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

Abdelbaki, Asmaa ; Udelhoven, Thomas

Remote sensing (Basel, Switzerland), 2022-08, Vol.14 (15), p.3515 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions
Autor: Abdelbaki, Asmaa ; Udelhoven, Thomas
Assuntos: Agricultural ecosystems ; Agriculture ; Artificial neural networks ; Chlorophyll ; chlorophyll content ; Climate change ; Crop growth ; Crops ; fractional vegetation cover ; Growth conditions ; Growth models ; hybrid-based nonparametric regression model ; hybrid-based parametric regression model ; Leaf area ; Leaf area index ; Leaves ; Literature reviews ; Machine learning ; Methods ; Neural networks ; Nonparametric statistics ; Optical properties ; Precision farming ; Radiation ; Radiative transfer ; radiative transfer models ; Regression analysis ; Regression models ; Remote sensing ; Retrieval ; Unmanned aerial vehicles ; Vegetation ; Vegetation cover
É parte de: Remote sensing (Basel, Switzerland), 2022-08, Vol.14 (15), p.3515
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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

Abdelbaki, Asmaa ; Udelhoven, Thomas

Basel: MDPI AG

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