An inversion algorithm for retrieving soil moisture and surface roughness from polarimetric radar observation

Oh, Yisok; Sarabandi, Kamal; Ulaby, F. T.

doi:10.1109/igarss.1994.399504

Cited by 57 publications

(45 citation statements)

References 4 publications

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“…Moreover, the increasing availability of multiband or multifrequency SAR data allows independent assessment of soil surface moisture, which could then be used to interpret optically-based tillage mapping. Specifically for SAR, Quad-Pol configurations help separate roughness from other factors, such as soil moisture [151]. With the exception of Radarsat-2, current (like Sentinel-1) and future radar missions (like Radarsat-constellation) do not consider Quad-Pol, or their configuration is infrequent (e.g., ALOS-2 and TerraSAR-X).…”

Section: Soil Tillagementioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Section: Soil Tillagementioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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“…Oh et al [8][9][10][11] developed between 1992 and 2004 several versions of a semi empirical backscattering model. Basing on theoretical models, scatterometer measurements and airborne SAR observations, the Oh model is built over a wide variety of bare soil surfaces.…”

Section: The Semi-empirical Oh Modelmentioning

confidence: 99%

“…Moreover, the validity domain of semi-empirical models is limited to the range of data used for calibration. The most commonly empirical models are the models of Oh [8][9][10][11] and Dubois [12]; while, the most popular physical models are Integral equation model (IEM) [13], IEM calibrated by Baghdadi, called in this paper "IEM_B" [14][15][16][17][18][19], and Advanced Integral Equation Model (AIEM) [20]. For bare soils, SAR backscattering models allow backscattering coefficients simulation by using soil parameters (mainly dielectric constant, and roughness) and SAR configurations (frequency, incidence angle, polarization) as input.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements

Choker

Baghdadi

Zribi

et al. 2017

Water

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Abstract:The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model "IEM", Oh, Dubois, and Advanced Integral Equation Model "AIEM") using a wide dataset of SAR (Synthetic Aperture Radar) data and experimental soil measurements. These forward models reproduce the radar backscattering coefficients (σ 0 ) from soil surface characteristics (dielectric constant, roughness) and SAR sensor parameters (radar wavelength, incidence angle, polarization). The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness). Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB) and better correlation with real data in VV polarization (RMSE = 2.9 dB). The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM). Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B) with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B) is the most adequate to estimate soil moisture and roughness from SAR data.

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“…This task can be undertaken by means of many existing models. Amongst these, the Oh and Dubois surface models, referred to as semi-empirical [7] and empirical [8], respectively, might initially appear suitable for the soil surfaces conditions given in the study area. In addition, each of these models has also the capacity to compute at least two polarization states of the backscattering coefficient, which is also important, as the radar data used in this work is a full polarimetric mode.…”

Section: Simulation Of the Dielectric Surfacementioning

confidence: 99%

“…Soil surface scattering models need to take into account statistical roughness parameters, such as profile height displacement standard deviation and correlation length, in addition to the dielectric properties that are responsible of the soil reflectivity properties [4,5]. These variables are common hydrological parameters, and their knowledge allows establishing the corresponding surface scattering models, so that they can be further inverted in order to locally or regionally retrieve hydrologic or vegetation related information of the surface [6,7]. A certain number of theoretical and empirical models are available for deriving these values, and they are commonly defined by some specific validity ranges, as it is the case for the Oh and Dubois models [4,8].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

Molina

Morillo

García‐Meléndez

et al. 2011

Sensors

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Abstract:One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ 0 ).The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r 2 = 0.88). In turn, when effective LAI OPEN ACCESSSensors 2011, 11 7477(LAI eff ) has been taken into account, the parameters of the scattering canopy model are better estimated (r 2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values.

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An inversion algorithm for retrieving soil moisture and surface roughness from polarimetric radar observation

Cited by 57 publications

References 4 publications

Remote Sensing and Cropping Practices: A Review

Remote Sensing and Cropping Practices: A Review

Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements

Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

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