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

2016 ◽  
Vol 20 (10) ◽  
pp. 4191-4208 ◽  
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.

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

2015 ◽  
Vol 12 (11) ◽  
pp. 11549-11589 ◽  
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.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

scholarly journals Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

2019 ◽  
Vol 11 (9) ◽  
pp. 1113 ◽  
Author(s):  
Franklin Paredes-Trejo ◽  
Humberto Barbosa ◽  
Carlos A. C. dos Santos
Keyword(s):  
Soil Moisture ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Rainfall Events ◽  
Error Components ◽  
Space Agency ◽  
Daily Scale ◽  
Bioclimatic Conditions ◽  
Heavy Rainfall Events

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.

scholarly journals The International Soil Moisture Network: serving Earth system science for over a decade

2021 ◽  
Author(s):  
Wouter Dorigo ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
European Space Agency ◽  
Data Repository ◽  
Reference Database ◽  
Data Sets ◽  
Scientific Publications ◽  
Data Set ◽  
Space Agency

Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011a, b). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present.The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database continue to be updated. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade,including a description of network and data set updates and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.

scholarly journals The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France

2018 ◽  
Vol 10 (11) ◽  
pp. 1839 ◽  
Author(s):  
A. Al-Yaari ◽  
S. Dayau ◽  
C. Chipeaux ◽  
C. Aluome ◽  
A. Kruszewski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Agricultural Research ◽  
Pearson Correlation ◽  
European Space Agency ◽  
Microwave Remote Sensing ◽  
Climate Conditions ◽  
Space Agency ◽  
Satellite Microwave

Global soil moisture (SM) products are currently available thanks to microwave remote sensing techniques. Validation of these satellite-based SM products over different vegetation and climate conditions is a crucial step. INRA (National Institute of Agricultural Research) has set up the AQUI SM and soil temperature in situ network (composed of three main sites Bouron, Bilos, and Hermitage), over a flat area of dense pine forests, in South-Western France (the Bordeaux–Aquitaine region) to validate the Soil Moisture and Ocean salinity (SMOS) satellite SM products. SMOS was launched in 2009 by the European Space Agency (ESA). The aims of this study are to present the AQUI network and to evaluate the SMOS SM product (in the new SMOS-IC version) along with other microwave SM products such as the active ASCAT (Advanced Scatterometer) and the ESA combined (passive and active) CCI (Climate Change Initiative) SM retrievals. A first comparison, using Pearson correlation, Bias, RMSE (Root Mean Square Error), and Un biased RMSE (ubRMSE) scores, between the 0–5 cm AQUI network and ASCAT, CCI, and SMOS SM products was conducted. In general all the three products were able to reproduce the annual cycle of the AQUI in situ observations. CCI and ASCAT had best and similar correlations (R~0.72) over the Bouron and Bilos sites. All had comparable correlations over the Hermitage sites with overall average values of 0.74, 0.68, and 0.69 for CCI, SMOS-IC, and ASCAT, respectively. Considering anomalies, correlation values decreased for all products with best ability to capture day to day variations obtained by ASCAT. CCI (followed by SMOS-IC) had the best ubRMSE values (mostly < 0.04 m3/m3) over most of the stations. Although the region is highly impacted by radio frequency interferences, SMOS-IC followed correctly the in situ SM dynamics. All the three remotely-sensed SM products (except SMOS-IC over some stations) overestimated the AQUI in situ SM observations. These results demonstrate that the AQUI network is likely to be well-suited for satellite microwave remote sensing evaluations/validations.

The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Measurement Techniques ◽  
European Space Agency ◽  
Web Portal ◽  
Soil Moisture Data ◽  
Spatial Coverage

&lt;p&gt;The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&amp;#160;&lt;/p&gt;&lt;p&gt;In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.&lt;/p&gt;&lt;p&gt;The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.&lt;/p&gt;&lt;p&gt;Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community &gt; 3200 registered users strong. Time series with hourly timestamps from 1952 &amp;#8211; up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (&gt; 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).&lt;/p&gt;&lt;p&gt;About 10&amp;#8217;000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.&lt;/p&gt;&lt;p&gt;In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.&lt;/p&gt;

More than 10 years of The International Soil Moisture Network (ISMN) in support of EO science

2021 ◽  
Author(s):  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
European Space Agency ◽  
Data Availability ◽  
Data Repository ◽  
Source Surface ◽  
Web Portal ◽  
The Past

&lt;p&gt;&lt;span&gt;The International Soil Moisture Network (ISMN, &lt;/span&gt;&lt;span&gt;) is a unique centralized global and open freely available in-situ soil moisture data hosting facility. Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, WMO, etc.), with continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN is more than ever an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Following, building and improving standardized measurement protocols and quality techniques, the network evolved into a widely used, reliable and consistent in-situ data source (surface and sub-surface) collected by a myriad off data organizations on a voluntary basis. 66 networks are participating (status January 2021) with more than 2750 stations distributed on a global scale and a steadily increasing number of user community, &gt; 3200 registered users strong. Time series with hourly timestamps from 1952 &amp;#8211; up to near real time are stored in the database and are available through the ISMN web portal for free (&lt;/span&gt;&lt;span&gt;), including daily near-real time updates from 6 networks (~ 1000 stations). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;About 10&amp;#8217;000 datasets are available through the web portal and t&lt;/span&gt;&lt;span&gt;he number of&lt;/span&gt; &lt;span&gt;networks and stations covered by the ISMN is still growing as well as most datasets, that are already contained in the database, are continuously being updated.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The ISMN evolved in the past decade into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS), the online validation service Quality Assurance for Soil Moisture (QA4SM) and many more applications, services, products and tools. In general, ISMN data is widely used in a variety of scientific fields with hundreds of studies making use of ISMN data (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this session, we want to inform ISMN users about the evolution of the ISMN over the past decade, including a description of network and dataset updates and new quality control procedures. Besides, we provide a review of existing literature making use of ISMN data in order to identify current limitations in data availability&lt;/span&gt;&lt;span&gt;, &lt;/span&gt;&lt;span&gt;functionality and challenges in data usage in order to help shape potential future modes in operation of this unique community- based data repository.&lt;/span&gt;&lt;/p&gt;

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the unprecedented flooding on the Bahamas using SAR

2019 ◽  
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
European Space Agency ◽  
Sar Images ◽  
The Bahamas ◽  
In Situ Data ◽  
Space Agency ◽  
Continental Area ◽  
Flooded Areas

Abstract. Lack of real-time, in situ data on the extent of flooding in many parts of the world can hinder efficient disaster response. With the advent of satellite-based synthetic aperture radar (SAR) sensors, we can deploy techniques to identify flooded areas worldwide while storms are occurring. In this communication, we present an automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID), applying it to European Space Agency Sentinel-1 SAR images to produce flooding maps for Hurricane Dorian in the northern Bahamas. Images from RAPID released two days after the event show coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3,000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands.

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

2017 ◽  
Author(s):  
Nemesio Rodríguez-Fernández ◽  
Joaquin Muñoz Sabater ◽  
Philippe Richaume ◽  
Patricia de Rosnay ◽  
Yann Kerr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Weather Prediction ◽  
European Space Agency ◽  
Retrieval Algorithm ◽  
Space Agency ◽  
Wide Range ◽  
The Difference ◽  
Level 2

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 soil moisture information in near-real-time (NRT), typically not later than 3 hours after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil moisture 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 30º to 45º. 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 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 shows a standard deviation of the difference with respect to the L2 data of

scholarly journals Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2718 ◽  
Author(s):  
Luyao Zhu ◽  
Hongquan Wang ◽  
Cheng Tong ◽  
Wenbin Liu ◽  
Benxu Du
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Poor Performance ◽  
Temporal Variations ◽  
Simple Arithmetic ◽  
Carbon Cycles ◽  
Space Agency

The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product’s coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m3/m3 over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies.

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