Prediction of the Error Induced by Topography in Satellite Microwave Radiometric Observations

Pulvirenti, Luca; Pierdicca, Nazzareno; Marzano, Frank S.

doi:10.1109/tgrs.2010.2096514

Cited by 27 publications

(17 citation statements)

References 21 publications

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“…2, it can be seen that among the input data it is possible to distinguish: 1) Earth Observation (EO) data; 2) a thematic land cover map. The input EO data include: a) calibrated radar backscattering data in , provided as multilook images at resolution of about 90 m; b) local incidence angle in degrees, derived from a digital elevation model by a SAR geocoding software or as done in [32], [33]; c) (dimensionless) derived from the available optical images (e.g. Landsat, Sentinel-2) by computing the ratio between the difference and the sum of the near-infrared and red channels.…”

Section: B the Input Data And Their Pre-processingmentioning

confidence: 99%

A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

Pierdicca

Pulvirenti

Pace

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing for the generation of frequent soil moisture maps. This work presents a prototype software implementing a multitemporal approach to the problem of soil moisture retrieval using Synthetic Aperture Radar (SAR) data. The approach exploits the short revisit time of Sentinel-1 data by assuming the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum a posteriori probability statistical criterion. The paper focuses on the combination of on-line and off-line processing that has been designed in order to decrease the time necessary to produce a soil moisture map, which may be a critical aspect of multitemporal approaches. It describes also the optimization of the algorithm carried out to find the set of algorithm parameters that allow obtaining the best tradeoff between accuracy of the estimates and computational efficiency. A set of simulations of C-band SAR data, produced by applying a well-established radar-backscattering model, is used to perform the optimization. The designed system is tested on a series of ERS-1 SAR data acquired on February-April 1994 in Central Italy with a revisit time of three days. The results indicate that the temporal trend of estimated soil moisture is consistent with the succession of rain events occurred throughout the period of ERS-1 acquisitions over the observed geographic area

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Section: B the Input Data And Their Pre-processingmentioning

confidence: 99%

A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

Pierdicca

Pulvirenti

Pace

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The methodology, mainly based on the fuzzy logic 19 , consists of two main steps, i.e., the detection of low backscatter areas and the classification of each dark object present in the considered SAR image. The algorithm uses ancillary data, i.e., a land cover map and information about topography provided by a local incidence angle (θ l ) map derived from a Digital Elevation Model (DEM) as done in [20][21][22] . The block diagram of the proposed procedure is shown in Figure 2 and the procedure is summarized hereafter; more details can be found in 11 .…”

Section: Discriminating Water Surfaces From Artifacts Caused By Heavymentioning

confidence: 99%

Dealing with flood mapping using SAR data in the presence of wind or heavy precipitation

2013

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The latest generation synthetic aperture radar (SAR) systems allows providing emergency managers with near real time flood maps characterized by a very high spatial resolution. Near real time flood detection algorithms generally search for regions of low backscatter, thus assuming that floodwater appears dark in a SAR image. It is well known that this assumption is not always valid. For instance, vegetation emerging from floodwater may produce high radar returns because of the double bounce effect involving water surface and vertical stems. However, even mapping bare or scarcely vegetated inundated terrains, or crops totally submerged by water can turn out to be a difficult task. In the presence of wind that roughens the water surface, floodwater can appear bright in SAR images. Moreover, if X-band radars as TerraSAR-X or COSMO-SkyMed are used to map inundation, not only missed detection, but also false alarms may occur because of artifacts caused by heavy precipitating clouds that attenuate the radar signal. This paper proposes possible strategies to cope with flood mapping using SAR data in the presence of wind or heavy precipitation.

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“…Details of object extraction can be found in (Boulila et al, 2010) (Boulila et al, 2011) (Boulila et al, 2012). According to (Pulvirenti et al, 2011) (Benz et al, 2004), we determined the factors that have influence on the characteristics of O P .…”

Section: Step 1: Testing Gain Type By Neural Networkmentioning

confidence: 99%

An Approach based on Adaptive Decision Tree for Land Cover Change Prediction in Satellite Images

Ferchichi¹,

Boulila²,

Farah³

2013

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference O

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Decision tree (DT) prediction algorithms have significant potential for remote sensing data prediction. This paper presents an advanced approach for land-cover change prediction in remote-sensing imagery. Several methods for decision tree change prediction have been considered: probabilistic DT, belief DT, fuzzy DT, and possibilistic DT. The aim of this study is to provide an approach based on adaptive DT to predict land cover changes and to take into account several types of imperfection related to satellite images such as: uncertainty, imprecision, vagueness, conflict, ambiguity, etc. The proposed approach applies an artificial neural network (ANN) model to choose the appropriate gain formula to be applied on each DT node. The considered approach is validated using satellite images representing the Saint-Paul region, commune of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

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Prediction of the Error Induced by Topography in Satellite Microwave Radiometric Observations

Cited by 27 publications

References 21 publications

A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1 Data by Using a Bayesian Multitemporal Algorithm

Dealing with flood mapping using SAR data in the presence of wind or heavy precipitation

An Approach based on Adaptive Decision Tree for Land Cover Change Prediction in Satellite Images

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