المؤلفون: Ali M. Ali, Haytham M. Salem, Bijay-Singh

المصدر: Nitrogen, Vol 5, Iss 4, Pp 828-856 (2024)

مصطلحات موضوعية: site-specific nitrogen management, canopy reflectance sensors, chlorophyll meters, leaf color charts, Ecology, QH540-549.5

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2504-3129/5/4/54; https://doaj.org/toc/2504-3129

URL الوصول: https://doaj.org/article/253b92b8a5b045ff83198e41f1eed2ef

المؤلفون: Naisen Liu, Jingyu Guo, Fuxia Liu, Xuedong Zha, Jing Cao, Yuezhen Chen, Haixia Yan, Chenggong Du, Xuqi Wang, Jiping Li, Yongzhen Zhao

المصدر: Frontiers in Plant Science, Vol 15 (2025)

مصطلحات موضوعية: vegetation canopy reflectance, spectral reflectance, adaptability to varying light intensities, solar altitude correction, stability of full-daytime measurements, Plant culture, SB1-1110

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fpls.2024.1512660/full; https://doaj.org/toc/1664-462X

URL الوصول: https://doaj.org/article/c26f56b8bacf419a84cde25e06addc69

المؤلفون: Junying Li, Weichao Sun, Shuo Liu, Tao Cheng, Liang Tang, Wei Jiang, Feng Chen, Yuchao Li, Jianfei Cai

المصدر: Smart Agricultural Technology, Vol 8, Iss , Pp 100502- (2024)

مصطلحات موضوعية: Leaf area index, Canopy reflectance spectroscopy, Tobacco plant, Spectral variable importance, Spectral subset selection, Agriculture (General), S1-972, Agricultural industries, HD9000-9495

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2772375524001072; https://doaj.org/toc/2772-3755

URL الوصول: https://doaj.org/article/278bd0beded443a7a1e61172d60e3688

المؤلفون: Aichen Wang, Zishan Song, Yuwen Xie, Jin Hu, Liyuan Zhang, Qingzhen Zhu

المصدر: Agriculture, Vol 14, Iss 9, p 1471 (2024)

مصطلحات موضوعية: multispectral imaging, canopy reflectance, blast disease, SPAD value, data fusion, Agriculture (General), S1-972

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2077-0472/14/9/1471; https://doaj.org/toc/2077-0472

URL الوصول: https://doaj.org/article/76dbda49faaf42ebb2814499c46fef7b

المؤلفون: Liu, Chang, Calders, Kim, Origo, Niall, Terryn, Louise, Adams, Jennifer, Gastellu-Etchegorry, Jean-Philippe, Wang, Yingjie, Meunier, Félicien, Armston, John, Disney, Mathias, Woodgate, William, Nightingale, Joanne, Verbeeck, Hans

المصدر: REMOTE SENSING ; ISSN: 2072-4292

مصطلحات موضوعية: Earth and Environmental Sciences, radiative transfer, forest reconstruction, bitemporal, 3D-explicit, terrestrial LiDAR, remote sensing, DART, LEAF-AREA INDEX, CANOPY REFLECTANCE, BIDIRECTIONAL REFLECTANCE, LIGHT-SCATTERING, LIDAR, PLANT, TREE, SIMULATIONS, ATMOSPHERE, IMPACT

وصف الملف: application/pdf

Relation: https://biblio.ugent.be/publication/01J9R9997WMMPDPV2RMEMFHFHM; http://doi.org/10.3390/rs16193639; https://biblio.ugent.be/publication/01J9R9997WMMPDPV2RMEMFHFHM/file/01J9VBRDFMFVP7K60YFXKJ312C

الاتاحة: https://biblio.ugent.be/publication/01J9R9997WMMPDPV2RMEMFHFHM
http://hdl.handle.net/1854/LU-01J9R9997WMMPDPV2RMEMFHFHM
https://doi.org/10.3390/rs16193639
https://biblio.ugent.be/publication/01J9R9997WMMPDPV2RMEMFHFHM/file/01J9VBRDFMFVP7K60YFXKJ312C

المؤلفون: Karakoç, Ahmet, Karabulut, Murat

المصدر: Mediterranean Botany; Vol. 45 No. 2 (2024); e85161 ; Mediterranean Botany; Vol. 45 Núm. 2 (2024); e85161 ; 2603-9109

مصطلحات موضوعية: Canopy water content, Grasslands, Hhyperspectral remote sensing, Vegetation indices, Canopy reflectance

وصف الملف: application/pdf

Relation: https://revistas.ucm.es/index.php/MBOT/article/view/81561/4564456566947; Andrade, B.O., Koch, C., Boldrini, I.I., Vélez-Martin, E., Hasenack, H., Hermann, J.M., Kollmann, J., Pillar, V.D. & Overbeck, G.E. 2015. Grassland degradation and restoration: a conceptual framework of stages and thresholds illustrated by southern Brazilian grasslands. Natureza & Conservação 13(2): 95–104. doi:10.1016/j.ncon.2015.08.002; Apostolova, I., Dengler, J., Di Pietro, R., Gavilán, R.G. & Tsiripidis, I. 2014. Dry grasslands of Southern Europe: syntaxonomy, management and conservation. Hacquetia 13(1): 1–14. doi:10.2478/hacq-2014-0015; Atalay, I., Efe, R. & Öztürk, M. 2014. Effects of topography and climate on the ecology of Taurus mountains in the Mediterranean region of Turkey. Procedia Soc. Behav. Sci. 120: 142–156. doi:10.1016/j.sbspro.2014.02.091; Bangira, T., Mutanga, O., Sibanda, M., Dube, T & Mabhaudhi, T. 2023. Remote Sensing Grassland Productivity Attributes: A Systematic Review Remote Sens. 15(8): 2043. doi:10.3390/rs15082043; Bellini, E., Moriondo, M., Dibari, C., Leolini, L., Staglianò, N., Stendardi, L., Filippa, G., Galvagno, M. & Argenti, G. 2023. Impacts of Climate Change on European Grassland Phenology: A 20-Year Analysis of MODIS Satellite Data. Remote Sens. 15(1): 218. doi:10.3390/rs15010218; Blair, J., Nippert, J. & Briggs, J. 2014. Grassland Ecology. In: Monson, R.K. (Ed.). Ecology and the Environment; The Plant Sciences 8, Chapter 14. Pp. 389–423. Springer, New York.; Bora, Z., Xu, X., Angassa, A., Wang, Y. & Zhao, Y. 2020. Do herbaceous species functional groups have a uniform pattern along an elevation gradient? The case of a semi-arid savanna grasslands in Southern Ethiopia. International Int. J. Environ. Res. Public Health 17(8): 2817. doi:10.3390/ijerph17082817; Buschman, C. & Nagel, E. 1993. In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. Int. J. Remote Sens. 14: 711–722. doi:10.1080/01431169308904370; Chai, T. & Draxler, R.R. 2014. Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3): 1247–1250. doi:10.5194/gmd-7-1247-2014; Chang-Hua, J.U., Tian, Y., Yao, X., Cao, W., Zhu, Y. & Hannaway, D. 2010. Estimating Leaf Chlorophyll Content Using Red Edge Parameters. Pedosphere 20–25.doi: 1016/S10020160(10)60053-7; Cho, M.A., Skidmore, A.K., Corsi, F., van Wieren, S.E. & Sobhan, I. 2007. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. 9(4): 414–424. doi:10.1016/j.jag.2007.02.001; Colombo, R., Meroni, M., Marchesi, A., Busetto, L., Rossini, M., Giardino, C. & Panigada, C. 2008. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sens. Environ. 112(4): 1820–1834. doi:10.1016/J.RSE.2007.09.005; Danson, F.M. & Bowyer, P. 2004. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sens. Environ. 92(3): 309–321. doi:10.3390/rs11131575; Darvishzadeh, R. 2008. Hyperspectral remote sensing of vegetation parameters using statistical and physical models. PhD Thesis. International Institute for Geo-information Science and Earth Observation (ITC), Enschede.; Dechimo Jr, A.A. & Buot Jr, I.E. 2023. Biophysical assessment of the plant biodiversity of Northern Negros Natural Park, Negros Island, Philippines. Biodiversitas 24(1). doi:10.13057/biodiv/d240167; Dong, S., Shang, Z., Gao, J. & Boone, R.B. 2020. Enhancing sustainability of grassland ecosystems through ecological restoration and grazing management in an era of climate change on Qinghai-Tibetan Plateau. Agr. Ecosyst. Environ. 287: 106684. doi:10.1016/j.agee.2019.106684; Doygun, H., Zülkadiroğlu, D. & Ekşi, I. 2021. Başkonuş Mesire Yeri’nden (Kahramanmaraş) Sağlanan Ekosistem Hizmetlerine Yönelik Algının İncelenmesi. Çukurova Tarım ve Gıda Bilimleri Dergisi 36(2): 219–230. doi:10.36846/CJAFS.2021.50; Fan, L., Wigneron, J.P., Xiao, Q., Al-Yaari, A., Wen, J., Martin-StPaul, N., Dupuy, J.L, Pimont, F., Al Bitar, A., Fernandez-Moran, R. & Kerr, Y.H. 2018. Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region. Remote Sens. Environ. 205: 210–223. doi:10.1016/j.rse.2017.11.020; Feng, S., Qiu, J., Crow, W.T., Mo, X., Liu, S., Wang, S., Gao, L., Wang, X. & Chen, S. 2023. Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland. J. Hydrol. 617: 129015. doi:10.1016/j.jhydrol.2022.129015; Frank, T., Smith, A., Houston, B., Yang, X. & Guo, X. 2022. Estimating Biophysical Parameters of Native Grasslands Using Spectral Data Derived from Close Range Hyperspectral and Satellite Data. Can. J. Remote Sens. 48(5): 633–648. doi:10.1080/07038992.2022.2088486; Gao, Z., Wang, Q., Cao, X. & Gao, W. 2014. The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing. Gisci. Remote Sens. 51(1): 1–16. doi:10.1080/15481603.2014.882564; García-Haro, F.J., Campos-Taberner, M., Moreno, Á., Tagesson, H.T., Camacho, F., Martínez, B., Sánchez, S., Piles, M., Camps-Valls, G., Yebra, M. & Gilabert, M.A. 2020. A global canopy water content product from AVHRR/Metop. ISPRS. J. Photogramm. 162: 77–93. doi:10.1016/j.isprsjprs.2020.02.007; Gavilán, R.G., Díez-Monsalve, E., Izquierdo, J.L., Gutiérrez-Girón, A., Fernández-González, F. & Sánchez-Mata, D. 2012. An approach towards the knowledge of Iberian high-mountain calcareous grasslands. Lazaroa 33: 43–50. doi:10.5209/rev_LAZA.2012.v33.40285; Genceli, M. 2007. Kolmogorov-Smirnov, Lilliefors and Shapiro-Wilk tests for normality. Sigma Journal of Engineering and Natural Sciences 25(4): 306–328.; Grašič, M., Šabić, A. & Lukač, B. 2023. A review of methodology for grassland restoration and management with practical examples. Acta Biologica Slovenica 66(1): 52–77. doi:10.14720/abs.66.1.13230; Guo, X., Price, K.P. & Stiles, J.M. 2000. Modeling biophysical factors for grasslands in eastern Kansas using Landsat TM data. Transactions of the Kansas Academy of Science 103(3–4): 122–138. doi:10.2307/3628261; Guo, X., Zhang, C., Wilmshurst, J.F. & Sissons, R. 2005. Monitoring grassland health with remote sensing approaches. In: Noble, B.F., Martz, D.J.F. & Aitken, A.E. (Eds.). Prairie Perspectives: Geographical Essays, vol. 8. Pp. 11–22. Department of Geography, University of Saskatchewan, Saskatoon.; Gutiérrez-Girón, A. & Gavilán, R.G. 2013. Plant functional strategies and environmental constraints in Mediterranean high mountain grasslands in central Spain. Plant Ecol. Divers. 6(3–4): 435–446. doi:10.1080/17550874.2013.783641; Habel, J.C., Dengler, J., Janišová, M., Török, P., Wellstein, C. & Wiezik, M. 2013. European grassland ecosystems: threatened hotspots of biodiversity. Biodivers. Conserv. 22: 2131–2138. doi:10.1007/s10531-013-0537-x; He, Y. & Mui, A. 2010. Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: challenges and opportunities. Sensors-Basel. 10: 11072–11087. doi:10.3390/ s101211072; He, Y. 2008. Modeling grassland productivity through remote sensing products. PhD Thesis. University of Saskatchewan, Saskatoon.; He, Y., Guo, X. & Wilmshurst, J.F. 2006. Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices. Can. J. Remote Sens. 32(2): 98–107. doi:10.5589/m06-009; He, Y., Guo, X. & Wilmshurst, J.F. 2009. Reflectance measures of grassland biophysical structure. Int. J. Remote Sens. 30(10): 2509–2521. doi:10.1080/01431160802552751; He, Y., Yang, J. & Guo, X. 2020. Green vegetation cover dynamics in a heterogeneous grassland: spectral unmixing of Landsat time series from 1999 to 2014. Remote Sens. 12: 3826. doi:10.3390/rs12223826; Iqbal, N., Mumtaz, R., Shafi, U. & Zaidi, S.M.H. 2021. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ 7: e536. doi:10.7717/peerj-cs.536; Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P. & Hunt, E.R. 2004. Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybean. Remote Sens. Environ. 92(4): 475–482. doi:10.1016/j.rse.2003.10.021; Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C. & Ustin, S.L. 2009. PROSPECT+ SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 113: 56–66. doi:10.1016/j.rse.2008.01.026; Jiang, H., Yao, M., Guo, J., Zhang, Z., Wu, W. & Mao, Z. 2022. Vegetation monitoring of protected areas in rugged mountains using an improved Shadow-Eliminated Vegetation Index (SEVI). Remote Sens. 14(4): 882. doi:10.3390/rs14040882; Jiang, Z., Huete, A.R., Li, J. & Chen, Y. 2006. An analysis of angle-based with ratio-based vegetation indices. IEEE T. Geosci. Remote 44(9): 2506–2513. doi:10.1109/TGRS.2006.873205; Jin, Y., Yang, X., Qiu, J., Li, J., Gao, T., Wu, Q., Zhao, F., Ma, H., Yu, H. & Xu, B. 2014. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, northern China. Remote Sens. 6: 1496–1513. doi:10.3390/rs6021496; Karabörk, M. 2019. Başkonuş Dağının koruma kriterlerine göre değerlendirilmesi ve biyosfer rezervi açısından araştırılması. MA Thesis. Kahramanmaraş Sütçü İmam Universty, Institute of Social Sciences. Kahramanmaraş, Turkey.; Karabulut, M. 2018. An examination of spectral reflectance properties of some wetland plants in Göksu Delta, Turkey. J. Int. Environ. Appl. Sci. 13(4): 194–203.; Karakoç, A. & Karabulut, M. 2017. Otlaklarda kanopi nem içeriği tahmininde uygun vejetasyon indekslerinin belirlenmesi. Proceedings of the 3rd International Symposium on Social Science. Pp. 237–237. Kahramanmaraş.; Karakoç, A. & Karabulut, M. 2019. Ratio-based vegetation indices for biomass estimation depending on grassland characteristics. Turk. J. Bot. 43: 619–633. doi:10.3906/bot-1902-50; Karakoç, A. & Karabulut, M. 2021. Estimating grassland chlorophyll content at canopy scales using hyperspectral vegetation indices. J Geog. 43: 77–91. doi:10.26650/JGEOG2021-865289; Karakoç, A. 2019. Otlaklardaki Biyofiziksel Ve Biyokimyasal Özelliklerin Hiperspektral Uzaktan Algilama Verileri İle İncelenmesi. PhD Thesis. Kahramanmaraş Sütçü İmam Universty, Institute of Social Sciences, Kahramanmaraş.; Kim, H.Y. 2013. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry and Endodontics 38(1): 52–54. doi:10.5395/rde.2013.38.1.52; Kowalski, K., Okujeni, A. & Hostert, P. 2023. A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series. Remote Sens. Environ. 286: 113449. doi:10.1016/j.rse.2022.113449; Kycko, M., Zagajewski, B., Lavender, S. & Dabija, A. 2019. In situ hyperspectral remote sensing for monitoring of alpine trampled and recultivated species. Remote Sens. 11(11), 1296. doi:10.3390/rs11111296; Le Vine, D.M. & Karam, M.A. 1996. Dependence of attenuation in a vegetation canopy on frequency and plant water content. IEEE T. Geosci. Remote 34(5): 1090–1096. doi:10.1109/36.536525; Lei, J., Yang, W. & Yang, X. 2023. Estimation and Mapping of Aboveground Vegetation Water Storage in Jiuzhaigou Nature Reserve Using Sentinel Imagery. Pol. J. Environ. Stud. 32(1): 599–608. doi:10.15244/pjoes/155052; Liang, S. 2004. Quantitative Remote Sensing of Land Surfaces, vol. 30. John Wiley & Sons, Hoboken.; Lillesand, T.M. & Kiefer, R.W. 1994. Remote Sensing and Image Interpretation John Wiley & Sons, Toronto.; Liu, L., Zhang, S. & Zhang, B. 2016. Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content. Int. J. Remote Sens. 37(14): 3384–3399. doi:10.1080/01431161.2016.1199083; Lu, B., He, Y. & Liu, H.H. 2018. Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery. Int. J. Remote Sens. 39(15–16): 5265–5287. doi:10.1080/01431161.2017.1363441; Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J.M., McNairn, H. & Rao, Y.S. 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sens. Environ. 247: 111954. doi:10.1016/j.rse.2020.111954; Meiyan, S., Qizhou, D., ShuaiPeng, F., Xiaohong, Y., Jinyu, Z., Lei, M., Baoguo, L. & Yuntao, M. 2022. Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images. Comput. Electron. Agr. 197: 106982. doi:10.1016/j.compag.2022.106982; Mendiguren, G., Pilar Martín, M., Nieto, H., Pacheco-Labrador, J. & Jurdao, S. 2015. Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean fluxnet site. Biogeosciences 12(18): 5523–5535. doi:10.5194/bg-12-5523-2015; Mistele, B. & Schmidhalter, U. 2010. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total nitrogen in winter wheat. Agron. J. 102(2): 499–506. doi:10.2134/agronj2009.0282; Montalvo, J., Casado, M.A., Levassor, C. & Pineda, F.D. 1993. Species diversity patterns in Mediterranean grasslands. J. Veg. Sci. 4(2): 213–222. doi:10.2307/3236107; Monteiro, L., Machado, N., Martins, E., Pougy, N., Verdi, M., Martinelli, G. & Loyola, R. 2018. Conservation priorities for the threatened flora of mountaintop grasslands in Brazil. Flora 238: 234–243. doi:10.1016/j.flora.2017.03.007; Mutanga, O., Masenyama, A. & Sibanda, M. 2023. Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects. ISPRS J. Photogramm. Remote Sens. 198: 297–309. doi:10.1016/j.isprsjprs.2023.03.010; Olmeda, C., Šefferová, V., Underwood, E., Millan, L., Gil, T. & Naumann, S. (compilers). 2019. EU Action plan to maintain and restore to favourable conservation status the habitat type 6210 Semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) (*important orchid sites). European Commission Technical Report, Brussels.; Peddle, D.R., White, H.P., Soffer, R.J., Miller, J.R. & Ledrew, E.F. 2001. Reflectance processing of remote sensing spectroradiometer data. Comput. Geosci. 27: 203–213. doi:10.1016/S0098-3004(00)00096-0; Peng, Y., Chen, H.Y. & Yang, Y. 2020. Global pattern and drivers of nitrogen saturation threshold of grassland productivity. Funct. Ecol. 34(9): 1979–1990. doi:10.1111/1365-2435.13622; Pescador, D.S., de Bello, F., Valladares, F. & Escudero, A. 2015. Plant trait variation along an altitudinal gradient in Mediterranean high mountain grasslands: controlling the species turnover effect. PLoS One 10(3): e0118876. doi:10.1371/journal.pone.0118876; Prasad, B., Carver, B.F., Stone, M.L., Babar, M.A., Raun, W.R. & Klatt, A.R. 2007. Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Sci. 47(4): 1426–1440. doi:10.2135/cropsci2006.07.0492; Tong, X., Duan, L., Liu, T., Yang, Z., Wang, Y. & Singh, V.P. 2023. Estimation of grassland aboveground biomass combining optimal derivative and raw reflectance vegetation indices at peak productive growth stage. Geocarto Int. 38(1): 2186497. doi:10.1080/10106049.2023.2186497; Quan, X., He, B., Li, X. & Tang, Z. 2015. Estimation of grassland live fuel moisture content from ratio of canopy water content and foliage dry biomass. IEEE Geosci. Remote. Sens. Lett. 12(9): 1903–1907. doi:10.1109/LGRS.2015.2437391; Ravi, J., Nigam, R., Bhattacharya, B.K., Desai, D. & Patel, P. 2022. Retrieval of crop biophysical-biochemical variables from airborne AVIRIS-NG data using hybrid inversion of PROSAIL-D. Adv. Space Res. doi:10.1016/j.asr.2022.11.047; Ren, H. & Zhou, G. 2019. Estimating green biomass ratio with remote sensing in arid grasslands. Ecol. Indic. 98: 568–574. doi:10.1016/j.ecolind.2018.11.043; Sibanda, M., Mutanga, O., Dube, T., Mothapo, M.C. & Mafongoya, P.L. 2019. Remote sensing equivalent water thickness of grass treated with different fertiliser regimes using resample HyspIRI and EnMAP data. Phys. Chem. Earth. Pt. A/B/C 112: 246–254. doi:10.1016/j.pce.2018.12.003; Solgi, S., Ahmadi, S.H. & Seidel, S.J. 2023. Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields. Agr. Water Manage. 280: 108226. doi:10.1016/j.agwat.2023.108226; Varela, E. & Robles-Cruz, A.B. 2016. Ecosystem services and socio-economic benefits of Mediterranean grasslands. In: Kyriazopoulos, A.P. López-Francos, A., Porqueddu, C. & Sklavou P. (Eds.). Options Mediterranéennes: Serie A. Mediterranean Seminars 114: 13–27.; Varol, Ö. 2003. Flora of Başkonuş Mountain (Kahramanmaraş). Turk. J. Bot. 27(2): 117–139.; Vescovo, L., Wohlfahrt, G., Balzarolo, M., Pilloni, S., Sottocornola, M., Rodeghiero, M. & Gianelle, D. 2012. New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass. Int. J. Remote Sens. 33(7): 2178–2195. doi:10.1080/01431161.2011.607195; Vié, J.C., Hilton-Taylor, C. & Stuart, S.N. (Eds.). 2009. Wildlife in a changing world: an analysis of the 2008 IUCN Red List of threatened species. IUCN.; Wang, J., Xu, R. & Yang, S. 2009. Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions. Environ. Monit. Assess. 157: 459–469. doi:10.1007/s10661-008-0548-3; Wu, C. Y., Niu, Z., Tang, Q. & Huang, W. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agr. Forest Meteorol. 148(8–9): 1230–1241. doi:10.1016/j.agrformet.2008.03.005; Xie, Y., Sha, Z., Yu, M., Bai, Y. & Zhang, L. 2009. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecol. Model. 220(15): 1810–1818. doi:10.1016/j.ecolmodel.2009.04.025; Xu, D., An, D. & Guo, X. 2020. The impact of non-photosynthetic vegetation on LAI estimation by NDVI in mixed grassland. Remote Sens. 12(12): 1979. doi:10.3390/rs12121979; Xu, C., Qu, J.J., Hao, X., Cosh, M.H., Zhu, Z. & Gutenberg, L. 2020. Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa. Agr. Water Manage. 227: 105844. doi:10.1016/j.agwat.2019.105844; Xu, S., Rowntree, J., Borrelli, P., Hodbod, J. & Raven, M.R. 2019. Ecological health index: A short term monitoring method for land managers to assess grazing lands ecological health. Environments 6(6): 67. doi:10.3390/environments6060067; Yang, X. & Guo, X. 2014. Quantifying responses of spectral vegetation indices to dead materials in mixed grasslands. Remote Sens-Basel 6(5): 4289–4304. doi:10.3390/rs6054289; Yebra, M., Dennison, P.E., Chuvieco, E., Riaño, D., Zylstra, P., Hunt Jr, E.R., Danson, M.F., Qi, Y. & Jurdao, S. 2013. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sens. Environ. 136: 455–468. doi:10.1016/j.rse.2013.05.029; Yebra, M., Quan, X., Riaño, D., Larraondo, P.R., van Dijk, A.I. & Cary, G. J. 2018. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens. Environ. 212: 260–272. doi:10.1016/j.rse.2018.04.053; Yuan, Z., Tong, S., Bao, G., Chen, J., Yin, S., Li, F., Chula, S. & Bao, Y. 2023. Spatiotemporal variation of autumn phenology responses to preseason drought and temperature in alpine and temperate grasslands in China. Sci. Total Environ. 859: 160373. doi:10.1016/j.scitotenv.2022.160373; Zahid, A., Abbas, H.T., Ren, A., Zoha, A., Heidari, H., Shah, S.A., Imran, M.A., Alomainy, A. & Abbasi, Q.H. 2019. Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves. Plant Methods 15(1): 1–13. doi:10.1186/s13007-019 0522-9; Zhang, F. & Zhou, G. 2019. Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol. 19: 1–12. doi:10.1186/s12898-019-0233-0; Zhao, F., Xu, B., Yang, X., Jin, Y., Li, J., Xia, L., Shi, C. & Ma, H. 2014. Remote sensing estimates of grassland aboveground biomass based on Modis net primary productivity (NPP): a case study in the Xilingol grassland of Northern China. Remote Sens-Basel 6(6): 5368–5386. doi:10.3390/rs6065368; Zhao, G. 2023. Trends in grassland science: Based on the shift analysis of research themes since the early 1900s. Fundamental Research 3(2): 201–208. doi:10.1016/j.fmre.2022.05.008; Zou, L., Tian, F., Liang, T., Eklundh, L., Tong, X., Tagesson, T., Dou, Y., He, T., Liang, S. & Fensholt, R. (2023). Assessing the upper elevational limits of vegetation growth in global high mountains. Remote Sens. Environ. 286: 113423. doi:10.1016/j.rse.2022.113423; https://revistas.ucm.es/index.php/MBOT/article/view/81561

الاتاحة: https://revistas.ucm.es/index.php/MBOT/article/view/81561

المؤلفون: Mathyam Prabhakar, Kodigal A. Gopinath, Nakka Ravi Kumar, Merugu Thirupathi, Uppu Sai Sravan, Golla Srasvan Kumar, Gutti Samba Siva, Pebbeti Chandana, Vinod Kumar Singh

المصدر: Remote Sensing, Vol 16, Iss 6, p 954 (2024)

مصطلحات موضوعية: hyperspectral remote sensing, vegetation indices, canopy reflectance, leaf area, Science

Relation: https://www.mdpi.com/2072-4292/16/6/954; https://doaj.org/toc/2072-4292; https://doaj.org/article/386f55895e7d4ea4af36de8c77d35f17

الاتاحة: https://doi.org/10.3390/rs16060954
https://doaj.org/article/386f55895e7d4ea4af36de8c77d35f17

المؤلفون: Ahmet Karakoç, Murat Karabulut

المصدر: Mediterranean Botany, Vol Online first (2023)

مصطلحات موضوعية: Canopy water content, Grasslands, Hhyperspectral remote sensing, Vegetation indices, Canopy reflectance, Botany, QK1-989

وصف الملف: electronic resource

Relation: https://revistas.ucm.es/index.php/MBOT/article/view/81561; https://doaj.org/toc/2603-9109

URL الوصول: https://doaj.org/article/4f92c8feeff646d3a220664c0a5dd64a

المؤلفون: Qiaoli Wu, Shenhui Yang, Jie Jiang

المصدر: Frontiers in Forests and Global Change, Vol 6 (2023)

مصطلحات موضوعية: LESS, radiative transfer model, BRDF, canopy reflectance, CI, MODIS MCD43A1, Forestry, SD1-669.5, Environmental sciences, GE1-350

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/ffgc.2023.1106773/full; https://doaj.org/toc/2624-893X

URL الوصول: https://doaj.org/article/63ceafd9e1cc4238bcb939dbb56f18c7

المؤلفون: Xiaojuan Liu, Xianting Wu, Yi Peng, Jiacai Mo, Shenghui Fang, Yan Gong, Renshan Zhu, Jing Wang, Chaoran Zhang

المصدر: Science of Remote Sensing, Vol 7, Iss , Pp 100090- (2023)

مصطلحات موضوعية: Rice breeding, Rice full heading date, Unmanned aerial vehicle imaging, Canopy reflectance, Vegetation indices, Physical geography, GB3-5030, Science

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666017223000159; https://doaj.org/toc/2666-0172

URL الوصول: https://doaj.org/article/7b94e00748c741bfa302f41be62112d2

المؤلفون: Wenbo Li, Ke Wang, Guiqi Han, Hai Wang, Ningbo Tan, Zhuyun Yan

المصدر: Frontiers in Plant Science, Vol 13 (2023)

مصطلحات موضوعية: nutrient deficiency, symptom identification, unmanned aerial vehicle (UAV), canopy reflectance, medicinal plants, ligusticum chuanxiong Hort, Plant culture, SB1-1110

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fpls.2022.1092610/full; https://doaj.org/toc/1664-462X

URL الوصول: https://doaj.org/article/9fe01df7fea244839c453940ecd840f9

المؤلفون: Cihan Karaca, Rodney B. Thompson, M. Teresa Peña-Fleitas, Marisa Gallardo, Francisco M. Padilla

المصدر: Remote Sensing; Volume 15; Issue 8; Pages: 2174

مصطلحات موضوعية: canopy reflectance, chlorophyll meter, flavonols, fluorescence meter, fully expanded leaf, leaf position

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Remote Sensing in Agriculture and Vegetation; https://dx.doi.org/10.3390/rs15082174

الاتاحة: https://doi.org/10.3390/rs15082174

المؤلفون: Hongfeng Yu, Yongqian Ding, Huanliang Xu, Xueni Wu, Xianglin Dou

المصدر: Plant Methods, Vol 17, Iss 1, Pp 1-12 (2021)

مصطلحات موضوعية: Canopy reflectance spectrometer, Irradiation characteristics, GreenSeeker, NDVI, Plant culture, SB1-1110, Biology (General), QH301-705.5

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1746-4811

URL الوصول: https://doaj.org/article/ffa68c1663be4a61b2a8b7ae1d536e84

المؤلفون: Yan Gong, Kaili Yang, Zhiheng Lin, Shenghui Fang, Xianting Wu, Renshan Zhu, Yi Peng

المصدر: Plant Methods, Vol 17, Iss 1, Pp 1-16 (2021)

مصطلحات موضوعية: Leaf area index, Rice phenology, Unmanned aerial vehicle, Vegetation index, Canopy reflectance, Canopy height, Plant culture, SB1-1110, Biology (General), QH301-705.5

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1746-4811

URL الوصول: https://doaj.org/article/75693058a6a94fbf9b7b5e1f0dd9d9c8

المؤلفون: Severtson, Dustin, Callow, Nik, Flower, Ken, Neuhaus, Andreas, Olejnik, Matt, Nansen, Christian

المصدر: Precision Agriculture. 17(6)

مصطلحات موضوعية: Zero Hunger, Potassium deficiency, Arthropod performance, Remote sensing, Green peach aphid, Canopy reflectance, Crop and Pasture Production, Agronomy & Agriculture

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/1vh132kr

المؤلفون: Jiang, Jingyi, Weiss, Marie, Liu, Shouyang, Baret, Frédéric

المساهمون: Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The Key Laboratory for Silviculture and Conservation of Ministry of Education, Nanjing Agricultural University (NAU), Fundamental Research Funds for the Central Universities2021ZY13National Natural Science Foundation of China (NSFC)42101329Open Fund of State Key Laboratory of Remote Sensing ScienceOFSLRSS202115

المصدر: ISSN: 0378-4290 ; Field Crops Research ; https://hal.inrae.fr/hal-03634995 ; Field Crops Research, 2022, 283, pp.108538. ⟨10.1016/j.fcr.2022.108538⟩.

مصطلحات موضوعية: Effective GAI, Wheat Maize, 3D radiative transfer model, Canopy reflectance, [SDE]Environmental Sciences

Relation: hal-03634995; https://hal.inrae.fr/hal-03634995; https://hal.inrae.fr/hal-03634995/document; https://hal.inrae.fr/hal-03634995/file/S0378429022001095.pdf; PII: S0378-4290(22)00109-5; WOS: 000793177900005

الاتاحة: https://hal.inrae.fr/hal-03634995
https://hal.inrae.fr/hal-03634995/document
https://hal.inrae.fr/hal-03634995/file/S0378429022001095.pdf
https://doi.org/10.1016/j.fcr.2022.108538

المؤلفون: Yinghao Lin, Qingjiu Tian, Baojun Qiao, Yu Wu, Xianyu Zuo, Yi Xie, Yang Lian

المصدر: Agriculture; Volume 12; Issue 10; Pages: 1658

مصطلحات موضوعية: angle normalization, vegetation canopy reflectance, geostationary satellite, path length correction, Minnaert model, GOCI

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Digital Agriculture; https://dx.doi.org/10.3390/agriculture12101658

الاتاحة: https://doi.org/10.3390/agriculture12101658

المؤلفون: Luís Guilherme Teixeira Crusiol, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma, Chenxi Song

المصدر: Sustainability; Volume 14; Issue 15; Pages: 9039

مصطلحات موضوعية: Zea mays L., leaf reflectance, canopy reflectance, hyperspectral vegetation index, partial least squares regression

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Environmental Sustainability and Applications; https://dx.doi.org/10.3390/su14159039

الاتاحة: https://doi.org/10.3390/su14159039

المؤلفون: Telha H. Rehman, Mark E. Lundy, Bruce A. Linquist

المصدر: Remote Sensing; Volume 14; Issue 12; Pages: 2770

مصطلحات موضوعية: rice, nitrogen, precision management, grain yield, panicle initiation, canopy reflectance, Sufficiency-Index, NDVI, NDRE, UAS, GreenSeeker

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/rs14122770

الاتاحة: https://doi.org/10.3390/rs14122770

المؤلفون: Guyue Hu, Ainong Li

المصدر: Remote Sensing; Volume 14; Issue 8; Pages: 1821

مصطلحات موضوعية: canopy reflectance, geometric-optical model, scene components, topographic effect

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: Remote Sensing in Geology, Geomorphology and Hydrology; https://dx.doi.org/10.3390/rs14081821

الاتاحة: https://doi.org/10.3390/rs14081821