The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km2pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.

Food and Agriculture Organization (FAO) dual crop coefficient (FAO-2Kc) is one of the widely used formulation to estimate soil evaporation (E) due to its operationality and simplicity. The FAO-2Kc method could explicitly distinguish the contribution of E and plant transpiration, separately. However, systematic and random uncertainty in E observations still exist. In this vein, this paper attempts to improve FAO-2Kc evapotranspiration estimates through assimilating SMAP-based disaggregated soil moisture (SM) into FAO-2Kc E component via the soil evaporation coefficient. Sequential data assimilation methods (Kalman filter) was used for this purpose, where E is strongly linked to SM especially under arid atmospheric conditions where energy is not the limited factor. The proposed approach is applied over a semi-arid site in central Morocco. Results revealed that the assimilation approach provides better results in term of evapotranspiration by decreasing the root mean square error from 0.98 mm/day to 0.75 mm/day compared to the standard FAO-2Kc.

A precise estimate of surface evapotranspiration (ET) is fundamental in water science for determining the crop water needs and for optimizing water management practices and irrigation regimes. FAO-56 dual crop coefficient (FAO-2Kc) based on a water balance model allows the partitioning between bare soil evaporation (E) and plant transpiration (Tr). However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning [1], [2] due to its simplicity. This paper aims to improve the accuracy of the ET components, through assimilating remotely sensed data. Remotely sensed soil moisture (SM) and land surface temperature (LST) are simultaneously assimilated into FAO-2Kc. SM was used to improve the E component while LST to update the plant Tr element. SM and LST data were derived from SMAP and Landsat 7/8 remotely sensed observations during the 2015–2016 wheat season, respectively. The standard FAO-2Kc yields an error of 0.98 mm/day with a bias of 0.47 mm/day. Assimilating combined SM and LST into FAO-2Kc leads to an improvement of the ET prediction with an error of 0.73 mm/day compared to eddy correlation measurements.

Surface soil moisture (SM) is an essential component for crop water stress detection and irrigation management. It controls soil evaporation and plant transpiration. SM dynamics is temporally related to root zone soil moisture which is the primary measure of the plant's water status. The aim of this work is to assess the robustness of high-resolution SM product derived from remote sensing on the energy balance based latent and sensible heat fluxes. Radar SM products retrieved from Sentinel-1 data only combined with Landsat Normalized Difference Vegetation index and land surface temperature are used together to feed the energy balance model TSEB-SM to estimate turbulent fluxes. The model estimates have been evaluated against the Eddy-covariance measurements over an irrigated wheat field situated in the Haouz plain in the center of Morocco. The results are very encouraging, with few observed anomalies meanly linked to the retrieved Priestley Taylor coefficient that is affected by SM.

Optimizing irrigation (timing and amount) is a worldwide requirement for water resource management, especially in semi-arid regions suffering already from limited water supply. In this study, an approach for estimating daily to seasonal irrigation amount is developed. The approach assimilates the surface soil moisture (SSM) estimated from Sentinel-1 radar data using a particle filter algorithm into the FAO-56 double coefficient model. The approach is tested over a drip irrigated wheat field located in the center of Morocco during two successive growing seasons. It is evaluated using in situ SSM measurements and using Sentinel-1 SSM products. Assimilation of Sentinel-1 SSM products, available every 6 days, yielded accurate irrigation estimates. The seasonal amounts are estimated with a maximum difference of 28 mm (8%) and 15-days cumulative irrigation amounts are reasonable with R=0.64, RMSE=28.78 mm and bias=1.99 mm.

Indian Rainwater Harvesting System (RHS) is an essential source of irrigation water in upstream agricultural areas. It is composed of hundreds of thousands of Small Reservoirs (SR) often disconnected from any perennial rivers. This study aims at quantifying the RHS Maximum Water Storage Capacity (MWSC) in the Telangana State, South India. The true bathymetries of 545 dry SR, located from Sentinel-2 (SENT-MWAE) and Landsat Maximal Water Area Extent (GSW-MWAE), are extracted from four Very High Resolution (VHR) Pleiades Digital Elevation Model (DEM). The average water depth at full capacity ranges from 22 cm to 4.6 m (average 1.3 m, std 0.6). The MWSC estimated within the Pleiades ground-coverage, for 62% of the total SR, accounts for 37.2 mm on average. The estimated capacity highly depends on the MWAE data source, varying from 5 to 30%. The Telangana RHS MWSC based on the RHS GSW-MWAE (1.6% of the Telangana area) is estimated at 29.7 mm +/− 9 mm. This capacity seems small compared to the large dam capacity (113mm for 126 registered dams in Telangana), but matters in upstream areas, far from irrigated command areas, to complement local groundwater pumping (from 62 to 295 mm). These preliminary results show the high interest of VHR DEM to evaluate uncertainties derived from MWAE products and medium to high resolution DEM to map water storage in RHS.

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

For sustainable irrigation water management as well as ecosystem health monitoring, it is important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data is thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data.

The vegetation optical depth measured at L-Band (LVOD) by the SMOS satellite provides a high temporal resolution information of the vegetation water content that can be linked to the total above ground biomass (AGB). Nevertheless, its coarse spatial resolution (~40 km) can be limiting for a number of applications. This study is devoted to the downscaling of the SMOS LVOD using high spatial resolution L-Band backscatter data from ALOS1 synthetic aperture radar. The goal is to improve the spatial resolution of the LVOD to estimate AGB at 1 km.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

This paper presents the the first attempt to include soil moisture information from remote sensing in the tools available to desert locust managers. The soil moisture requirements were first assessed with the users. The main objectives of this paper are: i) to describe and validate the algorithms used to produce a soil moisture dataset at 1 km resolution relevant to desert locust management based on DisPATCh methodology applied to SMOS and ii) the development of an innovative approach to derive high (100 m) resolution soil moisture products from Sentinel-1 in synergy with SMOS data. For the purpose of soil moisture validation, 4 soil moisture stations where installed in desert areas (one in each user country). The soil moisture 1km product was thoroughly validated and its accuracy is amongst the best available soil moisture products. Current comparison with in-situ soil moisture stations shows good values of correlation (R > 0.7) and low RMSE (below 0.04 m3m-3). The low number of acquisitions on wet dates has limited the development of the soil moisture 100m product over the Users Areas. The Soil Moisture product at 1km will be integrated into the national and global Desert Locust early warning systems in national locust centres and at DLIS-FAO, respectively.

The SMOS (Soil Moisture and Ocean Salinity) mission, together with other passive microwave based missions (AMSR, SMAP), provides soil moisture estimates at resolutions ranging from 30 to 55 km. These estimates are validated by direct comparison to in situ measurements that typically measure over an area of a few centimeters. There exist a spatial scale mismatch between the satellite (large support) and the in situ measurements (point support), which contributes to the differences observed. Their magnitude depends on the spatial representativeness of the in situ measurements, which varies in time and with the selected location. This communication will show how the spatial scale mismatch evolves through timescales. It is characterized by using modeled, in situ and satellite soil moisture time series. Timescales, from 0.5 to 128 days, are obtained using wavelet transforms and the spatial representativeness is assessed with a new approach that uses wavelet-based correlations (WCor).

The disaggregation based on physical and theoretical scale change (DISPATCH) algorithm was developed to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) using 1 km resolution Moderate resolution Imaging Spectroradiometer (MODIS) data. The main objective of this paper is firstly, to adapt the DISPATCH algorithm to the 100 m resolution Landsat data and secondly, to determine an optimal intermediate spatial resolution (ISR) between the original (40 km) SMOS resolution and the targeted 100 m resolution. It is found that the ISR (set to 1 km, 3 km and 5 km) impacts the accuracy in the sequentially downscaled 100 m resolution SM depending on both the SM heterogeneity present within the spatial extent considered, and the gap between the low to high resolution ratio.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0-5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Données SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m3/m3for 1-km resolution and 0.037 m3/m3for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.

The L-band (1 - 2 GHz) microwave remote sensing has been widely acknowledged as the most promising method to monitor regional to global soil moisture. Consequently, the Soil Moisture Active Passive (SMAP) satellite applied this technique to provide global soil moisture every 2 to 3 days. To verify the performance of SMAP, the fourth and fifth campaign of SMAP Experiments (SMAPEx-4 & -5) were carried out at the beginning of the SMAP operational phase in the Murrumbidgee River catchment, southeast Australia. The airborne radar and radiometer observations together with ground sampling on soil moisture, vegetation water content, and surface roughness were collected in coincidence with SMAP overpasses. The SMAPEx-4 & -5 data sets will benefit to SMAP post-launch calibration and validation under Australian land surface conditions.

Evapotranspiration is an essential component for the assessment of water dynamic in agriculture and the estimation of water demand by crops. Its estimation becomes more difficult over row crops due to differences between canopy and inter-row space. In this work, lysimeter data was combined and integrated with radiative and meteorological data to evaluate energy and water fluxes over a raspberry crop during a summer month. These measurements allowed to determine differences in the water fluxes between vegetation and bare soil of the inter-row. It was determined that 15% of the precipitation was intercepted by vegetation, and 29% of the monthly precipitation could be attributed to dew. The pattern of evaporation was affected by the shadow of crops, thus the peak of evaporation occurs after direct sun irradiance reaches the lysimeter and therefore the evaporation tend to decrease. Combined radiative and mass balance instrumentation can be a useful tool to validate the partitioning of evapotranspiration over row crops.

A physically based downscaling algorithm, DISPATCH (DISaggregation based on Physical And Theoretical scale CHange), has been used to estimate soil moisture at 1 km scale from coarse resolution SMOS soil moisture estimates over the agricultural site of the CanEx-SM10 campaign. In this paper, we test the applicability of the algorithm and analyze the linearity of the relationship between the soil evaporative efficiency (SEE) and the near-surface soil moisture (SM) for very wet soils. In such conditions, the results show that the linear model is not suitable due to a difficulty in estimating Tsmax (dry edge) within the SMOS pixel. The use of a neighboring area to estimate a reliable value of Tsmax was tested and validated in order to improve the linear model results.

The objective of the SMOScat project is to operationally provide soil moisture at 1 km resolution or better over Catalonia. A downscaling algorithm is applied to 40 km resolution L2 SMOS (Soil Moisture and Ocean Salinity) soil moisture product using 1 km resolutionMODIS (MODerate resolution Imaging Spectroradiometer) data. High resolution soil moisture is compared with in situ measurements collected each month from April to October 2011 in a dryland and irrigated area. Our results show and increase of correlation coefficient with in-situ measurements when high resolution soil moisture is used. The 2012 experimental field campaign will address topography issues.

The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3over specific nominal cases, but differences are observed over many sites and need to be addressed.

Land surface temperature data are rarely available at high temporal and spatial resolutions at the same locations. To fill this gap, the low spatial resolution data can be disaggregated at high temporal frequency using empirical relationships between remotely sensed temperature and fractional green (photosynthetically active) and senescent vegetation covers. In this paper, a new disaggregation methodology is developed by physically linking remotely sensed surface temperature to fractional green and senescent vegetation covers using a radiative transfer equation. Moreover, the methodology is implemented with two additional factors related to the energy budget of irrigated areas, being the fraction of open water and soil evaporative efficiency (ratio of actual to potential soil evaporation). The approach is tested over a 5 km by 32 km irrigated agricultural area in Australia using airborne Polarimetric L-band Multibeam Radiometer brightness temperature and spaceborne Advanced Scanning Thermal Emission and Reflection radiometer (ASTER) multispectral data. Fractional green vegetation cover, fractional senescent vegetation cover, fractional open water, and soil evaporative efficiency are derived from red, near-infrared, shortwave-infrared, and microwave-L band data. Low-resolution land surface temperature is simulated by aggregating ASTER land surface temperature to 1-km resolution, and the disaggregated temperature is verified against the high-resolution ASTER temperature data initially used in the aggregation process. The error in disaggregated temperature is successively reduced from 1.65$^{\circ}\hbox{C}$to 1.16$^{\circ}\hbox{C}$by including each of the four parameters. The correlation coefficient and slope between the disaggregated and ASTER temperatures are improved from 0.79 to 0.89 and from 0.63 to 0.88, respectively. Moreover, the radiative transfer equation allows quantification of the impact on disaggregation of the temperature at high resolution for each parameter: fractional green vegetation cover is responsible for 42% of the variability in disaggregated temperature, fractional senescent vegetation cover for 11%, fractional open water for 20%, and soil evaporative efficiency for 27%.

Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies.

The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was -0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and thein situmeasurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.

Surface roughness parameterization plays an important role in soil moisture retrieval from passive microwave observations. This letter investigates the parameterization of surface roughness in the retrieval algorithm adopted by the Soil Moisture and Ocean Salinity mission, making use of experimental airborne and ground data from the National Airborne Field Experiment held in Australia in 2005. The surface roughness parameter is retrieved from high-resolution (60 m) airborne data in different soil moisture conditions, using the ground soil moisture as input of the model. The effect of surface roughness on the emitted signal is found to change with the soil moisture conditions with a law different from that proposed in previous studies. The magnitude of this change is found to be related to soil textural properties: in clay soils, the effect of surface roughness is higher in intermediate wetness conditions (0.2-0.3 v/v) and decreases on both the dry and wet ends. Consequently, this letter calls for a rethink of surface roughness parameterization in microwave emission modeling.

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-m resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-m ldquocoarse-scalerdquo passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): (1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and (2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture.

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: (1) the development of SMOS soil moisture retrieval algorithms; (2) developing approaches for downscaling the low-resolution data from SMOS; and (3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km × 40 km coverage flights at 500-m and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km × 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb.edu.au.

A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolution SMOS multiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface-atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relationship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0-5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach.