Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Zhang, Yangyang; Yang, Jian; Du, Lina

doi:10.3390/s21051869

Cited by 12 publications

(9 citation statements)

References 67 publications

(81 reference statements)

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“…Hence, it is important to choose a suitable resolution for LAI mapping with a high spatial resolution satellite. Moreover, Zhuhai‐1 with 10 m resolution provided a similar estimation accuracy of LAI to Worldview‐2 with 2 m resolution, suggesting that hyperspectral image had great potential in accurate estimation of mangrove LAI, which was similar to the findings of Zhang, Yang, and Du (2021).…”

Section: Discussionsupporting

confidence: 77%

See 1 more Smart Citation

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Zhao

Zhen

Zhang

et al. 2022

Remote Sens Ecol Conserv

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Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy (RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 (RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 (RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 (RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs (B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.

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Section: Discussionsupporting

confidence: 77%

“…Though hyperspectral remote sensing images (e.g. Zhuhai‐1and Hyperion) have 10–30 m resolution, they could obtain higher accuracy in retrieving LAI than moderate‐resolution multispectral images (Das et al., 2020; Zhang, Yang, et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Zhao

Zhen

Zhang

et al. 2022

Remote Sens Ecol Conserv

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“…where only the σ hyperparameter needs to be adjusted. The squared exponential is a widely accepted kernel function embedded in the GPR model for trait retrieval from both hyperspectral [11,81] and multispectral [80,82] datasets. The squared exponential kernel function is mostly used to reduce the total time taken for model training [81].…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

“…The squared exponential is a widely accepted kernel function embedded in the GPR model for trait retrieval from both hyperspectral [11,81] and multispectral [80,82] datasets. The squared exponential kernel function is mostly used to reduce the total time taken for model training [81]. So, it is most commonly employed for retrieving crop traits from multispectral (e.g., [82]) and hyperspectral datasets (e.g., [57]).…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Optimizing the Retrieval of Wheat Crop Traits from UAV-Borne Hyperspectral Image with Radiative Transfer Modelling Using Gaussian Process Regression

Sahoo,

Gakhar,

Rejith

et al. 2023

Remote Sensing

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The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R2 values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers’ fields, facilitating efficient crop monitoring and management.

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“…The high spatial resolution combined with the high spectral bands provided by Zhuhai-1 are expected to offer useful information for ecology applications. Zhuhai-1 hyperspectral data has proved beneficial in various applications, including land cover classification, vegetation parameter estimation, and water quality parameter estimation with positive outcomes [30][31][32]. However, no attempt has been made to use Zhuhai-1 hyperspectral data to identify the urban dominant tree species.…”

Section: Introductionmentioning

confidence: 99%

First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China

et al. 2023

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An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data have a low spatial resolution (pixel size ≥ 30 m), which limits their ability to differentiate tree species in urban areas characterized by fragmented patches and robust spatial heterogeneity. Zhuhai-1 is a new hyperspectral satellite sensor with a higher spatial resolution of 10 m. This study aimed to evaluate the potential of Zhuhai-1 hyperspectral imagery for classifying the urban dominant tree species. We first extracted 32 reflectance bands and 18 vegetation indices from Zhuhai-1 hyperspectral data. We then used the random forest classifier to differentiate 28 dominant tree species in Shenzhen based on these hyperspectral features. Finally, we analyzed the effects of the classification paradigm, classifier, and species number on the classification accuracy. We found that combining the hyperspectral reflectance bands and vegetation indices could effectively distinguish the 28 dominant tree species in Shenzhen, obtaining an overall accuracy of 76.8%. Sensitivity analysis results indicated that the pixel-based classification paradigm was slightly superior to the object-based paradigm. The random forest classifier proved to be the optimal classifier for distinguishing tree species using Zhuhai-1 hyperspectral imagery. Moreover, reducing the species number could slowly improve the classification accuracy. These findings suggest that Zhuhai-1 hyperspectral data can identify the urban dominant tree species with accuracy and holds potential for application in other cities.

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Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Cited by 12 publications

References 67 publications

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

Optimizing the Retrieval of Wheat Crop Traits from UAV-Borne Hyperspectral Image with Radiative Transfer Modelling Using Gaussian Process Regression

First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China

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