SMOS near real time soil moisture product: processor overview and first validation results

Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

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Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

10.20944/preprints201810.0453.v1 ◽

2018 ◽

Author(s):

Katarzyna Dabrowska-Zielinska ◽

Jan Musial ◽

Alicja Malinska ◽

Maria Budzynska ◽

Radoslaw Gurdak ◽

...

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Incidence Angle ◽

Temporal Frequency ◽

European Space Agency ◽

Wetland Ecosystems ◽

Space Agency ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

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Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs12223737 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3737

Author(s):

Nicola Paciolla ◽

Chiara Corbari ◽

Ahmad Al Bitar ◽

Yann Kerr ◽

Marco Mancini

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

European Space Agency ◽

Irrigation Season ◽

Surface Soil Moisture ◽

Irrigation Period ◽

Space Agency ◽

Ocean Salinity ◽

Passive Data ◽

Active Data

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data.

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ESA CCI and C3S Soil Moisture: latest product updates and climate assessments

10.5194/egusphere-egu2020-19198 ◽

2020 ◽

Author(s):

Wouter Dorigo ◽

Wolfgang Preimesberger ◽

Adam Pasik ◽

Alexander Gruber ◽

Leander Moesinger ◽

...

Keyword(s):

Climate Change ◽

Soil Moisture ◽

European Space Agency ◽

Climate Data ◽

Change Initiative ◽

Space Agency ◽

Data Store ◽

Climate Data Record ◽

Level 2

As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a more than 40 year long climate data record (CDR) is produced by systematically combining Level-2 datasets from separate missions. Combining multiple level 2 datasets into a single consistent long-term product combines the advantages of individual missions and allows deriving a harmonised long-term record with optimal spatial and temporal coverage. The current version of ESA CCI Soil Moisture includes a PASSIVE (radiometer-based) dataset covering the period 1978 to 2019, an ACTIVE (scatterometer-based) product covering the period 1991-2019 and a COMBINED product (1978-2019).&#160;The European Commission&#8217;s Copernicus Climate Changes Service (C3S) uses the ESA CCI soil moisture algorithm to produce similar climate data records from near-real-time Level-2 data streams.&#160; These products are continuously extended within 10 days after data acquisition and instantaneously made available through the C3S Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced.In this presentation we give an overview of the latest developments of the ESA CCI and C3S Soil Moisture datasets, which include the integration of SMAP and various algorithmic updates, and use the datasets to assess the hydrological conditions of 2019 with respect to a 30-year historical baseline.The development of the ESA CCI products has been supported by ESA&#8217;s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). The Copernicus Climate Change Service (C3S) soil moisture product is funded by the Copernicus Climate Change Service implemented by ECMWF through C3S 312b Lot 7 Soil Moisture service.

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Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

Remote Sensing ◽

10.3390/rs10121979 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1979 ◽

Cited By ~ 17

Author(s):

Katarzyna Dabrowska-Zielinska ◽

Jan Musial ◽

Alicja Malinska ◽

Maria Budzynska ◽

Radoslaw Gurdak ◽

...

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Incidence Angle ◽

European Space Agency ◽

Wetland Ecosystems ◽

Space Agency ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference

The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ°) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ° VH and VV, or the ratio of σ° VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas.

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Combining satellite observations to develop a global soil moisture product for near-real-time applications

Hydrology and Earth System Sciences ◽

10.5194/hess-20-4191-2016 ◽

2016 ◽

Vol 20 (10) ◽

pp. 4191-4208 ◽

Cited By ~ 16

Author(s):

Markus Enenkel ◽

Christoph Reimer ◽

Wouter Dorigo ◽

Wolfgang Wagner ◽

Isabella Pfeil ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

Weather Forecasting ◽

European Space Agency ◽

Space Agency ◽

Global Correlation ◽

Global Soil ◽

The Individual

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting.

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Combining satellite observations to develop a daily global soil moisture product for a wide range of applications

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-11549-2015 ◽

2015 ◽

Vol 12 (11) ◽

pp. 11549-11589 ◽

Cited By ~ 5

Author(s):

M. Enenkel ◽

C. Reimer ◽

W. Dorigo ◽

W. Wagner ◽

I. Pfeil ◽

...

Keyword(s):

Soil Moisture ◽

Weather Forecasting ◽

European Space Agency ◽

Space Agency ◽

Global Correlation ◽

Wide Range ◽

Global Soil ◽

Agricultural Applications ◽

The Individual

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from nine different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI processing chain for daily global updates via satellite-derived near real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced SCATterometer on-board the MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases, and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. Our findings indicate that, despite issues in arid regions, the new "CCI NRT" dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.8. An initial validation with 40 in-situ observations in France, Kenya, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT respectively. In summary, the CCI NRT dataset is getting ready for operational use, supporting applications such as drought and flood monitoring, weather forecasting or agricultural applications.

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Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

Journal of Geophysical Research Atmospheres ◽

10.1029/2006jd007478 ◽

2007 ◽

Vol 112 (D3) ◽

Cited By ~ 126

Author(s):

M. Drusch

Keyword(s):

Soil Moisture ◽

Numerical Weather Prediction ◽

Prediction Models ◽

Surface Soil ◽

Weather Prediction ◽

Surface Soil Moisture ◽

Forecast System ◽

Data Set ◽

Soil Moisture Data ◽

Numerical Weather Prediction Models

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The European Space Agency free SBAS software developments: Learning, practising and accessing the EGNOS performances in real-time

2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing ◽

10.1109/navitec.2012.6423118 ◽

2012 ◽

Cited By ~ 2

Author(s):

A. R. Mathur ◽

F. Toran ◽

C. A. Pandele ◽

M. Houdek ◽

J. Doubek

Keyword(s):

Real Time ◽

European Space Agency ◽

Space Agency

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A virtual test environment for validating spacecraft optical navigation

The Aeronautical Journal ◽

10.1017/s000192400000871x ◽

2013 ◽

Vol 117 (1197) ◽

pp. 1075-1101 ◽

Cited By ~ 1

Author(s):

S. M. Parkes ◽

I. Martin ◽

M. N. Dunstan ◽

N. Rowell ◽

O. Dubois-Matra ◽

...

Keyword(s):

Small Body ◽

European Space Agency ◽

Ground Truth ◽

Camera Motion ◽

Test Environment ◽

Space Applications ◽

Vision Guidance ◽

Space Agency ◽

Wide Range ◽

And Performance

Abstract The use of machine vision to guide robotic spacecraft is being considered for a wide range of missions, such as planetary approach and landing, asteroid and small body sampling operations and in-orbit rendezvous and docking. Numerical simulation plays an essential role in the development and testing of such systems, which in the context of vision-guidance means that realistic sequences of navigation images are required, together with knowledge of the ground-truth camera motion. Computer generated imagery (CGI) offers a variety of benefits over real images, such as availability, cost, flexibility and knowledge of the ground truth camera motion to high precision. However, standard CGI methods developed for terrestrial applications lack the realism, fidelity and performance required for engineering simulations. In this paper, we present the results of our ongoing work to develop a suitable CGI-based test environment for spacecraft vision guidance systems. We focus on the various issues involved with image simulation, including the selection of standard CGI techniques and the adaptations required for use in space applications. We also describe our approach to integration with high-fidelity end-to-end mission simulators, and summarise a variety of European Space Agency research and development projects that used our test environment.

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