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The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters. [ABSTRACT FROM AUTHOR]

Furthermore, previous studies generally focused on estimating soil moisture in mineral soils since the soil dielectric models used in soil moisture retrieval algorithms were usually mineral-soil-based models. Therefore, satellite soil moisture products have become increasingly abundant, greatly promoting the various application of satellite soil moisture datasets [[13], [15]]. Soil moisture is an important component of the global terrestrial ecosystem and has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) [[1]]. In Wu and Wen [[26]], the research progress in observing and simulating L-band microwave emissions, ground soil moisture measurements, and soil moisture retrieval from L-band passive microwave observations over the Third Pole, i.e., the Tibetan Plateau, was summarized. [Extracted from the article]

Microwave remote sensing instrument like Soil Moisture Active Passive ranging from 1 cm to 1 m has provided spatial soil moisture information over the entire globe. However, Soil Moisture Active Passive satellite soil moisture products have a coarse spatial resolution (36km x 36km), limiting its application at the basin scale. This research, subsequently plans to; (1) Evaluate the capability of SAR for the retrieval of surface roughness variables in the Awash River basin; (2) Measure the performance of Random Forest (RF) regression model to downscale SMAP satellite soil moisture over the Awash River basin; (3) validate downscaled soil moisture data with In-situ measurements in the river basin. Random Forest (RF) based downscaling approach was applied to downscale satellite-based soil moisture product (36km x 36km) to fine resolution (1km x 1km). Fine spatial resolution (1km) soil moisture data for the Awash River basin was generated. The downscaled soil moisture product also has a strong spatial correlation with the original one, allowing it to deliver more soil moisture information than the original one. In-situ soil moisture and downscaled soil moisture had a 0.69 Pearson correlation value, compared to a 0.53 correlation between the original and In-situ soil moisture. In-situ soil moisture measurements were obtained from the Middle and Upper Awash sub-basins for validation purposes. In the case of Upper Awash, downscaled soil moisture shows a variation of 0.07 cm3 /cm3, -0.036 cm3 /cm3, and 0.112 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Following that, the accuracy of downscaled soil moisture against the Middle Awash Sub-basin reveals a variance of 0.1320 cm3 /cm3, -0.033 cm3 /cm3, and 0.148 cm3 /cm3 with Root Mean Square Error, Bias error, and Unbiased Root Mean Square Error respectively. Future studies should take into account the temporal domain of Soil Moisture Active Passive satellite soil moisture product downscaling over the study region. [ABSTRACT FROM AUTHOR]

The Terrestrial Water Resources Satellite (TWRS) campaign is a planned Chinese candidate satellite mission, and a one-dimensional synthetic aperture technology will be used, resulting in variant incidence angles for collecting synchronous active-passive observations at L-band, which would make brightness temperature (Tb) downscaling especially challenging when aiming to improve the spatial resolution of soil moisture measurements. In this study, two active-passive Tb downscaling algorithms, the time-series regression (TSR) and spectral analysis (SA) algorithms, are assessed comprehensively based on airborne experimental datasets. The results with data collected during the Soil Moisture Experiment 2002 (SMEX02) showed that both approaches could provide a reliable downscaled Tb at the same incidence angle. Based on the ground and airborne active-passive observations under variant incidence angles from the Soil Moisture Experiment in the Luan River (SMELR) it can be shown that the linear relationship between Tb and σ is still robust under the case of variant incidence angles, and Tb (both h- and v-pol) is better correlated to σvv for most cases than σhh. Both downscaling approaches can be applied to active-passive observations under varying incidence angles. Moreover, SA method performed better than the TSR method according to the lower RMSE values and higher correlation. [ABSTRACT FROM AUTHOR]

Measurements of soil moisture have great significance for agricultural production and environmental protection. A variety of technologies have been introduced into the current monitoring of surface soil moisture, particularly widely-used optical remote sensing and synthetic aperture radar (SAR) microwave remote sensing. In this study, retrieval for surface soil moisture in farmland was proposed using multi-source remote sensing data and feature dimension reduction with GA-BP neural networks. Sentinel-1 microwave and Sentinel-2 optical remote sensing data were used with a high spatial and temporal resolution. Three field surveys and sampling were carried out simultaneously during the transit time of the Sentinel-1 satellite. A total of 20 sampling points were set on the ground in the study area to collect soil moisture in the longitude and latitude coordinates. Firstly, Sentinel-1 and Sentinel-2 data were preprocessed to extract 21 feature parameters, including 9 backscatter coefficient, 5 polarization characteristic parameters, surface roughness, and 6 vegetation indices. Then, the differential evolution feature selection (DEFS) was utilized to obtain an optimal feature subset with 10 parameters, including the incident angle, ..., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Water Band Index (WBI), and Fusion Vegetation Index (FVI). Principal component analysis (PCA) was used to further reduce the dimension of the optimal feature subset. The feature subset was reduced to eight dimensions, where an eight-dimensional feature matrix was obtained. After that, back propagation (BP) neural network was established to describe the nonlinear relationship between characteristic parameters and surface soil moisture, whereas, genetic algorithm (GA) was used to optimize the node weights and accelerated the learning speed of the BP neural network. The feature matrix after dimension reduction and some measured data of soil moisture were input into the GA-BP network for training, where the distribution map of soil moisture was obtained in the study area. Finally, taking a winter wheat field in Henan Province as the study area, three comparative experimental schemes were set to verify the accuracy of inversion using the measured data. The experimental schemes with DEFS and PCA presented the highest accuracy, where the coefficient of determination was 0.789 3, and the root mean square error was 0.028 7 cm³/cm³, compared with the genetic BP neural network, indicating the coefficient of determination increased by 0.215 7, and the root mean square error was reduced by 0.029 5 cm³/cm³. Meanwhile, the frequency distribution of soil moisture inversion was basically consistent with the measured soil moisture of sampling points. The experimental results demonstrated that the GA-BP network combining with DEFS and PCA can eliminate the redundant characteristic parameters for high inversion accuracy, and a high-resolution distribution map of surface soil moisture with a large area. The finding can offer some advantages and application potentials to the surface soil moisture retrieval in farmland. [ABSTRACT FROM AUTHOR]

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The Advanced Microwave Scanning Radiometer for the Earth Observing System and Advanced Microwave Scanning Radiometer 2 sensors (AMSR) have provided multifrequency microwave measurements of the global terrestrial water cycle since 2002. A new AMSR surface wetness index (ASWI) was developed by analyzing the near‐surface atmospheric vapor pressure deficit (VPD), surface volumetric soil moisture (VSM), and land surface fractional open water (FW) time series from an established AMSR Land Parameter Data Record (LPDR). The ASWI allows for multicomponent and independent satellite assessments of near‐surface drought conditions by exploiting the weighted anomalies of VPD, VSM, and FW. Comparisons between ASWI and more traditional drought metrics, including the Palmer moisture anomaly index (PDSI‐Z) and the U.S. Drought Monitor, showed generally consistent classifications of drought severity for three major droughts over the Contiguous United States since 2002. The AWSI showed moderate (0.3 ≤ R ≤ 0.7 for 56% of area) to strong (R > 0.7 for 29% of area) correlations with the PDSI‐Z during the summer months (June–August) from 2002 to 2017. ASWI and PDSI‐Z differences were attributed to AMSR retrieval uncertainties and the different aspects of drought represented by the indices. Comparisons between ASWI and the Gravity Recovery and Climate Experiment drought severity index (GRACE‐DSI) showed strong correspondence (R = 0.61) in regions where possible long‐term total water storage changes occurred. The sole reliance of the ASWI on satellite microwave remote sensing and continuing AMSR2 operations enables effective global monitoring of drought conditions while providing new information on the atmosphere, soil, and surface water components of drought. Plain Language Summary: Extreme hydrologic events affect the economy, environment, and society. The anomalies of the water cycle components are associated with drought events. Considering the capability of multifrequency microwave remote sensing in observing multiple water cycle components, this study developed a new surface wetness index by analyzing near‐surface atmospheric vapor pressure deficit, volumetric soil moisture, and land surface water inundation time series from an established satellite data record. The new index showed consistent assessment of major drought events relative to traditional drought monitors while providing enhanced information on the water cycle components of drought. A combined analysis of the index with satellite total water storage measurements provides complementary information on the different aspects of droughts, including relatively dynamic surface water changes and slower evolving groundwater conditions. The index enables independent and continuous satellite assessment of global drought conditions. Key Points: A new surface wetness index was developed from satellite‐observed vapor pressure deficit, soil moisture, and surface water coverThe index is consistent with traditional drought monitors but allows for analyzing the relative importance of water cycle componentsThe satellite‐based multicomponent index allows for independent monitoring of global drought conditions [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management. [Display omitted] • Soil moisture was estimated in rain-fed and irrigated wheat fields using Sentinel-1 data. • Three change detection methods were evaluated. • Different methodological alternatives were proposed for applying change detection methods at the field scale. • The results of two methods improved when correcting for the vegetation influence on backscatter. • Soil moisture was generally overestimated for dry conditions. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies. [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m 3 /m 3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m 3 /m 3 . The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture. [ABSTRACT FROM AUTHOR]

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer – Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators – based on microwave brightness temperature, land cover types, and long-term averaged vegetation index – are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals. [ABSTRACT FROM AUTHOR]

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R2) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for the application of the products and improvement of the retrieval algorithm. Since the SMAP SM products were released, much effort has been invested in the evaluation of the SMAP radiometer SM product (SMAP_P). However, there has been little validation of SMAP radar (SMAP_A) and active/passive combined (SMAP_AP) SM products. This paper presents an evaluation of SMAP_P, SMAP_A and SMAP_AP SM products by using distributed ground observations networks in different landscapes in the Heihe River Basin of northwestern China. The standard error metrics of SMAP products and relative error are applied to measure the products' performances. The results show that the SMAP SM products exhibit consistent spatial-temporal variation with the ground measurements and typical precipitation events. Three products show various types of performance capability (e.g., active, passive and combined), surface coverage (e.g., bare, vegetated) and climatic region (e.g., cold, arid). Relatively, the SMAP_P shows the best performance, while the SMAP_A performs the worst. The best performances are observed over bare soils but with overestimation and the largest relative error, and unsatisfactory accuracies are observed over cold regions and woody vegetated surfaces with underestimation. The vegetation effect and the freezing-thawing cycle may be major factors that led to an unsatisfactory performance. Efforts on resolving the influence of these factors are expected to improve the accuracy and to promote the application of SMAP SM products over these regions. Overall, this evaluation provides an understanding of SMAP SM products over cold and arid regions, and suggestions for the further refinement of the SMAP SM retrieval algorithms. [ABSTRACT FROM AUTHOR]

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements. [ABSTRACT FROM AUTHOR]

The role of soil moisture in the hydrological cycle and climate change monitoring is discussed widely after its designation as an Essential Climate Variable (ECV) by the European Space Agency (ESA). This paper reviews the methods of soil moisture estimation using microwave remote sensing sensors, including the Synthetic Aperture Radar (SAR), Scatterometer, and Radiometer; and the advantages and limitations of using them to estimate soil moisture over tropical regions. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite equipped with an L-band radiometer to provide soil moisture products with the best spatiotemporal advantages. Its products have been validated across the world except for the humid tropical regions. An in-situ soil moisture data collection network was set up in Malaysia aimed to provide the first available data to validate SMOS products in the region. This network also aimed to provide data for SMOS retrieval algorithm calibration in the near future. Preliminary result shows unconvincing accuracy between SMOS product and in-situ data. [ABSTRACT FROM AUTHOR]

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest on the part of the remote sensing community in using the Antarctic area for calibrating and validating data of low-frequency satellite-borne microwave radiometers. In particular, the East Antarctic Plateau appears to be suited for this purpose. The reasons for this interest are the size, structure, spatial homogeneity, and thermal stability of this area. This is particularly interesting for low-frequency microwave radiometers since, due to the low extinction of dry snow, the upper ice-sheet layer is almost transparent and the brightness temperature variability is therefore extremely small. In the context of calibration and validation activities of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite, an experiment called DOMEX-2, which included radiometric L-band measurements, was carried out at the Italian–French base of Concordia located at Dome C in the East Antarctic Plateau from December 2008 to December 2010. Ground measurements (i.e., snow temperature at different depths, snow structure, meteorological data, etc.) were also collected during the experiment. This paper presents information on the experimental campaign, the characteristics of the radiometric measurements, and the main results. A comparison with SMOS data is also presented. [ABSTRACT FROM AUTHOR]

In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanaiysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanaiysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988-2010 suggests a decrease of surface soil mois-ture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanaiysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil). [ABSTRACT FROM AUTHOR]

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The best wS estimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions. [ABSTRACT FROM AUTHOR]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text. [ABSTRACT FROM AUTHOR]

Summary: Soil moisture is an important variable in the hydrological cycle and plays a vital role in agronomy, meteorology, and hydrology. It regulates the exchange of water and heat between land surface and atmosphere and thus plays an important role in the development of weather patterns. It is difficult to obtain a comprehensive spatio-temporal map of soil moisture because of expensive installation of soil moisture measuring instruments. In this paper, a model to estimate soil moisture (ms ) using Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) backscatter (σ°) and Normalized Difference Vegetation Index (NDVI) is developed for the Southern United States. Soil moisture data from Soil and Climate Analysis Network (SCAN) stations is used to calibrate and validate the model. The estimated values of ms compare well with the ground measurements of soil moisture. The model works well for various landcovers but works best for low density vegetated areas (closed shrubland). All the soil moisture estimates in this landcover have an absolute error of less than 8%. The model performance deteriorates with increase in vegetation density (crops and forest). Overall, the model performance is satisfactory for all landcover types with RMSE less than 6.3% and absolute error of 10% or less for 90% of the estimates. Estimation of soil moisture over a large area with low error provides another use of TRMMPR data. [Copyright &y& Elsevier]

Accounting for small-scale spatial heterogeneity of soil moisture () is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft-based (800 m) estimates collected during the southern Great Plains experiment in 1997 (SGP97). We first demonstrate the presence of scale invariance and multifractality in fields of nine square domains of size 25.6 × 25.6 km2, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale distribution required by land surface models and data assimilation systems. [ABSTRACT FROM AUTHOR]

A change detection algorithm has been developed in order to obtain high-resolution soil moisture estimates from future Soil Moisture Active and Passive (SMAP) L-band radar and radiometer observations. The approach combines the relatively noisy 3-km radar backscatter coefficients and the more accurate 36-km radiometer brightness temperature into an optimal 10-km product. In preparation for the SMAP mission, an observation system simulation experiment (OSSE) and field experimental campaigns using the Passive and Active L- and S-band Airborne Sensor (PALS) have been conducted. We use the PALS airborne observations and OSSE data to test the algorithm and develop an error budget table. When applied to four-month OSSE data, the change detection method is shown to perform better than direct inversion of the radiometer brightness temperatures alone, improving the root mean square error by 2% volumetric soil moisture content. The main assumptions of the algorithm are verified using PALS data from the soil moisture experiments held during June--July 2002 (Soil Moisture Experiment 2002) in Iowa. The algorithm error budget is estimated and shown to meet SMAP science requirements. [ABSTRACT FROM AUTHOR]

Radar and Optical sensor integration combines traditional space-borne optical data from the visible and infrared wavelengths with the longer wavelengths of radar to improve land cover classification and the potential of land use classification. Optical data gives the details of vegetation and microwave data on the other hand provide the texture and terrain characteristics. The indicators of vegetation stress in optical include NDVI analysis and texture and Dielectric Constant of microwave. The LISS III data for optical and EnviSAT ASAR data for backscattering coefficient and dielectric constant were used. Soil Moisture plays an important role in the interactions between the land surface and the atmosphere. Soil moisture is a highly variable component in land surface hydrology and plays a critical role in agriculture and hydrometeorology and has been found not easy to map with only optical data. Therefore, a study to fit a mathematical equation to relate soil moisture condition mid the micro wave parameters in a part of Cumbum valley in south west Tamil Nadu. The optical vegetation stress indicators were studied along with the micro wave parameters were analyzed to judge upon the vegetation stress. The present work is to have synergetic use of the bands from both optical and microwave data which are quite complementary in nature. The optical and microwave data are processed using separate tools. An index called Soil Moisture Index function was generated for the estimation of the wet and dry nature of the soil. The NDVI image was generated and it was cross verified with land cover of the study area. As the NDVI is a consistent parameter of vegetation stress, this image was used as the base for the soil moisture equation. The dielectric constant generated from the back scatter image was referenced with the NDVI image. From the generated back scatter and dielectric constant values, soil moisture index was estimated. Regression and correlation analysis was carried out for the prediction of the values and the error estimation of the generated empirical relation of BSC and DC with respect to the SMI. The result showed a good agreement of dielectric constant with soil moisture index yielding R²=0.958 and for backscatter and soil moisture indent yielding R²=0.694 for the generated SMI relation. A map was generated to indicate the soil moisture across space. It is observed that an empirical relation between the dielectric constant and the soil moisture stress is possible to draw by correlation and the study has to be perfected with more critical field observed data. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging radiometers are powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the Soil Moisture and Ocean Salinity (SMOS) mission devoted to the monitoring of SMOS at global scale from L-band spaceborne radiometric observations obtained with a 2-D interferometer. This paper is concerned with the reconstruction of radiometric brightness temperature maps from interferometric measurements. More exactly, it extends the concept of "band-limited resolving matrix" to the case of the processing of dual-polarimetric data. [ABSTRACT FROM AUTHOR]

Methods to retrieve surface soil moisture were assessed at the global scale for one entire year by using simulated Soil Moisture and Ocean Salinity brightness temperatures (TB) and vegetation coverage information which can be derived from optical sensors. The global TB database consists of half-degree continental pixels and accounts for within-pixel heterogeneity, based on 1-km resolution land cover maps. The retrievals were performed by using a three-parameter inversion method applied to the L-band Microwave Emission of the Biosphere model. By using a Bayesian approach, vegetation data were injected as a priori information. Two options were investigated to profit from normalized difference vegetation index products: providing an a priori knowledge either on vegetation optical depth or on the vegetation cover fraction (fcover). The latter option allows for a better description of the surface heterogeneity by considering a bare soil fraction. When an error of 1 K is applied to the TB, both synergistic schemes significantly improved the soil moisture accuracy compared with methods using microwave data only. Using the vegetation a priori information, about 80% of the pixels present soil moisture retrieval accuracy less than 0.04 m³ m-3 in terms of root-mean-square error, whereas methods based only on the microwave data provide 63% of pixels of the studied area with this accuracy. If the error in TB is larger (2 or 3 K), the soil moisture retrieval accuracy decreases significantly for both methods. The use of optical data to give a priori value of vegetation optical option is then the best for these cases. [ABSTRACT FROM AUTHOR]

Water and energy fluxes at the interface between the land surface and atmosphere are affected by the surface water content of the soil, 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 mission concepts as, for example, the Soil Moisture and Ocean Salinity (SMOS) mission, are dedicated to provide global soil moisture information with a temporal frequency of a few days to capture the high temporal dynamics of surface soil moisture. SMOS soil moisture products are expected to have geometric resolutions on the order of 40 km. Mesoscale flood forecasting or water balance models typically operate at much higher spatial resolutions on the order of 1 km. It seems therefore essential to develop appropriate disaggregation schemes to benefit from the high temporal frequency of the SMOS data for hydrological applications as well as, for example, local numerical weather prediction models that are operated at a resolution of a few kilometers. This paper investigates the potential of using prior information on spatially persistent soil moisture fields to disaggregate SMOS scale soil moisture products. The approach is based on a ten-year soil moisture climatology for a mesoscale hydrological catchment, situated in southern Germany, which was generated using a state-of-the-art land-surface process model. The performance of the disaggregation algorithm is verified by comparison of disaggregated soil moisture fields with another ten-year period. To investigate the potential of the suggested disaggregation method for SMOS soil moisture products, a ten-year synthetic brightness temperature data set is generated at the 1-km scale. soil moisture is then retrieved from the aggregated brightness temperature data at the SMOS type scale of 40 km and then disaggregated using the suggested approach. The results are compared against reference soil moisture at the 1-km scale. Uncertainties in the retrieval of the SMOS soil moisture products are explicitly considered, and the uncertainties of the disaggregated fields are quantified. The developed method shows a generally good performance for large parts of the test site, where soil moisture can be disaggregated with an accuracy that is better than the 4 vol.% benchmark of the SMOS mission. As the suggested method shows high sensitivity to biased soil moisture retrievals, uncertainties of the SMOS soil moisture products will directly reflect on the absolute accuracy of the disaggregated soil moisture fields, resulting in a much worse performance under noisy conditions. Nevertheless, the resulting soil moisture distributions show that it is feasible to derive relative soil moisture distributions in these cases. [ABSTRACT FROM AUTHOR]

In this paper, the L-band Microwave Emission of the Biosphere (L-MEB) model used in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm is calibrated using L-band (1.4 GHz) microwave measurements over a coniferous (Pine) and a deciduous (mixed/Beech) forest. This resulted in working values of the main canopy parameters optical depth (τ), single scattering albedo (ω), and structural parameters tt(H) and tt(V), besides the soil roughness parameters HR and NR. Using these calibrated values in the forward model resulted in a root mean-square error in brightness temperatures from 2.8 to 3.8 K, depending on data set and polarization. Furthermore, the relationship between canopy optical depth and leaf area index is investigated for the deciduous site. Finally, a sensitivity study is conducted for the focus parameters, temperature, soil moisture, and precipitation. The results found in this paper will be integrated in the operational SMOS Level 2 Soil Moisture algorithm and used in future inversions of the L-MEB model, for soil moisture retrievals over heterogeneous, partly forested areas. [ABSTRACT FROM AUTHOR]

To retrieve soil moisture from L-band microwave radiometry, it is necessary to account for the effects of temperature within both vegetation and soil media. To compute the effective soil temperature TG, several simple formulations accounting for soil temperatures at the surface and at depth and surface soil moisture have been developed. However, the effects of the soil physical properties in terms of texture, density, or structure, which all may be important variables in the modeling of TG, have never been investigated. In this paper, several simple formulations of TG at L-band, accounting for or ignoring the effects of soil texture and density, were developed and compared based on a very large simulated data set. The best configurations and parameterizations of these simple formulations were computed and could be directly used for operational applications in future soil moisture retrieval studies. For instance, we showed that the use of the surface temperature in the estimation of TG can be significantly improved by using additional information on the soil temperature at depth (the average error in the estimation of TG decreased from ∼4 to ∼1.8 K). On the contrary, almost no improvement was obtained if air temperature was used instead of surface temperature. Also, it is shown that the use of additional information on the soil properties, mainly the soil clay content and density, led to improved results by about 0.2 K in the estimation of TG. The improvement was found to be larger for sandy and dry soils: simplified formulations accounting for soil properties are able to represent the fact that TG is closer to the soil temperature at depth for these soil conditions. [ABSTRACT FROM AUTHOR]

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-rn resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-rn "coarse-scale" 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. [ABSTRACT FROM AUTHOR]

Soil Moisture and Ocean Salinity (SMOS) level 2 and level 3 products are simulated and characterized over a one-year time period. A simulator is first used to evaluate the sea surface salinity (SSS) error of level 2 SMOS products. An optimal interpolation method is then adapted to map the surface salinity in order to simulate a level 3 SMOS product. The quality of the simulated products is satisfactory. The mean error of the SSS at pixel scale is around 1 psu, and the error on the final gridded product fits the Global Ocean Data Assimilation Experiment requirements (0.2 psu). [ABSTRACT FROM AUTHOR]

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 x 40 km coverage flights at 500-rn 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 x 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. [ABSTRACT FROM AUTHOR]

The microwave Forest Soil Moisture Experiment (FOSMEX) was performed at a deciduous forest site at the Research Centre Jülich (Germany). An L-and an X-band radiometer were mounted 100 m above ground and directed to the canopy. The measurements consist of dual- and single-polarized L- and X-band data and simultaneously recorded ground moisture, temperature, and meteorological data. The canopy L-band transmissivity was estimated from a subset of the FOSMEX data, where the ground was masked with a metalized foil. For the foliage-free canopy, the reflecting foil diminished the L-band brightness by ≈24 K, whereas brightness increased by ≈14 K when the foil was removed from below the foliated canopy. Depending on the assumption made on the scattering albedo of the canopy, the transmissivities were between 0.2 and 0.51. Furthermore, the contribution of the foliage was quantified. Although, the evaluation revealed the semitransparency of the canopy for L-band frequencies, the brightness sensitivity with respect to ground moisture was substantially reduced for all foliation states. The effect of ground surface moisture was explored in an irrigation experiment. The L-band measurements were only affected for a few hours until the water drained through the litter layer. This emphasizes the significance of the presence of litter for soil moisture retrieval from remotely sensed L-band brightness data. The FOSMEX database serves for further testing and improving radiative transfer models used for interpreting microwave data received from future spaceborne L-band radiometers flying over areas comprising a considerable fraction of deciduous forests. [ABSTRACT FROM AUTHOR]

An airborne L-band 2-D interferometric radiometer for Soil Moisture and Ocean Salinity (SMOS) measurements has been developed in the Helsinki University of Technology, Laboratory of Space Technology. The first successful flights were conducted in spring 2006. In this paper, the technical description, calibration, and image reconstruction philosophy and the latest results from the use of the instrument are discussed. One of the key goals of the instrument design has been to acquire L-band interferometric data to support the European Space Agency's SMOS mission that employs the L-band interferometric radiometer Microwave Interferometric Radiometer using Aperture Synthesis. Both instruments use aperture synthesis technology for the target image reconstruction in two dimensions. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission is a joint ESA-CNES (F)-CDTI (E) mission within the ESA Living Planet Program, and it was the second ESA Earth Explorer Opportunity Mission to be selected. The mission objectives of SMOS are to provide soil moisture and ocean salinity observations for weather forecasting, climate monitoring, and the global freshwater cycle. This paper will describe the scientific campaigns performed to date, as well as the plans for the on-orbit calibration and validation activities. [ABSTRACT FROM AUTHOR]

Soil moisture retrieval models from the Soil Moisture and Ocean Salinity (SMOS) mission, which is an L-band microwave interferometer, are based on multiangular measurements and make use of the emissivity angular signature. Mountainous areas modify local incidence angles, implying significant impacts on brightness temperatures and, consequently, on soil moisture retrievals. The purpose of this paper is to establish a criterion in quantifying the relevance of topographic impacts at the SMOS scale (∼40 km). The goal is thus to define a method of flagging the pixels according to the relative impact of topography on the brightness temperature. The proposed method uses the variogram of digital elevation model images. As a result, a map of the pixels to be flagged is produced to ensure that no soil moisture retrievals are carried out on pixels that are affected by strong topographic effects. As validation, a model was also used to simulate differences between brightness temperature variations between mountainous areas and flat surfaces. [ABSTRACT FROM AUTHOR]

The calibration of the Soil Moisture and Ocean Salinity (SMOS) payload instrument, known as Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), is based on characterization measurements which are performed initially on-ground prior to launch and, subsequently, in-flight. A good calibration is a prerequisite to ensure the quality of the geophysical data. The calibration scheme encompasses both the spaceborne instrument and the ground data processing. Once the system has been calibrated, the instrument performance can be verified, and the higher level geophysical variables, soil moisture and ocean salinity, can be validated. In this paper, the overall calibration approach is presented, focusing on the main aspects relevant to the SMOS instrument design and mission requirements. The distributed instrument, comprising 72 receivers, leads to a distributed internal calibration approach supported by specific external calibration measurements. The relationship between the calibration data and the routine ground processing is summarized, demonstrating the inherent link between them. Finally, the approach to the in-flight commissioning activities is discussed. [ABSTRACT FROM AUTHOR]

The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15 °C. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 m . s-1, the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity satellite comprises a single payload instrument known as the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) coupled to a PROTEUS platform. MIRAS synthesizes a large aperture from a reasonably sized 2-D array of passive microwave radiometers. By using interferometric techniques, the required coverage and spatial resolution can be achieved without the need for a large antenna. This paper describes the MIRAS instrument, its observation modes, the imaging geometry, and data products. [ABSTRACT FROM AUTHOR]

Soil Moisture and Ocean Salinity (SMOS) is an Earth observation mission developed by the European Space Agency in cooperation with the Centre National d'Etudes Spatiales, France and the Centre for the Development of Industrial Technology, Spain, whose main objective is to provide global maps of soil moisture over land and sea surface salinity over oceans. This paper describes the SMOS mission in terms of the mission objectives and associated key system requirements, the conceptual implementation of the mission and corresponding system architecture, major building blocks and associated functions, the SMOS selected polar orbit and characteristics, and SMOS satellite attitude modes for the different phases of the mission and for the calibration of the Microwave Imaging Radiometer with Aperture Synthesis instrument. [ABSTRACT FROM AUTHOR]

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two-dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L-band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively. [ABSTRACT FROM AUTHOR]

Active microwave remote sensing observations of backscattering, such as C?band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS?2) satellite, have the potential to measure moisture content in a near?surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near?surface soil moisture with the ERS?2 satellite is explored by comparing model estimates of backscattering with ERS?2 SAR observations. This comparison was made for two ERS?2 overpasses coincident with near?surface soil moisture measurements in a 6 ha catchment using 15?cm time domain reflectometry probes on a 20 m grid. In addition, 1?cm soil moisture data were obtained from a calibrated soil moisture model. Using state?of?the?art theoretical, semi?empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r2 values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r2 values. Likewise, using the first day of ERS?2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r2 values. Moreover, RMS differences were as large as 3·7 dB and r2 values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS?2 SAR data may be useful for estimating fields?scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Priroda microwave radiometer satellite observations were acquired and analysed in conjunction with soil moisture and other important ground and meteorological observations. Priroda was a remote sensing module on the MIR Space Station. The experiment was a component of the Southern Great Plains 1997 (SGP97) hydrology experiment. Problems with the MIR platform during the experiment resulted in a very limited set of data. Priroda sensor performance and some analyses of soil moisture conditions were conducted. These results provide important information for utilizing Priroda data and for soil moisture algorithm development. [ABSTRACT FROM AUTHOR]

Microwave remote sensing techniques provide a direct measurement of surface soil moisture (SM), with advantages for all-weather observations and solid physics. However, most satellite microwave soil moisture products fail to meet the requirements of land surface studies for high-resolution surface soil moisture data due to their coarse spatial resolutions. Although many approaches have been proposed to downscale the spatial resolution of satellite soil moisture products, most of them have been tested in flat areas where the surface is relatively homogeneous. Thus, those established approaches are often inapplicable for downscaling in cold alpine areas with complex terrain where multiple factors control the variations in surface soil moisture. In this work, we re-inferred and verified the mathematical assumption behind a semi-physical approach for downscaling satellite soil moisture data and extended this approach for cold alpine areas. Instead of directly deriving SM from proxy variables, this approach relies on a relationship between two standardized variables of SM and apparent thermal inertia (ATI), in which the sub grid standard deviation for SM is estimated by a physical hydraulic model taking soil texture data as input. The approach was applied to downscale the soil moisture active passive (SMAP) daily data in a typical cold alpine basin, i.e., the Babao River basin located in the Qilian Mountains of Northwest China. We observed good linearity between the computed ATI and SM observations on most wireless sensor network sites installed in the study basin, which justifies the underlying assumption. The sub grid standard deviations for the SMAP grid estimated through the Mualem-van Genuchten model can broadly represent the real characteristics. The downscaled 1-km resolution results correlated well with the in-situ SM observations, with an average correlation coefficient of 0.74 and a small root mean square error (0.096 cm3/cm3). The downscaled results show more and consistent textural details than the original SMAP data. After removal of biases in the original SMAP data even higher agreements with the observations can be achieved. These results demonstrate the adequacy of the proposed semi-physical approach for downscaling satellite soil moisture data in cold alpine areas, and the resultant fine-resolution data can serve as useful databases for land surface and hydrological studies in those areas. [ABSTRACT FROM AUTHOR]

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model. [ABSTRACT FROM AUTHOR]

Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp's model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land. [ABSTRACT FROM AUTHOR]

Climatology (mean seasonal cycle) errors often cause large differences between soil moisture products. However, relatively little work has been done to objectively evaluate soil moisture mean seasonal cycle (SMC) information acquired from different sources. This study evaluates surface (0–10 cm) SMCs obtained from four land surface models (LSMs), two C/X-band and two L-band remote-sensing (RS) products using 5 dense networks and 75 sparse in-situ soil moisture measurement sites located within the contiguous United States. Results show that relative to older C/X-band products derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), newer L-band products derived from the Soil Moisture Ocean and Salinity (SMOS) mission provide more accurate SMC estimates (with an average of 35% root-mean-square-error reduction). In fact, the latest SMOS INRA-CESBIO (SMOS-IC) product provides SMC intra-seasonal variability and dynamic range information that is 3–34% and 2–37% more accurate, respectively, than all four LSM-based SMCs examined here. Hence, SMC validation against SMOS-IC SMC results may improve LSMs' ability to accurately capture SMC characteristics, and the common strategy of scaling remote sensing SMC information to match LSM SMC estimates is likely sub-optimal for assimilating L-band soil moisture retrievals. Although the SMOS-IC product has made significant progresses towards retrieving absolute soil moisture values, a temporally constant dry bias is found in SMOS-IC surface SMCs over all land cover types. Addressing this bias should be a priority for future generations of SMOS retrieval algorithms. • Soil moisture climatological (SMC) error of multiple sources are investigated. • Sparse network based SMC evaluation is unbiased, when averaged regionally. • L-band remotely sensed (RS) SMC significantly outperforms C/X-band retrievals. • Relative to modeled SMC, L-band RS can better describe SMC temporal variability. • L-band RS provides a new data source for large-scale model parameterization. [ABSTRACT FROM AUTHOR]

A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of Sensors is dedicated to papers that describe such advances in the field of Earth remote sensing and their applications to advance understanding of Earth's planetary system and applying the resulting knowledge and information to meet the societal needs during recent decades. The papers accepted and published in this issue convey the exciting scientific and technical challenges and opportunities for remote sensing of all domains of Earth system, including terrestrial, aquatic and coastal ecosystems; bathymetry of coasts and islands; oceans and lakes; measurement of soil moisture and land surface temperature that affects both water resources and food production; and advances in use of sun-induced fluorescence (SIF) in measuring and monitoring the contribution of terrestrial vegetation in the cycling of carbon in Earth's system. Measurements of SIF, for example, has had a profound impact on the field of terrestrial ecosystems research and modelling. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCVR) satellite located at the Sun–Earth Lagrange Point One, about 1.5 million miles away from Earth, is providing unique observations of the Earth's full sun-lit disk from pole-to-pole and minute-by-minute, which overcomes a major limitation in temporal coverage of Earth by other polar-orbiting Earth-observing satellites. Active and passive microwave remote sensing instruments allow all-weather measurements and monitoring of clouds, weather phenomena, land-surface temperature and soil moisture by overcoming the presence of clouds that affect measurements by visible and infrared sensors. The use of powerful in-space lasers is allowing scientists and engineers to measure and monitor rapidly changing ice sheets in polar regions and mountain glaciers. These sensors and their measurements that are deployed on major space-based observatories and small- and micro-satellites, and the scientific knowledge they provide, are enhancing our understanding of planet Earth and development of Earth system models that are used increasingly to project future conditions due to Earth's rapidly changing environmental conditions. Such knowledge and information are benefiting people, businesses and governments worldwide. [ABSTRACT FROM AUTHOR]