Soil Moisture And Ocean Salinity Mission Research Articles

Quantitative Measurement of Radio Frequency Interference for SMOS Mission

The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by RFI impeding ocean salinity retrieval, especially in coastal areas such as the SCS (South China Sea), where the observed data has been discarded massively. At present, there is no way to eliminate the RFI impact on the retrieved salinity, other than by detecting and shutting down the emissions from the sources. However, it may be effective in a scientific sense if RFI can be quantified and applied to the salinity retrieval process. Therefore, this study proposes an RFI measuring method that can investigate contamination in both prominent and moderate respects, aroused either by on-site emissions or nearby continents. Based on the proposed method, two levels of hierarchical RFI maps of the SCS region, including the separated one and the merged one, are presented and discussed, indicating more severe contamination in northern and western SCS. Moreover, to verify the generalization of the method on open oceans far from continents, an area in the middle central Pacific is selected and tested. The result shows few or no RFI in this unattended region, which is consistent with the authors’ knowledge. This study presents the concept of the “RFI map” to describe the contamination, which will hopefully help researchers comprehend the RFI state intuitively and assist in ocean salinity retrieval statistically.

Satellite-Based Data Assimilation System for the Initialization of Arctic Sea Ice Concentration and Thickness Using CICE5

The satellite-derived sea ice concentration (SIC) and thickness (SIT) observation over the Arctic region are assimilated by implementing the Ensemble Optimal Interpolation (EnOI) into the Community Ice CodE version 5.1.2 (CICE5) model. The assimilated observations are derived from Special Sensor Microwave Imager/Sounder (SSMIS) for the SIC, European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission (SMOS) for the SIT of the thin ice, and ESA's CryoSat-2 satellite for the SIT of the thick ice. The SIC, and SIT observations are assimilated during 2000–2019, and 2011–2019, respectively. The quality of the reanalysis is evaluated by comparing with observation and modeled data. Three data assimilation experiments are conducted: noDA without data assimilation, Ver1 with SIC assimilation, and Ver2 with SIC and SIT assimilation. The climatological bias of the SIC in noDA was reduced in Ver1 by 29% in marginal ice zones during boreal winter, and 82% in pan-Arctic ocean during boreal summer. The quality of simulating the interannual variation of sea ice extent (SIE) is improved in all months particularly during boreal summer. The root-mean-square errors (RMSEs) of SIE anomaly in August are significantly reduced compared to noDA. However, the interannual variations of SIT is unrealistic in Ver1 which requires the additional assimilation of the SIT observation. The climatological bias of SIT over the Arctic was further reduced in Ver2 by 28% during boreal winter compared to that in Ver1. The interannual variability of SIT anomalies is realistically simulated in Ver2 by reducing the RMSEs of SIT anomalies by 33% in February, and 28% in August by compared to that in Ver1. The dominant interannual variation extracted by empirical orthogonal function (EOF) of SIT anomalies in Ver2 is better simulated than Ver1. The additional assimilation of SIT improves not only SIT, but also SIC. The climatological bias of SIE and the errors in leading EOF of SIC anomalies in Ver2 is further reduced compared to those in Ver1 during boreal winter. However, improvements led by assimilating additional SIT observation is not clear during boreal summer, possible due to the lack of available SIT observation during this season.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

Spaceborne High Precision Sea Surface Salinity Remote Sensing by Interferometric Radiometry

The global sea surface salinity (SSS) can be accomplished by means of the L-band microwave radiometers from space, such as European Space Agency's SMOS (soil moisture and ocean salinity) and NASA's Aquarius/SAC-D. However, the microwave imaging radiometer using aperture synthesis in SMOS mission suffers from various errors and unexpected in-orbit phenomena, which degrade SSS accuracy. To further improve SSS accuracy, a full polarization aperture synthesis microwave radiometer (FPASMR) has been proposed for China's next-generation Haiyang satellite program with two important improvements, which is composed by an L- and X-band aperture synthesis radiometer (ASR) with two dimensions. One important improvement is that the sea surface roughness and sea surface temperature are simultaneously measured by the full polarization X-band ASR, and another important improvement is that the microwave-optical receiving technology and in-orbit thermal control subsystem are employed to improve the stability of the FPASMR for reducing visibility phase errors. The system of the FPASMR is introduced in terms of L-band subsystem, X-band subsystem, and the calibration subsystem. The main specifications of the FPASMR are analyzed in terms of field of view, angular resolution, and radiometric sensitivity. In addition, the FPASMR calibration is also introduced. The FPASMR demonstrator has also been developed and a series of experiments is performed to assess the performance of the FPASMR demonstrator and the improvements in the FPASMR.

A Matrix Completion Based Method for RFI Source Localization in Microwave Interferometric Radiometry

The Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency (ESA) is aimed to globally monitor the Earth surface moisture and ocean salinity. As the single payload of SMOS satellite, the Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) operates in the protected L-band. Nonetheless, the artificial sources emitting close to or/and fully in this band are contaminating the collected remote sensing data and deteriorating the performance of the SMOS mission. Identifying and localizing such sources is crucial to improve the quality of SMOS scientific products. In this article, we propose a method based on matrix completion (MC) for localization of radio frequency interference (RFI) sources. This method mainly exploits the low-rank property of the augmented covariance matrix (ACM) of the sparse array and addresses the ACM incompleteness (i.e., sampling data loss) due to inherent array geometry (e.g., the SMOS Y-shaped array) or potential hardware malfunction (e.g., the correlator failure). Validation results show that, compared with existing RFI localization approaches such as the discrete Fourier transformation (DFT) inversion and covariance-based direction-of-arrival (DOA) estimation, the proposed MC method possesses competitive localization accuracy, superior spatial resolution, and reduced artifacts.

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.

Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

RFI Source Localization in Microwave Interferometric Radiometry: A Sparse Signal Reconstruction Perspective

The Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) is the payload of the Soil Moisture and Ocean Salinity (SMOS) satellite mission led by the European Space Agency. Although the MIRAS operates at the protected L-band, it is perturbed by radio frequency interferences (RFIs) that contaminate the acquired remote sensing data and further deteriorate the total performance of SMOS mission. Accurate location information of these sources is crucial for switching off illegal RFI emitters or mitigating RFI impacts from contaminated data. This article addresses the localization of SMOS RFI sources from a perspective of sparse signal reconstruction (SSR), which exploits the sparsity of RFI sources in the spatial domain. Such an SSR strategy possesses superior (at least comparable) performances over existing RFI localization methods [e.g., discrete Fourier transformation (DFT) inversion and subspace-based direction-of-arrival (DOA) estimation] using only SMOS measurements and even under situations in the presence of data missing due to correlator failures.

A New Metric for Estimating the Disparity of Antenna Patterns in Synthetic Aperture Imaging Radiometry

The soil moisture and ocean salinity (SMOS) satellite has provided for about ten years systematic passive L -band measurements from space. For several months, phase 0 studies are conducted by the French space agency for a second-generation high resolution (HR) follow-on mission. This contribution is making the connection between this SMOS hr project and the SMOS mission by revisiting the following problematic: the impact of the disparity of the antenna patterns on the reconstruction floor error observed in the retrieved brightness temperatures. This impact is revisited in light of the progress made since that time and a new metric is introduced for estimating the disparity between antenna patterns. It would be helpful for the design of future missions based on imaging by aperture synthesis with interferometric arrays comprising a large number of antennas.

Evaluating the utility of remotely sensed soil moisture for the characterization of runoff response over Canadian watersheds

Remotely sensed soil moisture measurements from satellite platforms are increasingly reliable, cost-effective and widely available data sources where in situ measurements are unavailable. This research uses the Soil Moisture and Ocean Salinity mission (SMOS) satellite-derived soil moisture anomalies over a database of 65 watersheds across Canada from 2011 to 2014 to analyze the soil moisture-runoff relationship. A spatial analysis of the variability and influences on the strength of this relationship revealed that 32% of catchments showed significant (1 tailed, p < 0.05) correlations between the weekly antecedent soil moisture state of the catchment and the weekly runoff ratio. Regions of strongest correlation were related to the topographic variables of slope and elevation. These results support the use of coarse-scale satellite remote sensing as a valuable data source in hydrological studies, but recommend caution when applying the data to regions where the accuracy of satellite soil moisture data sets is less certain (such as wetlands and areas with high topography) or areas where the runoff generation mechanisms are complex (frozen soils, wetlands or prairie environments).

Response of deep aquifers to climate variability

There is a general agreement that deep aquifers experience significant lag time in their response to climatic variations. Analysis of Temporal Gravity Recovery and Climate Experiment (GRACE), Soil Moisture and Ocean Salinity mission (SMOS), satellite altimetry, stable isotopic composition of groundwater, and precipitation and static global geopotential models over the Nubian Sandstone Aquifer System (NSAS) revealed rapid aquifer response to climate variability. Findings include: (1) The recharge areas of the NSAS (Northern Sudan Platform subbasin) witnessed a dry period (2002−2012), where average annual precipitation (AAP) was modest (85 mm) followed by a wet period (2013–2016; AAP: 107 mm), and during both periods the AAP remained negligible (<10 mm) over the northern parts of the NSAS (Dakhla subbasin); (2) the secular trends in terrestrial water storage (TWS) over the Dakhla subbasin were estimated at −3.8 ± 1.3 mm/yr and + 7.8 ± 1 mm/yr for the dry and wet periods, respectively; (3) spatial variations in TWS values and phase are consistent with rapid groundwater flow from the Northern Sudan Platform subbasin and Lake Nasser towards the Dakhla subbasin during the wet period and from the lake during the dry period; and (4) networks of densely fractured and karstified bedrocks provide preferential pathways for groundwater flow. The proposed model is supported by (1) rapid response in groundwater levels in distant wells (>280 km from source areas) and in soil moisture content in areas with shallow (<2 m) groundwater levels to fluctuations in Lake Nasser surface water, and (2) the isotopic composition (O, H) of groundwater along the preferred pathways, consistent with mixing of enriched (Lake Nasser water or precipitation over Sudan) and depleted (NSAS fossil water) endmembers. Findings provide new insights into the response of large, deep aquifers to climate variability and address the sustainability of the NSAS and similar fossil aquifers worldwide.

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SMOS brightness temperature assimilation into the Community Land Model

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

The global SMOS Level 3 daily soil moisture and brightness temperature maps

Abstract. The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:catds2010@ftp.ifremer.fr/Land_products/GRIDDED/L3SM/RE04/.

Sea Surface Salinity with SMOS

The mission to observe the Sea Surface Salinity (SSS) from the space is not really new because it has been started from long time ago. The first mission was the Skylab which used a 1.4 GHz microwave radiometer in 1970’s. But this mission is still not as comprehensive as other missions which observe such as Sea Surface Temperature (SST), Sea Surface Height (SSH), Ocean Color, and so on. Realizing the importance of SSS distribution in the ocean and its influences to the Earth’s climate system has motivated the scientists to develop a new technique in observing the SSS from space and lead a mission called the SMOS mission which was launched in November 2, 2011. Besides observing the SSS, this mission observes the Soil Moisture as well. The Soil Moisture and Ocean Salinity (SMOS) mission aims to obtain global and regular measurements on the soil moisture and the ocean salinity. These measurements are essential for climate and hydrological models, among other purposes. SMOS payload is a L band (21 cm, 1.4 GHz) 2D interferometric radiometer on a generic Proteus platform. The mission lifetime is at least 3 years (0.5 for commissioning and 2.5 for normal operation) + 2 years (extended operation) + 10 years for the post-mission processing. Raw physical data, level 1 and level 2 products will be produced by the PDPC (SMOS Payload Data and Processing Centre). It is an ESA center located in Villafranca (Spain) and operated under the responsibility of ESA. The SMOS Ocean Salinity objective is accuracy better than 0.1 psu, with 10 days to monthly grid scale (200 km).

Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China

Soil moisture (SM) retrieval from SMOS (the Soil Moisture and Ocean Salinity mission) and SMAP (the Soil Moisture Active/Passive mission) passive microwave data over forested areas with required accuracy is of great significance and poses some challenges. In this paper, we used Ground Wireless Sensor Network (GWSN) SM measurements from 9 September to 5 November 2015 to validate SMOS and SMAP Level 3 (L3) SM products over forested areas in northeastern China. Our results found that neither SMOS nor SMAP L3 SM products were ideal, with respective RMSE (root mean square error) values of 0.31 cm3/cm3 and 0.17 cm3/cm3. Nevertheless, some improvements in SM retrieval might be achievable through refinements of the soil dielectric model with respect to high percentage of soil organic matter (SOM) in the forested area. To that end, the potential of the semi-empirical soil dielectric model proposed by Jun Liu (Liu’s model) in improving SM retrieval results over forested areas was investigated. Introducing Liu’s model into the retrieval algorithms of both SMOS and SMAP missions produced promising results. For SMAP, the RMSE of L3 SM products improved from 0.16 cm3/cm3 to 0.07 cm3/cm3 for AM (local solar time around 06:00 am) data, and from 0.17 cm3/cm3 to 0.05 cm3/cm3 for PM (local solar time around 06:00 pm) data. For SMOS ascending orbit products, the accuracy was improved by 56%, while descending orbit products improved by 45%.

The Role of Climatic Anomalies and Soil Moisture in the Decline of Drought-Prone Forests

Increased intensity and duration of droughts and high-temperature events have been associated with forest decline in many parts of the world, and these decline events are expected to become more common under climate change. There is, therefore, a need for monitoring and modeling of forest decline. We used a regional forest condition monitoring program (DEBOSCAT) to study the spatial distribution of decline events in 2012 in Catalonia (Northeastern Spain) and their relationship with climatic factors. In 2012, this dataset was collected after an extraordinarily dry summer, and allowed the study of decline events in eight dominant tree species. We fitted a logistic model to predict forest decline probability as a function of species, precipitation and temperature anomalies, solar radiation, and remotely sensed soil moisture data from the Soil Moisture and Ocean Salinity Mission (SMOS). Broadleaved species were more affected by decline events than conifers. The statistical model explained almost 40% of forest decline occurrence, wherein almost 50% of this variability was explained by species effect, with broadleaved trees being generally more sensitive to the studied factors than conifers. Climatically wetter areas and those more exposed to radiation were more likely to be affected, suggesting better adaptation of forests in dry areas. In general, more damaged forests were characterized by high-positive temperature anomalies, lower than average rainfall, and low soil moisture in summer 2012. The most vulnerable species was Fagus sylvatica, a Euro-Siberian species, contrasting with Pinus halepensis, a typically Mediterranean species, which showed low sensitivity to drought.

High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China

Soil moisture distributions with high spatio-temporal resolution are scarce but beneficial for understanding eco-hydrological processes and closing the water cycle at the basin scale. Sensor networks are innovative in their ability to capture the spatio-temporal heterogeneity and dynamics of soil moisture; however, they cannot be used to directly derive spatially continuous soil moisture distributions. A Bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia is used to map daily soil moisture spatial patterns with a resolution of 1km in the Babao River Basin, China. The 2–4cm soil moisture observations from seven automatic meteorological stations located in different elevation zones from 3000m to 3500m are employed to validate the mapping algorithm. The correlation coefficient and unbiased root-mean-square error (RMSE) averaged 0.880 and 0.031cm3/cm3, respectively, which indicate satisfactory estimation accuracy. The 1km resolution soil moisture products are re-sampled to a resolution of 25km and then compared to the level 3 Soil Moisture and Ocean Salinity Mission (SMOS) soil moisture product. The results show that both products exhibit strong temporal consistency; however, due to complex topography, the SMOS soil moisture is generally lower than that of the upscaling results. Semivariograms and an empirical orthogonal function (EOF) analysis are used to analyze the space-time heterogeneities of soil moisture at the 1km scale. In the summer, rainfall results in soil moisture with low spatial variability and a complex spatial structure. After the rainy season, the spatial heterogeneity of soil moisture is affected by other factors, such as soil texture, evapotranspiration and topography. From the perspective of temporal variation, the upscaled soil moisture shows a well-defined seasonal cycle, which represents the effects of decreased rainfall from August to October. Because more rain falls in the summer due to the mountain microclimate, the oscillation in soil moisture is more pronounced over 20% of the area compared to that in other regions. Based on a validation analysis of the mapping results, the upscaling method is proven feasible, and the upscaled soil moisture can be used to analyze eco-hydrological processes and validate remote sensing products.

A method for soil moisture probes calibration and validation of satellite estimates

Optimization of field techniques is crucial to ensure high quality soil moisture data. The aim of the work is to present a sampling method for undisturbed soil and soil water content to calibrated soil moisture probes, in a context of the SMOS (Soil Moisture and Ocean Salinity) mission MIRAS Level 2 soil moisture product validation in Pampean Region of Argentina. The method avoids soil alteration and is recommended to calibrated probes based on soil type under a freely drying process at ambient temperature. A detailed explanation of field and laboratory procedures to obtain reference soil moisture is shown. The calibration results reflected accurate operation for the Delta-T thetaProbe ML2x probes in most of analyzed cases (RMSE and bias≤0.05m3/m3). Post-calibration results indicated that the accuracy improves significantly applying the adjustments of the calibration based on soil types (RMSE≤0.022m3/m3, bias≤−0.010m3/m3).•A sampling method that provides high quality data of soil water content for calibration of probes is described.•Importance of calibration based on soil types.•A calibration process for similar soil types could be suitable in practical terms, depending on the required accuracy level.

From field experiments to salinity products: a tribute to the contributions of Jordi Font to the SMOS mission

This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciencies del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politecnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre.