ПРЕПРИНТ
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.
Gross Primary Production (GPP) is an important metric for tracking vegetation health on a large scale and plays a vital role in the Earth's carbon cycle. Understanding the daily fluctuations in GPP is key for grasping how plants respond to environmental stress, which are likely to occur more frequently due to climate change. With advanced satellites, we can now gather surface data like solar radiation and land surface temperature more frequently, potentially helping us to estimate GPP daily.
Sokolov M. 2024. Modeling primary production from carbon flux and satellite data. PREPRINTS.RU. https://doi.org/10.24108/preprints-3112997
1. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 2006. FAO Irrigation and Drainage Paper No. 56, Crop Evapotranspiration. https://doi.org/10.3390/agronomy9100614
2. Alton, P.B., North, P.R., Los, S.O., 2007. The impact of diffuse sunlight on canopy light-use efficiency , gross photosynthetic product and net ecosystem exchange in three forest biomes. Glob. Chang. Biol. 13, 776–787. https://doi.org/10.1111/j.1365-2486.2007.01316.x
3. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M.A., Baldocchi, D., Bonan, G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F.I., Papale, D., 2010. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science (80-. ). 329, 834–838. https://doi.org/10.1126/science.1184984
4. Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyasaka, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesaka, D., Yokota, H., Yoshida, R., 2016. An introduction to Himawari-8/9 — Japan’s new-generation geostationary meteorological satellites. J. Meteorol. Soc. Japan. Ser. II 94, 151–183. https://doi.org/10.2151/jmsj.2016-009
5. Boland, J., Ridley, B., Brown, B., 2008. Models of diffuse solar radiation. Renew. Energy 33, 575–584. https://doi.org/10.1016/j.renene.2007.04.012
6. Cai, W., Prentice, I.C., 2019. Recent trends in gross primary production and their drivers: Analysis and modelling at flux-site and global scales. Environ. Res. Lett. 15. https://doi.org/10.1088/1748-9326/abc64e
7. Chen, J., Shen, M., Kato, T., 2009. Diurnal and seasonal variations in light-use efficiency in an alpine meadow ecosystem: Causes and implications for remote sensing. J. Plant Ecol. 2, 173–185. https://doi.org/10.1093/jpe/rtp020
8. Chen, W., Pinker, R.T., Ma, Y., Hulley, G., Borbas, E., Islam, T., Cawse-Nicholson, K.A., Hook, S., Hain, C., Basara, J., 2021. Land surface temperature from GOES-East and GOES-West. J. Atmos. Ocean. Technol. 38, 843–858. https://doi.org/10.1175/JTECH-D-20-0086.1
9. Chen, W., Zhu, D., Huang, C., Ciais, P., Yao, Y., Friedlingstein, P., Sitch, S., Haverd, V., Jain, A.K., Kato, E., Kautz, M., Lienert, S., Lombardozzi, D., Poulter, B., Tian, H., Vuichard, N., Walker, A.P., Zeng, N., 2019. Negative extreme events in gross primary productivity and their drivers in China during the past three decades. Agric. For. Meteorol. 275, 47–58. https://doi.org/10.1016/j.agrformet.2019.05.002
10. Cheng, J., Liang, S., Yao, Y., Zhang, X., 2013. Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. IEEE Geosci. Remote Sens. Lett. 10, 401–405.
11. Cheng, P., Pour-Biazar, A., McNider, R.T., Mecikalski, J.R., 2020. Validation of GOES-Based Surface Insolation Retrievals and Its Utility for Model Evaluation. J. Atmos. Ocean. Technol. 37, 553–571. https://doi.org/10.1175/JTECH-D-19-0058.1
12. Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A., Chevallier, F., De Noblet, N., Friend, A.D., Friedlingstein, P., Grünwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J.M., Papale, D., Pilegaard, K., Rambal, S., Seufert, G., Soussana, J.F., Sanz, M.J., Schulze, E.D., Vesala, T., Valentini, R., 2005. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533. https://doi.org/10.1038/nature03972
13. Cox, P.M., Pearson, D., Booth, B.B., Friedlingstein, P., Huntingford, C., Jones, C.D., Luke, C.M., 2013. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344. https://doi.org/10.1038/nature11882
14. Duan, S.B., Li, Z.L., Zhao, W., Wu, P., Huang, C., Han, X.J., Gao, M., Leng, P., Shang, G., 2021. Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites. Int. J. Digit. Earth 14, 640–660. https://doi.org/10.1080/17538947.2020.1862319
15. Fan, W., Liu, Y., Xu, X., Chen, G., Zhang, B., 2014. A new FAPAR analytical model based on the law of energy conservation: A case study in China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 3945–3955. https://doi.org/10.1109/JSTARS.2014.2325673
16. Farella, M.M., Fisher, J.B., Jiao, W., Key, K.B., Barnes, M.L., 2022. Thermal remote sensing for plant ecology from leaf to globe. Jouranl Ecol. 110, 1–19. https://doi.org/10.1111/1365-2745.13957
17. Friedlingstein, P., Jones, M.W., O’Sullivan, M., Andrew, R.M.., Hauck, J., Peters, G.P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D.C.E., Canadell, J.G., Ciais, P., Jackson, R.B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E., Chandra, N., Chevallier, F., Chini, L.P., Currie, K.I., Feely, R.A., Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, D.S., Gruber, N., Gutekunst, S., Harris, I., Haverd, V., Houghton, R.A., Hurtt, G., Ilyina, T., Jain, A.K., Joetzjer, E., Kaplan, J.O., Kato, E., Klein Goldewijk, K., Korsbakken, J.I., Landschützer, P., Lauvset, S.K., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, P.C., Melton, J.R., Metzl, N., Munro, D.R., Nabel, J.E.M.S., Nakaoka, S.-I., Neill, C., Omar, A.M., Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, P.P., Tian, H., Tilbrook, B., Tubiello, F.N., van der Werf, G.R., Wiltshire, A.J., Zaehle, S., 2019. Global Carbon Budget 2019. Earth Syst. Sci. Data 11, 1783–1838. https://doi.org/10.5194/essd-11-1783-2019
18. Goetz, S.J., Prince, S.D., Goward, S.N., Thawley, M.M., Small, J., 1999. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecol. Modell. 122, 239–255. https://doi.org/10.1016/S0304-3800(99)00140-4
19. Goetz, S.J., Prince, S.D., Small, J., Gleason, A.C.R., 2000. Interannual variability of global terrestrial primary production: Results of a model driven with satellite observations. J. Geophys. Res. Atmos. 105, 20077–20091. https://doi.org/10.1029/2000JD900274
20. Gu, L., Fuentes, J.D., Shugart, H.H., Staebler, R.M., Black, T.A., 1999. Responses of net ecosystem exchanges of carbon dioxide to changes in cloudiness: Results from two North American deciduous forests. J. Geophys. Res. 104, 31421–31434.
21. He, M., Ju, W., Zhou, Y., Chen, J., He, H., Wang, S., Wang, H., Guan, D., Yan, J., Li, Y., Hao, Y., Zhao, F., 2013. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agric. For. Meteorol. 173, 28–39. https://doi.org/10.1016/j.agrformet.2013.01.003
22. Heinsch, F.A., Maosheng Zhao, Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., Bolstad, P.V., Cook, B.D., Desai, A.R., Ricciuto, D.M., Law, B.E., Oechel, W.C., Hyojung Kwon, Hongyan Luo, Wofsy, S.C., Dunn, A.L., Munger, J.W., Baldocchi, D.D., Liukang Xu, Hollinger, D.Y., Richardson, A.D., Stoy, P.C., Siqueira, M.B.S., Monson, R.K., Burns, S.P., Flanagan, L.B., 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote Sens. 44, 1908–1925. https://doi.org/10.1109/TGRS.2005.853936
23. Hulley, G.C., Hook, S.J., Abbott, E., Malakar, N., Islam, T., Abrams, M., 2015. The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth’s emissivity at 100 meter spatial scale. Geophys. Res. Lett. 42, 7966–7976. https://doi.org/10.1002/2015GL065564
24. Imada, Y., Shiogama, H., Takahashi, C., Watanabe, M., Mori, M., Kamae, Y., Maeda, S., 2018. Climate Change Increased the Likelihood of the 2016 Heat Extremes in Asia. Bull. Am. Meteorol. Soc. 99, S97–S101. https://doi.org/10.1175/BAMS-D-17-0109.1
25. Janzen, H.H., 2004. Carbon cycling in earth systems—a soil science perspective. Agric. Ecosyst. Environ. 104, 399–417. https://doi.org/10.1016/j.agee.2004.01.040
26. Jia, A., Liang, S., Wang, D., 2022. Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data. Remote Sens. Environ. 278, 113105. https://doi.org/10.1016/j.rse.2022.113105
27. Jiang, S., Zhao, L., Liang, C., Cui, N., Gong, D., Wang, Y., Feng, Y., Hu, X., Zou, Q., 2021. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agric. For. Meteorol. 297, 108253. https://doi.org/10.1016/j.agrformet.2020.108253
28. Jung, M., Reichstein, M., Margolis, H.A., Cescatti, A., Richardson, A.D., Arain, M.A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B.E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E.J., Papale, D., Sottocornola, M., Vaccari, F., Williams, C., 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, G00J07. https://doi.org/10.1029/2010JG001566
29. Kalfas, J.L., Xiao, X., Vanegas, D.X., Verma, S.B., Suyker, A.E., 2011. Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data. Agric. For. Meteorol. 151, 1514–1528. https://doi.org/10.1016/j.agrformet.2011.06.007
30. Khan, A.M., Stoy, P.C., Joiner, J., Baldocchi, D., Verfaillie, J., Chen, M., Otkin, J.A., 2022. The diurnal dynamics of Gross Primary Productivity using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an oak savanna ecosystem. J. Geophys. Res. Biogeosciences. https://doi.org/10.1029/2021jg006701
31. Kustas, W.P., Li, F., Jackson, T.J., Prueger, J.H., Macpherson, J.I., Wolde, M., 2004. Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sens. Environ. 92, 535–547. https://doi.org/10.1016/j.rse.2004.02.020
32. Li, L., Du, Y., Tang, Y., Xin, X., Zhang, H., Wen, J., Liu, Q., 2015. A New Algorithm of the FPAR Product in the Heihe River Basin Considering the Contributions of Direct and Diffuse Solar Radiation Separately. Remote Sens. 7, 6414–6432. https://doi.org/10.3390/rs70506414
33. Li, Xin, Liang, H., Cheng, W., 2021b. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269. https://doi.org/10.1016/j.jag.2020.102269
34. Li, Xing, Xiao, J., Fisher, J.B., Baldocchi, D.D., 2021a. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station. Remote Sens. Environ. 258, 112360. https://doi.org/10.1016/j.rse.2021.112360
35. Liu, L., Guan, L., Liu, X., 2017. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agric. For. Meteorol. 232, 1–9. https://doi.org/10.1016/j.agrformet.2016.06.014
36. Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018. An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Trans. Geosci. Remote Sens. 56, 5717–5735. https://doi.org/10.1109/TGRS.2018.2824828
37. Mccree, K.J., 1981. Photosynthetically Active Radiation in Physiological Plant Ecology I. https://doi.org/10.1007/978-3-642-68090-8_3
38. Mercado, L.M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild, M., Cox, P.M., 2009. Impact of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017. https://doi.org/10.1038/nature07949
39. Michaelis, L., Menten, M.L., 1913. Die Kinetik der Invertinwirkung. Biochem. Z. 49, 333–369.
40. Monteith, J.L., 1972. Solar Radiation and Productivity in Tropical Ecosystems. J. Appl. Ecol. 9, 747–766. https://doi.org/10.2307/2401901
41. Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536. https://doi.org/10.1016/j.rse.2006.07.007
42. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3
43. Ozanne, C.H.P., Anhuf, D., Boulter, S.L., Keller, H., Kitching, R.L., Körner, C., Meinzer, F.C., Mitchell, A.W., Nakashizuka, T., Silva Dias, P.L., Stork, N.E., Wright, S.J., Yoshimura, M., 2003. Biodiversity meets the atmosphere: A global view of forest canopies. Science (80-. ). 301, 183–186. https://doi.org/10.1126/science.1084507
44. Park, H., Lee, J., Yoo, C., Sim, S., Im, J., 2021. Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 8614–8626. https://doi.org/10.1109/JSTARS.2021.3103754
45. Paul-Limoges, E., Damm, A., Hueni, A., Liebisch, F., Eugster, W., Schaepman, M.E., Buchmann, N., 2018. Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland. Remote Sens. Environ. 219, 310–323. https://doi.org/10.1016/j.rse.2018.10.018
46. Peng, Y., Kira, O., Nguy-Robertson, A., Suyker, A., Arkebauer, T., Sun, Y., Gitelson, A.A., 2019. Gross primary production estimation in crops using solely remotely sensed data. Agron. J. 111, 2981–2990. https://doi.org/10.2134/agronj2019.05.0332
47. Reeves, M.C., Zhao, M., Running, S.W., 2005. Usefulness and limits on MODIS GPP for estimating wheat yield. Int. J. Remote Sens. 26, 1403–1421. https://doi.org/10.1080/01431160512331326567
48. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560. https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
49. Running, S.W., Thornton, P.E., Nemani, R., Glassy, J.M., 2000. Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System, Methods in Ecosystem Science. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4612-1224-9
50. Sulla-Menashe, D., Gray, J.M., Abercrombie, S.P., Friedl, M.A., 2019. Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. Remote Sens. Environ. 222, 183–194. https://doi.org/10.1016/j.rse.2018.12.013
51. Thomas, V., Finch, D.A., McCaughey, J.H., Noland, T., Rich, L., Treitz, P., 2006. Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar-hyperspectral approach. Agric. For. Meteorol. 140, 287–307. https://doi.org/10.1016/j.agrformet.2006.04.008
52. Wang, W., Liang, S., Meyers, T., 2008. Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sens. Environ. 112, 623–635. https://doi.org/10.1016/j.rse.2007.05.024
53. Wang, Y., Li, R., Hu, J., Fu, Y., Duan, J., Cheng, Y., 2021. Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements. Remote Sens. Environ. 267, 112721. https://doi.org/10.1016/j.rse.2021.112721
54. Xia, J., Niu, S., Ciais, P., Janssens, I.A., Chen, J., Ammann, C., Arain, A., Blanken, P.D., Cescatti, A., Bonal, D., Buchmann, N., Curtis, P.S., Chen, S., Dong, J., Flanagan, L.B., Frankenberg, C., Georgiadis, T., Gough, C.M., Hui, D., Kiely, G., Li, J., Lund, M., Magliulo, V., Marcolla, B., Merbold, L., Montagnani, L., Moors, E.J., Olesen, J.E., Piao, S., Raschi, A., Roupsard, O., Suyker, A.E., Urbaniak, M., Vaccari, F.P., Varlagin, A., Vesala, T., Wilkinson, M., Weng, E., Wohlfahrt, G., Yan, L., Luo, Y., 2015. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. U. S. A. 112, 2788–2793. https://doi.org/10.1073/pnas.1413090112
55. Xu, H., Xiao, J., Zhang, Z., 2020. Heatwave effects on gross primary production of northern mid-latitude ecosystems. Environ. Res. Lett. 15, 074027. https://doi.org/10.1088/1748-9326/ab8760
56. Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., Nemani, R.R., Knyazikhin, Y., Myneni, R.B., 2016. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: Consistency and improvements. Remote Sens. 8, 1–16. https://doi.org/10.3390/rs8050359
57. Yang, X., Li, J., Yu, Q., Ma, Y., Tong, X., Feng, Y., Tong, Y., 2019. Impacts of diffuse radiation fraction on light use efficiency and gross primary production of winter wheat in the North China Plain. Agric. For. Meteorol. 275, 233–242. https://doi.org/10.1016/j.agrformet.2019.05.028
58. Yoo, C., Im, J., Cho, D., Lee, Y., Bae, D., Sismanidis, P., 2022. Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest. Int. J. Appl. Earth Obs. Geoinf. 110, 102827. https://doi.org/10.1016/j.jag.2022.102827
59. Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P.D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I.A., Jung, M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A., Roupsard, O., Varlagin, A., Wohlfahrt, G., 2014. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 192–193, 108–120. https://doi.org/10.1016/j.agrformet.2014.03.007
