50 citations as recorded by crossref.

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7. Mitigating Topographic Effects in Optical Remote Sensing: From surface reflectance to high-level products Y. Ma et al. https://doi.org/10.1109/MGRS.2026.3690171
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9. An Improved Physics-Based Model for Topographic Correction of Landsat TM Images A. Li et al. https://doi.org/10.3390/rs70506296
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11. Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China X. Xie et al. https://doi.org/10.3390/rs13183567
12. Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin T. Hu et al. https://doi.org/10.3390/rs70506489
13. Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an N. Shi et al. https://doi.org/10.1016/j.ecolind.2023.111427
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18. An improved instantaneous gross primary productivity model considering the difference in contributions of sunlit and shaded leaves to canopy sun-induced chlorophyll fluorescence X. Wang et al. https://doi.org/10.1016/j.gloplacha.2024.104627
19. Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study P. Preetha et al. https://doi.org/10.3390/w18080894
20. Characterizing the effect of scaling errors on the spatial downscaling of mountain vegetation gross primary productivity X. Xie & A. Li https://doi.org/10.1080/10095020.2023.2265149
21. Field‐Based Estimation of Net Primary Productivity and Its Above‐ and Belowground Partitioning in Global Grasslands Y. Sun et al. https://doi.org/10.1029/2021JG006472
22. Editorial Foreword to the Special Issue on Advances in Remote Sensing of Mountain Surface Processes L. Peng et al. https://doi.org/10.1109/JSTARS.2025.3576138
23. The Interplay between Canopy Structure and Topography and Its Impacts on Seasonal Variations in Surface Reflectance Patterns in the Boreal Region of Alaska—Implications for Surface Radiation Budget B. Nath & W. Ni-Meister https://doi.org/10.3390/rs13163108
24. A Machine Learning Approach to Relate Green Space Landscape Metrics to Net Primary Production Across Shanghai’s Built Environment R. Chen et al. https://doi.org/10.3390/land14122349
25. Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites J. Assmann et al. https://doi.org/10.1088/1748-9326/abbf7d
26. Spatiotemporal Distribution and Influencing Factors of the Net Primary Productivity in the Datai Mine in Western Beijing L. Dai et al. https://doi.org/10.3390/su142315567
27. Scale-dependency of Arctic ecosystem properties revealed by UAV M. Siewert & J. Olofsson https://doi.org/10.1088/1748-9326/aba20b
28. Machine learning and global vegetation: random forests for downscaling and gap filling B. van Jaarsveld et al. https://doi.org/10.5194/hess-28-2357-2024
29. Plant–soil microbial diversity and structural attributes jointly dominate the multifunctionality of the temperate forest Y. Che & G. Jin https://doi.org/10.1016/j.ecolind.2024.112282
30. A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas X. Xie et al. https://doi.org/10.1016/j.jag.2021.102522
31. A Monthly High-Resolution Net Primary Productivity Dataset (30 m) of Qinghai Plateau From 1987 to 2021 F. Yang et al. https://doi.org/10.1109/JSTARS.2023.3312518
32. Impact of local slope and aspect assessed from LiDAR records on tree diameter in radiata pine (Pinus radiata D. Don) plantations H. Saremi et al. https://doi.org/10.1007/s13595-014-0374-4
33. A downscaling algorithm for obtaining hourly gross primary productivity maps at the global scale Y. Wang et al. https://doi.org/10.1016/j.jag.2025.105059
34. Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China X. Xie et al. https://doi.org/10.3390/rs10040647
35. Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data X. Wang & H. Jiao https://doi.org/10.1109/ACCESS.2020.3027361
36. A global hourly gross primary production dataset from 2001 to 2020 Y. Wang et al. https://doi.org/10.1038/s41597-025-06371-0
37. A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes X. Xie et al. https://doi.org/10.1016/j.jhydrol.2022.128833
38. Assessment of flow characteristics through a grassed canal M. Gad et al. https://doi.org/10.1007/s41062-021-00716-5
39. Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations J. Leng et al. https://doi.org/10.5194/essd-16-1283-2024
40. Spatiotemporal dynamics and coupling analysis of gross primary productivity and evapotranspiration in the Qinling–Daba Mountains B. Cheng et al. https://doi.org/10.1016/j.jag.2026.105356
41. Long-term topographic effect on remotely sensed vegetation index-based gross primary productivity (GPP) estimation at the watershed scale X. Xie et al. https://doi.org/10.1016/j.jag.2022.102755
42. A two-level parallelization method for distributed hydrological models J. Liu et al. https://doi.org/10.1016/j.envsoft.2016.02.032
43. Dual carbon-sink pathways of urban parks: linking direct carbon sequestration with indirect emission reduction M. Liu et al. https://doi.org/10.1016/j.landurbplan.2026.105668
44. Enhancing Machine Learning-Based GPP Upscaling Error Correction: An Equidistant Sampling Method with Optimized Step Size and Intervals Z. Wang et al. https://doi.org/10.3390/rs18010023
45. TAVIs: Topographically Adjusted Vegetation Index for a Reliable Proxy of Gross Primary Productivity in Mountain Ecosystems X. Xie et al. https://doi.org/10.1109/TGRS.2023.3336727
46. Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review C. Matyukira & P. Mhangara https://doi.org/10.1080/22797254.2024.2422330
47. How is the performance of satellite-based product suites in monitoring long-term dynamics of vegetation photosynthesis over global mountainous areas? X. Xie & A. Li https://doi.org/10.1016/j.jag.2023.103325
48. RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China M. Zhang et al. https://doi.org/10.3390/rs11020133
49. Spatial Scaling of Gross Primary Productivity Over Sixteen Mountainous Watersheds Using Vegetation Heterogeneity and Surface Topography X. Xie et al. https://doi.org/10.1029/2020JG005848
50. Extracting subpixel vegetation NDVI time series for evaluating the mixed pixel effect on GPP estimation in urban areas S. Li et al. https://doi.org/10.1080/17538947.2023.2246945