Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter

Sun, Bo; Wang, Chufeng; Yang, Chenghai; Xu, Baodong; Zhou, Guangsheng; Li, Xiaoyong; Xie, Jing; Xu, Shijie; Liu, Bin; Xie, Tianjin; Kuai, Jie; Zhang, Jian

doi:10.1016/j.jag.2021.102373

Cited by 25 publications

(21 citation statements)

References 38 publications

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“…The applicability of PROSAIL to UAV data has been explored in various studies for various crops. The most widely retrieved variables are green fraction (GF), LAI, LCC and canopy chlorophyll content (CCC) [34][35][36][37][38][39]. In the majority of these studies, UAVs were either flown at high altitudes to produce coarse-resolution imagery that mimics airborne or satellite data [34,35,38], or the resolution of the final orthomosaic was artificially reduced [34].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy

et al. 2022

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Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf–canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 µg/m2, silage maize RMSE = 3.74 µg/m2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70 m2/m2, silage RMSE = 0.61 m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 µg/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy

et al. 2022

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“…In recent years, with the compactness and convenience of UAVs and the ability to customize their missions, they had played an extremely important role in information-based agriculture (Sampson et al, 2003 ; Sun et al, 2021 ). The UAV was used as a spaceflight vehicle to carry various sensors, such as hyperspectral sensors, multispectral sensors, RGB cameras, and thermal infrared sensors (Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning

et al. 2022

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To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (r), root means square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those underwater limitation treatment at different fertility stages; several vegetation indices and CC values showed a highly significant correlation, with the highest correlation reaching.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model (r = 0.63, RMSE = 3.28, NRMSE = 16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model (r = 0.63, RMSE = 3.47, NRMSE = 19.2%).

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“…Studies have found that the combination of vegetation index and canopy height can effectively improve the estimation accuracy of LAI [26], biomass [16,27,28], forest volume [29] and crop yield [30]. Other studies showed that vegetation index combined with canopy coverage can also be used to estimate LAI [31,32]. However, the above studies are rarely used for LAI estimation of crops such as wheat.…”

Section: Introductionmentioning

confidence: 99%

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

Deng

2022

Remote Sensing

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Leaf area index (LAI), which is an important vegetation structure parameter, plays a crucial role in evaluating crop growth and yield. Generally, it is difficult to accurately estimate LAI only using vegetation index in remote sensing (RS), especially for unmanned aerial vehicle (UAV) based RS, as its high-resolution advantage has not been fully utilized. This study aims to propose an improved LAI estimation method that comprehensively considers spectral information and structural information provided by the UAV-based RS to improve the LAI estimation accuracy of field-grown wheat. Specifically, this method introduces the canopy height model (CHM) to compensate for the lack of structural information in LAI estimation, and then takes canopy coverage (CC) as a correction parameter to alleviate the LAI overestimation. Finally, the performance of this method is verified on RGB and multispectral images, respectively. The results show that canopy structure, namely CHM and CC, can significantly improve the accuracy of LAI estimation. Compared with the traditional method, the proposed method improves the accuracy by 22.6% on multispectral images (R2 = 0.72, RMSE = 0.556) and by 43.6% on RGB images (R2 = 0.742, RMSE = 0.534). This study provides a simple and practical method for UAV-based LAI estimation, especially for the application of low-cost RGB sensors in precision agriculture and other fields.

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Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter

Cited by 25 publications

References 38 publications

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy

Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

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