Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.&#160;&#160;ReferencesBalenzano. A., et al., &#8220;Sentinel-1 soil moisture at 1km resolution: a validation study&#8221;, submitted to Remote Sensing of Environment (2020).Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., &#8220;ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,&#8221; final report, contract number 4000118762/16/I-NB, 2020.

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Soil Hydraulic Parameters and Surface Soil Moisture of a Tilled Bare Soil Plot Inversely Derived from L-Band Brightness Temperatures

Vadose Zone Journal ◽

10.2136/vzj2013.04.0075 ◽

2014 ◽

Vol 13 (1) ◽

pp. vzj2013.04.0075 ◽

Cited By ~ 10

Author(s):

M. Dimitrov ◽

J. Vanderborght ◽

K. G. Kostov ◽

K. Z. Jadoon ◽

L. Weihermüller ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Bare Soil ◽

Hydraulic Parameters ◽

Surface Soil Moisture ◽

Brightness Temperatures ◽

Soil Hydraulic Parameters ◽

L Band ◽

Soil Hydraulic

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A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2005.863319 ◽

2006 ◽

Vol 44 (6) ◽

pp. 1534-1544 ◽

Cited By ~ 70

Author(s):

Xiwu Zhan ◽

P.R. Houser ◽

J.P. Walker ◽

W.T. Crow

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Surface Soil ◽

Surface Soil Moisture ◽

Radar Observations ◽

L Band ◽

Band Radiometer

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Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

10.5194/egusphere-egu2020-7408 ◽

2020 ◽

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Jamal Ezzahar ◽

Saïd Khabba ◽

Mehrez Zribi ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Cloud Model ◽

Microwave Remote Sensing ◽

Radar Data ◽

Optical Data ◽

Surface Soil Moisture ◽

Backscattering Coefficient ◽

New Approach

High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2&#176; for VV, with R=0.82, RMSE=0.05m3/m3 and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m3/m3 and 0.03 m3/m3, respectively even that the incidence angle is higher (40&#176;). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.

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Evaluation of near-surface soil moisture data from an AAFC monitoring network in Manitoba, Canada: Implications for L-band satellite validation

Journal of Hydrology ◽

10.1016/j.jhydrol.2014.10.024 ◽

2015 ◽

Vol 521 ◽

pp. 582-592 ◽

Cited By ~ 26

Author(s):

Justin R. Adams ◽

Heather McNairn ◽

Aaron A. Berg ◽

Catherine Champagne

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Monitoring Network ◽

Surface Soil Moisture ◽

Near Surface ◽

Soil Moisture Data ◽

Satellite Validation ◽

L Band

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Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0280.1 ◽

2018 ◽

Vol 19 (1) ◽

pp. 183-200 ◽

Cited By ~ 15

Author(s):

Y. Malbéteau ◽

O. Merlin ◽

G. Balsamo ◽

S. Er-Raki ◽

S. Khabba ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Large Scale ◽

Surface Soil ◽

Land Surface Model ◽

Open Loop ◽

Surface Model ◽

Surface Soil Moisture ◽

Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

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Rock Fraction Effects on the Surface Soil Moisture Estimates From L-Band Radiometric Measurements

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2008.4779092 ◽

2008 ◽

Author(s):

A. Monerris ◽

M. Vall-llossera ◽

A. Camps ◽

M. Piles

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Surface Soil Moisture ◽

Radiometric Measurements ◽

L Band

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Mapping surface soil moisture with L-band radiometric measurements

Remote Sensing of Environment ◽

10.1016/0034-4257(89)90090-4 ◽

1989 ◽

Vol 27 (3) ◽

pp. 305-311 ◽

Cited By ~ 45

Author(s):

James R. Wang ◽

James C. Shiue ◽

Thomas J. Schmugge ◽

Edwin T. Engman

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Surface Soil Moisture ◽

Radiometric Measurements ◽

L Band

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Estimating the best spatial resolution of remotely sensed surface soil moisture based on their uncertainty

10.5194/egusphere-egu21-12826 ◽

2021 ◽

Author(s):

Theresa C. van Hateren ◽

Marco Chini ◽

Patrick Matgen ◽

Luca Pulvirenti ◽

Nazzareno Pierdicca ◽

...

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Temporal Resolution ◽

Surface Soil ◽

Reference Data ◽

Computing Time ◽

Ground Truth ◽

Remotely Sensed ◽

Surface Soil Moisture ◽

Trade Offs

Validation of remotely sensed soil moisture is a well-known issue. Reference data with the correct spatial and temporal resolution on large scales are sparse and lack spatial representativeness. Moreover, due to the heterogeneity of soil moisture in both space and time, even reference data cannot be considered to be &#8220;ground truth&#8221;. As such, uncertainties are difficult to quantify. Additionally, in remotely sensed soil moisture there are trade-offs between spatial resolution and temporal resolution, resolution and accuracy, and resolution and computing time. Here, we try to identify the best spatial resolution for Sentinel-1 based soil moisture estimation, considering the trade-off between product resolution and accuracy. We use the uncertainty&#160; of the soil moisture estimate as a guide parameter, and focus on how product accuracy depends on factors as soil wetness, and characteristics of the vegetated canopy.&#160; To this end, we compare Sentinel-1 soil moisture estimates to both in situ data and global reference data sets with a lower spatial resolution. Remotely sensed surface soil moisture data were obtained by applying the MULESME algorithm&#160; (Pulvirenti et al., 2018) on Sentinel-1 data throughout 2020. An extensive field campaign was performed, where TDR data and volumetric soil samples were gathered. A nearby setup of permanent soil moisture probes additionally provided continuous measurements of soil moisture at different depths, from 10 to 60 centimetres. Global datasets were obtained from the SMOS satellite constellation, GLDAS, MERRA-2 and ESA CCI.Pulvirenti, L., Squicciarino, G., Cenci, L., Boni, G., Pierdicca, N., Chini, M., Versace, P. & Campanella, P. (2018). A surface soil moisture mapping service at national (Italian) scale based on Sentinel-1 data. Environmental Modelling & Software, 102, 13-28.

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