On the use of compact L-band Dicke radiometer (ARIEL) and UAV for soil moisture and salinity map retrieval: 2008/2009 field experiments

Since 2012, the experts of Space Technology Institute have carried out the field experiments to obtain a high-resolution dataset of microwave radiometers for land surface parameters (soil moisture, soil temperature, vegetation water content), in order to improve the soil moisture retrieval methodology. L-band radiometers were used for measuring the brightness temperature of the bare soil. Field experiments for passive microwave remote sensing of soil moisture were carried out in Hoai Duc District in 2012. L-band microwave radiometers were used for measuring the microwave emission of bare agricultural fields. The radiometers, which are used for soil moisture measurement, worked well during the experimental campaign and produced volumetric soil moisture estimates that compared well with the ground-truth measurements. Explanations for the observed discrepancies are presented. The experimental results showed that the model of Choudhury et al. for surface roughness correction provides a better fit to radiometric data over the angular range between 20° and 50° for \(n = 0\) (i.e., the \(\cos ^{2}\theta\) factor in the exponential in (15) is suppressed). Based on the results of the experiments conducted over two experimental sites with different soils, namely sandy loam at Hoai Duc Agrometeorologyl Center, it may be concluded that the testing of both the radiometric equipment and the method for soil moisture retrieval was very successful, and the main goal of the experiments was fulfilled.

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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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Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

Journal of Hydrometeorology ◽

10.1175/jhm554.1 ◽

2007 ◽

Vol 8 (1) ◽

pp. 68-87 ◽

Cited By ~ 71

Author(s):

Margaret A. LeMone ◽

Fei Chen ◽

Joseph G. Alfieri ◽

Mukul Tewari ◽

Bart Geerts ◽

...

Keyword(s):

Soil Moisture ◽

Latent Heat ◽

Land Surface ◽

Field Experiments ◽

Time Of Day ◽

Horizontal Distribution ◽

Heat Fluxes ◽

Surface Model ◽

Green Vegetation ◽

The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

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

Remote Sensing ◽

10.3390/rs2010352 ◽

2010 ◽

Vol 2 (1) ◽

pp. 352-374 ◽

Cited By ~ 4

Author(s):

María Piles ◽

Mercè Vall-llossera ◽

Adriano Camps ◽

Marco Talone ◽

Alessandra Monerris

Keyword(s):

Soil Moisture ◽

Least Squares ◽

Retrieval Algorithm ◽

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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