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.
... In this integration, the soil moisture status is modeled first and the SAR soil moisture retrieval algorithm is implemented to improve the spatial details and accuracy of the modeled soil moisture. Griffiths and Wooding (1996) Gogieneni et al ( ...
he Prairie Pothole Region occupies a large portion of North American Great Plains and is characterized by millions of depressions (potholes) that form wetlands. Although the wetlands have various hydro-ecological and socio-cultural functions and services, they are under immense pressure from the impacts of land use and climate change. Therefore, there is a need to characterize these wetlands, not only to understand their services and functions but also to design management practices for effective wetland protection and restoration. Wetlands are often characterized using surface and near-surface hydrological data acquired in situ or via remote sensing observation. In this research, the in situ-measured hydrological data was able to characterize the variability of near-surface hydrology in the Prairie Potholes Region of Central Canada. However, owing to the limitations of in situ measurements, the research established functional relations between remote sensing (RADAR/LiDAR) and near-surface hydrological data. Empirical models developed from these relationships effectively mapped aerial soil moisture and monitored regional and local soil moisture dynamics during the snow-free periods. Furthermore, a more accurate way of delineating prairie wetlands, classifying wetland types and monitoring the wetland boundary changes, IS explored using integrated RADAR/LiDAR/optical data. In conclusion, the research has produced a novel mapping and monitoring technique and results that significantly advance the understanding of hydrodynamics and important hydrologic controls of wetlands and the surrounding uplands at various spatial scales.
... Multi-spectral images are now commercially available from satellites, and the future might bring higher spatial and spectral resolution images. Satellite based radar systems (SAR) have been effectively used for studying soil (Leconte et al., 2004) and crop moisture contents (Griffiths and Wooding, 1996). New opportunities for distinguishing spatial details have been offered by the successful launch of high-resolution satellite sensors i.e. ...
The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in complexity and optimality have been proposed for this purpose. This paper reviews various commonly used non-destructive methods for estimating foliar N status of plants.
... The surface soil moisture content influences the radar signatures because the backscattered energy is dependent on the dielectric constant of the soil, which varies rapidly as a function of its water content (Ulaby, 1974;Brisco et al., 1997;Schmugge and Wang, 1980). Numerous studies including Wooding et al. (1992), Boisvert et al. (1996), andGeng et al. (1996), using Synthetic Aperture Radar (SAR) and operating at C-band, have demonstrated the effectiveness of radar for surface soil moisture monitoring (0 to 10 ern), Results from previous work relating radar backscatter to the surface soil water content in an agricultural environment of the County of Buenos Aires, Argentina, were very promising and encouraged further evaluation of this application (Salgado, 1998). This project was part of the GlobeSAR-2 program which was a technology transfer and training program involving RADARSAT applications (Brisco et al., 1997). ...
L'objectif de cette étude, réalisée dans le cadre du programme GlobeSAR-2 en Argentine, était d'estimer l'humidité de surface du sol des champs agricoles dans la province de Buenos Aires. Quatre images RADARSAT en mode standard (S1) ont été acquises et des campagnes de terrain ont été menées quasi simultanément en support au traitement des images RADARSAT. Le contenu volumétrique d'eau du sol a été déterminé pour des champs sélectionnés utilisant la méthode gravimétrique. Les images RSO ont été corrigées géométriquement et fusionnées avec deux images Landsat TM de la même période. Les données TM ont été utilisées pour améliorer la géoréférenciation et la détermination de l'utilisation du sol. Des données météorologiques, pédologiques, topographiques et d'utilisation du sol ont aussi été acquises et superposées aux données images. L'humidité du sol mesurée a été corrélée avec les coefficients de rétrodiffusion radar extraits des images des champs expérimentaux, qui ont été stratifiés selon leur type de couvert. La meilleure corrélation entre l'humidité volumétrique de surface du sol et le coefficient de rétrodiffusion radar a été observée pour les sols nus en état de jachère (chaume constituée de blé et de maïs, R2=0,66), alors que la pire corrélation a été notée pour les sols complètement recouverts de plants de soja de 60–80 centimètres (R2=0,50). Des cartes d'humidité du sol ont été créées en inversant le modèle de régression et en utilisant cette équation pour estimer l'humidité du sol sur la base des valeurs de rétrodiffusion mesurées à partir des images RADARSAT. L'analyse de ces cartes montre qu'il est possible, grâce aux données RSO, de réaliser efficacement le suivi et la cartographie de l'humidité de surface du sol dans les zones agricoles, en autant qu'une information complémentaire soit disponible quant au couvert, incluant la rugosité de surface, le couvert végétal et la direction des axes de culture.
... For vegetated fields sharing similar structural characteristics with tallgrass prairie flora, they reported correlations of r = 0.44 for cereal grains and r = 0.23 for grass pastures. In the U.K., Griffiths and Wooding (1996) also used ERS-1 data to examine the relationship between s o total and q v for three bare soil fields and three grass fields. While bare soil correlations were high, ranging from a low of r = 0.76 to a high of r = 0.99, the relationships between s o total and soil moisture in the grass fields were referred to only as statistically insignificant. ...
Recent advances in radar remote sensing techniques illustrate the potential for monitoring soil moisture conditions at spatial and temporal scales required for regional and local modeling efforts. This research examined the feasibility of producing accurate and spatially distributed estimates of soil moisture using a time series of ERS-2 radar images for a tallgrass prairie ecosystem in northeast Kansas. Methods used included field data collection of soil moisture, digital image interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS-2) imagery, and environmental modeling in a raster geographic information system (GIS) and image processing environment. Critical to this study was determining the scattering behavior of overlying vegetation, or the contribution of vegetation backscatter (σoveg) to the total backscatter coefficient (σototal), which was simulated using a modified water cloud model. By removing σoveg from σototal, the amount of backscatter contributed by the soil surface (σosoil) was isolated and the linear relationship between σosoil and volumetric soil moisture determined. Single-date correlations averaged r = 0.62 and r = 0.67 for a burned and unburned watershed, respectively, within the study area. While previous studies have questioned the sensitivity of C-band radars to near-surface soil moisture conditions, these results show that ERS-2 data may be capable of monitoring soil moisture conditions over even extremely dense natural grassland vegetation.
... While synoptic and spatially covering surface soil moisture measurements are not feasible using conventional measuring techniques, remote sensing of the backscatter in the microwave region provides a means to calculate surface soil moisture. This is due to the dependency of the backscatter upon the dielectric constant which is strongly dependent upon the water content [1,9,15]. However, the backscatter intensity is also dependent upon the surface roughness and vegetational parameters [13 -15]. ...
A new approach for calculating mesoscale soil moisture maps from coarse resolution (500 m - 1 km) SAR data utilizing spatially reduced ERS data is presented. The processing of the radar data is described which includes a correction for the impact of surface roughness, plant water content and soil texture upon of the backscatter intensity. First, the portion of the pixel which does not provide soil moisture information (forest, built-up areas, water) is corrected, then the corrected backscatter intensity is normalized to a reference landuse (cereals). The required landuse map was derived from LANDSAT data. However, these landuse classes which must be distinguished to account for differences in surface roughness and plant water content, may also be derived from AVHRR spectro-temporal unmixing. Using model results and measurements together with the landuse map, the impact of the plant water content was corrected. Finally, the soil texture was taken into account to calculate the mesoscale surface soil moisture. The resulting soil moisture was validated quantitatively using ground truth measurements. A qualitative validation of the spatial patterns was carried out through by comparison of the calculated soil moisture with precipitation patterns. A good agreement was found between ground based and satellite derived soil moisture (RMSQ less than 5 VOL%).
... Past empirical and theoretical studies have demonstrated the dependence of satellite based SAR C-band backscatter on soil moisture conditions in bare to low vegetated landscapes including rangeland, agricultural fields, and recently burned boreal forest fire scars (e.g. Dobson et al. 1992, Wooding et al. 1992, French et al. 1996, Ulaby et al. 1996, Moran et al. 2000). Newly burned boreal forest fire scars of Alaska, Canada and Russia appear brighter than adjacent unburned forest in the European Space Agency's ERS C-band imagery when the soil is wet (Kasischke et al. 1995, Bourgeau-Chavez et al. 1997, 2000a) and darker than the unburned forest when the soil is very dry. ...
Due to the large volume of carbon currently stored in boreal regions and the high frequency of wildfire, the prospects of a warming climate would have important implications for the ecology of boreal forests which in turn would have significant feedbacks for carbon cycling, fire frequency, and global climate change. Since ecological studies and climate change models require routine information on surface soil moisture, the ability to remotely sense this variable is highly desirable. Toward this end research was conducted on developing methods for the retrieval of spatially and temporally varying patterns of soil moisture from recently burned boreal forest ecosystems of Alaska using C-band satellite radar data. To do this we focused on both individual date and temporal SAR datasets to develop techniques and algorithms which indicate how moisture varies across a recently burned boreal forest. For each of the methods developed we focused on reducing errors of SAR-derived soil moisture estimates due to confounding factors of variations in vegetative biomass and surface roughness. For the individual date soil moisture monitoring, we grouped test sites by a measurable biophysical variable, burn severity, and then developed algorithms relating moisture to SAR backscatter for each burn severity group. The algorithms developed had high coefficients of determination (0.56-0.82) and the moisture maps produced had high accuracy (3.61 rms error) based on the minimal validation conducted. For the seasonal soil moisture mapping we used principal component analysis to capture the time-variant feature of soil moisture and minimize the relatively time-invariant features that confound SAR backscatter. This resulted in good agreement between the drainage maps produced and our limited in situ observations and weather data. However, further validation, with larger sample sizes, is needed. While this study focuses on Alaska, research indicates that the techniques developed should be applicable to boreal forests worldwide.
... statistical relationships to theoretical backscattering models, have been proposed and applied. Often, a linear relationship between the backscattering coefficient and soil moisture is assumed [Bradley and Ulaby, 1981; Dobson and Ulaby, 1986; Griffiths and Wooding, 1996; Le Hégarat-Mascle et al., 2002; Haider et al., 2004]. However, it is well known that the relationship is nonlinear, showing higher sensitivities of the backscattering coefficient at lower moisture contents [Altese et al., 1996; Walker et al., 2004]. ...
Radar remote sensing of bare soil surfaces has been shown to be very useful for retrieving soil moisture. However, the error on the retrieved value depends on the accuracy of the roughness parameters (RMS height and correlation length). Several studies have demonstrated that these parameters show a high variability within a field, and therefore a lot of soil roughness profiles need to be measured to obtain accurate estimates. However, in an operational mode, soil roughness measurements are not available and therefore, for different types of tillage, roughness parameters are ill known. Possibility theory offers a way of handling this type of uncertainty, by modeling roughness parameters by means of possibility distributions. Inverting the integral equation model then leads to a possibility distribution for soil moisture. After transforming these possibilities into probabilities, mean soil moisture values and the uncertainty thereupon (given by the standard deviation) are obtained. It is found that the uncertainty depends on the wetness state of the soil. An application of our possibilistic retrieval algorithm to field observations at two sites in Belgium and one site in Italy resulted in accurate soil moisture observations (RMS error less than 6 vol %).
... The commercial available satellites can now provide multi-spectral images with meters level and in future may provide higher spatial and spectral resolution images. Some research has considered the satellite based radar system (SAR) to study the soil and crops moisture content (Leconte et al., 2004; Griffiths and Wooding, 1996). Another important question is the cost of this new technology used for crop management. ...
Nitrogen (N) applications often increase crop yields significantly, but N needs vary spatially across fields and landscapes. The color of the wheat plant is sensitive to N status and may provide a means to accurately predict N fertilizer rates matching spatial variability. Previous researches have reported that remote sensing may contribute to N management decisions by collecting spatially dense information. The objective of this study was to determine the feasibility of using high-resolution satellite imaging for evaluating N status of winter wheat in the North China Plain. High-resolution images from a QuickBird satellite were taken on April 1, 2002 at booting stage of wheat with multi-spectral wavelengths (blue, green, red, and near-infrared). Correlation analyses indicated that all the broadband indices derived from the satellite images correlated well with sap nitrate concentration, SPAD readings, total N concentration, and aboveground biomass. The individual band reflectance values R, G, B correlated well with sap nitrate concentration, SPAD readings, total N concentration, and aboveground biomass. These results demonstrated the potential of using new generation high-resolution satellite imaging for large area wheat N status diagnosis.
... Lower (0– 10%) and upper (25– 40%) parts of this range occur at widely scattered points. This range corresponds with the study of Griffiths and Wooding (1996), who reported that there is a significant positive correlation between VSM in the 10– 40% range and the ERS-1 SAR backscatter. Shoshany et al. (2000) and Svoray (2000) had further implemented Eq. (6) for an area of wide variation of soil properties along a climatic gradient. ...
Intrinsic soil factors affect and are affected by the spatial variation of soil properties. Therefore, intrinsic soil factors may both characterize and serve as an indicator for soil taxonomy. Difficulties in inferring intrinsic soil properties hamper attempts to assess their variability, on both local and regional/broad scales. Radar remote sensing might facilitate a breakthrough in this field, due to its sensitivity to the soil water content. In this research, a raster Geographic Information System (GIS) methodology is developed for combining multi-temporal ERS-2 SAR and Landsat TM data, which allows the estimation of drying rate patterns in bare soil surfaces. The drying rates provide further indication about intrinsic soil properties. The multi-scale behaviour of soil-drying rates is described using the richness – area curves and characteristic curves are determined to four soil formations typical to a climatic gradient between Mediterranean and semi-arid environments in Israel. To the best of our knowledge, this is one of the first attempts to document the effect of intrinsic soil factors on the soil system at the regional scale. The results achieved here demonstrate the connection between drying rates, richness – area variation and soil hydraulic conductivity of the four soil formations.
... While synoptic and spatially covering surface soil moisture measurements are not feasible using conventional measuring techniques, remote sensing of the backscatter in the microwave region provides a means to calculate surface soil moisture [1,2]. Although most of the experiments which have proven that soil moisture can be detected using ERS data are dealing with bare soil conditions [3,2], a number of experiments show good results in estimating soil moisture under vegetation [11,13]. To detect soil moisture under vegetation cover, the influence of the canopy has to be eliminated. ...
2) , Hans van Leeuwen (2) , Heike Bach (3) (1) Institute for Geography, University of Munich Luisenstr. 37, 80333Munich, Germany r.stolz@iggf.geo.uni-muenchen.de k.schneider@iggf.geo.uni-muenchen.de (2) SYNOPTICS Integrated Remote Sensing & GIS Applications BV P.O. Box 117, 6700 AC Wageningen The Netherlands main@synoptics.nl (3) VISTA-Remote Sensing Applications in Geosciences GmbH Luisenstr.45, 80333 Munich Germany Bach@vista-geo.de ABSTRACT Within the frame of the project GeoBIRD (ESA contract 12950/98/NL/GD entitled "Retrieval of geo-and bio-physical information from remote sensing data") an attempt was made to derive soil moisture by combining the microwave remote sensing model CLOUD and the land surface process model PROMET-V (PROcess oriented Modular Environment and Vegetation model. Three test sites in Europe, which show different dominant vegetation types were chosen. The CLOUD model was fitted for each of the vegetation types. The plant water content which is a central vegetation parameter required for the CLOUD model was derived from PROMET-V and the backscatter values are extracted from corrected and calibrated ERS data. By inverting the CLOUD model, the spatially distributed soil moisture values can be calculated. These modelled soil moisture results are compared to in situ soil moisture measurements which are available for selected fields in the different testsites. A sensitivity study was carried out and the achieved accuracies are compared and discussed.
... On the other hand, remotely sensed observations of model outputs, more specifically surface soil moisture values, can be used to validate model results. Examples of studies that have focused on the retrieval of these variables can be found in [10]–[15]. Furthermore, these data can also be assimilated into the models, reducing the error in the model predictions. ...
It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.
... A large number of reports suggest the usefulness of microwave backscatter in soil and plant variables estimation (Brisco and Brown, 1998;Le Toan et al., 1984). Many authors describe the relationship of the backscattering coefficient (r o ) to the Leaf Area Index, plant biomass, plant water content, and soil moisture content (Dabrowska-Zielinska et al., 1994;Daughtry et al., 1991;Moran et al., 1998;Ulaby et al., 1984;Wooding et al., 1992 the smaller incident angles (<30°) have been found to be quite useful in soil moisture estimation due to the decreased effects of roughness and of vegetation attenuation. The higher frequencies have been widely used for crop classification (there may be some regional differences depending on crop type and development). ...
The goal of this study was to extract from dual-frequency satellite SAR signatures consistent information about moisture in soils and about various features of plants for analyzing crop growth conditions in any agricultural region. The study was carried out on Polish agricultural regions but it is hoped that it will be applicable anywhere on the planet. During a satellite overpass on a particular date, the ground-based measurements required such as soil moisture (SM), Leaf Area Index (LAI), and biomass were collected from 10 to14 May 1998. The backscattering coefficients at various frequencies were collected from ERS-2.SAR (C-VV) on May 10, 1998 and from JERS-SAR (L-HH) on May 14, 1998. The applicability of three different vegetation descriptors to the semi-empirical water-cloud model was investigated. The contribution to the backscatter values of vegetation features such as leaf area expressed in the Leaf Area Index and the dielectric properties of leaf surface expressed in the Leaf Water Area Index (LWAI) and the Vegetation Water Mass (VWM) was examined in order to reveal the best fit of the model. It was found that in C-band, which had an incidence angle of 23°, the soil moisture contribution to the sigma value was predominant over the vegetation contribution. When the canopy cover increases, the sensitivity of a radar signal to dry soil conditions (SM < 0.1) decreased. The sigma value was the most sensitive to vegetation descriptor VWM which described the amount of water in vegetation. Attenuation of soil signal by the canopy was found in all three vegetation descriptors types; the strongest attenuation effect was observed in the case of VWM. In L-band (where the incidence angle was 35°), the dominant signal to total σo value comes from volume scattering of vegetation for LAI > 3. When LAI < 3 the vegetation contribution to total σo value appeared in two-way attenuation. The results gave us the possibility of comparing the modeled with the measured soil and vegetation parameters.
... Haferkamp and MacNeil (2004) stressed the importance of April–June precipitation when more than 90% of the biomass of cool-season grasses and 75% of the biomass of warm-season grasses are produced by the end of June. NDVI presently acts as a de facto surrogate for water availability, but spatial data quantifying soil moisture are under investigation by NASA, NOAA, USDA, and others (Griffiths & Wooding, 1996; Lakshimi, 2004; Lu & Meyer, 2002). Other aggregations of precipitation, such as precipitation lags or a moving window of precipitation accumulation, will be investigated. ...
Rangeland carbon fluxes are highly variable in both space and time. Given the expansive areas of rangelands, how rangelands respond to climatic variation, management, and soil potential is important to understanding carbon dynamics. Rangeland carbon fluxes associated with Net Ecosystem Exchange (NEE) were measured from multiple year data sets at five flux tower locations in the Northern Great Plains. These flux tower measurements were combined with 1-km2 spatial data sets of Photosynthetically Active Radiation (PAR), Normalized Difference Vegetation Index (NDVI), temperature, precipitation, seasonal NDVI metrics, and soil characteristics. Flux tower measurements were used to train and select variables for a rule-based piece-wise regression model. The accuracy and stability of the model were assessed through random cross-validation and cross-validation by site and year.
Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.
... The fast development of the satellite technologies have made the acquisition of high spatial and spectral resolution satellite images become cheaper and easier than before. In recent years, some researchers have considered hyperspectral sensors to monitor rice nitrogen status and nutrients disorder[31] and satellites with radar systems (SAR) to study soil and crop moisture3233 and forest biomass [34](Austin et al., 2003). Although more sensors have been used in monitoring vegetation growth status than before, satellite images for agriculture uses still face many challenges, such as clouds or dust which interfere the acquisition or quality of the image, low spectral resolution in explaining the canopy and canopy reflectance disorders caused by other factors except nutrients, etc. ...
The objective of this study was to determine relationship between high resolution satellite image and wheat N status, and develop a methodology to predict wheat N status in the farmers' fields. Field experiment with 5 different N rates was conducted in Huimin County in the North China Plain, and farmers' fields in 3 separated sites were selected as validation plots. The IKONOS image covering all research sites was obtained at shooting stage in 2006. The results showed that single band reflectance of NIR, Red and Green and vegetation indices of NDVI, GNDVI, RVI and OSAVI all well correlated with wheat N status parameters. Field validation results indicated that the prediction models using OSAVI performed well in predicting N uptake in the farmers' fields (R 2 = 0.735). We conclude that high resolution satellite images like IKONOS are useful tools in N fertilization management in the North China Plain. © 2012 IFIP International Federation for Information Processing.
... This is mainly due to the difficulties in measuring soil moisture consistently over large areas [2]. Due to the dependency of the radar backscatter intensity upon the dielectic constant of the observed medium, which in the case of agricultural landuse depends upon the surface soil water content, surface soil moisture patterns can be determined from radar backscatter [5,[7][8][9]. The algorithms described in the literature require an unequivocal assignment of land use and soil texture to each pixel in order to correct the radar backscatter for that part of the backscatter signal which is not due to soil moisture. ...
Mesoscale soil moisture maps can be determined quantitatively from
coarse resolution radar backscatter data, which will be available in the
near future from ENVISAT. The method requires the correction of the
backscatter signal for the impact of forests, built-up areas, and water
surfaces as well as a normalization of the backscatter to a reference
crop and the correction of the plant water content. The required landuse
data can be derived from AVHRR. Plant water content can be derived from
observations, plant growth models, or estimated from NDVI
... The area of those fields ranged from 1.3 (field 196) to 11.1 ha (field 511). Wooding et al. [27] found that a minimum field size of 1 ha would give a variability in the estimated backscatter smaller than 0.25 dB on ERS-1 scenes, overcoming the effects of the SAR speckle. On the other hand, very large fields can show differentiated in-field SM patterns due to their great spatial variability, complicating the calculation of representative average SM values. ...
The present paper focuses on the ability of currently available RADARSAT-1 data to estimate surface soil moisture over an agricultural catchment using the theoretical integral equation model (IEM). Five RADARSAT-1 scenes acquired over Navarre (north of Spain) between February 27, 2003 and April 2, 2003 have been processed. Soil moisture was measured at different fields within the catchment. Roughness measurements were collected in order to obtain representative roughness parameters for the different tillage classes. The influence of the cereal crop that covered most of the fields was taken into account using the semiempirical water cloud model. The IEM was run in forward and inverse mode using vegetation corrected RADARSAT-1 data and surface roughness observations. Results showed a great dispersion between IEM simulations and observations at the field scale, leading to inaccurate estimations. As the surface correlation length is the most difficult parameter to measure, different approaches for its estimation have been tested. This analysis revealed that the spatial variability in the surface roughness parameters seems to be the reason for the dispersion observed rather than a deficient measurement of the correlation length. At the catchment scale, IEM simulations were in good agreement with observations. The error values obtained in the inverse simulations were in the range of in situ soil moisture measuring methods (0.04 cm<sup>3</sup>·cm<sup>-3</sup>). Taking into account the small size of the catchment studied, these results are encouraging from a hydrological point of view.
Le caractère exceptionnel du contenu informationnel d'une série de données multidates de surfaces terrestres à des latitudes septentrionales polaires, obtenues à partir d'un radar à antenne synthètique embarqué à bord du satellite ERS-1, est illustré à l'aide de trois exemples. Plus particulièrement, on a démontré que les images RAS en bande C étaient utiles pour distinguer la végétation palustre de la végétation non palustre dans les biomes de type toundra, déceler les différences de niveau des eaux dans la toundra, surveiller les variations saisonnières relatives à la végétation et au niveau des eaux dans la toundra, surveiller la forme générale de la prise en glace des chutes d'eau dans différentes régions de végétation en Alaska, surveiller les variations intra et inter-saisonnières de l'humidité du sol dans les forêts perturbées par le feu et, potentiellement, estimer les niveaux de biomasse des strates herbacées consumées par le feu dans les forêts d'épinettes noires. Ces résultats ont une incidence importante pour le radar à antenne synthétique monté à bord de RADARSAT qui pourra couvrir de vastes territoires aux latitudes septentrionales à des fréquences élevées d'échantillonnage. Cette capacité peut se révéler importante pour surveiller des processus essentiels au cycle du carbone dans la toundra et la taïga, y compris la surveillance des effets engendrés par les feux de forêt, la durée de la saison de croissance et le niveau des eaux dans la toundra (principal facteur de contrôle de la production de méthane).
In order tofacilitate the incorporation of RADARSAT data into on-going programs for monitoring land use and land cover changes in the Brazilian portion of the Amazon basin, a rapid assessment of the visibility and detectability of features of interest was carried out and on the dynamics of change of thesefeatures with time. RADARSAT images were acquired for five dates in 1996: one in April, one in May and three in October These have been analyzed qualitatively and quantitatively,in conjunction with aerial photographs. Field trips were made concomitantly with both first and second series of RADARSAT overpasses; a Landsat - TM image was obtained from June 14, 1996. The most significant findings were: Visual interpretation of Standard Mode RADARSAT images could be used to detect urban areas, streams, and the general pattern of deforestation resultingfrom human settlement on all five images. The contrast between deforested areas and the primary forest was variable, being the greatest on an afternoon overpass at the end of the dry season, when no rain was recorded in the previous 24 hours. Compared to the undisturbed or minimally disturbed upland rainforest, anthropic areas were observed to exhibit much larger temporal variations in backscatter (up to 4 dB in the case of recently-burned forest); under the proper conditions of recent precipitation at the end of the dry season, RADARSAT imagery appears to be a very sensitive indicator of forests which have been recently burned. With C-HH radar the use of multitemporal data will increase the reliability of detecting and mapping deforestation; the ideal two date combination would be to obtain one image under very dry conditions and a second image following a recent rainfall. Pages: 350-359
Research activities aiming at integrating Earth Observation data in large scale distributed hydrological modelling will be presented. The focus will be on the Senegal River basin in West Africa where satellite data from various sensors and from different parts of the electromagnetic spectrum will be used in combination with a distributed model specifically adapted to utilize Earth Observation data. The work represents the initial results obtained by the ongoing INTEO project. The outcome and experiences gained so far are primarily related to low resolution optical data from the NOAA-AVHRR sensor, and results related to land cover classification, vegetation and soil moisture will be presented.
Use of SAR images for soil surface moisture estimation requires taking into account the other factors that influence the radar backscattering signal, among which the surface cover roughness at centimeter scale is very important. There are several methods to determine the roughness, but many are expensive or complex field operation. A versatile and economic method that uses a photographic camera and a girded screen is presented. Each picture is numerically processed obtaining the RMS height, as parameter of the crop-soil complex roughness. By means of krigging geostatistics techniques the spatial distribution of roughness is estimated. Experiences in parcels with wheat cover, located in the hill agricultural area of Buenos Aires Province, Argentina are shown. The found RMS values (29 mm < RMS < 48 mm) are analyzed with four roughness approaches. Their utility in order to estimate soil surface moisture status in agricultural parcels by means of their application like input into the SAR images backscattering models is stated.
Surface and near-surface hydrological conditions are not generally considered in the selection of protected-area boundaries; however, these hydrological conditions may be critical in the maintenance of hydrologically driven biodiversity areas and should factor heavily in systematic conservation planning and impact-mitigation efforts. Here we demonstrate the use of Synthetic Aperture Radar (SAR) satellite imagery to model surface and near-surface hydrological dynamics and related vascular-plant species richness surrounding Sundance Provincial Park, Alberta, Canada. Based on a relationship established between field-sampled soil moisture and remote-sensed SAR backscatter coefficient, we generated landscape-scale maps of surface hydrological condition and related vascular-plant species richness surrounding the protected area. After processing, SAR data were classified into three hydrological classes: unsaturated, saturated and inundated. By assembling a multi-year series of SAR images acquired across the natural range of hydrological conditions, the probability of surface saturation and inundation was developed. Using the resulting probability map to select sites for biodiversity assessment, we found relatively strong relationships between total vascular plants (species richness, especially ferns and herbs), and the probability of wet area (both saturated and inundated areas) occurrence. Using this relationship, we mapped vascular-plant species richness across the study region revealing opportunities to mitigate impacts from adjacent developments and to expand the park to increase its ecological value. This study presents an important approach for assessing biodiversity of existing parks and generating valuable input layers that help to define ecologically relevant protected-area boundaries.
The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.
The semi-empirical water cloud model can be implemented to derive spatial values of biophysical parameters from remotely sensed radar data for use throughout different disciplines of physical geography. The water cloud model formulation and development are detailed in this review paper, incorporating the major advances and publications related to the model.
The goal of this study was to evaluate the potential use of RADARSAT‐1 images to assess daily variations in dead fuel moisture over a northern boreal forest area, as parameterized by the Canadian Forest Fire Weather Index (FWI) System. The study area was located in the south‐central region of Canada's Northwest Territories and was comprised of three land cover classes (mature forest, burned forest, and fireguard areas). Nineteen RADARSAT‐1 images were acquired over the study area in June 2000 and August 2002, and their backscatter was compared to weather data and to the FWI System components. In both cases, the influences of incident angle and land cover class were measured. Radar backscatter was related to rainfall, and strong relationships were observed with the FWI System codes and indexes, particularly for Duff Moisture Code (R = 0.68–0.83), Drought Code (R = 0.77–0.82), Build‐up Index (R = 0.72–0.86), and FWI (R = 0.62–0.85). The best regression models were obtained using a stepwise regression procedure in which radar backscatter from the burned forest was used as the independent variable.
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.
The paper describes the results of a study based on the integration of remote sensing and geographical information system techniques to evaluate a distributed unit hydrograph model linked to an excess rainfall model for estimating the streamflow response at the outlet of a watershed. Travel time computation, based on the definition of a distributed unit hydrograph, has been performed, implementing a procedure using (1) a cell-to-cell flow path through the landscape determined from a digital elevation model (DEM); and (2) roughness parameters obtained from remote sensing data. This procedure allows the taking into account of the differences, in terms of velocity, between the hillslopes and the stream system. The proposed procedure has been applied to two watersheds in Sicily, in order to establish the level of agreement between the estimated and recorded hydrographs, using as a tool to calculate the excess rainfall a simplified version of the probability distributed model.
The feasibility of using multi-temporal spaceborne Synthetic Aperture Radar (SAR) imagery for the evaluation of soil moisture at a field and regional scale is evaluated through the use of European Remote Sensing Satellite (ERS-1 and ERS-2) images. Six pairs of Single Look Complex (SLC) images and six Precision Images (PRI) were acquired during the 1996 Tandem mission for the test site of Gembloux, in Belgium. The SLC images of each Tandem pair were processed by the “Centre Spatial de Liège” (CSL) in order to produce interferometric coherence images, and the PRI were calibrated; all the images were coregistered and rectified. The backscattering coefficient values and the mean coherence values were calculated for each pilot field and compared with soil moisture and crop characteristics measurements. Our study confirms that for bare soil fields, a linear relationship exists between the volumetric near-surface soil moisture and the backscattering coefficient for unsaturated soils, with a determination coefficient of 0.75. The innovative key point of the study shows an excellent correlation between the backscatter value of the image subset of the test site (80sq.km), and the corresponding mean soil moisture measurements, with a determination coefficient of 0.98. The image subset comprises mostly agricultural fields but also forests, meadows and urban areas. Moreover, the relation is as valid in the spring, before the growing season, as in the summer, when the vegetation cover is high. The study also reveals that coherence images are useful to identify bare soil fields.
Previous studies to determine the utility of satellite remote sensing for soil moisture monitoring have concentrated on field scale assessment. The European Remote Sensing satellites (ERS-1 and ERS-2) Synthetic Aperture Radar (SAR) instruments have a spatial resolution of less than 30 m in azimuth and 26 m in range, and potentially offer a considerably finer scale of soil moisture assessment. A series of soil moisture measurements were taken within a 15 ha bare field on the Essex (UK) coast at times to coincide with ERS-1 overpasses in the autumn and winter of 1995-96. This dataset has been used to investigate the ability of ERS-1 SAR backscatter response to detect spatial variation in soil moisture within the field. Analysis of variograms of SAR backscatter show that spatial variations in the ERS SAR data can be detected within the field and that spatial dependency increases when the soil is wet. Field measurements of soil moisture do not reflect these findings in quite the same way; during periods of low soil moisture content, the spatial variation of soil moisture cannot be easily characterized by the variograms. When the field is wet, spatial autocorrelation is more evident in the variograms. Regression analysis of paired observations of measured soil moisture and ERS SAR backscatter demonstrated that no significant relationship could be obtained for each day. However, when paired data for different dates were combined, a significant relationship between SAR backscatter and soil moisture could be obtained. The results suggest, therefore, that for this case study, soil moisture can be predicted at the field scale but not at the within-field/pixel scale.
Principal components analysis (PCA) is applied to a time series of European Remote Sensing (ERS) synthetic aperture radar (SAR) scenes of the Alzette River floodplain (Grand-Duchy of Luxembourg). These images cover markedly different hydrological conditions during several winter seasons in order to enable the examination of the decrease of the radar backscattering signal during drying-up phases following important flood events. At the floodplain scale, with homogeneous land use and constant topography, the first principal components (PCs) are mainly dominated by the variance related to the changing areas. The PCs are thus mainly controlled by subsurface and surface water dynamics. The field observations of a densely equipped piezometric network in the floodplain are used to calculate a mean soil saturation index (SSI) continuously. A classification scheme, based on the PCs and k-means algorithm, leads to the segmentation of the floodplain into several hydrological behaviour classes with distinctive responses versus changing moisture conditions. To validate this classification method with ground-based estimations, the relation between the mean backscattering values of microplots within each PCA-derived hydrological class and the water table measurements, expressed by means of the SSI, is evaluated. Results show that each class of microplots is characterized by the slope of the ‘backscattering–SSI’ function and by the SSI threshold value at which groundwater resurgence appears. The water ponding implies very low signal return due to the specular backscattering effect on the water surface. Based on established relationships between measured initial water table depths, runoff coefficients and rainfall-induced water table rises, these results are used to discuss the potential of SAR-derived information in flood management applications. Copyright © 2005 John Wiley & Sons, Ltd.
Characterizing the spatial and temporal variation in surface hydrological dynamics of large boreal landscapes is vital, since these patterns define the occurrence of key areas of land-to-lake and land-to-atmosphere hydrological and biogeochemical linkages that are critical in the movement of matter and energy at local to global scales. However, monitoring surface hydrological dynamics over large geographic extents and over long periods of time is a challenge for hydrologists, as traditional point measurements are not practical. In this study we used European Remote Sensing satellite radar imagery to monitor the variation in surface hydrological patterns over a 12-year period and to assess the change in the organization of saturated and inundated areas of the landscape. Using the regional Utikuma River drainage basin (2900 km2) as the test area, the analyses of patterns of wetlands indicated that, during dry climatic conditions, wetland sizes were small and disconnected from each other and receiving bodies of water. As climatic conditions changed from dry to mesic, wetland numbers increased but were still disconnected. Very wet climatic conditions were required before the disconnected wetlands coalesced and connected to lakes. During these wet conditions, the response of the lake level at Utikuma Lake was observed to be much higher than under drier conditions. Analyses of individual wetland maps and integrated wetland probability maps have the potential to inform future biogeochemical and ecological investigations and forest management on the Boreal Plain. Copyright © 2007 John Wiley & Sons, Ltd.
The status quo in forestry practice is to leave standard width buffers around water bodies in order to prevent the excess transport of sediments and nutrients from terrestrial to aquatic systems. This practice does not seem to be effective in the sub-humid boreal forest where climatic and physiographic variability produces complex hydrologic pathways not well protected by standard width buffers. We developed a remote sensing technique that forms a hydrologic basis for buffer strip design. Synthetic aperture radar (SAR) imagery was used to map, both inundated and saturated areas (herein called wet areas) amenable for surface transport of nutrients and sediments on a low relief landscape in northern Alberta, Canada. Wet areas coverage of the Moose Lake drainage basin showed a semi-logarithmic relationship with daily discharge (r2 = 0.72, p < 0.001, n = 18). This relationship was used to define a flow–duration curve for wet areas that could be used as an aspatial assessment of what proportion of a drainage basin should be protected. A probability map of wet areas formation was calculated from the database of 18 images. We demonstrated how the probability map may be used to design adaptive buffer strips for the mitigation of increased nutrient loading to boreal lakes following timber harvesting.
1.Britain is unusual in the quantity and quality of species and habitat data available, at both national and regional scales. This paper reviews the sources, coverage and quality of these data.
2.Habitat and species data are used by conservation agencies in England, Scotland and Wales for site selection and for monitoring habitat quality. The paper argues, however, that neither habitat data nor species distribution data on their own are sufficient to locate and monitor habitats for nature conservation purposes effectively.
3.Differences in sampling methodologies between habitat and species surveys present methodological difficulties for the development of an integrated monitoring system that uses both types of data. These problems need to be overcome if habitat and species data are to be used more effectively for nature conservation in the wider countryside.
4.A more integrated system based on the concept of biotope occupancy is proposed and discussed. The implementation of the system would assist with understanding those factors that explain observed patterns in species distribution and diversity, thereby helping to improve the effectiveness of policies for nature conservation.
The radar data have been used for establishing the proper crop information system in Poland. The objective of the study is to find an efficient method of crop classification based on satellite microwave data and to find the relationship of different soil - vegetation parameters on backscatter. There is a large demand of microwave images as due to often cloud effect these satellite data are available during a certain growth season. Wielkopolska region located in western Poland was selected for the research works. This region, characterized by intensive agricultural practices and diversified agricultural pattern, was equipped with ground truth information, which enabled to make properly the whole classification process.
Block kriging is applied to geographically register digital images from the RADARSAT-1 satellite to soil moisture samples. Both satellite and soil moisture data are interpolated in this process to obtain precise registration. Median and adaptive Lee filtering of images are also used to correlate pixel values with soil moisture. A case study is presented using a playa in the western Great Basin, Nevada, of North America. A statistically significant correlation is found between interpolated RADARSAT-1 digital numbers and interpolated soil moisture. Results indicate that RADARSAT-1 is sensitive to median soil moisture levels; however, filtering does not significantly improve this sensitivity. The study results indicate the ability of synthetic aperture radar to delineate and map temporal soil moisture variability with the use of geostatistical methods to interpolate values over pixel areas.
The COST 717 action is implemented by three working groups, each investigating different aspects of the use of radar observations in hydrological and NWP models. Working Group 1 (WG-1), the subject of this paper, looks at the use of radar observations in hydrological models. It has divided the work into 9 subject topics each being undertaken by teams of 2, 3 or 4 members of the WG. Initially these focus on (i) identifying the current state of radar use in Europe and compiling a database of operational systems from which data may be obtained or research undertaken, and (ii) on identifying the sensitivity of hydrological model outputs to the type of high resolution, spatially distributed, precipitation inputs which can be provided by radar. This is the first step in identifying the need for and benefits from such inputs. Account will be taken of the differences between urban and rural catchment requirements. Once these have been done, WG-1 will investigate the use of combined radar/ hydrological modelling systems and these together with NWP systems.
Remote sensing of near-surface hydrological conditions within northern peatlands has the potential to provide important large-scale hydrological information regarding ecological and carbon-balance processes occurring within such systems. This article details how field knowledge of the spectral properties of Sphagnum spp., airborne remote sensing data and a range of image analysis approaches, may be combined to provide a suitable proxy for near-surface wetness. Co-incident field and airborne remote sensing data were acquired in May and September 2002 over an important UK raised bog (Cors Fochno). A combination of laboratory-tested NIR and SWIR water-based and biophysical spectral reflectance indices were applied to field and airborne reflectance spectra of Sphagnum pulchrum to elucidate changes in near-surface moisture conditions. Field results showed significant correlations between water-based indices (moisture stress index (MSI) and floating water band indices (fWBI980 and fWBI1200))) and measures of both near-surface volumetric moisture content (VMC) and water-table position. Spectral indices formulated from the NIR (fWBI980 and fWBI1200) proved to be the most useful for indicating near-surface wetness across the widest range of moisture conditions because of their ability to penetrate deeper into the Sphagnum canopy. Correlations between a biophysical index based upon chlorophyll content and both hydrological measures were not significant, possibly due to relatively high levels of surface wetness at the field site in both May and September. S. pulchrum lawns were successfully located and mapped from airborne imagery using the mixed tuned match filtering (MTMF) algorithm. Importantly, MSI derived from airborne data was significantly correlated with both field moisture and the water-table position. Relationships between measures of near-surface wetness and the MSI for naturally heterogeneous canopies were, however, found to be weaker for airborne imagery than for associated field data. This is likely to be a result of the formulation of the MSI itself and the possible preferential detection of “wetter” pixels within the imagery. This effectively reduced the ability of MSI to detect subtle changes in near-surface wetness under high moisture conditions, but would not impede the use of the index under drier conditions. Results from the field data suggest that indices formulated from the NIR may be more suitable for detailed estimations of near-surface and surface wetness at the landscape-scale although reliable hyperspectral data are required to test fully the performance of such indices. The relative merits of using such an approach to determine near-surface hydrological conditions across entire peatland complexes are also discussed.
The NASA Langley Research Center's L -band pushbroom
microwave radiometer (PBMR) aboard the NASA C-130 aircraft was used to
map surface soil moisture at and around the Konza Prairie Natural
Research Area in Kansas during the four intensive field campaigns of
FIFE in May-October 1987. A total of 11 measurements were made when
soils were known to be saturated. This measurement was used for the
calibration of the vegetation effect on the microwave absorption. Based
on this calibration, the data from other measurements on other days were
inverted to generate soil moisture maps. Good agreement was found when
the estimated soil moisture values were compared with those
independently measured on the ground at a number of widely separated
locations. There was a slight bias between the estimated and measured
values, the estimated soil moisture on the average being lower by about
1.8%
Most attempts at predicting soil moisture from C-band microwave backscattering coefficients for bare soil are made by fitting experimental calibration relations obtained for limited ranges of incidence angle and soil surface roughness. In this paper, a more general approach is discussed using an inversion procedure to extend the use of a single experimental calibration relation to a wider range of incidence angle and surface roughness. A correcting function is proposed to normalize the backscattering coefficients to the conditions (incidence angle and surface roughness) of the calibration relation. This correcting function was derived from simulated data using the physical optics or KirchhofTs scatter model using the scalar approximation. Before discussing the inversion procedure, the backscattering coefficients calculated by the model have been compared with experimental data measured in the C-band, HH polarization and three incidence angles (Θ= 15°, 23°, 50°) under a wide range of surface soil moisture conditions (0.02Hv 0.35cm3 cm-3) and for a single quite smooth soil surface roughness (0–011s OOI4/n)m. The model was found to be experimentally validated from 15° to 23° of incidence and for surface soil moistures higher than 0-I0cm3cm-3. For the inversion procedure, it is assumed to have a wider range of validity (15° Θ 35° ) for ihc incidence angle. A sensitivity analysis of the model to errors on roughness parameter and incidence angle was performed in order to assess the feasability and suitability of the described inversion procedure.
Increased interest in soil moisture information for applications in such
disciplines as hydrology, meteorology, and agriculture has necessitated
an overview of both existing and proposed methods for determination of
soil moisture. This paper thus discusses methods of in situ soil
moisture determination including gravimetric, nuclear, and
electromagnetic techniques; soil physics models that track the behavior
of water in the soil in response to meteorological inputs
(precipitation) and demands (evapotranspiration); and remote sensing
approaches that use the solar, thermal infrared, and microwave portions
of the electromagnetic spectrum. The abilities of these approaches to
satisfy various user needs for soil moisture information vary from
application to application. Therefore we have proposed a conceptual
scheme for merging these approaches into an integrated system. This
system should provide soil moisture information having the potential for
meeting the requirements of various applications.
Multifrequency microwave backscatter from soils under different agricultural crops and different moisture conditions was measured during the LOTREX campaign (Land Surface Transverse Experiment. 26 June-21 July, 1989) in northern West Germany (LOTREX is part of the International Satellite Land-Surface Climatology Project (ISLSCP)). The data were gathered with an airborne coherent Doppler radar scatterometer at an off-nadir angle of 23 as it was multiplexed through its L-, C-, X- and Ku-bahds. The frequency dependency of the backscatter power spectra was analysed and published elsewhere. In this Letter we discuss polarization effects in the C-band.
Radar-backscatter measurements were made to estimate soil moisture. 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 degrees. Vertical polarization was used for two days and horizontal polarization was used for three days. 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 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 incidences angles near vertical.
An analysis is presented of aircraft response to soil moisture in the upper surface layer of agricultural fields. Measurements (taken at 1.6 GHz and 4.75 GHz using HH and HV polarizations, and at 13.3 GHz using VV polarization from an experiment conducted in 1978 at Colby, Kansas) are used to derive the radar soil moisture sensitivities and correlations. It is shown that the aircraft response to soil moisture is optimum at C-band frequencies and incidence angles of 10-20 deg. confirming previous truck-radar results. Like-polarization radar response is unaffected by vegetation but is dependent on row-tillage patterns; cross-polarization response also is unaffected by vegetation but is approximately independent of tillage patterns. These results show that remote sensing radars can be used effectively for the detection and estimation of near-surface soil moisture in agricultural fields.
stract-Two pre-dawn ascending data-akes (dt. 49.2 and 97.2) by the Shuttle Imaging Radar (SIR-B) are used to evaluate the effects of soil moisture, surface roughness, and crop canopy cover on radar backscattering. The two images are separated in acquisition date by three days and were obtained at the same local angle of incidence (¿1 = 30°) but with opposite azimuth viewing directions (¿¿ = 180°). The digitally recorded and processed SIR-B imagery is externallyi-calibrated with respect to the radar backscattering coefficient ¿° via response to arrays of point and area-extended targets of known radar cross section. Extensive ancillary data pertaining to scene physical and biophysical al conditions were collected from approximately 400 agricultural fields within a 20 km à 20 km test site in west-central Illinois. The test site was largely agricultural and consisted primarily of corn and soybeans at harvest-ready conditions. For the agricultural portions of the scene, ¿°SIR-B is found to vary over a 16-to 20-dB dynamic range for a given observation date. The magnitude of ¿°SIR-B is found to be proportional to soil moisture, surface roughness, and canopy biomass. Although the SIR-B sensor parameters es ( L-band, HH polarization, ¿nd 0 ° 300) were not expected to be optimum for estimation of near-surface soil moisture, significant linear correlations are observed between ¿°SIR-B (in decibels) and 0-5-cm volumetric moisture m¿. For a given target class as broadly defined by surface roughness and canopy cover, linear regression of ¿°SIR-B (dB) to m¿ generally yields correlation coefficients of r2 0.8.
The effect of the multiscale surface geometry on the sensitivity
of C band synthetic aperture radar (SAR) data to soil moisture
is studied. The experimental data consist of C -band SAR images
of an agricultural site, including fields with various combinations of
three distinct roughness components from small to large scale. The
backscatter variability due to surface roughness has been analyzed. The
effect of random roughness associated with soil clods is never less than
2 dB, and the effect of a row pattern can be as strong as 10 dB. In
addition, the periodic drainage topography induces a backscatter
variability due to soil moisture variation and drainage relief. The
results indicate that airborne C -band SAR data cannot be easily
inverted into soil moisture data. However, with ERS-1 or Radarsat data
at an incidence angle of about 20°, the effect of random and
periodic roughness can be reduced to about 2 dB if the look angle is
less than 50°
The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system mounted atop a 75-ft truck-mounted boom was used to measure the return at 10 frequency points across the 4-8 GHz band, at 8 different look angles ( 0deg through 70deg ), and for all polarization combinations. A total of 17 sets of data were collected covering the range 4-36 percent soil moisture content by weight. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear, however, over the range 15-30 percent moisture content for all angles, frequencies, polarizations, and surface conditions.
SAR observations and modeling of the C-band backscatter variability due toThe use of a microwave backscatter model for retrieving soil moistAircraft radar response to soil moisture
- A Beaudoin
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Radar estimates of soil moisture over the Konza Prairie', In?An experiment for the radiometric calibration of the ERS-1 SAR
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‘An experiment for the radiometric calibration of the ERS-1 SAR Proc. First ERS-1 Symposium Cannes France
- G E Keyte
- D R D Kenward
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