Publications

Follow us on 

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).

Morata, Miguel, Bastian Siegmann, Adrian Perez-Suay, Jose Luis Garcia-Soria, Juan Pablo Rivera-Caicedo, and Jochem Verrelst (2023) Neural Network Emulation of Synthetic Hyperspectral Sentinel-2-Like Imagery With Uncertainty. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16: 762–72. https://doi.org/10.1109/JSTARS.2022.3231380 

Prikaziuk, Egor, Mirco Migliavacca, Zhongbo (Bob) Su, and Christiaan van der Tol (2023) “Simulation of Ecosystem Fluxes with the SCOPE Model: Sensitivity to Parametrization and Evaluation with Flux Tower Observations.” Remote Sensing of Environment 284 (January): 113324. https://doi.org/10.1016/j.rse.2022.113324 

Wang, Na, Peiqi Yang, Jan G.P.W. Clevers, Sebastian Wieneke, and Lammert Kooistra (2023) “Decoupling Physiological and Non-Physiological Responses of Sugar Beet to Water Stress from Sun-Induced Chlorophyll Fluorescence.” Remote Sensing of Environment 286 (March): 113445. https://doi.org/10.1016/j.rse.2022.113445 

Amin, E.; Belda, S.; Pipia, L.; Szantoi, Z.; El Baroudy, A.; Moreno, J.; Verrelst, J. (2022) Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI. Remote Sens. 14, 1812. https://doi.org/10.3390/rs14081812

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. https://doi.org/10.1016/j.rse.2022.113198 

Binh, Nguyen An, Leon T. Hauser, Pham Viet Hoa, Giang Thi Phuong Thao, Nguyen Ngoc An, Huynh Song Nhut, Tran Anh Phuong, and Jochem Verrelst (2022) Quantifying Mangrove Leaf Area Index from Sentinel-2 Imagery Using Hybrid Models and Active Learning. International Journal of Remote Sensing 43 (15–16): 5636–57. https://doi.org/10.1080/01431161.2021.2024912 

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. https://doi.org/10.1007/s11119-022-09918-y 

Buchaillot, M.L.; Cairns, J.; Hamadziripi, E.; Wilson, K.; Hughes, D.; Chelal, J.; McCloskey, P.; Kehs, A.; Clinton, N.; Araus, J.L.; Kefauver, S.C. (2022) Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems. Remote Sens., 14, 5003. https://doi.org/10.3390/rs14195003 

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. https://doi.org/10.1007/s00425-022-03867-6

Caballero, G.; Pezzola, A.; Winschel, C.; Casella, A.; Sanchez Angonova, P.; Rivera-Caicedo, J.P.; Berger, K.; Verrelst, J.; Delegido, J. (2022) Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery. Remote Sens., 14, 4531. https://doi.org/10.3390/rs14184531 

Caballero, Gabriel, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Luciano Orden, Katja Berger, Jochem Verrelst, and Jesús Delegido (2022) Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles. Remote Sensing 14 (22): 5867. https://doi.org/10.3390/rs14225867 

Chakhvashvili, Erekle, Bastian Siegmann, Onno Muller, Jochem Verrelst, Juliane Bendig, Thorsten Kraska, and Uwe Rascher (2022) Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy Remote Sensing 14 (5): 1247. https://doi.org/10.3390/rs14051247 

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. https://doi.org/10.1016/j.jag.2022.102817 

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. https://doi.org/10.1016/j.agwat.2022.107664 

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. https://doi.org/10.1016/j.rse.2022.112958

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. https://doi.org/10.1038/s41561-022-00935-0 

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 Sens, 14, 2448. https://doi.org/10.3390/rs14102448 

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. https://doi.org/10.1007/978-3-030-84144-7_8

Prikaziuk, Egor, Georgios Ntakos, Tamara ten Den, Pytrik Reidsma, Tamme van der Wal, and Christiaan van der Tol (2022) Using the SCOPE Model for Potato Growth, Productivity and Yield Monitoring under Different Levels of Nitrogen Fertilization. International Journal of Applied Earth Observation and Geoinformation 114 (July): 102997. https://doi.org/10.1016/j.jag.2022.102997

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 Sens, 14, 1347. https://doi.org/10.3390/rs14061347 

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. https://doi.org/10.3390/rs14010146 

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. https://doi.org/10.1016/j.jag.2022.102697

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. https://doi.org/10.1016/j.agrformet.2022.109033 

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. https://doi.org/10.1016/j.agrformet.2022.109081 

Wocher, Matthias, Katja Berger, Jochem Verrelst, and Tobias Hank. (2022) Retrieval of Carbon Content and Biomass from Hyperspectral Imagery over Cultivated Areas. ISPRS Journal of Photogrammetry and Remote Sensing 193 (November): 104–14. https://doi.org/10.1016/j.isprsjprs.2022.09.003 

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. https://doi.org/10.3390/rs13091748

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. https://doi.org/10.3390/rs13010134 

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. https://doi.org/10.3390/rs13132545

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. https://doi.org/10.3390/rs13224711

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. https://doi.org/10.3390/rs13020287

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, https://doi.org/10.1016/j.isprsjprs.2021.01.017

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. https://doi.org/https://doi.org/10.3390/rs13081419 

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. https://doi.org/10.3390/rs13081589

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:https://doi.org/10.1016/j.rse.2020.112173 

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. https://doi.org/10.3390/rs13173488

Herrmann, I.; Berger, K. (2021). Remote Sensing Editorial: Remote and Proximal Assessment of Plant Traits. Remote Sens. 13, 1893. https://doi.org/10.3390/rs13101893

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). https://doi.org/10.1093/insilicoplants/diab026

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. https://doi.org/10.3389/fpls.2021.749374

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 Sens, 13, 4368. https://doi.org/10.3390/rs13214368 

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. https://doi.org/10.5194/bg-18-621-2021

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. https://doi.org/10.3389/frsen.2021.652436 

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. https://doi.org/10.3390/rs13061098

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. https://doi.org/10.1016/j.ecoinf.2020.101195

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. https://doi.org/10.1111/geb.13270

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. https://doi.org/10.1007/s42974-021-00042-x

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. https://doi.org/10.32615/ps.2021.034

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. https://doi.org/10.1016/j.isprsjprs.2021.06.017

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. https://doi.org/10.3390/s21030958

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). https://doi.org/10.3389/fpls.2020.00593

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. https://doi.org/10.1016/j.jag.2020.102174

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:https://doi.org/10.1016/j.jag.2020.102069

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. https://doi.org/10.1016/j.envsoft.2020.104666

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. https://doi.org/10.1109/jstars.2020.3025117 

Kallel, A. (2020). FluLCVRT: Reflectance and Fluorescence of Leaf and Canopy Modeling Based on Monte Carlo Vector Radiative Transfer Simulation. Journal of Quantitative Spectroscopy and Radiative Transfer 253 (September): 107183. https://doi.org/10.1016/j.jqsrt.2020.107183 

Kallel, A. (2020). Two-Scale Monte Carlo Ray Tracing for Canopy-Leaf Vector Radiative Transfer Coupling. Journal of Quantitative Spectroscopy and Radiative Transfer 243 (March): 106815. https://doi.org/10.1016/j.jqsrt.2019.106815 

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. https://doi.org/10.1111/pce.13754 

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. https://doi.org/10.1007/s10712-019-09511-5 

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. https://doi.org/10.3390/rs11101244 

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. https://doi.org/10.3390/rs11101240

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.  https://doi.org/10.1016/j.rse.2019.111276 

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. https://doi.org/10.3390/f10030292 

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. https://doi.org/10.3390/rs11050567

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. https://doi.org/10.2134/agronj2019.05.0332 

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. https://doi.org/10.1016/j.ecolind.2019.105606

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. https://doi.org/10.1016/j.rse.2019.05.011

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 https://doi.org/10.3390/rs10071091