Nilda Sánchez

Validation of the SMOS L2 Soil Moisture Data in the REMEDHUS Network (Spain)

The Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission have been re- leased. The data must be validated under different scenarios of biophysical and climatic conditions. For the current study, the data from January to December 2010 from 20 in situ soil moisture stations from the REMEDHUS soil moisture measurement station network (Spain) were used. A comparison analysis was carried out in terms of the soil moisture content, its spatial variability, and temporal stability. The results show an acceptable level of agreement (R = 0.73, RMSD = 0.069 m3 · m-3, and bias = 0.053 m3 · m-3) between the in situ and satellite data. A slight constant underestimation from the SMOS data set was detected. A centered (bias removed) root-mean-square difference was calculated to account for this persistent bias (RMSDc = 0.044 m3 · m-3). This result is close to the SMOS accuracy objective of 0.04 m3 · m-3. Two conclusions can be drawn: First, SMOS is close to meet the mission accuracy requirements in REMEDHUS, and second, SMOS is able to detect temporal anomalies and the temporal evolution of ground soil moisture, even though the soil moisture was slightly underestimated. Despite a noticeably reduced spatial variability among the SMOS grid cells, the remotely sensed soil moisture shows a spatial pattern of the soil moisture fields on the area scale, in agreement with the site-specific characteristics of REMEDHUS. No differences were found between the use of ascending and descending orbits. In addition, no differences were detected between the use of time-overpass values of in situ soil moisture and that of the daily average.

Land Geophysical Parameters Retrieval Using the Interference Pattern GNSS-R Technique

In the past years, the scientific community has placed a special interest in remotely sensing soil moisture and vegetation parameters. Radiometry and radar techniques have been widely used for years. Global Navigation Satellite Systems opportunity signals Reflected (GNSS-R) over the earths surface are younger, but they have already shown their potential to perform these observations. This paper presents a GNSS-R technique, based on Global Positioning System (GPS) measurements, that allows the retrieval of several geophysical parameters from land surfaces. This technique measures the power of the interference signal between the direct GPS signal and the reflected one after scattering over the land, so it is called Interference Pattern Technique (IPT). This paper presents the results obtained after applying the IPT for topography, soil moisture, and vegetation height retrievals over vegetation-covered soils.

A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

The ESAs Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earths surface soil moisture. It has a spatial resolution of ~ 40 km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0-5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatio-temporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of in situ soil moisture measurements is reproduced in the high-resolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.

A New Soil Moisture Agricultural Drought Index (SMADI) Integrating MODIS and SMOS Products: A Case of Study over the Iberian Peninsula

A new index for agricultural drought monitoring is presented based on the integration of different soil/vegetation remote sensing observations. The synergistic fusion of the surface soil moisture (SSM) from the Soil Moisture and Ocean Salinity (SMOS) mission, with the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), and water/vegetation indices for agricultural drought monitoring was tested. The rationale of the approach is based on the inverse relationship between LST, vegetation condition and soil moisture content. Thus, the proposed Soil Moisture Agricultural Drought Index (SMADI) combines the soil and temperature conditions while including the lagged response of vegetation. SMADI was retrieved every eight days at 500 m spatial resolution for the whole Iberian Peninsula (IP) from 2010 to 2014, and a time lag of eight days was used to account for the plant response to the varying soil/climatic conditions. The results of SMADI compared well with other agricultural indices in a semiarid area in the Duero basin, in Spain, and also with a climatic index in areas of the Iberian Peninsula under contrasted climatic conditions. Based on a standard classification of drought severity, the proposed index allowed for a coherent description of the drought conditions of the IP during the study period.

Modeling of soil roughness using terrestrial laser scanner for soil moisture retrieval

The present work reports the bases of an ongoing research whose main objective is the development of a methodology to characterize surface roughness models using terrestrial laser scanning devices. The classical measurements take the profile as the valuable information on roughness variations but a brand new paradigm is applied here where an original three-dimensional, multi-scale framework leads towards an accurate characterization of patterns and roughness for different surfaces. Terrestrial laser scanners are able to provide a complete picture of the roughness properties over the spatial scale of a Synthetic Aperture Radar satellite resolution cell. The paper describes the methodology for measuring the roughness of different surfaces and analyzes parameters what can be used as ancillary data in soil moisture retrieval from satellite datasets.

On the synergy of airborne GNSS-R and landsat 8 for soil moisture estimation

While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politecnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data).

Review of the CALIMAS Team Contributions to European Space Agency’s Soil Moisture and Ocean Salinity Mission Calibration and Validation

This work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoring.

Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

Abstract Since its launch in 2009, the ESAs SMOS mission is providing global soil moisture (SM) maps at ∼40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (∼1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R≈-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R≈-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R≈0.55 to 0.85; ubRMSD≈0.04 to 0.06 m3/m3) than LST night (R∼0.45 to 0.80; ubRMSD=0.04 to 0.07 m3/m3) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R≈0.55 to 0.85; ubRMSD≈0.04 to 0.07 m3/m3) with a coverage improvement of ∼10-20 %.

Recent trends in rivers with near-natural flow regime The case of the river headwaters in Spain

The main objective of this research was to study the streamflow evolution of a representative sample of the Spanish near-natural-regime fluvial system over the last four decades of the 20th century. The focus of this study was on those headwater river basins that, not having been subject to substantial human alteration directly via the flow regime, might still have been affected by changes in land management. A representative sample of 74 rivers was selected and a statistical analysis was performed to detect seasonal and annual trends, and the magnitude of streamflow change. Almost all of the rivers studied experienced reductions in streamflow, and three quarters of them had negative and significant (p<0.05) trends in streamflow change. It was impossible to detect any spatial pattern in terms of the type or trend magnitude. The main decreases in the discharge of these near-natural rivers in Spain was observed in the spring and summer, when 81% and 70% of the rivers, respectively, exhibited significant negative trends. The magnitudes of the changes are also remarkable. The average annual percentage change in streamflow magnitude of the 74 basins was –1.45% per year, which corresponds to an average streamflow reduction equivalent to 153 hm3 every year. The results of this study are relevant in view of future climatic scenarios and the evolution of land management in rural and mountain areas, as has already been observed in many parts of the Mediterranean and other regions. Global warming, resulting in continuous temperature increases and therefore evapotranspiration increases, is clearly one factor potentially affecting streamflow, together with land abandonment and subsequent continuous forest expansion. These results obtained in Spain could be extrapolated to other areas in the Mediterranean and beyond and should be taken into account in any water policy and water management in the near future.

On the synergy of SMOS and Terra/Aqua MODIS: High resolution soil moisture maps in near real-time

An innovative downscaling approach to obtain fine-scale soil moisture estimates from 40 km SMOS observations has been developed. It optimally blends SMOS multi-angular and full-polarimetric information with MODIS visible/data into high resolution soil moisture maps. The core of the algorithm is a model that linksmicrowave/optical sensitivity to soilmoisture and linearly relates the two instruments across spatial scales. This algorithm has been implemented at SMOS-BEC facilities and near real-time maps of disaggregated soil moisture over the Iberian Peninsula are being distributed. In this work, the temporal and spatial variability of these maps is evaluated through comparison with ground-basedmesurements acquired at the REMEDHUS soil moisture network, in the central part of the Duero basin, Spain. Results from a two-year time-series comparison show that downscaled soil moisture maps compare well with in situ data and nicely reproduce soil moisture dynamics at a 1 km spatial scale.