58 citations as recorded by crossref.

1. Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm T. Taylor et al. https://doi.org/10.5194/amt-16-3173-2023
2. A more precise retrieval of sun-induced chlorophyll fluorescence from satellite data using artificial neural networks D. Li et al. https://doi.org/10.1016/j.rse.2025.114987
3. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. https://doi.org/10.1029/2022MS003259
4. Dryland evapotranspiration from remote sensing solar-induced chlorophyll fluorescence: Constraining an optimal stomatal model within a two-source energy balance model J. Bu et al. https://doi.org/10.1016/j.rse.2024.113999
5. CSIF and GOSIF Do Not Accurately Capture the Vegetation Greening During the Spring of 2020 Y. Du et al. https://doi.org/10.1109/LGRS.2024.3379255
6. Estimation of global GPP from GOME-2 and OCO-2 SIF by considering the dynamic variations of GPP-SIF relationship J. Bai et al. https://doi.org/10.1016/j.agrformet.2022.109180
7. Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. https://doi.org/10.1016/j.rse.2023.113699
8. Variations in Column Concentration of Greenhouse Gases in China and Their Response to the 2015–2016 El Niño Event N. Liu et al. https://doi.org/10.1007/s13351-024-3160-8
9. Integrated Analysis of Solar-Induced Chlorophyll Fluorescence, Normalized Difference Vegetation Index, and Column-Average CO2 Concentration in South-Central Brazilian Sugarcane Regions K. de Meneses et al. https://doi.org/10.3390/agronomy14102345
10. Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques S. Pais et al. https://doi.org/10.1007/s12524-024-02097-5
11. High Temperature and VPD Amplify Tidal Suppression of the SIF–GPP Relationship in a Mangrove Restoration Area in Southeast China Y. Huang et al. https://doi.org/10.1109/TGRS.2025.3608266
12. Widespread enhancement of ecosystem carbon fluxes during post moisture pulse Y. Bai et al. https://doi.org/10.1038/s43247-026-03191-x
13. CNSIF: A reconstructed monthly 500-meter spatial resolution solar-induced chlorophyll fluorescence dataset in China K. Du et al. https://doi.org/10.1016/j.agrformet.2025.110869
14. Long-term solar-induced fluorescence data record from GOME-2A and GOME-2B (2007–2023) using the SIFTER v3 algorithm J. Anema et al. https://doi.org/10.5194/essd-18-5643-2026
15. Improved consistency in solar-induced fluorescence retrievals from GOME-2A with the SIFTER v3 algorithm J. Anema et al. https://doi.org/10.5194/amt-18-1961-2025
16. Seasonal and Diurnal Variations in XCO2 Characteristics in China as Observed by OCO‐2/3 Satellites: Effects of Land Cover and Local Meteorology H. Zhao et al. https://doi.org/10.1029/2023JD038841
17. Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm K. Zhu et al. https://doi.org/10.3390/rs16183441
18. GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania S. Jeong et al. https://doi.org/10.1016/j.rse.2024.114284
19. Gross Primary Production Estimation of the Leningrad Region Ecosystem Based on OCO-2 Satellite Data S. Foka et al. https://doi.org/10.1134/S0001433825701506
20. Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field R. Wang et al. https://doi.org/10.1016/j.rse.2022.113268
21. Tracking drought in dryland vegetation through the photosynthetic afternoon depression index of Sun-induced chlorophyll fluorescence S. He et al. https://doi.org/10.1016/j.agrformet.2025.110799
22. MCF-XCO2: A cross-mission consistency and fusion framework for integrating multi-satellite XCO2 observations Y. Yu et al. https://doi.org/10.1016/j.atmosres.2026.108747
23. High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems L. da Costa et al. https://doi.org/10.1016/j.envres.2022.114991
24. Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. https://doi.org/10.3390/rs15164038
25. Stress-induced changes in photosynthesis and proximal fluorescence emission of turfgrass T. Jones et al. https://doi.org/10.1088/2515-7620/ad0b29
26. Disentangling vegetation physiological responses under extreme drought in the Amazon Rainforest: A multispectral remote sensing approach with insights from ET, SIF, and VOD X. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2025.09.027
27. Improving terrestrial solar-induced chlorophyll fluorescence (SIF) reconstruction through multi-satellite-derived SIF integration: a comparative study in regional scale M. Pan et al. https://doi.org/10.1080/01431161.2025.2550675
28. Estimation of GPP in northern hemisphere terrestrial ecosystems based on light use efficiency model and chlorophyll fluorescence Z. Deng et al. https://doi.org/10.3389/frsen.2026.1888267
29. Identifying and mapping open-boll cotton fields in the southeastern United States with time series PhenoCam, Sentinel-2 and Sentinel-1 images F. Liu et al. https://doi.org/10.1016/j.jag.2025.104723
30. Quantifying the changes in soil moisture caused by vegetation greening and climate change across different drought gradient in China Y. Zhao et al. https://doi.org/10.1016/j.eiar.2026.108384
31. WOMBAT v2.S: A Bayesian Inversion Framework for Attributing Global CO2 Flux Components From Multiprocess Data J. Jacobson et al. https://doi.org/10.1002/env.70052
32. Normalized Dynamic Fluorescence Height: An Alternative Algorithm for Chlorophyll a Estimation in Algae-Dominated Waters Using Hyperspectral Remote Sensing Reflectance from In Situ and Spaceborne Imagers D. Zhao et al. https://doi.org/10.3390/rs18142332
33. A high-resolution satellite-based solar-induced chlorophyll fluorescence dataset for China from 2000 to 2022 S. Tao et al. https://doi.org/10.1038/s41597-024-04101-6
34. Solar angle matters: Diurnal pattern of solar-induced chlorophyll fluorescence from OCO-3 and TROPOMI Z. Zhang & Y. Zhang https://doi.org/10.1016/j.rse.2022.113380
35. Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence Z. Zhang et al. https://doi.org/10.1016/j.rse.2022.113383
36. Drought Monitoring of Spring Maize in the Songnen Plain Using Multi-Source Remote Sensing Data Z. Pei et al. https://doi.org/10.3390/atmos14111614
37. Validation of solar-induced chlorophyll fluorescence products derived from OCO-2/3 observations using tower-based in situ measurements S. Du et al. https://doi.org/10.1080/2150704X.2023.2234551
38. A Geographically Random Machine Learning Model for GOME-2 Global Seamless Sun-Induced Chlorophyll Fluorescence Downscaling Products With High Spatiotemporal Resolution S. He et al. https://doi.org/10.1109/TGRS.2025.3552678
39. Machine Learning for Satellite Solar-Induced Fluorescence: Retrieval, Reconstruction, Downscaling, and Applications J. Verrelst et al. https://doi.org/10.3390/rs18040553
40. Seasonality and synchrony of photosynthesis in African forests inferred from spaceborne chlorophyll fluorescence and vegetation indices R. Doughty et al. https://doi.org/10.5194/bg-22-1985-2025
41. Regional‐Scale Wilting Point Estimation Using Satellite SIF, Radiative‐Transfer Inversion, and Soil‐Vegetation‐Atmosphere Transfer Simulation: A Grassland Study T. Kiyono et al. https://doi.org/10.1029/2022JG007074
42. Gross primary productivity of terrestrial ecosystems: a review of observations, remote sensing, and modelling studies over South Asia V. Pandey et al. https://doi.org/10.1007/s00704-024-05158-4
43. Large diurnal compensatory effects mitigate the response of Amazonian forests to atmospheric warming and drying Z. Zhang et al. https://doi.org/10.1126/sciadv.abq4974
44. Confronting the Amazon's fire crisis: Evidence of early fire occurrence and increased atmospheric gases from the GOSAT satellite L. de Souza Maria et al. https://doi.org/10.1016/j.jastp.2025.106652
45. Downscaling Solar-Induced Chlorophyll Fluorescence to a 0.05° Monthly Product Using AVHRR Data in East Asia (1995–2003) Y. Jin et al. https://doi.org/10.1109/JSTARS.2024.3369332
46. Response of Vegetation Photosynthesis to the 2022 Drought in Yangtze River Basin by Diurnal Orbiting Carbon Observatory-2/3 Satellite Observations Z. Deng et al. https://doi.org/10.34133/remotesensing.0445
47. Global 0.1-Degree Monthly Mean Hourly Total Canopy Solar-Induced Chlorophyll Fluorescence Dataset Derived from Random Forest Y. Liu et al. https://doi.org/10.3390/rs17203429
48. Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996–2020 Z. Sun et al. https://doi.org/10.1016/j.scitotenv.2023.168262
49. An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area Q. Yu et al. https://doi.org/10.1007/s11104-022-05863-x
50. A Mechanistic Model for the NIR SIF Escape Ratio Considering Interactions Between Soil and Canopy M. Qi et al. https://doi.org/10.1109/TGRS.2025.3601233
51. Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy V. Mayorova et al. https://doi.org/10.3390/s23156794
52. Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale N. Jantol et al. https://doi.org/10.3390/rs15194835
53. Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. https://doi.org/10.3390/rs16101707
54. Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method Y. Wang et al. https://doi.org/10.5194/essd-15-3597-2023
55. Estimation of district-level spring barley yield in southern Sweden using multi-source satellite data and random forest approach X. Li et al. https://doi.org/10.1016/j.jag.2024.104183
56. A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF Y. Zhang et al. https://doi.org/10.1016/j.jag.2026.105330
57. Spectroscopic techniques conceptualized with the remote sensing of atmospheric carbon dioxide and other greenhouse gases B. Raychaudhuri https://doi.org/10.1080/05704928.2024.2326527
58. Modeling the spatial distribution of carbon dioxide concentration by machine learning S. Dalantai et al. https://doi.org/10.5564/mjgg.v62i46.4252