Incidence Angle Diversity on L-Band Microwave Radiometry and Its Impact on Consistent Soil Moisture Retrievals

Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not yet fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe and furthermore shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows the estimation of effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Hydrology and Earth System Sciences ◽

10.5194/hess-15-151-2011 ◽

2011 ◽

Vol 15 (1) ◽

pp. 151-162 ◽

Cited By ~ 54

Author(s):

H. Lievens ◽

N. E. C. Verhoest ◽

E. De Keyser ◽

H. Vernieuwe ◽

P. Matgen ◽

...

Keyword(s):

Soil Moisture ◽

Incidence Angle ◽

Soil Surface ◽

Equation Model ◽

Roughness Parameters ◽

Effective Roughness ◽

Soil Surface Roughness ◽

L Band ◽

Microwave Backscatter ◽

Param Eters

Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art back\\-scatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that param\\-eters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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Soil moisture retrieval from multi-incidence angle observations at L-band

Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation. ◽

10.36334/modsim.2011.e4.peischl ◽

2011 ◽

Keyword(s):

Soil Moisture ◽

Incidence Angle ◽

L Band

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Assessing simultaneous mono- and bistatic airborne radar observations for soil moisture retrieval

10.5194/egusphere-egu2020-20744 ◽

2020 ◽

Author(s):

Emma Tronquo ◽

Hans Lievens ◽

Niko E.C. Verhoest

Keyword(s):

Surface Roughness ◽

Soil Moisture ◽

Incidence Angle ◽

Added Value ◽

Theoretical Research ◽

Initial Assessment ◽

Soil Conditions ◽

Airborne Measurements ◽

L Band ◽

The Impact

<div>&#160;</div>Current radar systems are generally monostatic. However, some theoretical research indicated the potential of bistatic radar measurements to improve applications. In the research presented, we explore the use of InSAR/PolInSAR mono- and bistatic measurements acquired in L-band for soil moisture monitoring. The main objective of this study is to compare the performance of soil moisture retrieval from monostatic with that obtained through bistatic observations.The recent BelSAR campaign (in 2018) provided time series of airborne mono- and bistatic measurements at L-band, recorded during the growing season including bare soil conditions. In addition, in situ measurements of soil moisture and surface roughness were acquired concurrently with the airborne flights. Here, we provide an initial assessment of the sensitivity of the scatter observations with respect to soil moisture and surface roughness. The literature suggests that the impact of surface roughness on the retrieval of soil moisture decreases due to the simultaneous use of the mono- and bistatic measurements. However, our preliminary results show that the bistatic data do not provide substantial added value to reduce the impact of surface roughness on soil moisture retrieval. Further, we validate both mono- and bistatic scatter simulations from the Advanced Integral Equation Model (AIEM) using the airborne measurements. The AIEM allows additional investigations with respect to the sensitivity towards surface roughness and soil moisture of both mono- and bistatic scattering signals, as well as the impacts of sensor-related parameters such as the incidence angle, the bistatic configuration (e.g. the location of the second sensor), the frequency and the polarization.&#160;&#160;

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Multiyear in-situ L-band microwave radiometry of land surface processes on the Tibetan Plateau

Scientific Data ◽

10.1038/s41597-020-00657-1 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Z. Su ◽

J. Wen ◽

Y. Zeng ◽

H. Zhao ◽

S. Lv ◽

...

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Microwave Radiometry ◽

Surface Processes ◽

Surface Model ◽

Transfer Model ◽

Turbulent Heat ◽

Land Surface Processes ◽

L Band

Abstract We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented.

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Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

Remote Sensing ◽

10.3390/rs10101637 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1637 ◽

Cited By ~ 9

Author(s):

Thomas Meyer ◽

Lutz Weihermüller ◽

Harry Vereecken ◽

François Jonard

Keyword(s):

Soil Moisture ◽

Winter Wheat ◽

Optical Depth ◽

Incidence Angle ◽

Soil Surface ◽

Growing Season ◽

Area Index ◽

Vegetation Height ◽

L Band

L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.

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Robust retrieval of surface soil moisture across wide-ranging incidence angles over short crops: for application to NI-SAR

10.5194/egusphere-egu2020-13213 ◽

2020 ◽

Author(s):

Seungbum Kim ◽

Tienhao Liao

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Incidence Angle ◽

Physical Models ◽

Growth Stages ◽

Agricultural Field ◽

Surface Soil Moisture ◽

Vegetation Growth ◽

L Band ◽

Sar Data

We present our ongoing efforts to deliver surface soil moisture information at agricultural field scales using airborne or satellite synthetic aperture radar (SAR) data through the development and inversion of physical models for forward radar scattering from vegetation surfaces. While the past successful results were validated at 40-deg incidence angle for the Soil Moisture Active passive mission, the current work extends the incidence angle range from 30 to 50 degs so that the algorithm may apply to the future L-band NASA-ISRO SAR (NI-SAR) mission. NI-SAR aims at providing global soil moisture data at 200m resolution every 6 days.The soil moisture retrievals were validated over agriculture sites in Canadian Prairies using L-band airborne SAR data, where the fields experienced entire crop growth stages and two cycles of wetting and drydowns. The forward models were developed over NI-SAR&#8217;s incidence angle range of 30 to 50 degs for individual crops.The estimates are accurate to unbiased rmse of 0.053, 0.058 and 0.047 m3/m3 in volumetric water content for soybean, wheat, and pasture fields respectively over diverse conditions of vegetation growth and soil wetness. Surface roughness and vegetation amount were retrieved simultaneous to the soil moisture solutions. The roughness estimates are realistic.There was no significant effect of the local incidence angle on the retrieval performance, most likely because the path length of the radar wave through the vegetation (and therefore extinction of the soil moisture signal) did not vary much with incidence angle. The results are encouraging for successful soil moisture mapping for the NI-SAR mission.

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Assessment of surface soil moisture distribution across small scale tomato fields using L band SAR data

10.5194/egusphere-egu2020-623 ◽

2020 ◽

Author(s):

Punithraj Gururaj ◽

Pruthviraj Umesh ◽

Amba Shetty

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Incidence Angle ◽

Moisture Distribution ◽

Small Scale ◽

Surface Soil Moisture ◽

Alos Palsar ◽

L Band ◽

Sar Data ◽

Soil Moisture Distribution

Soil moisture is very important in several disciplines such as agriculture, hydrology and meteorology. It can be mapped using active and passive microwave remote sensing techniques. From literature it is observed that quad polarized data acquired at L-band is sensitive to soil moisture and can map surface soil moisture at high spatial resolution. The main objective of this study is to analyze the potential use of L-band radar data for the retrieval of surface soil moisture over small scale agricultural areas under vegetation cover conditions. Study area selected for this study was Malavalli, village in Karnataka state India which falls in Tropical semi-arid region. Two radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the study area between 23/07/2018 and 17/09/2018 which has spatial resolution of 5m. Ground Soil moisture over 30 sample sites were collected in synchronization with satellite pass over the study area. Acquired ALOS PALSAR-2 images were processed using PolSARpro (Polarimetric SAR data Processing and Education Toolbox). ALOS PALSAR-2 has been processed and lee speckle filter is applied with window size of 3*3. Surface soil moisture distribution over small scale tomato fields are mapped by adding incidence angle using Oh Model. Incidence angle map which is not available with PolSARpro (Polarimetric SAR data Processing and Education Toolbox) software was derived using the polynomial given in the leader file which was required for oh model inversion. Study site clearly shown increasing trend of soil moisture from July to September. It is interesting to note that vegetation and urban areas are clearly discriminated in the PauliRGB images. The retrieval of soil moisture using Oh model is validated using Ground truth samples. The accuracy of Oh model over small scale tomato fields with RMSE of 1.83 m3/m-3.

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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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