[1] |
Sun W, Gong X, Zhou Y, Li H. 2020. Photosynthetic characteristics of transgenic poplars with maize PEPC and PPDK gene at young plant stage. Scientia Silvae Sinicae 56:33−43 doi: 10.11707/j.1001-7488.20200704 |
[2] |
Tang L, Cao P, Zhang S, Liu X, Ge X, et al. 2024. Two male poplar clones (Populus × euramericana 'Siyang-1' and Populus deltoides 'Nanlin 3804') exhibit distinctly different physiological responses to soil water deficit. Forests 15:1142 doi: 10.3390/f15071142 |
[3] |
Chen F, Movahedi A, Wei H, Qiang Z, Sun W. 2024. Glycine betaine enhances poplar cultivar (Populus deltoides × Populus euramericana) tolerance to confront NaCl stress. Forests 15:1295 doi: 10.3390/f15081295 |
[4] |
Meshkova V, Zhupinska K, Borysenko O, Zinchenko O, Skrylnyk Y, et al. 2024. Possible factors of poplar susceptibility to large poplar borer infestation. Forests 15:882 doi: 10.3390/f15050882 |
[5] |
Zhang J, Zhang W, Ding C, Yuan Z, Dai L, et al. 2024. Comparative analysis of growth, photosynthetic physiology and root tip ion flow characteristics of five poplar varieties. Bulletin of Botanical Research 44:96−106 doi: 10.7525/j.issn.1673-5102.2024.01.012 |
[6] |
Wang L, Zhang Y, Cui L. 2021. Photosynthetic characteristics of six poplar varieties in the Songnen Plain of Western Heilongjiang Province. Journal of Northeast Forestry University 49:40−44,63 doi: 10.3969/j.issn.1000-5382.2021.08.008 |
[7] |
Zong D, Wang J, Zhang Y, Ma D, Jiang F, et al. 2022. Comparison of photosynthetic characteristics of nine poplar species in Southwest China in Autumn. Journal of Northwest Forestry University 37:57−63 doi: 10.3969/j.issn.1001-7461.2022.04.08 |
[8] |
Song Y, Chen Q, Ci D, Shao X, Zhang D. 2014. Effects of high temperature on photosynthesis and related gene expression in poplar. BMC Plant Biology 14:111 doi: 10.1186/1471-2229-14-111 |
[9] |
Wu M, Ding W, Luo J, Wu C, Mei L. 2024. Transcriptome and protein-protein interaction analysis reveals the tolerance of poplar to high boron toxicity regulated by transport and cell wall synthesis pathways. Environmental and Experimental Botany 226:105922 doi: 10.1016/j.envexpbot.2024.105922 |
[10] |
Tao Y, Chiu LW, Hoyle JW, Dewhirst RA, Richey C, et al. 2023. Enhanced photosynthetic efficiency for increased carbon assimilation and woody biomass production in engineered hybrid poplar. Forests 14:827 doi: 10.3390/f14040827 |
[11] |
Kume A. 2017. Importance of the green color, absorption gradient, and spectral absorption of chloroplasts for the radiative energy balance of leaves. Journal of Plant Research 130:501−14 doi: 10.1007/s10265-017-0910-z |
[12] |
El Azizi S, Amharref M, Bernoussi AS. 2024. Assessment of water content in plant leaves using hyperspectral remote sensing and chemometrics, application: Rosmarinus officinalis. Journal of Biosystems Engineering 00:1−9 doi: 10.1007/s42853-024-00236-x |
[13] |
Adesokan M, Otegbayo B, Alamu EO, Olutoyin MA, Maziya-Dixon B. 2024. Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning. Journal of Food Composition and Analysis 135:106692 doi: 10.1016/j.jfca.2024.106692 |
[14] |
Lamour J, Davidson KJ, Ely KS, Anderson JA, Rogers A, et al. 2021. Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy. PLoS One 16:e0258791 doi: 10.1371/journal.pone.0258791 |
[15] |
Wang S, Guan K, Wang Z, Ainsworth EA, Zheng T, et al. 2021. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. Journal of Experimental Botany 72:341−54 doi: 10.1093/jxb/eraa432 |
[16] |
Liu Q, Zhang F, Chen J, Li Y. 2020. Water stress altered photosynthesis-vegetation index relationships for winter wheat. Agronomy Journal 112:2944−55 doi: 10.1002/agj2.20256 |
[17] |
Huang N, Niu Z, Zhan Y, Xu S, Tappert MC, et al. 2012. Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems. Agricultural and Forest Meteorology 160:80−89 doi: 10.1016/j.agrformet.2012.03.005 |
[18] |
Doughty R, Xiao X, Köhler P, Frankenberg C, Qin Y, et al. 2021. Global-scale consistency of spaceborne vegetation indices, chlorophyll fluorescence, and photosynthesis. Journal of Geophysical Research: Biogeosciences 126:e2020JG006136 doi: 10.1029/2020JG006136 |
[19] |
Muraoka H, Noda HM, Nagai S, Motohka T, Saitoh TM, et al. 2013. Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. Journal of Plant Ecology 6:393−407 doi: 10.1093/jpe/rts037 |
[20] |
Serbin SP, Dillaway DN, Kruger EL, Townsend PA. 2012. Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. Journal of Experimental Botany 63:489−502 doi: 10.1093/jxb/err294 |
[21] |
Sukhova E, Yudina L, Kior A, Kior D, Popova A, et al. 2022. Modified photochemical reflectance indices as new tool for revealing influence of drought and heat on pea and wheat plants. Plants 11:1308 doi: 10.3390/plants11101308 |
[22] |
Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:127−50 doi: 10.1016/0034-4257(79)90013-0 |
[23] |
Wang Z, Wang T, Darvishzadeh R, Skidmore AK, Jones S, et al. 2016. Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote Sensing 8:491 doi: 10.3390/rs8060491 |
[24] |
Yin G, Verger A, Filella I, Descals A, Peñuelas J. 2020. Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices. Geophysical Research Letters 47:e2020GL089167 doi: 10.1029/2020GL089167 |
[25] |
Fu P, Meacham-Hensold K, Guan K, Wu J, Bernacchi C. 2020. Estimating photosynthetic traits from reflectance spectra: a synthesis of spectral indices, numerical inversion, and partial least square regression. Plant, Cell & Environment 43:1241−58 doi: 10.1111/pce.13718 |
[26] |
Meacham-Hensold K, Montes CM, Wu J, Guan K, Fu P, et al. 2019. High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sensing of Environment 231:111176 doi: 10.1016/j.rse.2019.04.029 |
[27] |
Jin J, Wang Q, Song G. 2022. Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data. Photosynthesis Research 151:71−82 doi: 10.1007/s11120-021-00873-9 |
[28] |
Rouse JW Jr, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the great plains with ERTS. NTRS - NASA Technical Reports Server 1:309−17 |
[29] |
Jordan CF. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50:663−66 doi: 10.2307/1936256 |
[30] |
Richardson AJ, Wiegand C. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43:1541−52 |
[31] |
Dash J, Curran PJ. 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25:5403−13 doi: 10.1080/0143116042000274015 |
[32] |
Jiang Z, Huete AR, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112:3833−45 doi: 10.1016/j.rse.2008.06.006 |
[33] |
Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology 22:247−52 doi: 10.1016/1011-1344(93)06963-4 |
[34] |
Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I. 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sensing of Environment 115:281−97 doi: 10.1016/j.rse.2010.08.023 |
[35] |
Agapiou A, Hadjimitsis DG, Alexakis DD. 2012. Evaluation of broadband and narrowband vegetation indices for the identification of archaeological crop marks. Remote Sensing 4:3892−919 doi: 10.3390/rs4123892 |
[36] |
Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing 18:2869−75 doi: 10.1080/014311697217396 |
[37] |
Zhao B, Duan A, Ata-Ul-Karim ST, Liu Z, Chen Z, et al. 2018. Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy 93:113−25 doi: 10.1016/j.eja.2017.12.006 |
[38] |
Cho MA, Skidmore AK. 2006. A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sensing of Environment 101:181−93 doi: 10.1016/j.rse.2005.12.011 |
[39] |
Zarco-Tejada PJ, Berjón A, López-Lozano R, Miller JR, Martín P, et al. 2005. Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment 99:271−87 doi: 10.1016/j.rse.2005.09.002 |
[40] |
Li J, Cheng JH, Shi JY, Huang F. 2012. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In Advances in Computer Science and Information Engineering, volume 169, eds Jin D, Lin S. Berlin, Heidelberg: Springer. pp. 553−58. doi: 10.1007/978-3-642-30223-7_87 |
[41] |
Zhang Z. Improved adam optimizer for deep neural networks. Proc. 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS), Banff, AB, Canada, 2018. US: IEEE. pp. 1−2. doi: 10.1109/IWQoS.2018.8624183 |
[42] |
Li Z, Yang Q, Shi S, Feng J. 2017. The photosynthetic characteristics of Ammopiptanthus mongolicus and its affecting factors. Chinese Journal of Ecology 36:2481−88 doi: 10.13292/j.1000-4890.201709.037 |
[43] |
Zhi Y, Yang C, Li H, Zhang H, Hua Y, Zhao K, et al. 2014. The bioclimatology and photosynthetic characteristics for the ex-situ conservation of the endemic relict shrub Tetraena mongolica. Journal of Desert Research 34:88−97 |
[44] |
Gao J, Chen J, Tan X, Wu Y, Yang W, Yang F. 2023. Effect of light intensity on leaf hydraulic conductivity and vein traits of soybean at seedling stage. Scientia Agricultura Sinica 56:4417−27 |
[45] |
Che H, Quan X, Wang L, Li X, Xu Q, et al. 2023. Photosynthetic characteristics of leaves under different planting densities and canopies in young Cunninghamia lanceolata seedlings. Forest Research 36:151−61 |
[46] |
Liu Q, Zhang Z, Wang D, Li F, Xie L. 2024. Main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees. Forestry Research 4:e001 doi: 10.48130/fr-0023-0029 |
[47] |
Wu B, Zhang Y, Wu Y, Shi L, Liu L, et al. 2010. Research on the relationship between photosynthetic characteristics and anthocyanins during the Populus × euramericana leaf growing. Journal of Anhui Agriculture Science 38:4525−28 doi: 10.3969/j.issn.0517-6611.2010.09.039 |
[48] |
Liu F, Shen S, Yang B, Tao S. 2013. Spectral monitoring model of leaf/canopy stomatal conductance in maize under different soil moisture treatments. Chinese Journal of Agrometeorology 34:727−32 doi: 10.3969/j.issn.1000-6362.2013.06.017 |
[49] |
Inoue Y, Peñuelas J, Miyata A, Mano M. 2008. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sensing of Environment 112:156−72 doi: 10.1016/j.rse.2007.04.011 |
[50] |
Jin J, Arief Pratama B, Wang Q. 2020. Tracing leaf photosynthetic parameters using hyperspectral indices in an alpine deciduous forest. Remote Sensing 12:1124 doi: 10.3390/rs12071124 |
[51] |
Barnes ML, Breshears DD, Law DJ, Van Leeuwen WJD, Monson RK, et al. 2017. Beyond greenness: detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data. PLoS One 12:e0189539 doi: 10.1371/journal.pone.0189539 |
[52] |
Liu C, Peng Q, Fang S. 2020. Remote estimation of rice leaf net photisythetic rate based on hyperspectral reflactance. Journal of China Agricultural University 25:56−65 |
[53] |
Zhou J, Zhang Y, Han Z, Liu X, Jian Y, et al. 2021. Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities. Remote Sensing 13:2160 doi: 10.3390/rs13112160 |