390 citations as recorded by crossref.

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24. Comparison of total emitted solar-induced chlorophyll fluorescence (SIF) and top-of-canopy (TOC) SIF in estimating photosynthesis X. Lu et al. 10.1016/j.rse.2020.112083
25. DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence J. Gastellu-Etchegorry et al. 10.1109/JSTARS.2017.2685528
26. Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals J. Joiner et al. 10.3390/rs12152346
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30. SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra J. Verrelst et al. 10.3390/rs9090927
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36. Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model Q. Zhang et al. 10.1016/j.rse.2020.111677
37. Modelling sun-induced fluorescence for improved evaluation of forest carbon flux (GPP): Case study of tropical deciduous forest, India S. Sinha et al. 10.1016/j.ecolmodel.2021.109552
38. Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization E. Prikaziuk et al. 10.1016/j.jag.2022.102997
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46. Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity J. Hu et al. 10.3390/rs10101663
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52. A mechanism-guided machine learning method for mapping gapless land surface temperature J. Ma et al. 10.1016/j.rse.2024.114001
53. Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest J. Jin et al. 10.3390/rs12071124
54. Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions K. Berger et al. 10.1016/j.rse.2020.111758
55. Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content A. Norton et al. 10.5194/gmd-11-1517-2018
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58. Overview of Passive Optical Multispectral and Hyperspectral Image Simulation Techniques S. Han & J. Kerekes 10.1109/JSTARS.2017.2759240
59. Influence of the canopy BRDF characteristics and illumination conditions on the retrieval of solar-induced chlorophyll fluorescence X. Liu & L. Liu 10.1080/01431161.2017.1404165
60. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity G. Duveiller et al. 10.5194/essd-12-1101-2020
61. An angular normalization method for temperature vegetation dryness index (TVDI) in monitoring agricultural drought Z. Bian et al. 10.1016/j.rse.2022.113330
62. Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements W. Liu et al. 10.1016/j.rse.2019.111274
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65. New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis X. Liu et al. 10.3390/rs70810626
66. Influence of Karst Reservoir Capacity on Flood in Lijiang Basin Based on Modified HEC-HMS through Soil Moisture Accounting Loss J. Dai et al. 10.3390/atmos13101544
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76. Quantify Wheat Canopy Leaf Angle Distribution Using Terrestrial Laser Scanning Data Y. Wang et al. 10.1109/TGRS.2024.3353225
77. Do Daily and Seasonal Trends in Leaf Solar Induced Fluorescence Reflect Changes in Photosynthesis, Growth or Light Exposure? R. Wyber et al. 10.3390/rs9060604
78. A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance S. Cogliati et al. 10.3390/rs11161840
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81. Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy K. Yang et al. 10.1016/j.rse.2018.07.008
82. 3D radiative transfer modeling of structurally complex forest canopies through a lightweight boundary-based description of leaf clusters J. Qi et al. 10.1016/j.rse.2022.113301
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88. Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection L. Tian et al. 10.1016/j.rse.2021.112350
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