Добірка наукової літератури з теми "Canopy chlorophyll content (CCC)"
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Статті в журналах з теми "Canopy chlorophyll content (CCC)"
Ali, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden, and Sander Mücher. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11 (June 1, 2020): 1788. http://dx.doi.org/10.3390/rs12111788.
Повний текст джерелаSun, Qi, Quanjun Jiao, Xiaojin Qian, Liangyun Liu, Xinjie Liu, and Huayang Dai. "Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations." Remote Sensing 13, no. 3 (January 29, 2021): 470. http://dx.doi.org/10.3390/rs13030470.
Повний текст джерелаHoeppner, J. Malin, Andrew K. Skidmore, Roshanak Darvishzadeh, Marco Heurich, Hsing-Chung Chang, and Tawanda W. Gara. "Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data." Remote Sensing 12, no. 21 (October 31, 2020): 3573. http://dx.doi.org/10.3390/rs12213573.
Повний текст джерелаBai, Xueyuan, Yingqiang Song, Ruiyang Yu, Jingling Xiong, Yufeng Peng, Yuanmao Jiang, Guijun Yang, Zhenhai Li, and Xicun Zhu. "Hyperspectral Estimation of Apple Canopy Chlorophyll Content Using an Ensemble Learning Approach." Applied Engineering in Agriculture 37, no. 3 (2021): 505–11. http://dx.doi.org/10.13031/aea.13935.
Повний текст джерелаZillmann, E., M. Schönert, H. Lilienthal, B. Siegmann, T. Jarmer, P. Rosso, and T. Weichelt. "Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 149–55. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-149-2015.
Повний текст джерелаYang, Hongye, Bo Ming, Chenwei Nie, Beibei Xue, Jiangfeng Xin, Xingli Lu, Jun Xue, et al. "Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution." Remote Sensing 14, no. 9 (April 28, 2022): 2115. http://dx.doi.org/10.3390/rs14092115.
Повний текст джерелаJiao, Quanjun, Qi Sun, Bing Zhang, Wenjiang Huang, Huichun Ye, Zhaoming Zhang, Xiao Zhang, and Binxiang Qian. "A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle." Remote Sensing 14, no. 1 (December 25, 2021): 98. http://dx.doi.org/10.3390/rs14010098.
Повний текст джерелаČervená, L., G. Pinlová, Z. Lhotáková, E. Neuwirthová, L. Kupková, M. Potůčková, J. Lysák, P. Campbell, and J. Albrechtová. "DETERMINATION OF CHLOROPHYLL CONTENT IN SELECTED GRASS COMMUNITIES OF KRKONOŠE MTS. TUNDRA BASED ON LABORATORY SPECTROSCOPY AND AERIAL HYPERSPECTRAL DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 381–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-381-2022.
Повний текст джерелаKamenova, Ilina, Petar Dimitrov, and Rusina Yordanova. "Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat." Aerospace Research in Bulgaria 30 (2018): 63–74. http://dx.doi.org/10.3897/arb.v30.e06.
Повний текст джерелаBrown, Luke A., Booker O. Ogutu, and Jadunandan Dash. "Estimating Forest Leaf Area Index and Canopy Chlorophyll Content with Sentinel-2: An Evaluation of Two Hybrid Retrieval Algorithms." Remote Sensing 11, no. 15 (July 25, 2019): 1752. http://dx.doi.org/10.3390/rs11151752.
Повний текст джерелаДисертації з теми "Canopy chlorophyll content (CCC)"
Gao, Jincheng. "Canopy chlorophyll estimation with hyperspectral remote sensing." Diss., Manhattan, Kan. : Kansas State University, 2006. http://hdl.handle.net/2097/252.
Повний текст джерелаJiang, Jingyi. "Retrieving leaf and canopy characteristics from their radiative properties using physically based models : from laboratory to satellite observations Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model a model of leaf optical properties accounting for the differences between upper and lower faces Speeding up 3D radiative transfer simulations: a physically based approximation of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance Effective GAI for crops is best estimated from reflectance observations as compared to GAI and LAI Optimal learning for GAI and chlorophyll estimation from 1D and 3D radiative transfer model inversion: the case of wheat and maize crops observed by Sentinel2." Thesis, Avignon, 2019. http://www.theses.fr/2019AVIG0708.
Повний текст джерелаMeasuring leaf and canopy characteristics from remote sensing acquisitions is an effective and non destructive way to monitor crops both for decision making within the smart agriculture practices or for phenotyping under field conditions to improve the selection efficiency. With the advancement of computer computing power and the increasing availability of high spatial resolution images, retrieval methods can now benefit from more accurate simulations of the Radiative Transfer (RT) models within the vegetation. The objective of this work is to propose and evaluate efficient ways to retrieve leaf and canopy characteristics from close and remote sensing observations by using RT models based on a realistic description of the leaf and canopy structures. At the leaf level, we first evaluated the ability of the different versions of the PROSPECT model to estimate biochemical variables like chlorophyll (Cab), water and dry matter content. We then proposed the FASPECT model to describe the optical properties differences between the upper and lower leaf faces by considering a four-layer system. After calibrating the specific absorption coefficients of the main absorbing material, we validated FASPECT against eight measured ground datasets. We showed that FASPECT simulates accurately the reflectance and transmittance spectra of the two faces and overperforms PROSPECT for the upper face measurements. Moreover, in the inverse mode, the dry matter content estimation is significantly improved with FASPECT as compared to PROSPECT. At the canopy level, we used the physically based and unbiased rendering engine, LuxCoreRender to compute the radiative transfer from a realistic 3D description of the crop structure. We checked its good performances by comparison with the state of the art 3D RT models using the RAMI online model checker. Then, we designed a speed-up method to simulate canopy reflectance from a limited number of soil and leaf optical properties. Based on crop specific databases simulated from LuxCoreRender for wheat and maize and crop generic databases simulated from a 1D RT model, we trained some machine learning inversion algorithms to retrieve canopy state variables like Green Area Index GAI, Cab and Canopy Chlorophyll Content (CCC). Results on both simulations and in situ data combined with SENTINEL2 images showed that crop specific algorithms outperform the generic one for the three variables, especially when the canopy structure breaks the 1D turbid medium assumption such as in maize where rows are dominant during a significant part of the growing season
Schlemmer, Michael R. "Examining leaf and canopy optical properties for the assessment of chlorophyll content to determine nitrogen management strategies." 2008. http://proquest.umi.com/pqdweb?did=1625771201&sid=27&Fmt=2&clientId=14215&RQT=309&VName=PQD.
Повний текст джерелаTitle from title screen (site viewed Mar. 10, 2009). PDF text: vi, 121 p. : ill. (some col.) ; 1 Mb. UMI publication number: AAT 3336809. Includes bibliographical references. Also available in microfilm and microfiche formats.
Частини книг з теми "Canopy chlorophyll content (CCC)"
Shanahan, John F., Kyle H. Holland, James S. Schepers, Dennis D. Francis, Michael R. Schlemmer, and Robert Caldwell. "Use of a Crop Canopy Reflectance Sensor to Assess Corn Leaf Chlorophyll Content." In ASA Special Publications, 135–50. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub66.c11.
Повний текст джерелаТези доповідей конференцій з теми "Canopy chlorophyll content (CCC)"
Jin, Xu, and Meng Jihua. "Retrieval Of canopy chlorophyll content for spring corn using multispectral remote sensing data." In 2014 Third International Conference on Agro-Geoinformatics. IEEE, 2014. http://dx.doi.org/10.1109/agro-geoinformatics.2014.6910668.
Повний текст джерелаClevers, J. G. P. W., and L. Kooistra. "Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080916.
Повний текст джерелаXuqing Li, Xiangnan Liu, Zhihong Du, and Cuicui Wang. "A random forest model for estimating Canopy Chlorophyll Content in rice using hyperspectral measurements." In 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2013. http://dx.doi.org/10.1109/fskd.2013.6816256.
Повний текст джерелаAi, Jinquan, Wei Gao, Runhe Shi, Chao Zhang, Zhibin Sun, Wenhui Chen, Chaoshun Liu, and Yuyan Zeng. "In situ hyperspectral data analysis for canopy chlorophyll content estimation of an invasive speciesspartina alterniflorabased on PROSAIL canopy radiative transfer model." In SPIE Optical Engineering + Applications, edited by Wei Gao, Ni-Bin Chang, and Jinnian Wang. SPIE, 2015. http://dx.doi.org/10.1117/12.2186973.
Повний текст джерелаCui, Zhaoyu, and John Kerekes. "Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Chlorophyll Content Retrieval." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898783.
Повний текст джерелаZhang, Qingyuan, and Elizabeth M. Middleton. "Introduction to fraction of absorbed par by canopy chlorophyll (fAPARchl) and canopy leaf water content derived from hyperion, simulated HyspIRI and MODIS images." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5649467.
Повний текст джерелаLi, Dong, Hengbiao Zheng, Xiaoqing Xu, Ning Lu, Xia Yao, Jiale Jiang, Xue Wang, et al. "BRDF Effect on the Estimation of Canopy Chlorophyll Content in Paddy Rice from UAV-Based Hyperspectral Imagery." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517684.
Повний текст джерелаPasqualotto, Nieves, Salvatore Falanga Bolognesi, Oscar Rosario Belfiore, Jesus Delegido, Guido D'Urso, and Jose Moreno. "Canopy chlorophyll content and LAI estimation from Sentine1-2: vegetation indices and Sentine1-2 Leve1-2A automatic products comparison." In 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 2019. http://dx.doi.org/10.1109/metroagrifor.2019.8909218.
Повний текст джерелаLaurent, V. C. E., W. Verhoef, M. E. Schaepman, A. Damm, and J. G. P. W. Clevers. "Mapping LAI and chlorophyll content from at-sensor APEX data using a Bayesian optimisation of a coupled canopy-atmosphere model." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352321.
Повний текст джерелаJiang, J., M. Weiss, S. Liu, and F. Baret. "The impact of canopy structure assumption on the retrieval of GAI and Leaf Chlorophyll Content for wheat and maize crops." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8899064.
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