Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

Soil moisture plays an important role in the hydrologic water cycle. Relative to in-situ soil moisture measurements, remote sensing has been the only means of monitoring global scale soil moisture in near real-time over the past 40 years. Among these, soil moisture products from radiometry sensors operating at L-band, e.g., SMAP, SMOS, and SMOS-IC, are theoretically established to be more advantageous than previous C/X-band products. However, little effort has been made to investigate the inter-product differences of L-band soil moisture retrievals and provide insights into the optimal use of these products. In this regard, this study aims to identify the relative strengths and weaknesses of three L-band soil moisture products across diverse climate zones and land covers at the global scale using triple collocation analysis. Results show that SMOS-IC exhibits significantly improved soil moisture estimation skills, relative to the original SMOS product. This demonstrates the paramount importance of retrieval algorithm development in improving global soil moisture estimates—given both SMOS-IC and SMOS are using the same L-band brightness temperature information. Relative to SMOS-IC, SMAP is superior across 69% of global land surface in terms of error variances. However, SMOS-IC tends to outperform SMAP over temperate/arid regions including in the east of North America, South America, western Africa, northern China, and central Australia. Additionally, considerable performance degradation of all the L-band data products is observed over unvegetated areas. This may suggest that improving soil moisture retrieval accuracy over arid and semi-arid regions should be a key priority for future L-band soil moisture development, and model-based (e.g., GLDAS) soil moisture products appear to provide more accurate soil moisture estimates over these regions.

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Influence of surface water variations on VOD and biomass estimates from passive microwave sensors

10.5194/egusphere-egu21-5786 ◽

2021 ◽

Author(s):

Emma Bousquet ◽

Arnaud Mialon ◽

Nemesio Rodriguez-Fernandez ◽

Catherine Prigent ◽

Fabien Wagner ◽

...

Keyword(s):

Soil Moisture ◽

Surface Water ◽

Aboveground Biomass ◽

Passive Microwave ◽

Microwave Emission ◽

Retrieval Algorithm ◽

Water Fraction ◽

Standing Water ◽

Flooded Forests ◽

L Band

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the attenuation of the Earth's thermal emission at microwave wavelengths by the vegetation layer. At L-band, VOD can be used to estimate and monitor aboveground biomass (AGB), a key component of the Earth's surface and of the carbon cycle. We observed a strong anti-correlation between SMOS (Soil Moisture and Ocean Salinity) L-band VOD (L-VOD) and soil moisture (SM) anomalies over seasonally inundated areas, confirming previous observations of an unexpected decline in K-band VOD during flooding (Jones et al., 2011). These results could be, at least partially, due to artefacts affecting the retrieval and could lead to uncertainties on the derived L-VOD during flooding. To study the behaviour of SMOS satellite L-VOD retrieval algorithm over seasonally inundated areas, the passive microwave L-MEB (L-band Microwave Emission of the Biosphere) model was used to simulate the signal emitted by a mixed scene composed of soil and standing water. The retrieval over this inundated area shows an overestimation of SM and an underestimation of L-VOD. This underestimation increases non-linearly with the surface water fraction. The phenomenon is more pronounced over grasslands than over forests. The retrieved L-VOD is typically underestimated by ~10% over flooded forests and up to 100% over flooded grasslands. This is mainly due to the fact that i) low vegetation is mostly submerged under water and becomes invisible to the sensor; and ii) more standing water is seen by the sensor. Such effects can distort the analysis of aboveground biomass (AGB) and aboveground carbon (AGC) estimates and dynamics based on L-VOD. Using the L-VOD/AGB relationship from Rodriguez-Fernandez et al. (2018), we evaluated that AGB can be underestimated by 15/20Mg ha-1 in the largest wetlands, and up to higher values during exceptional meteorological years. Such values are more significant over herbaceous wetlands, where AGB is ~30 Mg ha-1, than over flooded forests, which have typical AGB values of 150-300 Mg ha-1. Consequently, to better estimate the global biomass, surface water seasonality has to be taken into account in passive microwave retrieval algorithms.

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Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

Hydrology and Earth System Sciences ◽

10.5194/hess-13-343-2009 ◽

2009 ◽

Vol 13 (3) ◽

pp. 343-356 ◽

Cited By ~ 39

Author(s):

F. Mattia ◽

G. Satalino ◽

V. R. N. Pauwels ◽

A. Loew

Keyword(s):

Soil Moisture ◽

Soil Moisture Content ◽

A Priori ◽

Retrieval Algorithm ◽

A Priori Information ◽

Hydrologic Modelling ◽

Multi Temporal ◽

L Band ◽

Sar Data ◽

Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

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A simple parameterisation for retrieving soil moisture from passive microwave data

Hydrology and Earth System Sciences ◽

10.5194/hess-5-39-2001 ◽

2001 ◽

Vol 5 (1) ◽

pp. 39-48 ◽

Cited By ~ 7

Author(s):

E. J. Burke ◽

L. P. Simmonds

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Passive Microwave ◽

Microwave Emission ◽

Balance Model ◽

Retrieval Algorithm ◽

Emission Model ◽

Near Surface ◽

Wide Range ◽

L Band

Abstract. MICRO-SWEAT, a physically based soil water and energy balance model coupled with a microwave emission model, was used to investigate the relationship between near surface soil moisture (θ0-5) and L-band microwave brightness temperature (TB) under a wide range of conditions. The effects of soil texture, look angle and vegetation on this relationship were parameterised and combined into a simple summary model relating θ0-5 to TB. This model retains much of the physical basis of MICRO-SWEAT but can be used in more data limiting circumstances. It was tested using a variety of truck-based L-band data sets collected between 1980 and 1982. This paper emphasises the need to have an accurate estimate of the vegetation optical depth (a parameter that describes the degree of influence of the vegetation on the microwave emission from the soil surface) in order to retrieve correctly the soil water content. Keywords: passive microwave, soil moisture, remote sensing, vegetation, retrieval algorithm

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Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-3479-2008 ◽

2008 ◽

Vol 5 (6) ◽

pp. 3479-3515 ◽

Cited By ~ 3

Author(s):

F. Mattia ◽

G. Satalino ◽

V. R. N. Pauwels ◽

A. Loew

Keyword(s):

Soil Moisture ◽

Soil Moisture Content ◽

A Priori ◽

Retrieval Algorithm ◽

A Priori Information ◽

Hydrologic Modelling ◽

Multi Temporal ◽

L Band ◽

Sar Data ◽

Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

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Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽

10.3390/agronomy11010035 ◽

2020 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

Xiaodong Huang ◽

Beth Ziniti ◽

Michael H. Cosh ◽

Michele Reba ◽

Jinfei Wang ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Canopy Cover ◽

The United States ◽

Optical Data ◽

Study Region ◽

Model Based ◽

Key Indicator ◽

Ground Measurement ◽

L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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Soil moisture and vegetation optical depth retrievals over heterogeneous scenes using LEWIS L-band radiometer

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102424 ◽

2021 ◽

Vol 102 ◽

pp. 102424

Author(s):

M. Barrée ◽

A. Mialon ◽

T. Pellarin ◽

M. Parrens ◽

R. Biron ◽

...

Keyword(s):

Soil Moisture ◽

Optical Depth ◽

L Band ◽

Band Radiometer

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Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112485 ◽

2021 ◽

Vol 261 ◽

pp. 112485

Author(s):

Hongtao Shi ◽

Lingli Zhao ◽

Jie Yang ◽

Juan M. Lopez-Sanchez ◽

Jinqi Zhao ◽

...

Keyword(s):

Soil Moisture ◽

Agricultural Fields ◽

Decomposition Techniques ◽

L Band

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

10.5194/egusphere-egu21-8660 ◽

2021 ◽

Author(s):

Anna Balenzano ◽

Giuseppe Satalino ◽

Francesco Lovergine ◽

Davide Palmisano ◽

Francesco Mattia ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Earth Science ◽

Observation System ◽

Final Report ◽

Surface Soil Moisture ◽

Error Characterization ◽

L Band

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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