Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data

Shi, Juncheng; Wang, Cheng; Xi, Xiaohuan; Yang, Xuebo; Wang, Jinliang; Ding, Xue

doi:10.1080/2150704x.2020.1807647

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…A common approach to assessing the goodness-of-fit is calculating the adjusted R 2 [43], which accounts for model complexity and degrees of freedom to mitigate overfitting. This metric ranges from 0 to 1, where values nearing 1 indicate that the model adequately explains variation in the data, while values approaching 0 denote poor explanatory power.…”

Section: Comparison Of Goodness-of-fit Between Bp Model and Grnn Modelmentioning

confidence: 99%

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Mei,

Yuan,

Guo

et al. 2024

Sensors

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InGaAs detection systems have been increasingly used in the aerospace field, and due to the high signal-to-noise ratio requirements of short-wave infrared quantitative payloads, there is an urgent need for methods for the rapid and precise evaluation and the optimal design of these systems. The rigid-flex printed circuit board (PCB) is a vital component of InGaAs detectors, as its grid ground plane design parameters impact parasitic capacitance and thus affect weak infrared analog signals. To address the time-intensive and costly nature of design optimization achieved with simulations and experimental measurements, we propose an innovative method based on a neural network to predict the scattering parameters of rigid-flex boards for InGaAs detection links. This is the first study in which the effects of rigid-flex boards on weak infrared detection signals have been considered. We first obtained sufficient samples with software simulation. A backpropagation (BP) neural network prediction model was trained on existing sample sets and then verified on a rigid-flex board used in a crucial aerospace short-wave infrared quantitative mission. The model efficiently and accurately predicted high-speed interconnect scattering parameters under various rigid-flex board grid plane parameter conditions. The prediction error was less than 1% compared with a 3D field solver, indicating an overcoming of the iterative optimization inefficiency and showing improved design quality for InGaAs detection circuits.

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Section: Comparison Of Goodness-of-fit Between Bp Model and Grnn Modelmentioning

confidence: 99%

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Mei,

Yuan,

Guo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…There are also machine learning algorithms that can estimate the FPAR. Shi et al [101] demonstrated the feasibility of estimating the maize FPAR using an ANN and stepwise multiple linear regression (SMLR) method (R 2 > 0.6). Although there were some errors in the FPAR estimation at the flowering and maturity stages, the hybrid model still exhibited acceptable performance during the seedling stage.…”

Section: Estimating the Fpar In Canola Growth Periods With Hybrid Modelsmentioning

confidence: 99%

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

et al. 2023

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The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

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Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data

Shi

Wang

et al. 2022

International Journal of Remote Sensing

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Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data

Cited by 3 publications

References 18 publications

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data

Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data

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