Publications in the context of SENSECO

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

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).

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.

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:

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. 

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: 

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. 

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. 

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. 

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. 

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. 

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. 

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