High resolution retrieval of leaf chlorophyll content over Himalayan pine forest using Visible/IR sensors mounted on UAV and radiative transfer model
暂无分享,去创建一个
[1] H. Fang,et al. Determination of the Leaf Inclination Angle (LIA) through Field and Remote Sensing Methods: Current Status and Future Prospects , 2023, Remote. Sens..
[2] M. Pal,et al. Cloud Detection using Sentinel 2 Imageries: A comparison of XGBoost, RF, SVM and CNN algorithms , 2022, Geocarto International.
[3] Guijun Yang,et al. Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery , 2022, Comput. Electron. Agric..
[4] Lunche Wang,et al. Leaf pigment retrieval using the PROSAIL model: influence of uncertainty in prior canopy-structure information , 2022, The Crop Journal.
[5] O. Muller,et al. Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy , 2022, Remote. Sens..
[6] P. Srivastava,et al. Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms , 2022, Comput. Electron. Agric..
[7] Chufeng Wang,et al. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[8] A. Anand,et al. Development of hyperspectral indices for anti-cancerous Taxol content estimation in the Himalayan region , 2021, Geocarto International.
[9] Thomas Udelhoven,et al. Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging , 2021, Remote. Sens..
[10] Ramandeep Kaur M. Malhi,et al. Denoising AVIRIS-NG Data for Generation of New Chlorophyll Indices , 2021, IEEE Sensors Journal.
[11] S. Schmidtlein,et al. The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology , 2021 .
[12] Huichun Ye,et al. Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data , 2020, Remote. Sens..
[13] Liangyun Liu,et al. Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds , 2020, Remote. Sens..
[14] Jinfei Wang,et al. Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn , 2020, Remote. Sens..
[15] Hitendra Padalia,et al. Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India , 2020, Int. J. Appl. Earth Obs. Geoinformation.
[16] Huaguo Huang,et al. Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar , 2019, Remote. Sens..
[17] Yang Li,et al. Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs , 2019, Remote. Sens..
[18] Frédéric Baret,et al. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops , 2019, Remote Sensing of Environment.
[19] A. B. M. Shawkat Ali,et al. A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat , 2019, Remote. Sens..
[20] Henning Buddenbaum,et al. Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems , 2019, Surveys in Geophysics.
[21] Gustau Camps-Valls,et al. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods , 2018, Surveys in Geophysics.
[22] Wolfram Mauser,et al. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study , 2018, Remote. Sens..
[23] Neus Sabater,et al. Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis , 2016, Remote. Sens..
[24] Yong Liu,et al. Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[25] J. Féret,et al. A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy , 2016 .
[26] C. Felby,et al. Light-driven oxidation of polysaccharides by photosynthetic pigments and a metalloenzyme , 2016, Nature Communications.
[27] Pablo J. Zarco-Tejada,et al. Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping , 2015, Remote. Sens..
[28] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[29] Jan G. P. W. Clevers,et al. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison , 2015 .
[30] Beatriz Fernández-Marín,et al. Opening Pandora's box: cause and impact of errors on plant pigment studies , 2015, Front. Plant Sci..
[31] José F. Moreno,et al. Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model , 2013, Remote. Sens..
[32] Peter R. J. North,et al. Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods , 2013, Remote. Sens..
[33] Craig S. T. Daughtry,et al. Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices , 2013 .
[34] Lammert Kooistra,et al. Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data , 2012, Remote. Sens..
[35] Roshanak Darvishzadeh,et al. Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[36] A. Skidmore,et al. Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models , 2011 .
[37] Clement Atzberger,et al. Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based LAI Estimation of Wheat, Sugar Beet, and Maize , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[38] Michael K. Danquah,et al. Chlorophyll Extraction from Microalgae: A Review on the Process Engineering Aspects , 2010 .
[39] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[40] Zhang Tianyi. LEAF AREA INDEX PETRIEVAL BASED ON REMOTELY SENSED DATA AND PROSPECT+SAIL MODEL , 2009 .
[41] Jing M. Chen,et al. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery , 2008 .
[42] A. Skidmore,et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .
[43] Michael E. Schaepman,et al. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.
[44] J. Hill,et al. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .
[45] Kenneth R. Richards,et al. A Review of Forest Carbon Sequestration Cost Studies: A Dozen Years of Research , 2004 .
[46] C. Bacour,et al. Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. , 2000 .
[47] F. López‐Serrano,et al. LAI estimation of natural pine forest using a non-standard sampling technique , 2000 .
[48] V. Demarez,et al. A Modeling Approach for Studying Forest Chlorophyll Content , 2000 .
[49] Ranga B. Myneni,et al. Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies , 1998 .
[50] Gregory P. Asner,et al. Ecological Research Needs from Multiangle Remote Sensing Data , 1998 .
[51] F. Baret,et al. Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems , 1996 .
[52] F. M. Danson,et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .
[53] John R. Miller,et al. Comparative Relationships between Some Red Edge Parameters and Seasonal Leaf Chlorophyll Concentrations , 1995 .
[54] Frédéric Baret,et al. Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties , 1994 .
[55] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[56] W. Verhoef. Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .
[57] Prashant K. Srivastava,et al. Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region , 2021 .
[58] J. Kumhálová,et al. Comparing RGB - based vegetation indices from UAV imageries to estimate hops canopy area , 2020 .
[59] Wolfram Mauser,et al. Remote Sens , 2015 .
[60] Luis Alonso,et al. Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[61] Peter R. J. North,et al. Retrieval of leaf area index from MODIS surface reflectance by model inversion using different minimization criteria , 2013 .
[62] M. Dalponte,et al. Remote Sensing of Environment , 2022 .
[63] R. Myneni,et al. Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .
[64] P. G. Jarvis,et al. Canopy Structure and Leaf Area Index in a Mature Scots Pine Forest , 1982 .