Surface soil moisture retrievals from remote sensing: Current status, products &amp; future trends

Petropoulos, George P.; Ireland, Gareth; Barrett, Brian

doi:10.1016/j.pce.2015.02.009

Cited by 369 publications

(270 citation statements)

References 188 publications

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“…With the advances in earth observation technology, the field of remote sensing of soil moisture has expanded greatly in the past decades [6]. Observations from both microwave and optical/thermal infrared sensors have been successfully used to retrieve surface soil moisture [7][8][9]. In particular, soil moisture retrieval from microwave observations has been highly advanced and several global soil moisture products have been produced [10,11], such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) [12], the advanced scatterometer (ASCAT) [13], the Soil Moisture and Ocean Salinity (SMOS) [14,15], the Soil Moisture Active Passive (SMAP) [16], and the European Space Agency's Climate Change Initiative (ESA CCI) soil moisture products [17,18].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Soil Moisture Estimation from Remote Sensing

Peng

Loew

2017

Water

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Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Soil Moisture Estimation from Remote Sensing

Peng

Loew

2017

Water

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“…Consequently, the ASAR WS data were aggregated to 1km spatial resolution, supporting the comparison with the ECV product and also improving the radiometric accuracy of the satellite data. There are different approaches to soil moisture estimation using SAR data (Barrett et al, 2009;Petropoulos et al, 2015) and in this study, soil moisture values were retrieved from the ASAR WS acquisitions by applying the TU Wien change detection algorithm (Wagner et al, 1999a;Wagner et al, 1999b). This technique was originally developed for ERS scatterometer and Advanced Scatterometer (ASCAT) data but has been successfully adapted to both ASAR WS and GM data (e.g.…”

Section: Asar Soil Moisture Observationsmentioning

confidence: 99%

“…Soil moisture dynamics are dependent on both meteorological conditions and soil physical characteristics and, as a result, exhibit large spatial and temporal variations between different areas, seasons, and years (Western and Blöschl, 1999;Schulte et al, 2005). The spatial and temporal coverage attainable by spaceborne remote sensing has demonstrated the capability to monitor soil moisture over large areas at regular time intervals, and several approaches for soil moisture retrieval have been developed using optical, thermal infrared (TIR), and microwave (MW) sensors over the last three decades (Barrett and Petropoulos, 2013;Petropoulos et al, 2015). Since the late 1970s, coarse resolution (25 -50km) soil moisture products derived from past and present microwave radiometers (Advanced Microwave Scanning Radiometer (AMSR-E) (Njoku et al, 2003) and WindSat (Li et al, 2010)) and scatterometers (European Remote Sensing satellites (ERS) scatterometer (SCAT) (Wagner a (Bartalis et al, 2007;Naeimi et al, 2009)) have been available on an operational basis.…”

Section: Introductionmentioning

confidence: 99%

“…Since the late 1970s, coarse resolution (25 -50km) soil moisture products derived from past and present microwave radiometers (Advanced Microwave Scanning Radiometer (AMSR-E) (Njoku et al, 2003) and WindSat (Li et al, 2010)) and scatterometers (European Remote Sensing satellites (ERS) scatterometer (SCAT) (Wagner a (Bartalis et al, 2007;Naeimi et al, 2009)) have been available on an operational basis. Data from the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) (Kerr et al, 2012;Mecklenburg et al, 2012) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) (Entekhabi et al, 2010) dedicated soil moisture missions are strengthening this record of observations and further facilitating the study of long term soil moisture behaviour (please see Petropoulos et al (2015) and Zeng et al (2015) for further details of available satellite derived soil moisture products). With the availability of these products, it is necessary to validate them using independently derived soil moisture observations obtained through in-situ monitoring, models, or with different satellite sensors (van Doninck et al, 2012;Ochsner et al, 2013;Al-Yaari et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Barrett¹,

Pratola²,

Gruber³

et al. 2016

Satellite Soil Moisture Retrieval

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The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology and climatic modelling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), using finer spatial resolution Synthetic Aperture Radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in-situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse scale CCI Essential Climate Variable (ECV) soil moisture product (ECV SM) in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture datasets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture datasets at the Finnish sites. Overall, the two different satellite derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse scale ECV SM product to track soil moisture variability in time.

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“…Reviews about these methods can be found [6][7][8]. However, developing a method that can avoid complex parameterization of aerodynamic and surface resistances for water and heat transfer and can enable the estimation using a remote sensing image without additional information is still a challenge [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Theoretical Methods for Determining Dry and Wet Edges of the LST/FVC Space: Revisit of Method Physics

et al. 2017

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Abstract:Land surface temperature and fractional vegetation coverage (LST/FVC) space is a classical model for estimating evapotranspiration, soil moisture, and drought monitoring based on remote sensing. One of the key issues in its utilization is to determine its boundaries, i.e., the dry and wet edges. In this study, we revisited and compared three methods that were presented by Moran et al. (1994), , and Sun (2016) for calculating the dry and wet edges theoretically. Results demonstrated that: (1) for the dry edge, the Sun method is equal to the Long method and they have greater vegetation temperature than that of the Moran method. (2) With respect to the wet edge, there are greater differences among the three methods. Generally, Long's wet edge is a horizontal line equaling air temperature. Sun's wet edge is an oblique line and is higher than that of the Long's. Moran's wet edge intersects them with a higher soil temperature and a lower vegetation temperature. (3) The Sun and Long methods are simpler in calculation and can circumvent some complex parameters as compared with the Moran method. Moreover, they outperformed the Moran method in a comparison of estimating latent heat flux (LE), where determination coefficients varied between 0.45~0.66 (Sun), 0.47~0.68 (Long), and 0.39~0.57 (Moran) among field stations.

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Surface soil moisture retrievals from remote sensing: Current status, products & future trends

Cited by 369 publications

References 188 publications

Recent Advances in Soil Moisture Estimation from Remote Sensing

Recent Advances in Soil Moisture Estimation from Remote Sensing

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Comparison of Three Theoretical Methods for Determining Dry and Wet Edges of the LST/FVC Space: Revisit of Method Physics

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