TY  - JOUR
AU  - Pacheco-Labrador, Javier
AU  - Cendrero-Mateo, M. Pilar
AU  - Van Wittenberghe, Shari
AU  - Hernandez-Sequeira, Itza
AU  - Koren, Gerbrand
AU  - Prikaziuk, Egor
AU  - Fóti, Szilvia
AU  - Tomelleri, Enrico
AU  - Maseyk, Kadmiel
AU  - Čereković, Nataša
AU  - Gonzalez-Cascon, Rosario
AU  - Malenovský, Zbyněk
AU  - Albert-Saiz, Mar
AU  - Antala, Michal
AU  - Balogh, János
AU  - Buddenbaum, Henning
AU  - Dehghan-Shoar, Mohammad Hossain
AU  - Fennell, Joseph T.
AU  - Féret, Jean-Baptiste
AU  - Balde, Hamadou
AU  - Machwitz, Miriam
AU  - Mészáros, Ádám
AU  - Miao, Guofang
AU  - Morata, Miguel
AU  - Naethe, Paul
AU  - Nagy, Zoltán
AU  - Pintér, Krisztina
AU  - Pullanagari, R. Reddy
AU  - Rastogi, Anshu
AU  - Siegmann, Bastian
AU  - Wang, Sheng
AU  - Zhang, Chenhui
AU  - Kopkáně, Daniel
TI  - Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise
JO  - International journal of remote sensing
VL  - 46
IS  - 1
SN  - 0143-1161
CY  - London
PB  - Taylor & Francis
M1  - FZJ-2024-06100
SP  - 443-468
PY  - 2025
AB  - The ability to access physiologically driven signals, such as surfacetemperature, photochemical reflectance index (PRI), and suninducedchlorophyll fluorescence (SIF), through remote sensing(RS) are exciting developments for vegetation studies. Accessingthis ecophysiological information requires considering processesoperating at scales from the top-of-the-canopy to the photosystems,adding complexity compared to reflectance index-basedapproaches. To investigate the maturity and knowledge of thegrowing RS community in this area, COST Action CA17134SENSECO organized a Spatial Scaling Challenge (SSC). Challengeparticipants were asked to retrieve four key ecophysiological variablesfor a field each of maize and wheat from a simulated fieldcampaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximumcarboxylation rate (Vcmax,25), and non-photochemicalquenching (NPQ). The simulated campaign data included hyperspectraloptical, thermal and SIF imagery, together with groundsampling of the four variables. Non-parametric methods that combinedmultiple spectral domains and field measurements were usedmost often, thereby indirectly performing the top-of-the-canopy tophotosystem scaling. LAI and Cab were reliably retrieved in mostcases, whereas Vcmax,25 and NPQ were less accurately estimated anddemanded information ancillary to RS imagery. The factors consideredleast by participants were the biophysical and physiologicalcanopy vertical profiles, the spatial mismatch between RS sensors,the temporal mismatch between field sampling and RS acquisition,and measurement uncertainty. Furthermore, few participantsdeveloped NPQ maps into stress maps or provided a deeper analysisof their parameter retrievals. The SSC shows that, despiteadvances in statistical and physically based models, the vegetationRS community should improve how field and RS data are integratedand scaled in space and time. We expect this work will guide newcomersand support robust advances in this research field.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001343869000001
DO  - DOI:10.1080/01431161.2024.2414435
UR  - https://juser.fz-juelich.de/record/1032256
ER  -