| Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop S Xu, J Atherton, A Riikonen, C Zhang, J Oivukkamäki, A MacArthur, ... Remote Sensing of Environment 263, 112555, 2021 | 88 | 2021 |
| Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives H Tao, S Xu, Y Tian, Z Li, Y Ge, J Zhang, Y Wang, G Zhou, X Deng, ... Plant Communications 3 (6), 2022 | 51 | 2022 |
| Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform S Xu, Z Liu, L Zhao, H Zhao, S Ren Remote Sensing 10 (10), 1510, 2018 | 45 | 2018 |
| Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing Z Sun, Q Li, S Jin, Y Song, S Xu, X Wang, J Cai, Q Zhou, Y Ge, R Zhang, ... Plant Phenomics, 2022 | 42 | 2022 |
| Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition? J Cao, Q An, X Zhang, S Xu, T Si, D Niyogi Science of the Total Environment 792, 148396, 2021 | 40 | 2021 |
| Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing Q Li, S Jin, J Zang, X Wang, Z Sun, Z Li, S Xu, Q Ma, Y Su, Q Guo, D Jiang The Crop Journal 10 (5), 1334-1345, 2022 | 24 | 2022 |
| Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions L Zhao, Z Liu, S Xu, X He, Z Ni, H Zhao, S Ren Sensors 18 (11), 3965, 2018 | 21 | 2018 |
| An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies X Zhou, Z Liu, S Xu, W Zhang, J Wu Sensors 16 (6), 775, 2016 | 21 | 2016 |
| Using the diurnal variation characteristics of effective quantum yield of PSII photochemistry for drought stress detection in maize Z Chen, Z Liu, S Han, H Jiang, S Xu, H Zhao, S Ren Ecological Indicators 138, 108842, 2022 | 13 | 2022 |
| Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: A case in Yunnan Province of China Z Ni, H Huo, S Tang, ZL Li, Z Liu, S Xu, B Chen International Journal of Remote Sensing 40 (5-6), 2278-2295, 2019 | 11 | 2019 |
| Exploring the sensitivity of solar-induced chlorophyll fluorescence at different wavelengths in response to drought S Xu, Z Liu, S Han, Z Chen, X He, H Zhao, S Ren Remote Sensing 15 (4), 1077, 2023 | 8 | 2023 |
| Using high-frequency PAR measurements to assess the quality of the SIF derived from continuous field observations S Han, Z Liu, Z Chen, H Jiang, S Xu, H Zhao, S Ren Remote Sensing 14 (9), 2083, 2022 | 8 | 2022 |
| What does the NDVI really tell us about crops? Insight from proximal spectral field sensors J Atherton, C Zhang, J Oivukkamäki, L Kulmala, S Xu, T Hakala, ... Information and Communication Technologies for Agriculture—Theme I: Sensors …, 2022 | 5 | 2022 |
| On the Estimation of the Leaf Angle Distribution from Drone Based Photogrammetry S Xu, MA Zaidan, E Honkavaara, T Hakala, N Viljanen, A Porcar-Castell, ... IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 5 | 2020 |
| Diurnal Pattern of Sun-Induced Chlorophyll Fluorescence as Reliable Indicators of Crop Water Stress Z Liu, X He, P Yang, H Jiang, S Xu, H Zhao, S Ren, M Chen IEEE Geoscience and Remote Sensing Letters, 2023 | 4 | 2023 |
| Investigating foliar macro-and micronutrient variation with chlorophyll fluorescence and reflectance measurements at the leaf and canopy scales in potato J Oivukkamäki, J Atherton, S Xu, A Riikonen, C Zhang, T Hakala, ... Remote Sensing 15 (10), 2498, 2023 | 3 | 2023 |
Jon AthertonVerified email at helsinki.fi
