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.     

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).     

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.     

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

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

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.     

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

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

Semi-empirical regressions at L-band applied to surface soil moisture retrievals over grass

The L-band brightness temperature of natural grass fields is strongly influenced by rainfall interception. In wet conditions, the contribution of the soil, mulch, and vegetation to the overall microwave emission is difficult to decouple, thus rendering the retrieval of surface soil moisture from a direct emission model difficult. This paper investigates the development and assesses the performances of statistical regressions linking passive microwave measurements to surface soil moisture in order to assess the potential of soil moisture retrievals over natural grass. First, statistical regressions were analytically derived from the L-Band Emission of the Biosphere model (L-MEB). Single configuration (1 angle, 1 polarisation), and multi-configuration regressions (2 angles, or 2 polarisations) were developed. Second, the performance of statistical regressions was evaluated under different rainfall interception conditions. For that purpose, a modified polarisation ratio at L-band was used to build three data sets with different interception levels. In the presence of interception, a regression based on one observation angle (50°) and two polarisations was able to reduce the effects of vegetation and soil roughness on the soil moisture retrievals. The methodology presented in this study is also able to provide estimates of the vegetation and soil roughness contribution to the brightness temperature.     

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

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

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.     

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

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

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

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

青藏高原L波段微波辐射观测与土壤水分反演研究进展

土壤水分是地气间水热交换的重要变量,影响着地表感热潜热划分、水分收支和植被蒸腾等过程,青藏高原土壤水分的研究对于改进高原水分循环和能量平衡的模拟研究具有重要意义。随着SMOS、SMAP等卫星的发射,L波段被动微波遥感技术成为大尺度监测土壤水分的主要手段。分别从L波段星—机—地观测与微波辐射模拟、区域尺度土壤水分观测、卫星产品评估与土壤水分反演算法发展等方面系统回顾和总结了近年来L波段被动微波遥感及其土壤水分反演算法、产品在青藏高原的主要应用与研究进展。在此基础上,归纳了当前高原L波段被动微波辐射模拟与土壤水分反演存在的问题,主要包括缺乏高原尺度的微波辐射模拟评估和改进的卫星土壤水分产品、土壤冻结时期的水分监测产品依然缺失等问题。针对存在的问题,进一步提出了相关建议与展望,建议今后的研究应加强高原尺度的微波辐射模拟评估与土壤水分产品改进工作,并积极拓展土壤水分产品在高原水分循环和能量平衡模拟、植被生长与干旱监测的应用研究。     

Sea surface salinity retrievals from HUT-2D L-band radiometric measurements

The interest of the scientific community in global climate has been constantly increasing in the last years. Much effort has been devoted to better understand the water cycle and its role in global climate regulation. This is one of the objectives of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, approved in May 1999 and successfully launched in November 2nd 2009. Collecting brightness temperature measurements by means of a new type of instrument, a synthetic aperture radiometer operating at L-Band (MIRAS: Microwave Imaging Radiometer by Aperture Synthesis), the SMOS mission will provide regular and global maps of sea surface salinity (SSS) and soil moisture (SM), two key parameters to describe the water cycle. Synthetic aperture brightness temperatures have been simulated since the early 90s, during the MIRAS technological studies, and both SSS and SM retrievals have been tested with simulated data first, and later with real data to assess and improve the instrument performance. Nevertheless, except for the processing of the data collected by the SMOSillo (MIRAS Demonstrator) on June 20th, 2006, Duffo et al. (2007) and Camps et al. (2008b), these outcomes have never been validated using sea surface brightness temperatures coming from a real synthetic aperture radiometer. The aim of this study is thus to test some of the techniques proposed in previous years to improve the SSS retrieval, in particular only Level 2 techniques (i.e. converting calibrated and geo-located brightness temperature into SSS maps) will be investigated. To do so, brightness temperatures resulting from the SMOS Salinity Demonstrator Campaign held in August 2007 will be used. In that campaign the Helsinki University of Technology-2 Dimensional (HUT-2D) radiometer flown over both over land and sea. The part of the campaign conducted over the sea consisted of two series of flights over a very fresh water plume characterized by a strong SSS gradient (from 0 to 4 psu) in the Gulf of Finland. In-situ auxiliary data was collected simultaneously with the radiometer measurements. The positive outcomes in these very challenging conditions (due to the very low brightness temperature sensitivity to SSS, the lack of accurate models of the sea water dielectric constant at low SSS (Klein & Swift (1977)), and the weak radiometric sensitivity of the HUT-2D radiometer) demonstrate the importance of data pre- and post-processing to improve the results. Removing both brightness temperature biases and salinity retrieval biases and further averaging of the results, permits estimating SSS with an rms error on the order of 1 psu, which is comparable to the SMOS Level 2 expected accuracy.     

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.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km² area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r² = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r² = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.     

Impact of rain interception by vegetation and mulch on the L-band emission of natural grass

This paper explores the effect of rain intercepted by vegetation and mulch on the L-band emission of natural grass. The study is based on radiometric, meteorological, and biophysical measurements obtained during the SMOSREX Experiment (Toulouse, France). Several approaches were followed to evaluate interception effects. Firstly, the analysis of microwave brightness temperature (TB) measurements at L-band indicated that interception increases vegetation emission at both polarisations. Secondly, the use of microwave indices to detect the presence/absence of interception was examined. In particular, a modified polarisation ratio at 50° was found to be well related to the interception status of the standing vegetation. Finally, the vegetation optical depth (τ), which parameterises the extinction across the vegetation layer, was retrieved from the TB observations. It was found that τ increases with the increase in the water content stored within the vegetation and mulch after rainfall. The study highlights the strong impact of intercepted water in otherwise weakly attenuating covers such as grasses. Interception might therefore be an issue to consider in order to improve soil moisture retrieval algorithms from L-band observations.     

Pixel-scalesoil Moisture Monitoring Network and Its Preliminary Validation of L-band Soil Moisture Products

Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP（Soil Moisture and Active and Passive） and SMOS（Soil Moisture and Ocean Salinity）passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained：（1）Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;（2）Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; （3）SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.