The Experts below are selected from a list of 66618 Experts worldwide ranked by ideXlab platform
Nathalie Gorretta - One of the best experts on this subject based on the ideXlab platform.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Sylvain Jay, Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
Fabienne Maupas - One of the best experts on this subject based on the ideXlab platform.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Sylvain Jay, Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
Ryad Bendoula - One of the best experts on this subject based on the ideXlab platform.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
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retrieving lai chlorophyll and nitrogen contents in sugar beet crops from multi angular optical Remote Sensing comparison of vegetation indices and prosail inversion for field phenotyping
Field Crops Research, 2017Co-Authors: Sylvain Jay, Fabienne Maupas, Ryad Bendoula, Nathalie GorrettaAbstract:Abstract Remote Sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered Remote-Sensing Method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to Remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet ( Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing Remote-Sensing Methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications. Overall, both approaches are promising to Remotely estimate LAI and canopy chlorophyll content (RMSE ≤ 10%). In addition, VIs show a great potential to retrieve canopy nitrogen content (RMSE = 10%). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs (RMSE = 17%). As expected when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively. Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAV-embedded multispectral cameras, with which only a few wavebands are available.
Jiayi Pan - One of the best experts on this subject based on the ideXlab platform.
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determining azimuthal variations in frontal froude number from sar imagery
Geophysical Research Letters, 2009Co-Authors: Jiayi Pan, David A Jay, Hui LinAbstract:[1] River plume fronts are the locus of strong mixing between plume and ambient coastal waters, contribute to coastal productivity, and exert a major impact on coastal ecosystems. The frontal Froude number Fr is an important parameter characterizing the frontal status with respect to both propagation and vertical mixing. In this study, we examine azimuthal variations in Fr using a new Remote Sensing Method. We derive Fr from SAR image data on the basis of the SAR imaging theory and the mechanism of internal wave fission at front. This Method is applied to a SAR image showing a front off the Columbia River (CR) mouth taken on 31 May 2003 at 14:33:19 UTC under weak wind conditions. Fr increases from south to north along the front. This variation is consistent with potential vorticity conservation and the influence of tidal currents on the plume. This calculation confirms arguments based on vessel observations by Jay et al. (2009).
Alexandre Lepoutre - One of the best experts on this subject based on the ideXlab platform.
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Satellite-based Remote Sensing of running water habitats at large riverscape scales: Tools to analyze habitat heterogeneity for river ecosystem management
Geomorphology, 2016Co-Authors: F. Hugue, B. C. Eaton, Michel Lapointe, Alexandre LepoutreAbstract:We illustrate an approach to quantify patterns in hydraulic habitat composition and local heterogeneity applicable at low cost over very large river extents, with selectable reach window scales. Ongoing developments in Remote Sensing and geographical information science massively improve efficiencies in analyzing earth surface features. With the development of new satellite sensors and drone platforms and with the lowered cost of high resolution multispectral imagery, fluvial geomorphology is experiencing a revolution in mapping streams at high resolution. Exploiting the power of aerial or satellite imagery is particularly useful in a riverscape research framework (Fausch et al., 2002), where high resolution sampling of fluvial features and very large coverage extents are needed. This study presents a satellite Remote Sensing Method that requires very limited field calibration data to estimate over various scales ranging from 1. m to many tens or river kilometers (i) spatial composition metrics for key hydraulic mesohabitat types and (ii) reach-scale wetted habitat heterogeneity indices such as the hydromorphological index of diversity (HMID). When the purpose is hydraulic habitat characterization applied over long river networks, the proposed Method (although less accurate) is much less computationally expensive and less data demanding than two dimensional computational fluid dynamics (CFD). Here, we illustrate the tools based on a Worldview 2 satellite image of the Kiamika River, near Mont Laurier, Quebec, Canada, specifically over a 17-km river reach below the Kiamika dam. In the first step, a high resolution water depth (D) map is produced from a spectral band ratio (calculated from the multispectral image), calibrated with limited field measurements. Next, based only on known river discharge and estimated cross section depths at time of image capture, empirical-based pseudo-2D hydraulic rules are used to rapidly generate a two-dimensional map of flow velocity (V) over the 17-km Kiamika reach. The joint distribution of D and V variables over wetted zones then is used to reveal structural patterns in hydraulic habitat availability at patch, reach, and segment scales. Here we analyze 156 bivariate (D, V) density function plots estimated over moving reach windows along the satellite scene extent to extract 14 physical habitat metrics (such as river width, mean and modal depths and velocity, variances and covariance in D and V over 1-m pixels, HMID, entropy). A principal component analysis on the set of metrics is then used to cluster river reaches in regard to similarity in their hydraulic habitat composition and heterogeneity. Applications of this approach can include (i) specific fish habitat detection at riverscape scales (e.g., large areas of riffle spawning beds, deeper pools) for regional management, (ii) studying how river habitat heterogeneity is correlated to fish distribution and (iii) guidance for site location for restoration of key habitats or for post regulation monitoring of representative reaches of various types.