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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).

2021

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.; 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 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://www.frontiersin.org/articles/10.3389/fpls.2021.749374/full

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

2020

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 

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 

2019

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

2018

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

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