A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

被引:22
作者
Abdelbaki, Asmaa [1 ,2 ]
Udelhoven, Thomas [1 ]
机构
[1] Trier Univ, Earth Observat & Climate Proc, D-54286 Trier, Germany
[2] Fayoum Univ, Fac Agr, Soils & Water Sci Dept, Al Fayyum 63514, Egypt
关键词
leaf area index; fractional vegetation cover; chlorophyll content; hybrid-based parametric regression model; hybrid-based nonparametric regression model; radiative transfer models; FRACTIONAL VEGETATION COVER; LEAF-AREA INDEX; GLOBAL SENSITIVITY-ANALYSIS; RADIATIVE-TRANSFER MODEL; RED EDGE POSITION; LEARNING REGRESSION ALGORITHMS; ESTIMATING CHLOROPHYLL CONTENT; HYPERSPECTRAL DATA; BIOPHYSICAL PARAMETERS; SPECTRAL INDEXES;
D O I
10.3390/rs14153515
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications.
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页数:39
相关论文
共 245 条
[1]   Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging [J].
Abdelbaki, Asmaa ;
Schlerf, Martin ;
Retzlaff, Rebecca ;
Machwitz, Miriam ;
Verrelst, Jochem ;
Udelhoven, Thomas .
REMOTE SENSING, 2021, 13 (09)
[2]   Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion [J].
Abdelbaki, Asmaa ;
Schlerf, Martin ;
Verhoef, Wout ;
Udelhoven, Thomas .
REMOTE SENSING, 2019, 11 (22)
[3]  
Abdi Herve, 2003, ENCY RES METHODS SOC, V6, P792
[4]  
Adeluyi Oluseun, 2021, Int J Appl Earth Obs Geoinf, V102, P102454, DOI 10.1016/j.jag.2021.102454
[5]  
[Anonymous], 2006, EFFECTIVE GROUNDWATE
[6]   A review of machine learning kernel methods in statistical process monitoring [J].
Apsemidis, Anastasios ;
Psarakis, Stelios ;
Moguerza, Javier M. .
COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 142
[7]   Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models [J].
Atzberger, C .
REMOTE SENSING OF ENVIRONMENT, 2004, 93 (1-2) :53-67
[8]   Suitability and adaptation of PROSAIL radiative transfer model for hyperspectral grassland studies [J].
Atzberger, Clement ;
Darvishzadeh, Roshanak ;
Schlerf, Martin ;
Le Maire, Guerric .
REMOTE SENSING LETTERS, 2013, 4 (01) :56-65
[9]   Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery [J].
Atzberger, Clement ;
Richter, Katja .
REMOTE SENSING OF ENVIRONMENT, 2012, 120 :208-218
[10]  
Gewali UB, 2019, Arxiv, DOI arXiv:1802.08701