7 results on '"Paz, Veronica Sobejano"'
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2. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
- Author
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Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin
- Published
- 2022
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3. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System
- Author
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Wang, Sheng, Garcia, Monica, Bauer-Gottwein, Peter, Jakobsen, Jakob, Zarco-Tejada, Pablo J., Bandini, Filippo, Paz, Verónica Sobejano, and Ibrom, Andreas
- Published
- 2019
- Full Text
- View/download PDF
4. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
- Author
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Global Ecohydrology and Sustainability, Environmental Sciences, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Global Ecohydrology and Sustainability, Environmental Sciences, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin
- Published
- 2022
5. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain:A review
- Author
-
Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin
- Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys
- Published
- 2022
6. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
- Author
-
Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, Schlerf, Martin, Berger, K, Machwitz, M, Kycko, M, Kefauver, S, Van Wittenberghe, S, Gerhards, M, Verrelst, J, Atzberger, C, van der Tol, C, Damm, A, Rascher, U, Herrmann, I, Paz, V, Fahrner, S, Pieruschka, R, Prikaziuk, E, Buchaillot, M, Halabuk, A, Celesti, M, Koren, G, Gormus, E, Rossini, M, Foerster, M, Siegmann, B, Abdelbaki, A, Tagliabue, G, Hank, T, Darvishzadeh, R, Aasen, H, Garcia, M, Pôças, I, Bandopadhyay, S, Sulis, M, Tomelleri, E, Rozenstein, O, Filchev, L, Stancile, G, Schlerf, M, Berger, Katja, Machwitz, Miriam, Kycko, Marlena, Kefauver, Shawn C., Van Wittenberghe, Shari, Gerhards, Max, Verrelst, Jochem, Atzberger, Clement, van der Tol, Christiaan, Damm, Alexander, Rascher, Uwe, Herrmann, Ittai, Paz, Veronica Sobejano, Fahrner, Sven, Pieruschka, Roland, Prikaziuk, Egor, Buchaillot, Ma. Luisa, Halabuk, Andrej, Celesti, Marco, Koren, Gerbrand, Gormus, Esra Tunc, Rossini, Micol, Foerster, Michael, Siegmann, Bastian, Abdelbaki, Asmaa, Tagliabue, Giulia, Hank, Tobias, Darvishzadeh, Roshanak, Aasen, Helge, Garcia, Monica, Pôças, Isabel, Bandopadhyay, Subhajit, Sulis, Mauro, Tomelleri, Enrico, Rozenstein, Offer, Filchev, Lachezar, Stancile, Gheorghe, and Schlerf, Martin
- Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys
- Published
- 2022
7. Hyperspectral and thermal sensing of stomatal conductance and photosynthesis under water stress for a C3 (soybean) and a C4 (maize) crop.
- Author
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Paz, Veronica Sobejano, Mikkelsen, Teis Nørgaard, Johnson, Mark, Mo, Xingguo, Morillas, Laura, Liu, Suxia, Shen, Liang, and Garcia, Monica
- Subjects
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NORMALIZED difference vegetation index , *SOYBEAN , *PLANT transpiration , *ENVIRONMENTAL risk assessment , *CORN , *PHOTOSYNTHESIS , *WATER levels - Abstract
Currently, the agricultural sector accounts for more than 90% of the water footprint of humanity. Reducing crop water footprints is urgent, especially considering that droughts are becoming more frequent. Unmanned Aerial Systems (UAS) carrying hyperspectral and thermal sensors can help by quantifying crop water use and photosynthesis at the farm scale. Remote sensing models could benefit from better parameterizations of plant responses to water stress. Current parameterizations group plants that might have different functional traits. In addition, they rely on meteorological variables and vegetation indices like the Normalized Difference Vegetation Index (NDVI). As a response to stress, crops modify leaf transpiration, photosynthesis and conductance, and the associated energy balance in the shortwave and longwave ranges. Therefore, UAS can help to better quantify water stress responses using ad-hoc dynamic parameterizations. This requires a careful identification of the spectral wavelengths which are most sensitive to changes in water stress. In this study we assess the sensitivity of different indices and spectral regions to changes in leaf conductance, photosynthesis and transpiration due to water stress in soybean and maize, with different photosynthetic paths (C3 and C4) and differing strategies for stomatal regulation (anisohydric/isohydric). The experiment was conducted in a growth chamber at the phytotron Risø Environmental Risk Assessment Facility (RERAF), Technical University of Denmark (DTU). The crops were grown under typical meteorological conditions of Southern Europe (25⁰C/50% RH daytime) with 3 water stress treatments: control (100% of field capacity, FC), medium (70% FC) and high-water stress (40% FC) with 6 replicates per treatment. Canopy reflectance and temperature were measured with a hyperspectral camera Cubert UHD 185 with 125 bands (450-950 nm) and a Flir-Tau2 thermal infrared camera, respectively. Canopy evapotranspiration was measured by gravimetry, and leaf stomatal conductance, transpiration and photosynthetic rates were measured with a Li-Cor-6400.We found contrasting responses in water and carbon regulation between the two crops for similar water stress levels. Our results suggest that for soybean and maize under the same levels of water stress, soybean undertakes physiological adjustments, while maize responds with reductions in biochemical constituents, such as pigments or leaf water content. In general, leaf conductance, transpiration and photosynthesis were lower and presented larger responses to water stress for soybean (C3) than for maize (C4). In soybean, the air–leaf temperature difference (ΔT) was the variable most sensitive to changes in conductance and transpiration, while this was not the case in maize. This is consistent with larger changes in conductance observed in soybean that will reduce transpiration cooling. The Photochemical Reflectance Index (PRI) and ΔT were also sensitive to photosynthesis for soybean. For maize, the Water Index (WI) was sensitive to conductance and transpiration, suggesting reductions in leaf water content but not in soybean. Pigment content indices such as the Optimized Soil-Adjusted Vegetation Index (OSAVI) as well as NDVI were responsive to transpiration and conductance for maize. These results highlight the benefit of using different spectral indices and regions to track plant water status and accounting for different functional traits when modeling evapotranspiration or photosynthesis. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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