The SMAP and Copernicus Sentinel 1A/B Microwave Active-Passive High Resolution Surface Soil Moisture Product and Its Applications

SMAP project released a new enhanced high-resolution (3km and 1 km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-1A and -1B Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-1A and -1B multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density (~< 3 kg/sq.m). A new version of this product is released to public in May 2018. This high resolution (3 km and 1 km) soil moisture product with reasonable accuracy of 0.05 m3/m3 is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications

Physics-Based Retrieval of Surface Roughness Parameters for Bare Soils from Combined Active-Passive Microwave Signatures

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two default parameters to describe the surface roughness are the standard deviation of the surface height variation and the surface correlation length with its corresponding autocorrelation function. Both parameters (,) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve and by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that and can be estimated simultaneously when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured -values, and of 3.13 cm and 3.04 cm for -values. The first validation of the results reveals that the influence of the autocorrelation function, needed within the retrieval, is distinct

HIGH-RESOLUTION ENHANCED PRODUCT BASED ON SMAP ACTIVE-PASSIVE APPROACH USING SENTINEL 1A AND 1B SAR DATA

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-1A and -1B Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program’s Sentinel-1A and -1B multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density (&sim;&thinsp;&lt;&thinsp;3&thinsp;kg&thinsp;m&minus;2). The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3&thinsp;km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications

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

Since its launch in 2009, the ESA’s 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 %.Peer ReviewedPostprint (published version

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

18 pages, 5 figures, 1 tableSince its launch in 2009, the ESA’s 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 m/m) than LST night (R≈0.45 to 0.80; ubRMSD≈0.04 to 0.07 m/m) 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 m/m) with a coverage improvement of~10-20 %This work was supported by the Spanish Ministry of Economy and Competitiveness, through a Formación Personal Investigador (FPI) grant BES-2011-043322, the project PROMISES: ESP2015-67549-C3, ERDF (European Regional Development Fund) and the BBVA foundationPeer Reviewe

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented

Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain

Soil moisture is an important component of the Earth system and plays a key role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several satellite-based coarse spatial resolution soil moisture datasets have been produced and widely used for various applications in climate science, hydrology, ecosystem research and agriculture. Owing to the strong demand for soil moisture data with high spatial resolution for regional applications, much effort has recently been devoted to the generation of high spatial resolution soil moisture data from either high-resolution satellite observations or by downscaling existing coarse-resolution satellite-based soil moisture datasets. In addition, land surface models provide an alternative way to obtain consistent high-resolution soil moisture information when forced with high-resolution inputs. The aim of this study is to create and evaluate high-resolution soil moisture products derived from multiple sources including satellite observations and land surface model simulations. The JULES-CHESS simulated soil moisture and satellite-based soil moisture datasets including SMAP L3E, SMAP L4, SMOS L4, Sentinel 1, ASCAT, and Sentinel 1/SMAP combined products were first validated against observed soil moisture from COSMOS-UK, a network of in-situ cosmic-ray based sensors. Second, an approach based on triple collocation was applied to compare these satellite products in the absence of a known reference dataset. Third, a combined soil moisture product was generated to integrate the better-performing soil moisture estimates based on triple collocation error estimation and a least-squares merging scheme. From further evaluation, it is found that the merged soil moisture integrates the characteristics of model simulation and satellite observations and particularly improves the limited temporal variability of the JULES-CHESS simulation. Therefore, we conclude that the triple collocation merging scheme is a simple and reliable way to combine satellite-based soil moisture products with outputs from the JULES-CHESS simulation for estimating model-data fused high-resolution soil moisture for the British mainland

Long-term and high-resolution global time series of brightness temperature from copula-based fusion of SMAP enhanced and SMOS data

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of 25km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture