Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H_R for crops is too low, and that variability of H_R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context.     

Appropriate scale of soil moisture retrieval from high resolution radar imagery for bare and minimally vegetated soils

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m² sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5 × 5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy.     

Estimates of surface soil moisture under grass covers using L-band radiometry

The scope of this study is to establish the parameters of the L-band (1.4 GHz) Microwave Emission of the Biosphere model (L-MEB) for grass covers, and to assess surface soil moisture retrievals in areas covered by grass. L-MEB parameters are key ancillary information for the Soil Moisture and Ocean Salinity mission (SMOS) retrieval algorithm that produces estimates of the surface soil moisture from measurements of the surface brightness temperature at L-band.L-band data sets from three ground-based experiments over grass are analysed in this paper: BARC (orchard grass and alfalfa), ELBARA-ETH (clover grass), and SMOSREX (grass and litter from a field left fallow). Modelling of the brightness temperature using the zero-th order radiative transfer model in L-MEB indicates that the vegetation appears isotropic to microwaves propagating with horizontal polarisation, and that the single scattering albedo can be neglected. At vertical polarisation, non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil and litter emission are calibrated beforehand. Then surface soil moisture and vegetation optical depth can be left as free parameters in the retrieval process. Finally, the study highlights the importance of detecting strong emission and attenuation by wet vegetation and litter due to rainfall interception in order to obtain accurate estimates of the surface soil moisture. The study illustrates how strong rainfall interception can be flagged straightforwardly using a microwave polarisation index.     

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02

Extensive reflected GPS data was collected using a GPS reflectometer installed on an HC130 aircraft during the Soil Moisture Experiment 2002 (SMEX02) near Ames, Iowa. At the same time, widespread surface truth data was acquired in the form of point soil moisture profiles, areal sampling of near-surface soil moisture, total green biomass and precipitation history, among others. Previously, there have been no reported efforts to calibrate reflected GPS data sets acquired over land. This paper reports the results of two approaches to calibration of the data that yield consistent results. It is shown that estimating the strength of the reflected signals by either (1) assuming an approximately specular surface reflection or (2) inferring the surface slope probability density and associated normalization constants give essentially the same results for the conditions encountered in SMEX02. The corrected data is converted to surface reflectivity and then to dielectric constant as a test of the calibration approaches. Utilizing the extensive in-situ soil moisture related data this paper also presents the results of comparing the GPS-inferred relative dielectric constant with the Wang-Schmugge model frequently used to relate volume moisture content to dielectric constant. It is shown that the calibrated GPS reflectivity estimates follow the expected dependence of permittivity with volume moisture, but with the following qualification: The soil moisture value governing the reflectivity appears to come from only the top 1-2 cm of soil, a result consistent with results found for other microwave techniques operating at L-band. Nevertheless, the experimentally derived dielectric constant is generally lower than predicted. Possible explanations are presented to explain this result.     

Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA

An inversion of linked radiative transfer models (RTM) through artificial neural networks (ANN) was applied to MODIS data to retrieve vegetation canopy water content (CWC). The estimates were calibrated and validated using water retrievals from AVIRIS data from study sites located around the United States that included a wide range of environmental conditions. The ANN algorithm showed good performance across different vegetation types, with high correlations and consistent determination coefficients. The approach outperformed a multiple linear regression approach used to independently retrieve the same variable. The calibrated algorithm was then applied at the MODIS 500 m scale to follow changes in CWC for the year 2005 across the continental United States, subdivided into three vegetation types (grassland, shrubland, and forest). The ANN estimates of CWC correlated well with rainfall, indicating a strong ecological response. The high correlations suggest that the inversion of RTM through an ANN provide a realistic basis for multi-temporal assessments of CWC over wide areas for continental and global studies.     

Vegetation and surface roughness effects on AMSR-E land observations

Characteristics of the land surface including soil moisture, vegetation cover, and soil roughness among others influence the microwave emissivity and brightness temperature of the surface as observed from space. Knowledge of the variability of microwave signatures of vegetation and soil roughness is necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. We describe here a characterization of vegetation and soil roughness at the frequencies and spatial resolution of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E). A single parameter has been used to approximate the combined effects of vegetation and roughness. AMSR-E data have been analyzed to determine the frequency dependence of this parameter and to generate a global vegetation/roughness map and an estimate of seasonal variability. A physical model is used for the analysis with approximations appropriate to the AMSR-E footprint scale and coefficients calibrated empirically against the AMSR-E data. The spatial variabilities of roughness and vegetation cannot be estimated independently using this approach, but their temporal dynamics allow separation of predominantly static roughness effects from time-varying vegetation effects using multitemporal analysis. Global signals of time-varying vegetation water content derived from this analysis of AMSR-E data are consistent with time-varying biomass estimates obtained by optical/infrared remote sensing techniques.     

Evaluation of two empirical wind erosion models in arid and semi-arid regions of China and the USA

Wind erosion models are important tools for assessing soil erodibility and identifying management practices to control erosion. The Agricultural Policy/Environmental eXtender (APEX) model and Revised Wind Erosion Equation (RWEQ) were tested using data collected from the Tarim Basin of China and Columbia Plateau of the United States of America. Adequate performance in simulating soil loss was achieved using the original APEX model and RWEQ in respectively a cotton field and desert-oasis ecotone in the Tarim Basin and winter wheat - summer fallow (WW-SF) field in the Columbia Plateau. We calibrated the APEX model and RWEQ to improve performance because both models have many empirical parameters. After calibration, both models adequately simulated soil loss from all land use types except the RWEQ from the red date orchard in the Tarim Basin. Inadequate performance of the calibrated RWEQ in the red date orchard was likely due to underestimating maximum mass transport.     

Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley-Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley-Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation (R_n), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature (T_max), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r² was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986-1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.     

Evaluation of a static water balance model in cropped and grazed systems of temperate Australia

Several water balance models have been developed for Australian conditions, however few of them were developed for the mixed cropping and grazing systems that are typical of temperate south-east Australia. HowLeaky? is a 1-dimensional water balance model that allows simulation of both cropped and grazed systems. This study tested the accuracy of HowLeaky? simulations of soil moisture content and surplus water (runoff plus deep drainage) for mixed farming systems in south-east Australia. Two datasets from the state of Victoria were used to validate model simulations: 1) Rutherglen, consisting of four years soil moisture observations and deep drainage estimates from seven treatments of crop rotations and annual pasture; 2) Vasey, consisting of three years soil moisture and runoff observations and deep drainage estimates from a perennial pasture. A static plant growth option was applied to simulate seasonal crop and pasture covers. Despite the static approach not accounting for inter-annual variation in crop development, HowLeaky? captured observed soil moisture trends, with the Nash Sutcliffe efficiency ranging from fair (0.34) in continuous crop, moderate (0.64–0.68) in annual and perennial pasture, to very good (>0.70) for continuous lucerne and lucerne-crop rotations. Annual water surplus was generally underestimated in this uncalibrated application of the model suggesting a need for some degree of model calibration for highly uncertain and influential parameters such as field capacity.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

Modelling above-ground biomass based on vegetation indexes: a modified approach for biomass estimation in semi-arid grasslands

Remote sensing could be the most effective means for scaling up grassland above-ground biomass (AGB) from the sample scale to the regional scale. Remote sensing approaches using statistical models based on vegetation indexes (VIs) are frequently used because of their simplicity and reliability. And many researchers have already proven the method is scientific, feasible, and can bring relatively better effects in practice. However, the only deficiency of the method has been criticized because of the uncertainties introduced by saturation of spectral reflectance at high-density vegetation levels and the soil surface at low-density vegetation levels. Therefore, in this study, we aimed to improve grassland AGB estimates by using modified VIs (MVIs) to minimize the influence of the soil background. The field study was conducted in the Chen Barag Banner, the Ewenkizu Banner, and the Xin Barag Left Banner in the Hulun Buir Grassland, Inner Mongolia, northern China. Field plots were photographed and AGB samples were collected during field sampling. Remote sensing data were obtained from MOD09A1 (TERRA satellite). Four MVIs were first calculated based on the corresponding VI: the Ratio Vegetation Index (RVI), the Normalized Differential Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the Modified Soil-Adjusted Vegetation Index (MSAVI), by improving estimates of vegetation cover (VC). Then, MVIs, i.e., MRVI, MDVI, MNDVI, and MMSAVI, were regressed with the sample-scale AGB using an exponential function, a linear function, a logarithmic function, and a power function. When the accuracy of the models was tested by comparing root mean square error (RMSE), relative error (RE), and coefficient of determination (R²), the results demonstrated that MVI-AGB models performed better than the VI-AGB models. The logarithmic MNDVI-AGB model was the best of the regression functions. This model gave the best estimates of AGB from remote sensing data, compared with the values measured in field analyses. Our proposed method provides a new way to estimate regional grassland AGB and will be useful to analyze ecosystem responses under climate change.     

Predicting water content using Gaussian model on soil spectra

This paper presents an approach to estimating soil moisture content through fitting an inverted Gaussian function to the continuum in soil spectra. The soil moisture Gaussian model (SMGM) estimates the water content by the declining reflectance in the near infrared (NIR) and shortwave infrared (SWIR) regions, 1.2-2.5 μm, due to the spreading of the fundamental water absorption at 2.8 μm. Convex hull boundary points were used to isolate the spectral continuum and to fit the inverted Gaussian function. The function extrapolates the continuum to the fundamental water absorption beyond the wavelength limits of common laboratory, field, and airborne instruments. Of the derived functional parameters, both amplitude and area on the shortwave side of the inverted Gaussian curve were highly correlated with soil water content.In this study, laboratory spectra, from 0.4 to 2.5 μm, were measured at sequential moisture levels in soil samples collected in Castilla-La Mancha, Spain and in California, USA. The Gaussian area was determined to be the best indicator of gravimetric water content with the initial modeling of 2592 spectra. The SMGM was validated with a separate set of 849 spectra. The model performance significantly improved for water contents below a critical level of 0.32 g water/g soil. Within this restricted range, the SMGM predicted water contents for all soils with a maximum of 0.027 RMSE for 1901 modeled spectra and 0.026 for 602 validation spectra. The water content estimates were improved slightly by stratifying the model and validation sets by the two locations, reducing the RMSE to 0.023 in Spain and 0.025 in California. Further stratifying the model spectra by landform and soil sodicity improved some predictions substantially, but less consistently. Stratifying the samples locally demonstrated that a priori knowledge of soil surfaces by landforms should be part of an image calibration strategy. The SMGM provides practical water content estimates and has a potential use in correcting the effects of soil moisture in hyperspectral images.     

Assessing FPAR source and parameter optimization scheme in application of a diagnostic carbon flux model

The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors. FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome or ecoregion for parameterization of diagnostic carbon flux models.     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study

The performance of the DNDC and Daisy model to simulate the water dynamics in a floodplain soil of the North China Plain was tested and compared. While the DNDC model uses a simple cascade approach, the Daisy model applies the physically based Richard's equation for simulating water movement in soil. For model testing a three years record of the soil water content from the Dong Bei Wang experimental station near Beijing was used. There, the effect of nitrogen fertilization, irrigation and straw removal on soil water and nitrogen dynamics was investigated in a three factorial field experiment applying a split-split-plot design with 4 replications. The dataset of one treatment was used for model testing and calibration. Two other independent datasets from further treatments were employed for validating the models. For both models, the simulation results were not satisfying using default parameters. After parameter optimisation and the use of site-specific van Genuchten parameters, however, the Daisy model performed well. But, for the DNDC model, parameter optimisation failed to improve the simulation result. Owing to the fact that many biological processes such as plant growth, nitrification or denitrification depend strongly on the soil water content, our findings bring us to the conclusion that the site-specific suitability of the DNDC model for simulating the soil water dynamics should be tested before further simulation of other processes.     

Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (?_n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ?_n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.     

Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondônia, Brazil

This study analyzed the relationships between soil fertility and remotely sensed measures over three pasture chronosequence sites in the state of Rondônia, in the western Brazilian Amazon region. Remotely sensed measures included shade, nonphotosynthetic vegetation (NPV), green vegetation (GV) and soil (derived from spectral mixture analysis), and the normalized difference vegetation index (NDVI). These were correlated against soil fertility parameters such as phosphorus, potassium, calcium, and base saturation. In temporal analysis, it was observed that NPV dominated the spectral responses of pasture canopies and tended to increase with pasture age as well. The increase of NPV appeared to be related to the decline of soil fertility, but soil texture variation also played a role. In the correlation analysis, soil P, known as the most limiting nutrient for pasture productivity, showed the highest correlation with remotely sensed measures, followed by soil K and base saturation. However, this result was not observed at the sites where nutrient availability was very low.     

Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation.Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~ 5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.