Ground Soil Moisture Measurements Research Articles

SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of $-0.02 \mbox{m}^3\mbox{m}^{-3}$ between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of $\pm 0.06 \mbox{m}^3\mbox{m}^{-3}$ . Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of $-0.05 \mbox{m}^3\mbox{m}^{-3}$ was observed, with a standard deviation of $\pm 0.04 \mbox{m}^3\mbox{m}^{-3}$ , which yields an uncertainty of $\pm 0.07 \mbox{m}^3\mbox{m}^{-3}$ for the SMOS product. Although the random error meets the SMOS mission's goal of $\pm 0.04 \mbox{m}^3\mbox{m}^{-3}$ , the product overall uncertainty is higher than that due to the significant dry bias, which is also found in other regions of the world.

Triangle Method for Estimating Soil Surface Wetness from Satellite Imagery in Allahabad District, Uttar Pradesh, India

Soil surface wetness is indispensable land surface parameter in agriculture, hydrology and environmental engineering. This paper explores the relationship between surface radiant temperature and fractional vegetation cover derived from satellite imagery to estimate soil surface wetness (triangle method) in Allahabad district. The pixel distributions create triangular shapes because the range of surface radiant temperature decreases as the amount of vegetation cover increases and sufficient number of pixels exists. A very weak correlation is found between the simulated soil surface wetness and ground measured soil moisture at deeper soil layers (R2

An approach to validate soil moisture derived from passive microwave sensors using SAR as an interface

Validation of soil moisture derived from space-borne passive microwave sensors remains a challenging task owing to the large variability in soil moisture within the coarse-resolution cell offered by passive sensors. In this article, nine spatial/temporal synthetic aperture radar (SAR) data sets from Radar Satellite-1 (Radarsat-1) and Environmental Satellite-1 (Envisat-1) Advanced Synthetic Aperture Radar (ASAR) were used as an interface to upscale the field-level soil moisture to soil moisture at a 25 km × 25 km grid, the scale at which the Advanced Microwave Scanning Radiometer for the EOS (Earth Observing System) (AMSR-E) level-3 soil moisture product is available, leading to a total of 68 such grids where there is a pair of SAR-derived and corresponding AMSR-E-derived values. Out of the these 68 grid cells, each of size 25 km × 25 km, pair data from nine dates, data corresponding to 51 grid cell pairs (from six dates) were used for the development of the approach and data corresponding to 17 grid cell pairs (from three dates) were used for validating the approach. It is observed that while the spatial variation in SAR- and AMSR-E-derived soil moisture is broadly similar, there exists a bias in the difference between AMSR-E- and SAR-derived soil moisture. The root mean square (RMS) difference between AMSR-E soil moisture and SAR-derived soil moisture at 25 km over the model development sample of 51 grid cells, each of 25 km × 25 km, was observed to be 4.2 (%g cm–3). The study shows that the bias is significantly affected by the AMSR-E vegetation/roughness parameter, g, which is an indicator of the prevailing vegetation/surface roughness conditions. This dependence of the bias on g was exploited to arrive at modified soil moisture estimates at 25 km. The RMS difference between the modified AMSR-E soil moisture at 25 km and the SAR-derived soil moisture was observed to be 2.13 (%g cm–3) over a validation sample of 17 grid cells each of size 25 km × 25 km, comprising extreme, dry and wet, and soil moisture conditions. This demonstrative study provides the feasibility of calibrating and improving the soil moisture product from coarse-resolution space-borne passive sensors with the help of soil moisture derived using SAR. The approach is demonstrated for validating passive radiometer-derived soil moisture over bare/crop-covered conditions through an experiment involving 68 25 km × 25 km grid cells spread over nine temporal/spatial Radarsat and Envisat-1 satellite SAR data sets and synchronous ground soil moisture measurements over farmers’ fields, covering a large range of soil/crop conditions prevailing during the nine months of SAR data acquisition. Hence the results reported in this manuscript are encouraging.

Soil moisture derivation using triangle method in the lighvan watershed, north western Iran

Soil moisture determination plays a major role in water and crop management. In this research, the accuracy of the Triangle Method (TM) was evaluated to predict surface soil moisture content using Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite images with 1km resolution. The investigation was carried out within an area of 76 km2 in Lighavn watershed in East Azerbaijan, North West of Iran. The analysis was based on ground measurements of soil moisture at 225 points (45 pixels) across the catchment on 2 different days. Ground measurements from the first day were used to train the models, while those from the second day were used for validation. A range of polynomial regressions from 1st to 4th orders were established between the ground measured soil moisture and MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). The best results were obtained for the 4th order polynomial of the TM with the efficiency error (ER) and adjusted determination coefficient (R2adj) criterions, respectively, equal to 11.0% and 0.63 for calibration and 15.9% and 0.60 for validation stage. Therefore, the TM was found to provide reliable estimates of soil moisture, without the need for prior information of the soil surface roughness or the vegetation type and water content.

Soil Moisture Estimation in a Vegetated Area Using Combination of AIRSAR and Landsat5-TM Images

High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively.

Modified Shortwave Infrared Perpendicular Water Stress Index: A Farmland Water Stress Monitoring Method

AbstractCrop water stress monitoring by remote sensing has been the focus of numerous studies. In this paper, specifically red (630–690 nm) and shortwave infrared (SWIR; 1550–1750 nm) wavelength bands are identified to monitor farmland water stress, and a method [modified shortwave infrared perpendicular water stress index (MSPSI)] is developed that is based on the spectral space constructed by SWIR − Red (Rd) and SWIR + Red (Rs). The MSPSI stayed at mostly the same water stress level for full vegetation coverage cases with high vegetation water content and saturated bare soil as well as full vegetation coverage with extremely low vegetation water and dry bare soil in the Rs–Rd spectral feature space. This approach makes the water stress conditions between different covers comparable and the MSPSI applicable to farmland water stress monitoring in different vegetation covers throughout the growing season. To validate the proposed index, the MSPSI calculated from Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance products (from March to October) in the Ningxia Hui Autonomous Region was compared with the ground-measured soil moisture content at different depths. It is evident from the results that the MSPSI derived from satellite imageries is highly correlated with ground-measured soil moisture at different depths (7.6 and 10 cm), with coefficients of determination R2 of 0.666, 0.512, 0.576, 0.361, 0.383, 0.391, 0.357, 0.410, and 0.418. The paper concludes that MSPSI is a promising index for crop water stress monitoring throughout the growing season.

Field Level Soil Moisture Variability at 6‐ and 3‐cm Sampling Depths: Implications for Microwave Sensor Validation

Calibration/validation experiments are critical to developing and testing soil moisture retrieval algorithms from microwave remote sensing platforms. Ground sampling must be optimally designed to minimize sources of error or bias during data collection. An important consideration in the design of ground sampling campaigns is that the in situ measured soil moisture is representative of the retrieval depth of a microwave sensor. Because of the popularity of portable impedance probe instruments, volumetric soil moisture is usually measured at a constant depth of approximately 0 to 6 cm and aggregated over a spatial extent representative of a pixel footprint. However, the retrieval depth of microwave signals can vary, and in agricultural soils is typically representative of the top ∼5 cm (or less) of the soil surface. It is unknown whether this mismatch between the depth of ground measured soil moisture and microwave retrieved soil moisture significantly contributes to error during sensor calibration/validation. This issue is addressed based on an analysis of over 3400 impedance probe measurements collected in 72 agricultural fields over four sampling occasions. Volumetric soil moisture was measured at depths of approximately 6 and 3 cm, at 24 locations in each field. The observed soil moisture sampling distributions and statistical moments are compared between these two measurement depths. Results demonstrate that the majority of fields exhibit a near‐normal sampling distribution at both the 6‐ (60 of 72) and 3‐cm (59 of 72) depths. An average difference of 0.016 m3 m−3 exists between all field means, generally indicating that wetter measurements are observed from the samples at 6 cm than those at 3 cm. Statistically significant differences were found between the measurement depths for field means (23 of 72), distributions (21 of 72), and variances (9 of 72). These findings demonstrate the importance of recognizing near‐surface volumetric soil moisture spatial and depth variability effects during ground sampling experiments to minimize error in calibration/validation of retrieval algorithms.

Soil Moisture Estimation Under Low Vegetation Cover Using a Multi-Angular Polarimetric Decomposition

The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multi-angular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6-8 vol.% including all test fields with a variety of crop types.

A First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy and Luxembourg

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils.

Soil moisture estimation over an arid environment in Mongolia from passive microwave remote sensing based on a simplified parameterization method

Satellite passive microwave remote sensing provides a major basis for the analysis of temporal and spatial variation of top soil moisture at large scales. A global soil moisture data product is currently released by the National Aeronautics and Space Administration (NASA) of USA based on AMSR-E (the Advanced Microwave Scanning Radiometer for the Earth Observing System onboard the NASA EOS Aqua satellite) global dual-polarization brightness temperature observations of the Earth. But the accuracy of this soil moisture product is not satisfactory in arid Mongolian regions. Based on a simplified parameterization method for characterizing the effects of the sparse vegetation cover and surface roughness on surface microwave emission, the estimation of daytime (local noon) and nighttime (local midnight) top soil moisture during a near two-year period from October 1, 2005 to September 20, 2007 over the arid CEOP(The Coordinated Energy and Water Cycle Observations Project)-Mongolia experiment area was conducted based on a multifrequency algorithm by mainly using AMSR-E 6.9 GHz, 10.7 GHz and 18.7 GHz vertical-polarized brightness temperature data. In the inversion model, the only model parameter asking for experimental calibration, i. e., the vegetation single scattering albedo, could be achieved through optimization technique by using in-situ soil moisture measurements. The results showed that the root mean square error (RMSE) between satellite estimated soil moisture and ground-measured soil moisture was close to 0.030 cm(3)/cm(3). Without other supplementary data else, the retrieval algorithm proposed in this paper may be applied to near real-time monitoring of the dynamic variation in top soil moisture at large scales, in an all-weather working manner, over arid and or semiarid environments characterized by natural sparse vegetation conditions. Therefore, it will be promising to provide sustaining and reliable initial near-surface soil moisture information for the long term study of climate change and water cycle over arid and or semiarid regions.

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.

Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali

This paper presents the ground soil moisture measurements performed over the so-called Gourma meso-scale site in Mali, Sahel , in the context of the African Monsoon Multidisciplinary Analysis (AMMA) project. The Gourma meso-scale soil moisture network is part of a complete land surface processes observing and modelling strategy and is associated to vegetation and meteorological field measurements as well as soil moisture remote sensing . It is spanning 2 ° in latitude between 15 ° N and 17 ° N. In 2007, it includes 10 soil moisture stations, of which three stations also have meteorological and flux measurements . A relevant spatial sampling strategy is proposed to characterise soil moisture at different scales including local, kilometer, super-site and meso-scales. In addition to the local stations network, transect measurements were performed on different coarse textured (sand to sandy-loam) sites, using portable impedance probes. They indicate mean value and standard deviation (STD) of the surface soil moisture (SSM) at the kilometer scale. This paper presents the data set and illustrates soil moisture spatial and temporal features over the Sahelian Gourma meso-scale site for 2005–2006. Up-scaling relation of SSM is investigated from (i) local to kilometer scale and (ii) from local to the super site scale. It is shown to be stable in space and time (2005–2006) for different coarse textured sites. For the Agoufou local site, the up-scaling relation captures SSM dynamics at the kilometer scale with a 0.9% accuracy in volumetric soil moisture. At the multi-site scale, an unique up-scaling relation is shown to be able to represent kilometer SSM for the coarse textured soils of the meso-scale site with an accuracy of 2.2% (volumetric). Spatial stability of the ground soil moisture stations network is also addressed by the Mean Relative Difference (MRD) approach for the Agoufou super site where five soil moisture stations are available (about 25 km × 25 km ). This allows the identification of the most representative ground soil moisture station which is shown to be an accurate indicator with low variance and bias of the soil moisture dynamics at the scale of the super site. Intensive local measurements, together with a robust up-scaling relation make the Gourma soil moisture network suitable for a large range of applications including remote sensing and land surface modelling at different spatial scales.

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

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

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.

Soil moisture initialization for climate prediction: Assimilation of scanning multifrequency microwave radiometer soil moisture data into a land surface model

Climate model prediction skill is currently limited in response to poor land surface soil moisture state initialization. However, initial soil moisture state prediction skill can potentially be enhanced by the assimilation of remotely sensed near‐surface soil moisture data in off‐line simulation. This study is one of the first to evaluate such potential using actual remote sensing data together with field observations. Here the ensemble Kalman filter (Kalman, 1960) is used to assimilate scanning multifrequency microwave radiometer derived near‐surface soil moisture data from 1979 to 1987 into the catchment‐based land surface model (CLSM). CLSM is used by the NASA Goddard Modeling and Assimilation Office global climate model. Enhancement to land surface soil moisture initialization skill is evaluated for Eurasia using the ground soil moisture measurements collected in Russia, Mongolia, and China. As initial model and observation error predictions were poor, the assimilation improved both the surface and root zone soil moisture estimates only when the observation error was less than the model error. This emphasizes the need for good quality remotely sensed soil moisture data sets, together with reliable observation and model error assessments, in order to ensure improved soil moisture estimates through data assimilation. When the relative magnitude of predicted observation and model error was matched to the error determined from field observation comparison, improvements in root zone and surface soil moisture estimates were guaranteed given unbiased model and satellite observations.

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

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

Correlation between Ground Measured Soil Moisture and RADARSAT-1 derived Backscattering Coefficient over an Agricultural Catchment of Navarre (North of Spain)

Surface soil moisture is a variable of great importance in several agronomic, hydrological and meteorological processes. The knowledge of its magnitude and its spatial distribution is essential to adequately describe and model the processes where it is involved. Radar remote sensors measure microwave energy backscattered by natural surfaces. This scattered energy depends on the geometrical and the dielectric properties of surfaces. In the case of bare soil surfaces, the dielectric properties are directly related to the soil water content, so theoretically, radar remote sensing allows the extraction of spatially distributed soil moisture information. However, the influence of surface roughness on the scattering process limits the ability to correctly estimate volumetric soil moisture values unless detailed roughness measurements are acquired. The present paper reports the results of a study where five images from the remote radar sensor on the satellite RADARSAT-1 were processed and correlated to ground measured soil moisture values over an agricultural catchment. Linear regression models were fitted between RADARSAT-1 derived backscattering coefficient σ 0 and the soil moisture at different spatial scales: point scale, field scale and catchment scale. Three soil moisture classes were identified according to their implications for crop growth: (1) low moisture values which contributed to water stress in plants; (2) medium moisture content that allowed an optimal crop growth; and (3) high moisture values which affected crop growth by other means, such as by fungal disease. Results show a direct relation between σ 0 and the soil moisture. At the catchment scale the observed correlation is high. At detailed scales, however, variability increases, causing a decrease of correlation values. The ability of σ 0 to discriminate between the considered moisture classes seems to be adequate. The accuracy of the estimation increases from the detailed to coarser scales. In the case of vegetated fields, the vegetation cover can cause a certain attenuation of the radar pulse resulting in reduced σ 0 values. In this research, vegetation-induced attenuation was considered by applying the semi-empirical ‘Water Cloud’ model. The presented technique is useful for crop growth monitoring and modelling at medium to large scales, particularly in early growing seasons where the attenuation of vegetation is not too high. It is also applicable to irrigation planning or crop health studies. However, regression lines are site specific and can be affected by the surface roughness variability and vegetation cover of fields.

A time series based method for estimating relative soil moisture with ERS wind scatterometer data

The radar backscattering coefficient is mainly determined by surface soil moisture, vegetation and land surface roughness under a given configuration of the satellite sensor. It is observed that the temporal variations of the three variables are different, the variation of vegetation and roughness are at the longer temporal scales corresponding to climate and cultivation practices, while soil moisture varies at a shorter temporal scale in response to weather forcing. Based on this hypothesis, a robust method for the determination of relative soil moisture is proposed for application to the ERS Wind Scatterometer database and the Pathfinder AVHRR dataset. Ground measured soil moisture data collected during GAME/Tibet field experiment are used to validate the proposed method. The results show that the estimated relative soil moisture corresponds closely to local precipitation at two GAME/Tibet field sites. The volumetric soil moisture converted from the Wind Scatterometer estimated relative soil moisture is in good agreement with the measured 0–4 cm topsoil volumetric soil moisture. The regional distributions of relative soil moisture are evaluated and correlated well to the ground measured volumetric soil moisture. It is concluded that the large footprint Wind Scatterometer dataset can capture ground soil wetness variation in arid and semi‐arid zone with the aid of vegetation information derived from other sensors.

Airborne soil moisture measurements for First International Satellite Land Surface Climatology Program Field Experiment

The airborne gamma radiation system of the National Weather Service (NWS) was used to measure soil moisture over a research area near Manhattan, Kansas, in 1987 and in 1989. The research was in support of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) by the National Aeronautics Space Administration. Twenty‐four flight lines were established in the FIFE research area. A large number of ground soil moisture measurements were collected for calibration of the flight lines. The NWS standard operational method to obtain soil moisture estimates for flight lines that average 16 km in length, using three airborne measurements of gamma radiation fluxes, was adapted for use with the shorter FIFE flight lines. Examples of temporal and spatial variations of soil moisture for the FIFE research area are presented.

Direct snow-water equivalent measurement by air-borne gamma-ray spectrometry

A direct air-borne technique to measure snow-water equivalent that utilizes the buildup of scattered gamma-radiation from 40K with increasing snow-water equivalent has been successfully tested. The variations in the shapes of the potassium, uranium and thorium gamma-ray spectra were determined from measurements on large radioactive concrete pads, using plywood sheets to simulate a uniform snow-water equivalent layer. The air-borne system was calibrated by flying at different altitudes over two test areas and converting the aircraft altitude to an equivalent layer of water. A series of nine flights at different altitudes over an area of unknown radioactivity gave results consistent with snow-water equivalent ground measurements of 142 mm and also with air-borne results from the standard two-flight technique. The single-flight technique has the advantage over the two-flight method that no ground soil-moisture measurements are necessary, no presnow flight is required and navigation problems in duplicating the two flight lines are avoided.