Surface Topography and Mixed-Pixel Effects on the Simulated L-Band Brightness Temperatures

Talone, Marco; Camps, A.; Monerris, A.; Vall-Ilossera, M.; Ferrazzoli, P.; Piles, María

doi:10.1109/tgrs.2007.898254

Cited by 28 publications

(17 citation statements)

References 12 publications

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“…For example, several retrieval algorithms usually use vegetation models formulated and calibrated from limited validation sites [4,5]. A change detection method makes an assumption that the effects of vegetation on backscattering is minimal at cross-over angle, based upon the empirical relations established for correcting the vegetation effects [3,6,7]. Similarly, as in the tree example described above, remotely sensed vegetation index such as Leaf Area Index (LAI) may be used for globally characterizing the height of vegetation, although vegetation reflects a remote sensor's signals.…”

Section: Scale Issuementioning

confidence: 99%

“…Verhoest et al [16] also addressed that a direct comparison with field measurements is not possible, because there is a scale dependency of satellite data in terms of land surface characteristics such as roughness [17,18]. Talone et al [7] previously stated that land surface inhomogeneity ultimately limits the capability to compare single point measurements with satellite measurements so that the ideal validation site should be spacious and homogeneous. However, in the real world, land surface is usually spatially heterogeneous.…”

Section: Issues With Current Retrieval Goalmentioning

confidence: 99%

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Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

2017

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Abstract:To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with.

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Section: Scale Issuementioning

confidence: 99%

Section: Issues With Current Retrieval Goalmentioning

confidence: 99%

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

2017

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“…The spatial scale mismatch becomes more prominent when the land surface is heterogeneous. This limits the competency to compare the point measurement with the satellite [65]. The error in the retrieval of soil moisture increases with the heterogeneity in land surface [66].…”

Section: Soil Moisture From Sentinel-1amentioning

confidence: 99%

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

et al. 2020

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Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

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“…In a modeling study using digital elevation models (DEMs), Kerr et al [20] find that modeled brightness temperatures in areas of variable topography can be several kelvins different than a corresponding flat surface. Talone et al [21] used a 30-m DEM and a 100-m land cover map to derive inputs to the SMOS RTM, in which the DEM is used to incorporate topographic effects on shadowing and local incidence angles. Further, Mialon et al [22] recently discussed these effects in the context of the SMOS mission and developed a criterion to identify SMOS brightness temperature pixels in which topographic effects on incidence angle are likely to result in observation errors greater than the required 4-K accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission

Flores

Ivanov

Entekhabi

et al. 2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-Microwave radiometry will emerge as an important tool for global remote sensing of near-surface soil moisture in the coming decade. In this modeling study, we find that hillslopescale topography (tens of meters) influences microwave brightness temperatures in a way that produces bias at coarser scales (kilometers). The physics underlying soil moisture remote sensing suggests that the effects of topography on brightness temperature observations are twofold: 1) the spatial distribution of vegetation, moisture, and surface and canopy temperature depends on topography and 2) topography determines the incidence angle and polarization rotation that the observing sensor makes with the local land surface. Here, we incorporate the important correlations between factors that affect emission (e.g., moisture, temperature, and vegetation) and topographic slope and aspect. Inputs to the radiative transfer model are obtained at hillslope scales from a mass-, energy-, and carbon-balance-resolving ecohydrology model. Local incidence and polarization rotation angles are explicitly computed, with knowledge of the local terrain slope and aspect as well as the sky position of the sensor. We investigate both the spatial organization of hillslope-scale brightness temperatures and the sensitivity of spatially aggregated brightness temperatures to satellite sky position. For one computational domain considered, hillslope-scale brightness temperatures vary from approximately 121 to 317 K in the horizontal polarization and from approximately 117 to 320 K in the vertical polarization. Including hillslope-scale heterogeneity in factors effecting emission can change watershed-aggregated brightness temperature by more than 2 K, depending on topographic ruggedness. These findings have implications for soil moisture data assimilation and disaggregation of brightness temperature observations to hillslope scales.

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Surface Topography and Mixed-Pixel Effects on the Simulated L-Band Brightness Temperatures

Cited by 28 publications

References 12 publications

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India

Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission

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