Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm³/cm³) was higher than over the Arizona domain (SEE = 0.014 cm³/cm³). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.     

The SMOS Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) over natural shrubs

In the framework of ESA's SMOS mission (Soil Moisture and Ocean Salinity), many studies have been carried out over different land surface types to model their microwave emission at L-band (1.4 GHz). Results of these studies have been integrated in the emission model L-MEB (L-Band Microwave Emission of the Biosphere), which is the core of the SMOS Level 2 soil moisture retrieval algorithm. The Mediterranean Ecosystem L-Band characterisation EXperiment (MELBEX-I) was carried out at the SMOS validation site near Valencia in autumn 2005. The main objective of MELBEX-I was to calibrate L-MEB over Mediterranean shrub land, as no data were available over this biome. For that purpose, multi-angular and dual polarimetric measurements (H, V) were obtained by the EMIRAD L-band radiometer from a 14-m tower. Results of this study indicate a small and constant impact of vegetation on the microwave emission of shrub land, and L-MEB parameters for shrub land were obtained. In addition, the study highlights the need for calibrating microwave soil roughness, which was found to be constant at the site. Depending on the number of retrieved parameters, soil moisture (SM) near the surface could be estimated with errors between 0.035 m³ m^− 3 (if only SM was retrieved) and 0.057 m³ m^− 3 (if SM, optical depth and a roughness parameter were simultaneously retrieved). Finally, no modelling improvements were observed when coarse estimates of the fraction of exposed rocks were accounted for in the model.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.     

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature (T_B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz-19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T_B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T_B and SWE.The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T_B observed from satellite measurements. AMSR-E brightness temperatures (2002/03-2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37 GHz T_B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r² = 0.77 with monthly averages; r² = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r² values calculated for January through April 2003-2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).     

The vegetation cycle in West Africa from AVHRR-NDVI data: Horizons of predictability versus spatial scales

The predictability of the vegetation cycle is analyzed as a function of the spatial scale over West Africa during the period 1982-2004. The NDVI-AVHRR satellite data time series are spatially aggregated over windows covering a range of sizes from 8 × 8 km² to 1024 × 1024 km². The times series are then embedded in a low-dimensional pseudo-phase space using a system of time delayed coordinates. The correlation dimension (D_c) and entropy of the underlying vegetation dynamics, as well as the noise level (σ) are extracted from a nonlinear analysis of the time series. The horizon of predictability (H_P) of the vegetation cycle defined as the time interval required for an n% RMS error on the vegetation state to double (i.e. reach 2n% RMS) is estimated from the entropy production. Compared to full resolution, the intermediate scales of aggregation (in the range of 64 × 64 km² to 256 × 256 km²) provide times series with a slightly improved signal to noise ratio, longer horizon of predictability (about 2 to 5 decades) and preserve the most salient spatial patterns of the vegetation cycle. Insights on the best aggregation scale and on the expected vegetation cycle predictability over West Africa are provided by a set of maps of the correlation dimension (D_c), the horizon of predictability (H_P) and the level of noise (σ).     

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

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

On the reproducibility of field-measured reflectance factors in the context of vegetation studies

This paper describes a study aimed at quantifying uncertainty in field measurements of vegetation canopy hemispherical conical reflectance factors (HCRF). The use of field spectroradiometers is common for this purpose, but the reliability of such measurements is still in question. In this paper we demonstrate the impact of various measurement uncertainties on vegetation canopy HCRF, using a combined laboratory and field experiment employing three spectroradiometers of the same broad specification (GER 1500). The results show that all three instruments performed similarly in the laboratory when a stable radiance source was measured (NEΔL < 1 mW m⁻² sr⁻¹ nm⁻¹ in the range of 400-1000 nm). In contrast, field-derived standard uncertainties (u = SD of 10 consecutive measurements of the same surface measured in ideal atmospheric conditions) significantly differed from the lab-based uncertainty characterisation for two targets: a control (75% Spectralon panel) and a cropped grassland surface. Results indicated that field measurements made by a single instrument of the vegetation surface were reproducible to within ± 0.015 HCRF and of the control surface to within ± 0.006 HCRF (400-1000 nm (± 1σ)). Field measurements made by all instruments of the vegetation surface were reproducible to within ± 0.019 HCRF and of the control surface to within ± 0.008 HCRF (400-1000 nm (± 1σ)). Statistical analysis revealed that even though the field conditions were carefully controlled and the absolute values of u were small, different instruments yielded significantly different reflectance values for the same target. The results also show that laboratory-derived uncertainty quantities do not present a useful means of quantifying all uncertainties in the field. The paper demonstrates a simple method for u characterisation, using internationally accepted terms, in field scenarios. This provides an experiment-specific measure of u that helps to put measurements in context and forms the basis for comparison with other studies.     

Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02)

The Soil Moisture Experiments in 2002 (SMEX02) were conducted in Iowa between June 25th and July 12th, 2002. A major aim of the experiments was examination of existing algorithms for soil moisture retrieval from active and passive microwave remote sensors under high vegetation water content conditions. The data obtained from the passive and active L and S band sensor (PALS) along with physical variables measured by in situ sampling have been used in this study to demonstrate the sensitivity of the instrument to soil moisture and perform soil moisture retrieval using statistical regression and physical modeling techniques. The land cover conditions in the region studied were predominantly soybean and corn crops with average vegetation water contents ranging from 0 to ∼5 kg/m². The PALS microwave sensitivity to soil moisture under these vegetation conditions was investigated for both passive and active measurements. The performance of the PALS instrument and retrieval algorithms has been analyzed, indicating soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture. Statistical regression techniques have been shown to perform satisfactorily with soil moisture retrieval error of around 0.05 g/g gravimetric soil moisture. The retrieval errors were higher for the corn than for the soybean fields due to the higher vegetation water content of the corn crops. However, the algorithms performed satisfactorily over the full range of vegetation conditions.     

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

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

A new empirical model to retrieve soil moisture and roughness from C-band radar data

A new empirical model for the retrieval, at a field scale, of the bare soil moisture content and the surface roughness characteristics from radar measurements is proposed. The derivation of the algorithm is based on the results of three experimental radar campaigns conducted under natural conditions over agricultural areas. Radar data were acquired by means of several C-band space borne (SIR-C, RADARSAT) or helicopter borne (ERASME) sensors, operating in different configurations of polarization (HH or VV) and incidence angle. Simultaneously to radar acquisitions, a complete ground truth data base was built up with different surface condition measurements of the mean standard deviation (rms) height s, the correlation length l, and the volumetric surface moisture M_v. This algorithm is more specifically developed using the radar cross-section σ⁰ (HH polarization and 39° incidence angle off nadir), namely, σ_0HH,39, and the differential (HH polarization) radar cross-section Δσ₀=σ_0,23°−σ_0,39° in terms of an original roughness parameter, Z_s, namely Z_s=s²/l, and M_v. A good agreement is observed between model outputs and backscattering measurements over different test fields. Eventually, an inversion technique is proposed to retrieve Z_s and M_v from radar measurements.     

Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006

AMSR-E has been extensively evaluated under a wide range of ground and climate conditions using in situ and aircraft data, where the latter were primarily used for assessing the T_B calibration accuracy. However, none of the previous work evaluates AMSR-E performance under the conditions of flood irrigation or other forms of standing water. Also, it should be mentioned that global soil moisture retrievals from AMSR-E typically utilize X-band data. Here, C-band based AMSR-E soil moisture estimates are evaluated using 1 km resolution retrievals derived from L-band aircraft data collected during the National Airborne Field Experiment (NAFE'06) field campaign in November 2006. NAFE'06 was conducted in the Murrumbidgee catchment area in southeastern Australia, which offers diverse ground conditions, including extensive areas with dryland, irrigation, and rice fields. The data allowed us to examine the impact of irrigation and standing water on the accuracy of satellite-derived soil moisture estimates from AMSR-E using passive microwave remote sensing. It was expected that in fields with standing water, the satellite estimates would have a lower accuracy as compared to soil moisture values over the rest of the domain. Results showed sensitivity of the AMSR-E to changes in soil moisture caused by both precipitation and irrigation, as well as good spatial (average R = 0.92 and RMSD = 0.049 m³/m³) and temporal (R = 0.94 and RMSD = 0.04 m³/m³) agreement between the satellite and aircraft soil moisture retrievals; however, under the NAFE'06 ground conditions, the satellite retrievals consistently overestimated the soil moisture conditions compared to the aircraft.     

Utilizing SAR imagery and aquatic vegetation to map fresh and brackish lakes in the Brazilian Pantanal wetland

South America's Pantanal, the world's largest tropical wetland, contains hundreds of thousands of geochemically diverse lakes, ranging from dilute to brackish to saline in composition. These lakes form the backbone of the habitat that supports the highly diverse flora and fauna of the Pantanal, yet the natural processes that create and destroy them are largely unknown. The quantities and types of lakes in the Pantanal and their spatial distribution are therefore essential, but missing information required to understand the dynamics of the Pantanal ecosystem.RADARSAT S1 and S7, and JERS-1 imagery were integrated with field measurements of water geochemistry and characteristics of emergent aquatic vegetation for fresh and brackish lakes of the Nhecolândia region of the Brazilian Pantanal. A supervised classification was used to classify forest, pasture, bare soil, and lakes. A mask is then applied to produce an image of only lakes. The radar backscattering values were found to have a strong relationship with the emergent aquatic plant assemblages of the lakes—S1 imagery was the most useful. The plant assemblages, in turn, were observed to be strongly controlled by the total dissolved solids (TDS) and pH of the lakes. The relationships between backscattering values, plant assemblages, and geochemistry were then exploited to map the type and distribution of the lakes in the study area.Threshold rules were used to perform Level 1 and Level 2 classifications of the lakes. For the Level 1 classification, the σ^o values of RADARSAT S1 effectively separated brackish (10,000 > TDS > 1000 mg/kg) from fresh water lakes (TDS < 1000 mg/kg) with a total accuracy of 91%. For the Level 2 classification, the σ^o values of RADARSAT S1 effectively separated lakes into three geochemical groups: brackish (10,000 > TDS > 1000 mg/kg), hard with only Typha (1000 > TDS > 100 mg/kg), and fresh water lakes (TDS mg/kg < 100 mg/kg) with a total accuracy of 83%. Considering that the area is very remote and the lakes are very numerous, this may be the most feasible way to map lake type in the Pantanal.     

Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

The objective of this investigation is to analyze the sensitivity of ASAR (Advanced Synthetic Aperture Radar) data to soil surface parameters (surface roughness and soil moisture) over bare fields, at various polarizations (HH, HV, and VV) and incidence angles (20°-43°). The relationships between backscattering coefficients and soil parameters were examined by means of 16 ASAR images and several field campaigns. We have found that HH and HV polarizations are more sensitive than VV polarization to surface roughness. The results also show that the radar signal is more sensitive to surface roughness at high incidence angle (43°). However, the dynamics of the radar signal as a function of soil roughness are weak for root mean square (rms) surface heights between 0.5 cm and 3.56 cm (only 3 dB for HH polarization and 43° incidence angle). The estimation of soil moisture is optimal at low and medium incidence angles (20°-37°). The backscattering coefficient is more sensitive to volumetric soil moisture in HH polarization than in HV polarization. In fact, the results show that the depolarization ratio σ_HH⁰/σ_HV⁰ is weakly dependent on the roughness condition, whatever the radar incidence. On the other hand, we observe a linear relationship between the ratio σ_HH⁰/σ_HV⁰ and the soil moisture. The backscattering coefficient ratio between a low and a high incidence angle decreases with the rms surface height, and minimizes the effect of the soil moisture.     

Soil moisture retrievals at L-band using a two-step inversion approach (COSMOS/NAFE'05 Experiment)

COSMOS (Campaign for validating the Operation of Soil Moisture and Ocean Salinity), and NAFE (National Airborne Field Experiment) were two airborne campaigns held in the Goulburn River catchment (Australia) at the end of 2005. These airborne measurements are being used as benchmark data sets for validating the SMOS (Soil Moisture and Ocean Salinity) ground segment processor over prairies and crops. This paper presents results of soil moisture inversions and brightness temperature simulations at different resolutions from dual-polarisation and multi-angular L-band (1.4 GHz) measurements obtained from two independent radiometers. The aim of the paper is to provide a method that could overcome the limitations of unknown surface roughness for soil moisture retrievals from L-band data. For that purpose, a two-step approach is proposed for areas with low to moderate vegetation. Firstly, a two-parameter inversion of surface roughness and optical depth is used to obtain a roughness correction dependent on land use only. This step is conducted over small areas with known soil moisture. Such roughness correction is then used in the second step, where soil moisture and optical depth are retrieved over larger areas including mixed pixels. This approach produces soil moisture retrievals with root mean square errors between 0.034 m³ m^− 3 and 0.054 m³ m^− 3 over crops, prairies, and mixtures of these two land uses at different resolutions.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.     

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.     

ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy

Reliable measurements of soil moisture at global scale might greatly improve many practical issues in hydrology, meteorology, climatology or agriculture such as water management, quantitative precipitation forecasting, irrigation scheduling, etc. Remote sensing offers the unique capability to monitor soil moisture over large areas but, nowadays, the spatio-temporal resolution and accuracy required for some hydrological applications (e.g., flood forecasting in medium to large basins) have still to be met. The Advanced SCATterometer (ASCAT) onboard the Metop satellite (VV polarization, C-band at 5.255 GHz), based on a large extent on the heritage of the ERS scatterometer, provides a soil moisture product available at a coarse spatial resolution (25 km and 50 km) and at a nearly daily time step. This study evaluates the accuracy of the new 25 km ASCAT derived saturation degree product by using in situ observations and the outcomes of a soil water balance model for three sites located in an inland region of central Italy. The comparison is carried out for a 2-year period (2007-2008) and three products derived from ASCAT: the surface saturation degree, m_s, the exponentially filtered soil wetness index, SWI, and its linear transformation, SWI*, matching the range of variability of ground data. Overall, the performance of the three products is found to be quite good with correlation coefficients higher than 0.92 and 0.80 when the SWI is compared with in situ and simulated saturation degree, respectively. Considering SWI*, the root mean square error is less than 0.035 m³/m³ and 0.042 m³/m³ for in situ and simulated saturation degree, respectively. More notably, when the m_s product is compared with modeled data at 3 cm depth, this index is found able to accurately reproduce the temporal pattern of the simulated saturation degree in terms of both timing and entity of its variations also at fine temporal scale. The daily temporal resolution and the reliability obtained with the ASCAT derived saturation degree products represent the preliminary step for its effective use in operational rainfall-runoff modeling.     

The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVI_o) and full vegetation NDVI (NDVI_∞). Usually it is assumed that NDVI_o is close to zero (NDVI_o ∼ 0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI = 0.2) and is highly variable (standard deviation = 0.1). We show that the underestimation of NDVI_o yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI_o and NDVI_∞ derived from global scenes yields overestimations of Fg that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2 < NDVI_pixel < 0.4. When using conterminous U.S. scenes to derive NDVI_o and NDVI_∞, the overestimation is less (0.10-0.17 for 0.2 < NDVI_pixel < 0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI_o along with information on historical NDVI_pixel values to compute a statistically most-likely estimate of Fg. Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI_o values estimated from whole scenes. At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.     

A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data

According to simulation analysis of the advanced integral equation model (AIEM), there is a good linear relationship between emissivity and soil moisture under conditions of given roughness. The normalized difference of emissivities at 19.35 GHz and 10.65 GHz with vertical polarization can partly eliminate the influence of roughness and the squared correlation coefficient is about 0.985. This paper uses the normalized brightness temperature for retrieving soil moisture in Tibet from TRMM/TMI data. This method avoids parametrizing the land surface temperature which is a key parameter for the computation of emissivity. We make some sensitivity analysis for the atmosphere which is the main influence factor for our method. The analysis results indicate that our method is very good for clear days but is not very good when there is rainfall. We evaluate our algorithm by using the ground truth data obtained from GAME‐Tibet and the retrieval error of soil moisture is about 0.04m³ m^?3 relative to experimental data. The analysis indicates that the relationship obtained from the theoretical model should be revised through the local ground measurement data because the method is still influenced by roughness and vegetation. After making a regression revision, the retrieval error of soil moisture is under 0.02m³ m^?3. Finally, we retrieve the soil moisture in Tibet from TRMM/TMI data, and the distribution trend of retrieval results is consistent with the real world.