Dr. Niko E. C. Verhoest Profile

Dr. Niko E. C. Verhoest

at Univ Gent

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Author

Publications (7)

SPIE Journal Paper | 20 March 2012

Influence of topographic normalization on the vegetation index-surface temperature relationship

Jasper Van doninck, Niko Verhoest, Jan Peters, Bernard De Baets, Eva De Clercq, Els Ducheyne

JARS, Vol. 6, Issue 01, 063518, (March 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063518

KEYWORDS: Vegetation, Soil science, Francium, Data modeling, MODIS, Temperature metrology, Visualization, In situ remote sensing, Remote sensing, Image acquisition

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Proceedings Article | 20 October 2005 Paper

Radar based surface soil moisture retrieval through the combined use of two backscattering models

Jesús Álvarez-Mozos, Niko Verhoest, Javier Casali, María González-Audícana

Proceedings Volume 5976, 59761O (2005) https://doi.org/10.1117/12.627513

KEYWORDS: Soil science, Backscatter, Surface roughness, Radar, Dielectrics, Error analysis, Agriculture, Sensors, Remote sensing, Polarization

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Proceedings Article | 19 October 2005 Paper

Assimilation of small scale soil moisture in a land-surface model

Gabriëlle De Lannoy, Niko Verhoest, Valentijn Pauwels, Paul Houser

Proceedings Volume 5976, 59760L (2005) https://doi.org/10.1117/12.627075

KEYWORDS: Soil science, Agriculture, Data modeling, Error analysis, Atmospheric modeling, Filtering (signal processing), Calibration, Remote sensing, Sensors, Vegetation

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Proceedings Article | 19 October 2005 Paper

Effect of soil roughness uncertainty on the accuracy of soil moisture retrieval from ERS SAR backscattering

Niko Verhoest, Bernard De Baets

Proceedings Volume 5976, 597607 (2005) https://doi.org/10.1117/12.627524

KEYWORDS: Soil science, Probability theory, Backscatter, Dielectrics, Remote sensing, Radar, Synthetic aperture radar, Data modeling, Hydrology, Stochastic processes

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Proceedings Article | 19 October 2005 Paper

Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model

Cozmin Lucau-Danila, Moira Callens, Pierre Defourny, Niko Verhoest, Valentijn Pauwels

Proceedings Volume 5976, 597603 (2005) https://doi.org/10.1117/12.627574

KEYWORDS: Soil science, Calibration, Synthetic aperture radar, Data modeling, Clouds, Error analysis, Vegetation, Backscatter, Radar, Agriculture

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Previous experiments demonstrated the relationships between the radar backscattering coefficient, σ_o and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from σ_o. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.

Proceedings Article | 26 October 2004 Paper

Surface soil moisture retrieval on agricultural catchments of Navarre, Spain through RADARSAT-1 SAR data: first results

Jesus Alvarez, Niko Verhoest, Javier Casali, Maria Gonzalez-Audicana, Jose Lopez

Proceedings Volume 5568, (2004) https://doi.org/10.1117/12.565239

KEYWORDS: Soil science, Vegetation, Surface roughness, Backscatter, Synthetic aperture radar, Agriculture, Clouds, Sensors, Remote sensing, Microwave radiation

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The importance of soil moisture on many scientific fields like hydrology, meteorology, crop growth or soil erosion has been addressed frequently. Its characterisation has been a difficult task because of its high spatial and temporal variability. Several point based measurement techniques have been developed with different degree of success, but their conversion to spatially distributed values depends on complex geostatistical techniques. Furthermore, sensor installation and maintenance can be quite tedious. In this background, SAR remote sensing sensors provide valuable information on land surface parameters. The backscattering of the SAR signal depends amongst others on the dielectric constant of the observed surface, which is mainly related to the soil surface water content. It also gives spatially distributed information with a resolution adequate for different spatial scales: from medium or small watersheds to agricultural fields. Its periodicity can be appropriate for calibrating, on a monthly basis, the simulations of distributed hydrologic modelling tools. The present paper reports the first results of an ongoing research of which the main objective is the development of a simple methodology for the calibration of the soil moisture component of distributed hydrological models using SAR data. Five RADARSAT-1 images, acquired between 27/02/2003 and 02/04/2003 over the Navarre region (Northern Spain) have been processed. The calculated backscattering values have been compared to soil moisture and surface roughness ground measurements. Empirical linear regression models have been fitted at three different scales: point scale, field scale and catchment scale, showing acceptable correlation between calculated backscattering values and ground measured soil moisture specially at field and watershed scale. However, consistent trends have not been found probably due to differing local conditions such as surface roughness or vegetation cover. Seeking for a more consistent approach, the physically based Integral Equation Method (IEM) model has been applied. Yet, simulations run by the IEM have not been completely successful probably due to an inadequate characterisation of surface roughness.

Proceedings Article | 30 December 1997 Paper

Radar backscattering sensitivity to soil moisture content of sugar beet fields

Gerrit Schoups, Peter Troch, Niko Verhoest

Proceedings Volume 3222, (1997) https://doi.org/10.1117/12.298139

KEYWORDS: Backscatter, Soil science, Radar, Vegetation, L band, Data modeling, Scattering, Polarization, S band, Dielectrics

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