Early validation study of the photochemical reflectance index (PRI) and the normalized difference vegetation index (NDVI) derived from the GCOM-C satellite in Mongolian grasslands

References

• Akitsu, T. K., T. Nakaji, H. Kobayashi, T. Okano, Y. Honda, U. Bayarsaikhan, Terigele, et al. 2020. “Large-Scale Ecological Field Data for Satellite Validation in Deciduous Forests and Grasslands.” Ecological Research 35: 1009–1028.
   Web of Science ®Google Scholar
• Akitsu, T. K., and K. N. Nasahara. 2022. “In-Situ Observations on a Moderate Resolution Scale for Validation of the Global Change Observation Mission-Climate Ecological Products: The Uncertainty Quantification in Ecological Reference Data.” International Journal of Applied Earth Observations and Geoinformation 107: 102639.
   Web of Science ®Google Scholar
• Akitsu, T., K. Nasahara, H. Kobayashi, N. Saigusa, M. Hayashi, T. Nakaji, H. Kobayashi, T. Okano, and Y. Honda JAXA Super Sites 500: Large-Scale Ecological Monitoring Sites for Satellite Validation in Japan. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan 2015, 2015, 3866–3869.
   Google Scholar
• Baret, F., M. Weiss, D. Allard, S. Garrigue, M. Leroy, Jeanjean, H., R. Fernandes, et al , et al. 2013. ”VALERI: A Network of Sites and Methodology for the Validation of Medium Spatial Resolution Land Products.” Remote Sensing of Environment.
   Google Scholar
• Baumgardner, M. F., L. F. Silva, L. L. Biehl, and E. R. Stoner. 1986. “Reflectance Properties of Soils.” Advances in Agronomy 38: 1–44.
   Web of Science ®Google Scholar
• Brown, L. A., F. Camacho, V. García-Santos, N. Origo, B. Fuster, H. Morris, J. Pastor- Guzman, et al. 2021. “Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-To-End Uncertainty Evaluation Framework.” Remote Sensing 13 (16): 3194. doi:10.3390/rs13163194.
   Web of Science ®Google Scholar
• Brown, L. A., C. Meier, H. Morris, J. Pastor-Guzman, G. Bai, C. Lerebourg, N. Gobron, C. Lanconelli, M. Clerici, and J. Dash. 2020. “Evaluation of Global Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation Products Over North America Using Copernicus Ground Based Observations for Validation Data.” Remote Sensing of Environment 247: 111935.
   Web of Science ®Google Scholar
• Chen, J., P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh. 2004. “A Simple Method for Reconstructing a High-Quality NDVI Time-Series Data Set Based on the Savitzky–Golay Filter.” Remote Sensing of Environment 91 (3–4): 332–344. doi:10.1016/j.rse.2004.03.014.
   Web of Science ®Google Scholar
• Cohen, W. B., and C. O. Justice. 1999. “Validating MODIS Terrestrial Ecology Products: Linking in situ and Satellite Measurements.” Remote Sensing of Environment 70 (1): 1–3. doi:10.1016/S0034-4257(99)00053-X.
   Web of Science ®Google Scholar
• Cohen, W. B., Maiersperger, T. K., Turner, D. P., Ritts, W. D., Pflugmacher, D., Kennedy, R. E., Kirschbaum, A. et al. 2006 ”MODIS land cover and LAI collection 4 product quality across nine states in the western hemisphere” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1843–1857. doi:10.1109/TGRS.2006.876026.
   Web of Science ®Google Scholar
• Dash, D. Natural Zone. 2005. Shapefile. Available online: https://eic.mn/geodata/download.php (accessed on May 10, 2020).
   Google Scholar
• Demmig-Adams, B., and W.W. Adams III. 1992. “Photoprotection and Other Responses of Plants to High Light Stress.” Annual Review of Plant Biology 43: 599–626.
   Web of Science ®Google Scholar
• Drolet, G. G., K. F. Huemmrich, F. G. Hall, E. M. Middleton, T. A. Black, A. G. Barr, and H. A. Margolis. 2005. “A MODIS-Derived Photochemical Reflectance Index to Detect Inter-Annual Variations in the Photosynthetic Light-Use Efficiency of a Boreal Deciduous Forest.” Remote Sensing of Environment 98 (2–3): 212–224. doi:10.1016/j.rse.2005.07.006.
   Web of Science ®Google Scholar
• Filella, I., J. Penuelas, L. Llorens, and M. Estiarte. 2004. “Reflectance Assessment of Seasonal and Annual Changes in Biomass and CO2 Uptake of a Mediterranean Shrubland Submitted to Experimental Warming and Drought.” Remote Sensing of Environment 90 (3): 308–318. doi:10.1016/j.rse.2004.01.010.
   Web of Science ®Google Scholar
• Fuster, B., J. Sánchez-Zapero, F. Camacho, V. García-Santos, A. Verger, R. Lacaze, M. Weiss, F. Baret, and B. Smets. 2020. “Quality Assessment of PROBA-V LAI, fApar and fCover Collection 300 M Products of Copernicus Global Land Service.” Remote Sensing 12: 1017.
   Web of Science ®Google Scholar
• Gamon, J. A., K. F. Huemmrich, C. Y. Wong, I. Ensminger, S. Garrity, D. Y. Hollinger, A. Noormets, and J. Peñuelas. 2016. “A Remotely Sensed Pigment Index Reveals Photosynthetic Phenology in Evergreen Conifers.“ Proceedings of the National Academy of Sciences , 113, 13087–13092.
   PubMed Web of Science ®Google Scholar
• Gamon, J., J. Penuelas, and C. Field. 1992. “A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency.” Remote Sensing of Environment 41 (1): 35–44. doi:10.1016/0034-4257(92)90059-S.
   Web of Science ®Google Scholar
• Gitelson, A. A., J. A. Gamon, and A. Solovchenko. 2017. “Multiple Drivers of Seasonal Change in PRI: Implications for Photosynthesis 2. Stand Level.” Remote Sensing of Environment 190: 198–206.
   Web of Science ®Google Scholar
• Goerner, A., M. Reichstein, and S. Rambal. 2009. “Tracking Seasonal Drought Effects on Ecosystem Light Use Efficiency with Satellite-Based PRI in a Mediterranean Forest.” Remote Sensing of Environment 113 (5): 1101–1111. doi:10.1016/j.rse.2009.02.001.
   Web of Science ®Google Scholar
• Gong, Z., K. Kawamura, N. Ishikawa, M. Goto, T. Wulan, D. Alateng, T. Yin, and Y. Ito. 2015. “MODIS Normalized Difference Vegetation Index (NDVI) and Vegetation Phenology Dynamics in the Inner Mongolia Grassland.” Solid Earth 6 (4): 1185–1194. doi:10.5194/se-6-1185-2015.
   Web of Science ®Google Scholar
• Hori, M., H. Murakami, R. Miyazaki, Y. Honda, K. Nasahara, K. Kajiwara, T. Y. Nakajima, et al. 2018. “GCOM-C Data Validation Plan for Land, Atmosphere, Ocean, and Cryosphere.” Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan 16 (3): 218–223. doi:10.2322/tastj.16.218.
   Google Scholar
• Ide, R., and H. Oguma. 2010. “Use of Digital Cameras for Phenological Observations.” Ecological Informatics 5 (5): 339–347. doi:10.1016/j.ecoinf.2010.07.002.
   Web of Science ®Google Scholar
• Ishihara, M., and M. Tamura. 2005. “Relationship Between Light Use Efficiency and Photochemical Reflectance Index Using MODIS Data in Japan.” Journal of Agricultural Meteorology 60 (5): 977–980. doi:10.2480/agrmet.977.
   Google Scholar
• Jones, H. G., and R. A. Vaughan. Remote Sensing of Vegetation: Principles, Techniques, and Applications, 2010. New York, US: Oxford University Press.
   Google Scholar
• Klinge, M., C. Dulamsuren, S. Erasmi, D. N. Karger, and M. Hauck. 2018. “Climate Effects on Vegetation Vitality at the Treeline of Boreal Forests of Mongolia.” Biogeosciences 15 (5): 1319–1333. doi:10.5194/bg-15-1319-2018.
   Web of Science ®Google Scholar
• Kohzuma, K., and K. Hikosaka. 2018. “Physiological Validation of Photochemical Reflectance Index (PRI) as a Photosynthetic Parameter Using Arabidopsis Thaliana Mutants.” Biochemical and Biophysical Research Communications 498 (1): 52–57. doi:10.1016/j.bbrc.2018.02.192.
   PubMed Web of Science ®Google Scholar
• Kohzuma, K., K. Sonoike, and K. Hikosaka. 2021. “With Gratitude from the Editor-In-Chief of the Journal of Plant Research.” Journal of Plant Research 134 (1): 1–3. doi:10.1007/s10265-021-01252-0.
   PubMed Web of Science ®Google Scholar
• Lim, C. H., J. H. An, S. H. Jung, G. B. Nam, Y. C. Cho, N. S. Kim, and C. S. Lee. 2018. “Ecological Consideration for Several Methodologies to Diagnose Vegetation Phenology.” Ecological Research 33 (2): 363–377. doi:10.1007/s11284-017-1551-3.
   Web of Science ®Google Scholar
• Mänd, P., L. Hallik, J. Peñuelas, T. Nilson, P. Duce, B. A. Emmett, C. Beier, et al. 2010. “Responses of the Reflectance Indices PRI and NDVI to Experimental Warming and Drought in European Shrublands Along a North–south Climatic Gradient.” Remote Sensing of Environment 114 (3): 626–636. doi:10.1016/j.rse.2009.11.003.
   Web of Science ®Google Scholar
• Middleton, E., K. Huemmrich, D. Landis, T. Black, A. Barr, and J. McCaughey. 2016. “Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI).” Remote Sensing of Environment 187: 345–366.
   Web of Science ®Google Scholar
• Miller, L. 1977. “Soil Spectra Contributions to Grass Canopy Spectral Reflectance.” Photogrammetric Engineering and Remote Sensing 43: 721–726.
   Web of Science ®Google Scholar
• Morisette, J. T., F. Baret, J. L. Privette, R. B. Myneni, J. E. Nickeson, S. Garrigues, N. V. Shabanov, et al. 2006. ”Validation of Global Moderate-Resolution LAI Products: A Framework Proposed Within the CEOS Land Product Validation Subgroup.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1804–1817. doi:10.1109/TGRS.2006.872529.
   Web of Science ®Google Scholar
• Murakami, H. Half-Pixel Shift of LTOA Q/K. Available online: https://suzaku.eorc.jaxa.jp/GCOM_C/data/files/About_LTOA_half_pixel_shift_Web_e.pdf (accessed on March 20, 2021).
   Google Scholar
• Nakano, T., G. Bavuudorj, Y. Iijima, and T. Y. Ito. 2020. “Quantitative Evaluation of Grazing Effect on Nomadically Grazed Grassland Ecosystems by Using Time-Lapse Cameras.” Agriculture, Ecosystems & Environment 287: 106685.
   Web of Science ®Google Scholar
• Nasahara, K. N., and S. Nagai. 2015. “Development of an in situ Observation Network for Terrestrial Ecological Remote Sensing: The Phenological Eyes Network (PEN).” Ecological Research 30 (2): 211–223. doi:10.1007/s11284-014-1239-x.
   Web of Science ®Google Scholar
• Penman, J., M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, et al. Good Practice Guidance for Land Use, Land-Use Change and Forestry. Good Practice Guidance for Land Use, Land-Use Change and Forestry. 2003.
   Google Scholar
• Peñuelas, J., M. F. Garbulsky, and I. Filella. 2011. “Photochemical Reflectance Index (PRI) and Remote Sensing of Plant CO 2 Uptake.” The New Phytologist 191 (3): 596–599. doi:10.1111/j.1469-8137.2011.03791.x.
   PubMed Web of Science ®Google Scholar
• Putzenlechner, B., S. Castro, R. Kiese, R. Ludwig, P. Marzahn, I. Sharp, and A. Sanchez-Azofeifa. 2019. “Validation of Sentinel-2 fApar Products Using Ground Observations Across Three Forest Ecosystems.” Remote Sensing of Environment 232: 111310.
   Web of Science ®Google Scholar
• Rao, M. P., N. K. Davi, R. DD’Arrigo, J. Skees, B. Nachin, C. Leland, B. Lyon et al . 2015. “Dzuds, Droughts, and Livestock Mortality in Mongolia.” Environmental Research Letters 10 (7): 074012. doi:10.1088/1748-9326/10/7/074012.
   Web of Science ®Google Scholar
• Suttie, J. 2005. “Grazing Management in Mongolia.” Grasslands of the World 34: 265.
   Google Scholar
• Thenot, F., M. Méthy, and T. Winkel. 2002. “The Photochemical Reflectance Index (PRI) as a Water-Stress Index.” International Journal of Remote Sensing 23 (23): 5135–5139. doi:10.1080/01431160210163100.
   Web of Science ®Google Scholar
• Wang, Y., C. E. Woodcock, W. Buermann, P. Stenberg, P. Voipio, H. Smolander, T. Häme, et al. 2004. ”Evaluation of the MODIS LAI Algorithm at a Coniferous Forest Site in Finland.” Remote Sensing of Environment 91 (1): 114–127. doi:10.1016/j.rse.2004.02.007.
   Web of Science ®Google Scholar
• Wong, C. Y., and J. A. Gamon. 2015. “Three Causes of Variation in the Photochemical Reflectance Index (PRI) in Evergreen Conifers.” The New Phytologist 206 (1): 187–195. doi:10.1111/nph.13159.
   PubMed Web of Science ®Google Scholar
• Xie, Y., D. L. Civco, and J. A. Silander Jr. 2018. “Species‐specific Spring and Autumn Leaf Phenology Captured by Time‐lapse Digital Cameras.” Ecosphere 9 (1): e02089. doi:10.1002/ecs2.2089.
   Web of Science ®Google Scholar
• Xie, X., Z. Gao, and W. Gao. 2009. ”Estimating Photosynthetic Light-Use Efficiency of Changbai Mountain by Using MODIS-Derived Photochemical Reflectance Index.” SPIE Proceedings. 7454. 745415. doi:10.1117/12.824644.
   Google Scholar
• Yang, W., B. Tan, D. Huang, M. Rautiainen, N. V. Shabanov, Y. Wang, J. L. Privette, et al. 2006. ”MODIS Leaf Area Index Products: From Validation to Algorithm Improvement.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1885–1898. doi:10.1109/TGRS.2006.871215.
   Web of Science ®Google Scholar
• Yan, D., X. Zhang, S. Nagai, Y. Yu, T. Akitsu, K. N. Nasahara, R. Ide, and T. Maeda. 2019. “Evaluating Land Surface Phenology from the Advanced Himawari Imager Using Observations from MODIS and the Phenological Eyes Network.” International Journal of Applied Earth Observations and Geoinformation 79: 71–83 doi:10.1016/j.jag.2019.02.011.
   Web of Science ®Google Scholar
• Zhang, X., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. Hodges, F. Gao, B. C. Reed, and A. Huete. 2003. “Cholesterol is Necessary Both for the Toxic Effect of Abeta Peptides on Vascular Smooth Muscle Cells and for Abeta Binding to Vascular Smooth Muscle Cell Membranes.” Journal of Neurochemistry 84 (3): 471–475. doi:10.1046/j.1471-4159.2003.01552.x.
   PubMed Web of Science ®Google Scholar