42 citations as recorded by crossref.

1. Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems W. Zhou et al. https://doi.org/10.1016/j.agrformet.2021.108521
2. Tracing changes in subsurface water storage through a novel satellite-based time-series of far-red solar-induced fluorescence quantum efficiency D. Herrera et al. https://doi.org/10.1016/j.rse.2026.115456
3. Remote sensing for crop mapping: A perspective on current and future crop-specific land cover data products C. Zhang et al. https://doi.org/10.1016/j.rse.2025.114995
4. Thermal, water, and land cover factors led to contrasting urban and rural vegetation resilience to extreme hot months Y. Wang et al. https://doi.org/10.1093/pnasnexus/pgae147
5. Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe X. Yang et al. https://doi.org/10.1146/annurev-arplant-090324-115537
6. River Bars and Vegetation Dynamics in Response to Upstream Damming: A Case Study of the Middle Yangtze River Y. Hu et al. https://doi.org/10.3390/rs15092324
7. Evaluation of Ecosystem Water Use Efficiency Based on Coupled and Uncoupled Remote Sensing Products for Maize and Soybean L. Huang et al. https://doi.org/10.3390/rs15204922
8. A Spatially Downscaled TROPOMI SIF Product at 0.005° Resolution With Bias Correction J. Hu et al. https://doi.org/10.1109/JSTARS.2024.3433371
9. Long-term, high-resolution GPP mapping in Qinghai using multi-source data and google earth engine F. Yang et al. https://doi.org/10.1080/17538947.2023.2288131
10. Remote‐Sensing Derived Trends in Gross Primary Production Explain Increases in the CO2 Seasonal Cycle Amplitude L. He et al. https://doi.org/10.1029/2021GB007220
11. Observed divergence in the trends of temperature controls on Chinese ecosystem water use efficiency X. Xu et al. https://doi.org/10.1016/j.ecolind.2023.111241
12. Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model Z. Zhang et al. https://doi.org/10.1016/j.scitotenv.2022.156172
13. Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production I. Filella et al. https://doi.org/10.3390/rs15082207
14. Spatiotemporal Variability of Gross Primary Productivity in Türkiye: A Multi-Source and Multi-Method Assessment E. Başakın et al. https://doi.org/10.3390/rs16111994
15. Fluorescence Quantum Efficiency Data for Europe 2018–2025 D. Herrera et al. https://doi.org/10.1002/gdj3.70068
16. A simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band X. Liu et al. https://doi.org/10.1016/j.rse.2022.113341
17. A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States R. Qin et al. https://doi.org/10.1016/j.agrformet.2025.110407
18. Spatiotemporal dynamics and driving factors of carbon sinks across ecosystems in Northwest China C. Xueye et al. https://doi.org/10.1016/j.jaridl.2026.05.001
19. How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest? W. Zhou et al. https://doi.org/10.1016/j.geoderma.2022.116254
20. Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands L. Gao et al. https://doi.org/10.1016/j.rse.2024.114516
21. Modelling belowground plant acclimation to low soil nitrogen – a heuristic optimality‐based approach A. Chakrawal et al. https://doi.org/10.1111/nph.71210
22. Uncovering the importance of spatiotemporal resolution in satellite-based rice yield estimation using a simple but effective proxy J. Kong et al. https://doi.org/10.1016/j.agrformet.2025.110892
23. Assessing Different Plant‐Centric Water Stress Metrics for Irrigation Efficacy Using Soil‐Plant‐Atmosphere‐Continuum Simulation J. Zhang et al. https://doi.org/10.1029/2021WR030211
24. Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale G. Abeshu et al. https://doi.org/10.5194/hess-29-1847-2025
25. Climate-driven divergence in biophysical and economic impacts of agrivoltaics M. Jia et al. https://doi.org/10.1073/pnas.2514380123
26. NIRP as a remote sensing proxy for measuring gross primary production across different biomes and climate zones: Performance and limitations S. Chen et al. https://doi.org/10.1016/j.jag.2023.103437
27. Knowledge-guided graph machine learning improves corn yield mapping in the U.S. Midwest J. Yang et al. https://doi.org/10.1016/j.rse.2026.115287
28. Tracking diurnal to seasonal variations of gross primary productivity using a geostationary satellite, GK-2A advanced meteorological imager S. Jeong et al. https://doi.org/10.1016/j.rse.2022.113365
29. Tropospheric ozone pollution increases the sensitivity of plant production to vapor pressure deficit across diverse ecosystems in the Northern Hemisphere L. Gao et al. https://doi.org/10.1016/j.scitotenv.2024.175748
30. Estimation of Live fuel moisture content via spectral–meteorological time-series fusion: a TCN–BiGRU deep learning framework C. Wang et al. https://doi.org/10.1080/01431161.2026.2633780
31. Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time J. Lee & J. Im https://doi.org/10.1080/15481603.2024.2319372
32. Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution J. Zhang et al. https://doi.org/10.1029/2022WR032967
33. Quantifying the accuracy of cos(SZA)-based upscaling from instantaneous to daily GPP: Implications for improving satellite-based SIF and GPP retrievals F. Xu et al. https://doi.org/10.1016/j.jaridenv.2025.105541
34. Improved estimation of gross primary production with NIRvP by incorporating a phenophase scheme for temperate deciduous forest ecosystems J. Jin et al. https://doi.org/10.1016/j.foreco.2024.121742
35. Rapid changes in terrestrial carbon dioxide uptake captured in near-real time from a geostationary satellite: The ALIVE framework D. Losos et al. https://doi.org/10.1016/j.rse.2025.114759
36. A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. https://doi.org/10.1016/j.rse.2023.113880
37. A vehicle imaging approach to acquire ground truth data for upscaling to satellite data: A case study for estimating harvesting dates C. Jiang et al. https://doi.org/10.1016/j.rse.2023.113894
38. Machine learning methods for assessing photosynthetic activity: environmental monitoring applications S. Khruschev et al. https://doi.org/10.1007/s12551-022-00982-2
39. Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation Y. Zhang et al. https://doi.org/10.1016/j.jag.2023.103360
40. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates J. Kong et al. https://doi.org/10.1016/j.agrformet.2022.108878
41. Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand J. Zhang et al. https://doi.org/10.1038/s41467-021-25254-7
42. TMF: Temporal multi-modal fusion framework for estimating wheat yield from multi-source satellite and environmental data across the European Union Z. Lin et al. https://doi.org/10.1016/j.isprsjprs.2026.05.041