Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

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Publication Details

Journal
International Journal of Remote Sensing
Published
2024-10-28
DOI
https://doi.org/10.1080/01431161.2024.2414435
Citations
3
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.38

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article

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

Egor Prikaziuk, Bastian Siegmann, Miguel Morata, Jean‐Baptiste Féret et al.
3 citations
International Journal of Remote Sensing
Remote Sensing in Agriculture
1.38
article

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

Egor Prikaziuk, Bastian Siegmann, Miguel Morata, Jean‐Baptiste Féret, Rosario González-Cascón, Mar Albert-Saiz, Michal Antala, Reddy Pullanagari, Guofang Miao, Joseph T. Fennell, Maria Pilar Cendrero‐Mateo, Anshu Rastogi, Kadmiel Maseyk, Krisztina Pintér, Javier Pacheco‐Labrador, Itza Hernandez-Sequeira, Zoltán Nagy, Shari Van Wittenberghe, Nataša Čereković, Paul Naethe, Hamadou Balde, Mohammad Hossain Dehghan-Shoar, Chengzhong Zhang, Zbyněk Malenovský, János Balogh, Miriam Machwitz, Daniel Kopkáně, Szilvia Fóti, Gerbrand Koren, Henning Buddenbaum, Sheng Wang, Enrico Tomelleri, Ádám Mészáros
article en
3 citations

Abstract

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

International Journal of Remote SensingVol. 46(1)
Universitat Jaume I (ES), Fujian Normal University (CN), Australian Centre for Plant Functional Genomics (AU), Magyar Agrár- és Élettudományi Egyetem (HU), Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Consejo Superior de Investigaciones Científicas (ES), University of Bonn (DE), École Polytechnique (FR), University of Illinois Urbana-Champaign (US), Free University of Bozen-Bolzano (IT), University of Banja Luka (BA), Utrecht University (NL), Université de Montpellier (FR), The Open University (GB), Aarhus University (DK), AgroParisTech (FR), Parc Científic de la Universitat de València (ES), Sorbonne Université (FR), Laboratoire de Météorologie Dynamique (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Czech Academy of Sciences, Global Change Research Institute (CZ), Luxembourg Institute of Science and Technology (LU), Institute of Plant Biology (HU), Max Planck Institute for Biogeochemistry (DE), Territoires, Environnement, Télédétection et Information Spatiale (FR), Massey University (NZ), University of Life Sciences in Poznań (PL), The University of Adelaide (AU), Massachusetts Institute of Technology (US), Universität Trier (DE), University of Twente (NL)
European Space Agency, European Cooperation in Science and Technology, Agence Nationale de la Recherche, National Natural Science Foundation of China, Bundesministerium für Bildung und Forschung, Centre National d’Etudes Spatiales, Deutsches Zentrum für Luft- und Raumfahrt, Narodowe Centrum Nauki, Ministerio de Ciencia e Innovación, Österreichische Forschungsförderungsgesellschaft, Horizon 2020 Framework Programme, H2020 European Research Council
Climate action
Openalex Percentile: Top 18%
Remote Sensing in Agriculture
1.38
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