Follow us on 

Acknowledgement of SENSECO COST action

Was SENSECO beneficial for your next paper? Then you should acknowledge the action by adapting the text below in the "Acknowledgement" paragraph!!!

This article/publication is based upon work from COST Action CA17134 "Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits" (SENSECO), supported by COST (European Cooperation in Science and Technology).


Amin, E.; Belda, S.; Pipia, L.; Szantoi, Z.; El Baroudy, A.; Moreno, J.; Verrelst, J. (2020) Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI. Remote Sens. 14, 1812.

Berger, Katja, Miriam Machwitz, Marlena Kycko, Shawn C. Kefauver, Shari Van Wittenberghe, Max Gerhards, Jochem Verrelst, et al. (2022). Multi-Sensor Spectral Synergies for Crop Stress Detection and Monitoring in the Optical Domain: A Review. Remote Sensing of Environment 280 (October): 113198. 

Bossung, Christian, Martin Schlerf, and Miriam Machwitz (2022) Estimation of Canopy Nitrogen Content in Winter Wheat from Sentinel-2 Images for Operational Agricultural Monitoring. Precision Agriculture, June. 

Buchaillot, Ma. Luisa, David Soba, Tianchu Shu, Juan Liu, Iker Aranjuelo, José Luis Araus, G. Brett Runion, Stephen A. Prior, Shawn C. Kefauver, and Alvaro Sanz-Saez (2022) Estimating Peanut and Soybean Photosynthetic Traits Using Leaf Spectral Reflectance and Advance Regression Models. Planta 255 (4): 93.

Döpper, Veronika, Alby Duarte Rocha, Katja Berger, Tobias Gränzig, Jochem Verrelst, Birgit Kleinschmit, and Michael Förster (2022) Estimating Soil Moisture Content under Grassland with Hyperspectral Data Using Radiative Transfer Modelling and Machine Learning. International Journal of Applied Earth Observation and Geoinformation 110 (June): 102817. 

Ekinzog, Elmer Kanjo, Martin Schlerf, Martin Kraft, Florian Werner, Angela Riedel, Gilles Rock, and Kaniska Mallick (2022) Revisiting Crop Water Stress Index Based on Potato Field Experiments in Northern Germany. Agricultural Water Management 269 (July): 107664. 

Estévez, José, Matías Salinero-Delgado, Katja Berger, Luca Pipia, Juan Pablo Rivera-Caicedo, Matthias Wocher, Pablo Reyes-Muñoz, Giulia Tagliabue, Mirco Boschetti, and Jochem Verrelst (2022) Gaussian Processes Retrieval of Crop Traits in Google Earth Engine Based on Sentinel-2 Top-of-Atmosphere Data. Remote Sensing of Environment 273 (May): 112958.

Hoek van Dijke, Anne J., Martin Herold, Kaniska Mallick, Imme Benedict, Miriam Machwitz, Martin Schlerf, Agnes Pranindita, Jolanda J. E. Theeuwen, Jean-François Bastin, and Adriaan J. Teuling (2022) Shifts in Regional Water Availability Due to Global Tree Restoration. Nature Geoscience 15 (5): 363–68. 

Pascual-Venteo, A.B.; Portalés, E.; Berger, K.; Tagliabue, G.; Garcia, J.L.; Pérez-Suay, A.; Rivera-Caicedo, J.P.; Verrelst, J. (2022) Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data. Remote Sens14, 2448. 

Pipia, L., Belda, S., Franch, B., Verrelst, J. (2022). Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring. In: Bochtis, D.D., Lampridi, M., Petropoulos, G.P., Ampatzidis, Y., Pardalos, P. (eds) Information and Communication Technologies for Agriculture—Theme I: Sensors. Springer Optimization and Its Applications, vol 182. Springer, Cham.

Reyes-Muñoz, P.; Pipia, L.; Salinero-Delgado, M.; Belda, S.; Berger, K.; Estévez, J.; Morata, M.; Rivera-Caicedo, J.P.; Verrelst, J. (2022) Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine. Remote Sens14, 1347. 

Salinero-Delgado, M.; Estévez, J.; Pipia, L.; Belda, S.; Berger, K.; Paredes Gómez, V.; Verrelst, J. (2022) Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression. Remote Sens.14, 146. 

Segarra, J.; Araus J.L.; and Kefauver. S.C. (2022) Farming and Earth Observation: Sentinel-2 Data to Estimate within-Field Wheat Grain Yield. International Journal of Applied Earth Observation and Geoinformation 107 (October 2021): 102697.

Wang, Na, Jan G.P.W. Clevers, Sebastian Wieneke, Harm Bartholomeus, and Lammert Kooistra (2022) Potential of UAV-Based Sun-Induced Chlorophyll Fluorescence to Detect Water Stress in Sugar Beet. Agricultural and Forest Meteorology 323 (August): 109033. 

Wang, Na, Bastian Siegmann, Uwe Rascher, Jan G.P.W. Clevers, Onno Muller, Harm Bartholomeus, Juliane Bendig, Dainius Masiliūnas, Ralf Pude, and Lammert Kooistra (2022) Comparison of a UAV- and an Airborne-Based System to Acquire Far-Red Sun-Induced Chlorophyll Fluorescence Measurements over Structurally Different Crops. Agricultural and Forest Meteorology 323 (August): 109081. 




Abdelbaki, A.; Schlerf, M.; Retzlaff, R.; Machwitz, M.; Verrelst, J.; Udelhoven, T. (2021). Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging. Remote Sens. 13, 1748.

Alessi, N., Wellstein, C., Rocchini, D., Midolo, G., Oeggl, K., & Zerbe, S. (2021). Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images. Remote Sensing, 13(1), 134. 

Bandopadhyay, S.; Rastogi, A.; Cogliati, S.; Rascher, U.; Gąbka, M.; Juszczak, R. (2021). Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data. Remote Sens. 13, 2545.

Berger, K.; Hank, T.; Halabuk, A.; Rivera-Caicedo, J.P.; Wocher, M.; Mojses, M.; Gerhátová, K.; Tagliabue, G.; Dolz, M.M.; Venteo, A.B.P.; Verrelst, J. (2021). Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery. Remote Sens.13, 4711.

Berger, K.; Rivera Caicedo, J.P.; Martino, L.; Wocher, M.; Hank, T.; Verrelst, J. (2021). A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data. Remote Sens. 13, 287.

Danner, M., Berger, K., Wocher, M., Mause,r W., Hank, T. (2021). Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops, ISPRS Journal of Photogrammetry and Remote Sensing. 173, 278-296,

De Grave, C., Pipia, L., Siegmann, B., Morcillo-Pallarés, P., Rivera-Caicedo, J. P., Moreno, J., & Verrelst, J. (2021). Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution : A Multiscale Analysis with the Sentinel-3 OLCI Sensor. Remote Sensing, 13, 1419. 

Estévez, J.; Berger, K.; Vicent, J.; Rivera-Caicedo, J.P.; Wocher, M.; Verrelst, J. (2021). Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow. Remote Sens. 13, 1589.

Féret, J.-B., Berger, K., de Boissieu, F., & Malenovský, Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. doi: 

Goldberg, K.; Herrmann, I.; Hochberg, U.; Rozenstein, O. (2021). Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel. Remote Sens. 13, 3488.

Herrmann, I.; Berger, K. (2021). Remote and Proximal Assessment of Plant Traits. Remote Sens. 13, 1893.

Janoutová, R., Homolová, L., Novotný, J., Navrátilová, B., Pikl, M., & Malenovský, Z. (2021). Detailed reconstruction of trees from terrestrial laser scans for remote sensing and radiative transfer modelling applications. In Silico Plants, 3(2).

Machwitz, M., Pieruschka, R., Berger, K., Schlerf, M., Aasen, H., Fahrner, S., … Rascher, U. (2021). Bridging the gap between remote sensing and plant phenotyping - challenges and opportunities for the next generation of sustainable agriculture. Frontiers in Plant Science, 0, 2334.

Morata, M.; Siegmann, B.; Morcillo-Pallarés, P.; Rivera-Caicedo, J.P.; Verrelst, J. (2021) Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer. Remote Sens13, 4368. 

Pisek, J., Erb, A., Korhonen, L., Biermann, T., Carrara, A., Cremonese, E., . . . Vincke, C. (2021). Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests, Biogeosciences, 18, 621–635.

Pisek, J., Arndt, S.K., Erb, A., Pendall, E., Schaaf, C., Wardlaw, T.J., Woodgate, W., Knyazikhin, Y. (2021). Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites. Frontiers in Remote Sensing, 2. 

Prikaziuk, E.; Yang, P.; Van der Tol, C. (2021) Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences. Remote Sens. 13, 1098.

Rocchini, D., Salvatori, N., Beierkuhnlein, C., Chiarucci, A., de Boissieu, F., Förster, M., … Féret, J.-B. (2021). From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing. Ecological Informatics, 61, 101195.

Rocchini, D., Marcantonio, M., Da Re, D., Bacaro, G., Feoli, E., Foody, G. M., … Ricotta, C. (2021). From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy. Global Ecology and Biogeography, 30(5), 1153–1162.

Thouverai, E., Marcantonio, M., Bacaro, G., Re, D. Da, Iannacito, M., Marchetto, E., … Rocchini, D. (2021). Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective. Community Ecology, 22(1), 1–11.

Van Wittenberghe, S., Sabater N., Cendrero-Mateo, M.P., Tenjo, C., Moncholi, A., Alonso, L.,  and Moreno, J. (2021). Towards the quantitative and physically-based interpretation of solar-induced vegetation fluorescence retrieved from global imaging. Photosynthetica 59, no. special issue, 438-57.

Verrelst, J., Rivera-Caicedo, J. P., Reyes-Muñoz, P., Morata, M., Amin, E., Tagliabue, G., … Berger, K. (2021). Mapping landscape canopy nitrogen content from space using PRISMA data. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 382–395.

Weksler, S.; Rozenstein, O.; Haish, N.; Moshelion, M.; Wallach, R.; Ben-Dor, E. (2021). Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting. Sensors, 21, 958.


Aasen, H., Kirchgessner, N., Walter, A., & Liebisch, F. (2020). PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits. Frontiers in Plant Science, 11(593).

Berger, K., Verrelst, J., Féret, J.-B., Hank, T., Wocher, M., Mauser, W., and Camps-Valls, G. (2020). Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92, 102174.

Biriukova, K., Celesti, M., Evdokimov, A., Pacheco-Labrador, J., Julitta, T., Migliavacca, M., . . . Rossini, M. (2020). Effects of varying solar-view geometry and canopy structure on solar-induced chlorophyll fluorescence and PRI. International Journal of Applied Earth Observation and Geoinformation, 89, 102069. doi:

Belda, S., Pipia, L., Morcillo-Pallarés, P., Rivera-Caicedo, J. P., Amin, E., De Grave, C., & Verrelst, J. (2020). DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. Environmental Modelling & Software, 127, 104666.

Hueni, A., Chisholm, L. A., Ong, C., Malthus, T. J., Wyatt, M., Trim, S. A., . . . Thankappan, M. (2020). The SPECCHIO Spectral Information System. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5789-5799. 

Pinto, F., Celesti, M., Acebron, K., Alberti, G., Cogliati, S., Colombo, R., . . . Rascher, U. (2020). Dynamics of sun-induced chlorophyll fluorescence and reflectance to detect stress-induced variations in canopy photosynthesis. Plant Cell Environ, 43(7), 1637-1654. 


Gamon, J. A., Somers, B., Malenovský, Z., Middleton, E. M., Rascher, U., & Schaepman, M. E. (2019). Assessing Vegetation Function with Imaging Spectroscopy. Surveys in Geophysics, 40(3), 489-513. 

Gracia-Romero, A.; Kefauver, S.C.; Fernandez-Gallego, J.A.; Vergara-Díaz, O.; Nieto-Taladriz, M.T.; Araus, J.L. (2019) UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat. Remote Sens. 11, 1244. 

Gerhards, M., Schlerf, M., Mallick, K., & Udelhoven, T. (2019). Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review. Remote Sensing, 11(10), 1240.

Gitelson, A., Viña, A., Solovchenko, A., Arkebauer, T., & Inoue, Y. (2019). Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sensing of Environment, 231, 111276. 

Janoutová, R., Homolová, L., Malenovský, Z., Hanuš, J., Lauret, N., & Gastellu-Etchegorry, J.-P. (2019). Influence of 3D Spruce Tree Representation on Accuracy ofAirborne and Satellite Forest Reflectance Simulated in DART. Forests, 10(3), 292. 

Kelly, J., Kljun, N., Olsson, P.-O., Mihai, L., Liljeblad, B., Weslien, P., . . . Eklundh, L. (2019). Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera. Remote Sensing, 11(5), 567.

Peng, Y., Kira, O., Nguy-Robertson, A., Suyker, A., Arkebauer, T., Sun, Y., &  Gitelson, A. A. (2019). Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data. Agronomy Journal, 111(6), 2981-2990. 

Ricotta, C., Acosta, A. T. R., Bacaro, G., Carboni, M., Chiarucci, A., Rocchini, D., & Pavoine, S. (2019). Rarefaction of beta diversity. Ecological Indicators, 107, 105606.

Rocchini, D., Marcantonio, M., Da Re, D., Chirici, G., Galluzzi, M., Lenoir, J., … Ziv, G. (2019). Time-lapsing biodiversity: An open source method for measuring diversity changes by remote sensing. Remote Sensing of Environment, 231, 111192.


Aasen, H., Honkavaara, E., Lucieer, A., Zarco-Tejada, P. (2018) “Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows,” Remote Sensing, vol. 10, no. 7, p. 1091

Share this page