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

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.

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

Remote Sensing ◽

10.3390/rs11091113 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1113 ◽

Cited By ~ 9

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.

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Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Water ◽

10.3390/w13192777 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2777

Author(s):

Tao Cheng ◽

Siyang Hong ◽

Bensheng Huang ◽

Jing Qiu ◽

Bikui Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Pearson Correlation ◽

Microwave Remote Sensing ◽

Northeastern China ◽

Passive Microwave ◽

Agricultural Drought ◽

Passive Microwave Remote Sensing ◽

Soil Moisture Data

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

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Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture

Hydrology and Earth System Sciences ◽

10.5194/hess-25-5029-2021 ◽

2021 ◽

Vol 25 (9) ◽

pp. 5029-5045

Author(s):

Bonan Li ◽

Stephen P. Good

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Model Uncertainty ◽

Pearson Correlation ◽

Microwave Remote Sensing ◽

Random Uncertainty ◽

Dual Channel ◽

Informational Model

Abstract. The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (TBh) and vertical (TBv) polarized microwave brightness temperatures through a modeled mechanistic relationship between vegetation opacity, surface scattering albedo, and soil effective temperature (Teff). Although this model has been validated against in situ soil moisture, there is a lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, we assess how the information content of in situ soil moisture observations from the US Climate Reference Network contrasts with (1) the information contained within raw SMAP observations (i.e., “informational random uncertainty”) derived from TBh, TBv, and Teff themselves and with (2) the information contained in SMAP's dual-channel algorithm (DCA) soil moisture estimates (i.e., “informational model uncertainty”) derived from the model's inherent structure and parameterizations. The results show that, on average, 80 % of the information in the in situ soil moisture is unexplained by SMAP DCA soil moisture estimates. Loss of information in the DCA modeling process contributes 35 % of the unexplained information, while the remainder is induced by a lack of additional explanatory power within TBh, TBv, and Teff. Overall, retrieval quality of SMAP DCA soil moisture, denoted as the Pearson correlation coefficient between SMAP DCA soil moisture and in situ soil moisture, is negatively correlated with the informational uncertainties, with slight differences across different land covers. The informational model uncertainty (Pearson correlation of −0.59) was found to be more influential than the informational random uncertainty (Pearson correlation of −0.34), suggesting that the poor performance of SMAP DCA at some locations is driven by model parameterization and/or structure and not underlying satellite measurements of TBh and TBv. A decomposition of mutual information between TBh, TBv, and DCA soil moisture shows that on average 58 % of information provided by TBh and TBv to DCA estimates is redundant. The amount of information redundantly and synergistically provided by TBh and TBv was found to be closely related (Pearson correlations of 0.79 and −0.82, respectively) to the retrieval quality of SMAP DCA. TBh and TBv tend to contribute large redundant information to DCA estimates under surfaces or conditions where DCA makes better retrievals. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end-user products.

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The Rosetta Mission to Primitive Bodies of the Solar System

Symposium - International Astronomical Union ◽

10.1017/s0074180900046660 ◽

1994 ◽

Vol 160 ◽

pp. 381-394

Author(s):

Yves Langevin

Keyword(s):

Remote Sensing ◽

Solar System ◽

European Space Agency ◽

Time Span ◽

Main Belt ◽

Space Agency ◽

Rosetta Mission ◽

One Year ◽

Definition Of

The European Space Agency (ESA) has selected Rosetta as the next cornerstone mission, to be launched in 2003. The goal is to perfom one or more fly-bys to main belt asteroids, followed by a rendez-vous with an active comet. Advanced in situ analysis, both in the coma and on the surfaces of the nucleus, will be possible, as well as monitoring by remote sensing instruments of the nucleus and of the inner coma for a time span of more than one year, until perihelion. This paper outlines the scientific and technological choices done in the definition of the mission.

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Information – based uncertainty decomposition in dual channel microwave remote sensing of soil moisture

10.5194/hess-2020-534 ◽

2020 ◽

Author(s):

Bonan Li ◽

Stephen P. Good

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Mutual Information ◽

Microwave Remote Sensing ◽

Reference Network ◽

Brightness Temperatures ◽

Dual Channel ◽

The Us ◽

In Situ Observations

Abstract. NASA's Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal, TBh, and vertical, TBv, polarized microwave brightness temperatures through a modeled relationship between vegetation opacity and surface scattering albedo (i.e. tau-omega model). Although this model has been validated against in situ soil moisture measurements across sparse validations sites, there is lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, soil moisture observations from the US Climate Reference Network are used within a mutual information framework to decompose the overall retrieval uncertainty from SMAPs Modified Dual Channel Algorithm (MDCA) into random uncertainty derived from raw data itself and model uncertainty derived from the model’s inherent structure. The results shown that, on average, 12 % of the uncertainty in SMAP soil moisture estimates is caused by the loss of information in the MDCA model itself while the remainder is induced by inadequacy of TBh and TBv observations. We find the fraction of algorithm induced uncertainty is negatively correlated (pearson r of −0.48) with correlations between in-situ observations and MDCA estimates. A decomposition of mutual information between TBh, TBv and MDCA soil moisture shows that on average 55 % of the mutual information is redundantly shared by TBh and TBv, while the information provided uniquely from both TBh and TBv is 15 %. The fraction of information redundantly provided by TBh and TBv was found to be tightly correlated (pearson r = −0.7) to how well the MDCA output correlated to in situ observations. Thus, MDCA overall quality improves as TBh and TBv provide more redundant information for the MDCA. This suggests the informational redundancy between these remotely sensed observations can be used as independent metric to assess the overall quality of algorithms using these data streams. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end user products.

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

10.5194/hess-2021-2 ◽

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.

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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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European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

Remote Sensing ◽

10.3390/rs13153003 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3003

Author(s):

Fabrizio Niro ◽

Philippe Goryl ◽

Steffen Dransfeld ◽

Valentina Boccia ◽

Ferran Gascon ◽

...

Keyword(s):

Remote Sensing ◽

European Space Agency ◽

Uncertainty Budget ◽

General Concept ◽

Satellite Validation ◽

Space Agency ◽

Reference Measurements ◽

Imaging Sensors ◽

Measurement Practices

Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems.

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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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