Publications
Google Scholar site Research Gate site
77. Pang, B. , S. Cheng , Y. Huang, Y. Jin, I. C. Prentice, Y. Guo, S. P. Harrison, and R. Arcucci (2024). FIDN: Fire-Image-DenseNet for predicting wildfire burnt area based on remote sensing data, Computers and Geosciences.
76 *Dixon, D., Y. Zhu, and Y. Jin (2024), Canopy height estimation from PlanetScope time series with spatio-temporal deep learning, Remote Sensing of Environment.
75. *Winsemius, S., C. Babcock, V. R. Kane, K. J. Bormann, H. D Safford, and Y. Jin (2024), Improving aboveground biomass estimation with aerial lidar in California’s subalpine forests, Carbon Balance and Management.
74. Dye, A., S. Rauschenbach, S. de Szoeke, A. Igel, Y. Jin, J. Kim, M. Krawchuk, K. Maes, L. O'Neill, K. T. Paw U, R. Samelson, D. Shaw, and C. Still (2024). Fog in western coastal ecosystems: inter-disciplinary challenges and opportunities with example from the Pacific Northwest, USA, Front. Environ. Sci., Volume 12, https://doi.org/10.3389/fenvs.2024.1488401
73. Farruggia, M. J., J. Brahney, A. J. Tanentzap, J. A. Brentrup, , L. S. Brighenti, S. Chandra, A. Cortés, R. L. Fernandez, J. M. Fischer, A. L. Forrest, Y. Jin, K. Larrieu, I. M. McCullough, I. A. Oleksy, R. M. Pilla, J. A. Rusak, F. Scordo, A. P. Smits, C. C. Symons, … Sadro, S. (2024). Wildfire smoke impacts lake ecosystems. Global Change Biology, 30, e17367. https://doi.org/10.1111/gcb.17367
72. Smith, Adrianne, Facundo Scordo , Minmeng Tang , Alicia Cortés , Mary Farruggia , Joshua Culpepper, Sudeep Chandra , Yufang Jin, Sergio Valbuena, Shohei Watanabe, Geoffrey Schladow, and Steven Sadro (2024). Variable impact of wildfire smoke on ecosystem metabolic rates in lakes, Nature Communications Earth and Environment, in press
71. Chen, B., S. Wu , Y. Jin , Y. Song, C. Wu , S. Venevsky, B. Xu , C. Webster , and P. Gong (2024), Wildfire risk for global wildland–urban interface (WUI) areas, Nature Sustainability, DOI: 10.1038/s41893-024-01291-0
70. *Dixon, D., C. Brown, Y. Zhu, and Y. Jin (2023). Satellite detection of canopy scale tree mortality and survival from California wildfires with spatio-temporal deep learning, Remote Sensing of Environment, 298(113842), https://doi.org/10.1016/j.rse.2023.113842.
69. *Tang, Z., Y.Jin, P. H. Brown, and M. Park (2023). “Estimation of Tomato Water Status with Photochemical Reflectance Index and Machine Learning: Assessment from Proximal Sensors and UAV Imagery”, Frontiers in Plant Sciences, doi: 10.3389/fpls.2023.1057733
68.*Tang, M., D. Sadowski, C. Peng, S. Vougioukas, B. Klever, Sat D. Khasla, P. Brown, and Y. Jin (2023). Tree-level Almond Yield Estimation from High Resolution Aerial Imagery with Convolutional Neural Network, Frontiers in Plant Science, Volume 14,| https://doi.org/10.3389/fpls.2023.1070699.
67. *Liu, Han, Yufang Jin, Leslie M Roche, Anthony T O'Geen, and Randy A Dahlgren (2023). Regional differences in the response of California’s rangeland production to climate and future projection, Environ. Res. Lett., 18(014011). https://doi.org/10.1088/1748-9326/aca689.
66. Masri, Shahir, Yufang Jin, and Jun Wu (2022). Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018–2020: Implications for Environmental Justice in the Context of Climate Change, Climate, 10, no. 10: 145. https://doi.org/10.3390/cli10100145.
65. *Huang, Yuhan, and Yufang Jin (2022). "Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping" Remote Sensing 14, no. 15: 3622. https://doi.org/10.3390/rs14153622.
64. Cheng, S., Y. Jin, S. P. Harrison, C. Quilodrán-Casas, I. C. Prentice, Y-K. Guo, and R. Arcucci (2022). Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling, Remote Sensing, 14(13):3228. https://doi.org/10.3390/rs14133228.
63. *He, R., Y. Jin, J. Jiang, M. Xu, and S. Jia (2022), Sensitivity of METRIC-based tree crop evapotranspiration to meteorological variables, land surface parameters and different study sizes, Agricultural Water Management, Volume 271, 107789, doi: 10.1016/j.agwat.2022.107789.
62. *Chen, B., Y. Tu, S. Wu, Y. Song, Y. Jin, C. Webster, B. Xu, and P. Gong (2022), Beyond green environments: multi-scale difference in human exposure to greenspace in China, Environmental International, doi: 10.1016/j.envint.2022.107348.
61. Cheng,S., I. C. Prentice, Y. Huang, Y. Jin, Y-K. Guo, and R. Arcucci (2022), Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting, Journal of Computational Physics, Volume 464, 111302, https://doi.org/10.1016/j.jcp.2022.111302.
60. *Tang, Z., Y. Jin, M. M. Alsina, A. J. McElrone, N. Bambach, W. P. Kustas (2022), Vine Water Status Mapping with Multispectral UAV imagery and Machine Learning, Irrigation Science, in press
59. *Chen, B., and Y. Jin (2022), Spatial Patterns and Drivers for Wildfire Ignitions in California, Environmental Research Letters, in press
58.Gutierrez, A., S Hantson, B. Langenbrunner, B. Chen, Y. Jin, M. Goulden, and J.T. Randerson (2021). Wildfire response to changing daily temperature extremes in California’s Sierra Nevada, Science Advances, 7(47), doi: 10.1126/sciadv.abe6417.
57. Masri, S., E.Scaduto, Y. Jin and J. Wu (2021), Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000–2020, Int. J. Environ. Res. Public Health, 18(8), 3921; https://doi.org/10.3390/ijerph18083921.
56. *Wong, A.J., Y. Jin, J. Medellín‐Azuara, K. T. Paw U, E. R. Kent, J. M. Clay, F. Gao, J. B. Fisher, G. Rivera, C. M. Lee, K. S. Hemes, E. Eichelmann, D. D. Baldocchi, and S. J. Hook (2021), Multi-scale Assessment of Agricultural Consumptive Water Use in California’s Central Valley, Water Resources Research, doi:10.1029/2020WR028876.
55. Sharp, S. L., A. L. Forrest, K. Bouma-Gregson, Y. Jin, A. Cortés, and S. G. Schladow (2021). Quantifying scales of spatial variability of cyanobacteria in a large, eutrophic lake using multiplatform remote sensing tools, Frontiers in Environmental Science, section Environmental Informatics and Remote Sensing.
54. *Chen, B., J. Li, an Y. Jin (2021). Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive. Remote Sens. 13, 167. https://doi.org/10.3390/rs13020167.
53. Han, R., A . Wong, Z. Tang, M. J. Truco, D. O. Lavelle, A. Kozik, Y. Jin, and M. W. Michelmore (2021), Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce, Journal of Experimental Botany, 72( 8), Pages 2979–2994, https://doi.org/10.1093/jxb/erab081.
52. Kohli, G., C. M. Lee, J. B. Fisher, G. Halverson, E. Variano, Y. Jin, D. Carney, B. A. Wilder, and A.M. Kinoshita (2020). ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California. Remote Sens. 12, 4126. https://doi.org/10.3390/rs12244126.
51. *Chen, B., Y. Jin, E. Scaduto. M. Mortitz, M. L. Goulden, and J. T, Randerson (2020). Climate, fuel, and land use controls on the spatial pattern of wildfire in California’s Sierra Nevada, Journal of Geophysical Research-Biogeosciences.
50. *Liu, H., Y. Jin, L. M. Roche, A. O’ Geen, and R. Dahlgren (2020). Understanding spatial variability in Mediterranean forage production: delineating climate, topography and soil controls, Environmental Research Letters.
49. *Scaduto, E., C. Bin, and Y. Jin (2020), Satellite-based fire progression mapping: a comprehensive assessment for large fires in Northern California, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, JSTARS-2020-00613.
48. *Huang, Y., Y. Jin, M. Schwartz, and J. Thorne (2020), Intensified burn severity in California’s northern coastal mountains by drier climatic condition, Environmental Research Letter, in press.
47. Jin, Y., B. Chen, B. Lampinen, and P. Brown, Advancing Agricultural Production with Machine Learning Analytics: Yield Determinants for California’s Almond Orchards, Frontiers in Plant Science, 2020.
46. Devine, S., A. O'Geen, H. Liu, Y. Jin, H. Dahlke, R.Larsen, and R. Dahlgren. Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks. Geoderma, 2020.
45.Devine, S., A. O'Geen, R. Larsen, H. Hahlke, H. Liu, Y. Jin, and R. Dahlgren. Microclimate-forage growth linkages across two strongly contrasting precipitation years in a Mediterranean catchment. Ecohydrology, 2019.
44. Ma, J., C. Brühl, Q. He, B. Steil, V. Karydis, K. Klingmüller, H. Tost, B. Chen,Y. Jin, N. Liu, X. Xu, P. Yan, X. Zhou, K. Abdelrahman, A. Pozzer, and J. Lelieveld. Modelling the aerosol chemical composition of the tropopause over the Tibetan Plateau during the Asian summer monsoon, Atmospheric Chemistry and Physics, 2019.
43. *Chen, B., Y. Jin, and P. Brown, An enhanced bloom index for quantifying floral phenology, ISPRS Journal of Photogrammetry and Remote Sensing, 2019.
42. *Zhang, Z., Y. Jin, B. Chen, and P. Brown, California Almond Yield Prediction At the Orchard Level With A Machine Learning Approach, Frontiers in Plant Science, 2019.
41. *Chen, B., Y. Jin, and P. Brown, Automatic Mapping of Planting Year For Tree Crops using Landsat Satellite Time Series Stacks, ISPRS Journal of Photogrammetry and Remote Sensing, 2019.
40. *Liu, H. R. Dahlgren, R. Larsen, S. Devin, L. Roche, A. O'Geen, A. Wong, S. Covello, and Y. Jin, Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite, Remote Sensing, 2019.
39. Jin, Y. et al. (2018), Spatially variable evaptranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates, Agricultural and Forest Meteorology.
38. *Favero, A., B.Sohngen, Y. Huang, and Y. Jin (2018), Global cost estimates of forest climate mitigation with albedo: a new integrative policy approach, Environmental Research Letters.
37. *Byer, S. and Y. Jin (2017), Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data, Remote Sensing, 9(929).
36. Adelaine, S., M. Sato, Y. Jin, and H. Godwin (2017), An Assessment of Climate Change Impacts on Los Angeles Hospitals, Wildfires Highest Priority, Prehospital and Disaster Medicine, in press.
35. *He, R., Y. Jin, M. M. Kandelous, D. Zaccaria, B. L. Sanden, R. L. Snyder, J. Jiang, and J. W. Hopmans (2017), Evapotranspiration estimate over an almond orchard using Landsat satellite observations, Remote Sensing, 9(5), 436; doi:10.3390/rs9050436.
34. *Faivre, N., Y. Jin, M. L. Goulden, and J. T. Randerson (2016), Spatial patterns and controls on burned area for two contrasting fire regimes in Southern California, Ecosphere,7(5), doi: 10.1002/ecs2.1210.
33. Jin, Y., M. L. Goulden, N. Faivre, S. Veraverbeke, F. Sun, A. Hall, M. S. Hand, S. Hook, and J. T. Randerson (2015), Identification of two distinct fire regimes in Southern California: implications for economic impact and future change, Environmental Research Letters, 10(9), doi: 10.1088/1748-9326/10/9/094005. [PDF] *IOPSELECT.
32. *Faivre, N., Y. Jin, M. L. Goulden, and J. T. Randerson (2014), Modeling the spatial pattern of wildfire ignitions in Southern Californian Mediterranean ecosystems, International Journal of Wildland Fire, doi: 10.1071/WF13136. [PDF]
31. Lin, H.-w., J. L. McCarty, D. Wang, B. M. Rogers, D. C. Morton, G. J. Collatz, Y. Jin, and J. T. Randerson (2014), Management and climate contributions to satellite-derived active fire trends in the contiguous United States, Journal of Geophysical Research-Biogeosciences, 119, 645-660, doi: 10.1002/2013JG002382. [PDF]
30. Jin, Y., J. T. Randerson, S. Capps, A. Hall, N. Faivre, and M. L. Goulden (2014), Contrasting controls on wildland fires in Southern California during periods with and without Santa Ana events, Journal of Geophysical Research-Biogeosciences, doi: 10.1002/2013JG002541. [PDF]
29. Veraverbeke, S., F. Sedano, S. J. Hook, J. T. Randerson, Y. Jin, and B. M. Rogers (2014), Mapping the daily progression of large wildland fires using MODIS active fire data, International Journal of Wildland Fire, doi: 10.1071/WF13015. [PDF]
28. Jin, Y. and M. L. Goulden (2013), Ecological consequences of precipitation variation: separating short- vs. long-term effects using satellite data, Global Ecology and Biogeography, doi: 10.1111/geb.12135. [PDF]
27. Chen, Y., D. C. Morton, Y. Jin, L. Giglio, G. J. Gollatz, P. S. Kasibhatla, G. R. van der Werf, R. S. DeFries, and J. T. Randerson (2013), Long-term trends and interannual variability of forest, savanna and agricultural fires in South America, Carbon Management, 4(6), 617-638, doi: 10.4155/cmt.13.61. [PDF]
26. Loranty, M. M., L. T. Berner, S. J. Goetz, Y. Jin, and J. T. Randerson (2013), Vegetation controls on northern high latitude snow-albedo feedback, Global Change Biology, 20(2), 594-606, doi: 10.1111/gcb.12391. [PDF]
25. Jin, Y., J. T. Randerson, M. L. Goulden, and S. J. Goetz (2012), Post-fire changes in net shortwave radiation along a latitudinal gradient in boreal North America, Geophysical Research Letters, 39, L13403, doi:10.1029/2012GL051790. [PDF]
24. Jin, Y., J. T. Randerson, S. J. Goetz, P. S. A. Beck, M. M. Loranty, and M. L. Goulden (2012), The influence of burn severity on post-fire vegetation recovery and albedo change during early succession in North American boreal forests, Journal of Geophysical Research-Biogeosciences, 117, G01036, doi:10.1029/2011JG001886. [PDF]
23. Anderson, R.G., Y. Jin, and M. L. Goulden (2012), Assessing regional evapotranspiration and water balance across a Mediterranean montane climate gradient, Agricultural and Forest Meteorology, 166-167, 10-22, doi:10.1016/j.agrformet.2012.07.004. [PDF]
22. Lin, H.-w., Y. Jin, L. Giglio, J.A. Foley, and J.T. Randerson (2012), Evaluating greenhouse gas emissions reporting systems for agricultural waste burning using satellite observations of active fires, Ecological Applications, 22(4),1345-1364. [PDF]
21. Chen, Y., J. T. Randerson, D. C. Morton, R. S. DeFries, G. J. Collatz, P. S. Kasibhatla, L. Giglio, Y. Jin, and M. E. Marlier (2011),Forecasting fire season severity in South America using Sea Surface Temperature Anomalies, Science, 334(6057), 787-791, doi: 10.1126/science.1209472. [PDF]
20. Jin, Y., J.T. Randerson, and M. L. Goulden (2011), Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations, Remote Sensing of Environment, 115(9), 2302-2319, doi: 10.1016/j.rse.2011.04.031. [PDF]
19. Beck, P. S. A., S. J. Goetz, M.C. Mack, H.D. Alexander, Y. Jin, J. T.Randerson, and M. M. Loranty (2011), The impacts and implications of an intensifying fire regime on Alaskan boreal forest composition and albedo, Global Change Biology, 17, 2853–2866. doi: 10.1111/j.1365-2486.2011.02412.x. [PDF]
18. van Leeuwen, T.T., A.J. Frank, Y. Jin, P.J. Smyth, M.L. Goulden, G.R. van der Werf, and J.T. Randerson (2011), Optimal use of land surface temperature data to detect changes in tropical forest cover, J. Geophysical Research – Biogeosciences, 116, G02002, doi:10.1029/2010JG001488. [PDF]
2010 and before
17. van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, M. Mu, P.S. Kasibhatla, D.C. Morton, R.S. DeFries, Y. Jin, and T.T. van Leeuwen (2010), Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmospheric Chemistry and Physics, 10, 1-28, doi:10.5194/acp-10-1-2010.
16. Lyons, E.A., Y. Jin, and J.T. Randerson (2008), Changes in surface albedo after fire in boreal forest ecosystems of interior Alaska assessed using MODIS satellite observations, Journal of Geophysical Research-Biogeosciences, vol. 113, G02012, doi:10.1029/2007JG000606.
15. Randerson, J. T., H. Liu, M.G. Flanner, S.D. Chambers, Y. Jin, P.G. Hess, G. Pfister, M.C. Mack, K.K. Treseder, L.R. Welp, F.S. Chapin, J.W. Harden, M.L. Goulden, E. Lyons, J.C. Neff, E.A.G. Schuur, and C.S. Zender (2006), The impact of boreal forest fire on climate warming, Science, 314(5802), 1130-1132, doi:10.1126/science.1132075.
14. Salomon, J., C. B. Schaaf, A. H. Strahler, F. Gao, and Y. Jin (2006), Validationof the MODIS Bidirectional Reflectance Distribution Function and albedo retrievals using combined observations from the Aqua and Terra platforms, IEEE Transaction on Geoscience and Remote Sensing, 44(6), 1555-1565, doi: 10.1109/TGRS.2006.871564.
13. Jin, Y., and D.P. Roy (2005), Fire-induced albedo change and its radiative forcing at the surface in northern Australia, Geophysical Research Letters, 32(13), L13401, doi:10.1029/2005GL022822.
12. Roy, D.P., Y. Jin, P. E. Lewis, and C. O. Justice (2005), Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data, Remote Sensing of Environment, 97(2), 137-162, doi:10.1016/j.rse.2005.04.007.
11. Diner, D. J., B. H. Braswell, R. Davies, N. Gobron, J. Hu, Y. Jin, R. A. Kahn, Y. Knyazikhin, N. Loeb, J.-P. Muller, A. W. Nolin, B. Pinty, C. B. Schaaf, G. Seiz, and J. Stroeve (2005), The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces, Remote Sensing of Environment, 97(4), 495-518, doi:10.1016/j.rse.2005.06.006.
10. Jin, Y., C. B. Schaaf, F. Gao, X. Li, A. H. Strahler, W. Lucht, and S. Liang (2003), Consistency of MODIS surface BRDF/Albedo retrieval, 1. Algorithom performance, Journal of Geophysical Research-Atmosphere, 108(D5), 4158, doi:10.1029/2002JD002803.
9. Jin, Y., C. B. Schaaf, C. E. Woodcock, F. Gao, X. Li, A. H. Strahler, W. Lucht, and S. Liang (2003), Consistency of MODIS surface BRDF/albedo retrieval, 2. Validation, Journal of Geophysical Research-Atmosphere, 108(D5), 4159, doi:10.1029/2002JD002804.
8. Oleson, K. W., G. B. Bonan, C. B. Schaaf, F. Gao, Y. Jin, and A. H. Strahler (2003), Assessment of global climate model land surface albedo using MODIS data, Geophysical Research Letter, 30(8), 1443, doi:10.1029/2002GL016749.
7. Gao, F., C. B. Schaaf, A. H. Strahler, Y. Jin, and X. Li (2003), Detecting vegetation structure using a kernel based BRDF model, Remote Sensing of Environment (2003), 86(2), 198-205, doi:10.1016/S0034-4257(03)00100-7.
6. Zhou, L., R. E. Dickinson, Y. Tian, X. Zeng, Y. Dai, Z. Yang, C. B. Schaaf, F. Gao, Y. Jin, A. Strahler, R.B. Myneni, H.Yu, W. Wu, and M. Shaikh (2003), Comparison of seasonal and spatial variations of albedos from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Common Land Model, Journal of Geophysical Research, 108(D15), 4488, doi:10.1029/2002JD003326.
5. Jin, Y., F. Gao, C. B. Schaaf, X. Li, A. H. Strahler, C. J. Bruegge, and J. V. Martonchik (2002), Improving MODIS surface BRDF/Albedo retrievals with MISR observations, IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1593-1604, doi:10.1109/TGRS.2002.801145.
4. Gao, F., Y. Jin, C.B. Schaaf, X. Li, and A.H. Strahler (2002), Bi-directional NDVI and atmospherically resistant BRDF inversion for vegetation canopy, IEEE Transactions on Geoscience Remote Sensing, 40(6), 1269-1278, doi:10.1109/TGRS.2002.800241.
3. Schaaf, C.B., F. Gao, A.H. Strahler, W. Lucht, X. Li, T. Tsang, N.C. Strugnell, X. Zhang, Y. Jin, J.-P. Muller, P. Lewis, M. Barnsley, P. Hobson, M. Disney, G. Roberts, M. Dunderdale, C. Doll, R. d'Entremont, B. Hu, S. Liang, and J.L. Privette (2002), First operational BRDF, albedo and nadir reflectance products from MODIS, Remote Sensing of Environment, 83(2), 135-148, doi: 10.1016/S0034-4257(02)00091-3.
2. Jin, Y., C.B. Schaaf, F. Gao, X. Li, A.H. Strahler, X. Zeng, and R.E. Dickinson (2002), How does snow impact the albedo of vegetated land surfaces as analyzed with MODIS data? Geophysical Research Letters, 29(10), 1374, doi:10.1029/2001GL014132.
1. Wu, B., and Y. Jin (1997), Twilight polarization and optical depth of stratospheric aerosols over Beijing after the Pinatubo volcanic eruption, Applied Optics, 36(27), 7009-7015, doi: 10.1364/AO.36.007009.
