Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)

AbstractGlobal surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS; SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TU-Wien-WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010–2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space–time analysis using Hovmöller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R>0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and south-eastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRA-Land SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records

SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation with GEDI LIDAR Products

In this article, L-band vegetation optical depth (L-VOD) retrieved by Soil Moisture and Ocean Salinity (SMOS) in four large continents is compared against vegetation parameters (RH100 and PAI) retrieved by Global Ecosystem Dynamics Investigation (GEDI) LIDAR instrument, recently launched by NASA. In order to manage the different spatial resolutions, GEDI parameters were averaged within SMOS pixels and a threshold to the minimum number of GEDI samples per SMOS pixel was applied. Spatial correlations between monthly averages were investigated from May 2019 to April 2020. For continents mostly covered by tropical vegetation (Africa and South America), the Pearson correlation coefficients between L-VOD and RH100 are higher than 0.8 in all months of the year. Conversely, seasonal effects are observed in North America and Asia, producing a lower correlation in colder months. RMS differences between L-VODs retrieved by SMOS and the ones obtained using a linear regression over RH100 are lower than 0.2 for all cases, and close to 0.1 for most cases. Using PAI in place of RH100 slightly lower spatial correlations are generally achieved. Overall, the obtained results confirm the good potential of L-VOD to monitor vegetation height in different environments

Analysis of vegetation optical depth and soil moisture retrieved by SMOS over tropical forests

In this letter, the results obtained with the lastversion (level 2, version 650) of SMOS retrieval algorithm are compared against independent measurements, over tropical forests. In particular, the climate research unit meteorological variables and data bases of forest height and forest biomass are considered. Comparisons with results obtained by AMSR- 2 under similar conditions are also illustrated. Vegetation optical depth shows a generally good correlation with forest height and forest biomass, particularly in Africa and South America. Spatial and temporal trends of retrieved soil moisture follow trends of rainfall, particularly in regions of dry winter

Immunological method for direct assessment of the functionality of a denitrifying strain of Pseudomonas fluorescens in soil.

International audienceThis work describes an immunological method for detection and quantification in complex environments of the dissimilative nitrate reductase (NRA) responsible for the reduction of nitrate to nitrite, which plays an important role in ecosystem functioning. The alpha-catalytic subunit of the enzyme was purified from the denitrifying strain Pseudomonas fluorescens YT101 and used for the production of polyclonal antibodies. These antibodies were used to detect and quantify the NRA by a chemifluorescence technique on Western blots after separation of total proteins from pure cultures and soil samples. The specificity, detection threshold and reproducibility of the proposed method were evaluated. A soil experiment showed that our method can be applied to complex environmental samples

Spatial and temporal properties of SMOS retrieval over tropical forests

In this paper, retrieval results obtained using the last version (V650) of SMOS level 2 algorithms are tested considering pixels of Africa and South America. Yearly average values of vegetation optical depth are compared against forest height estimates at continental scale. For selected areas of African woody savannah, multitemporal trends of SM and VOD are compared against environmental variables available from Climatic Research Unit data base

SMOS forest optical depth intercomparisons over pan-tropical biomes

The main objective of SMOS over land is to retrieve soil moisture. Anyway, for soils covered by vegetation two model parameters, soil moisture and vegetation optical depth, are provided as outputs of the Level 2 algorithm

Denitrification in the upper vadose zone layers : a comparison between a fluvic hypercalcaric cambisol and a haplic calcisol

National audienc

SMOS retrieval over forests: exploitation of optical depth and tests of soil moisture estimates

This research aims to test data obtained by level 2 retrieval algorithm of SMOS over land, in order to provide information regarding vegetation and soil moisture over forested areas. Results presented in this paperwere obtained using the last 620 version of the algorithm. The correlation between the new vegetation optical depth (VOD) product and the height of the forest estimated by ICES at GLAS lidar on a global scale is investigated. Over South American and African forests a good correspondence between the two variables is observed, with saturation occurring above about 30 m height. Moreover, the comparison between the VOD and the height of the forest shows good spatial and temporal stability, and the r2 correlation coefficient is within a 0.59–0.69 range. Conversely, discrepancies are observed in some Indonesian islands, particularly New Guinea. Over specific areas, the trends vs. forest height obtained with SMOS VOD are compared with the corresponding trends of AMSR-E VOD. Results are also validated at country-level scale. To this aim, accurate estimates of forest biomass derived from airborne lidar over selected forests of Peru, Columbia and Panama are used. Finally, the soil moisture retrieved over forests is investigated, reporting continental maps for Tropical areas and comparisons with ground measurements in selected forests of the US. Continental maps obtained with the new level 2 V620 algorithm cover almost all forest areas, and show seasonal variations which are dependent on climatic zones. Comparisons between soil moisture retrievals in forests and ground measurements of the US SCAN network produce worseRMSE valueswith respect to lowvegetation areas. Significant improvements however are achieved after averaging among close nodes of the ground network