Application of a Three-Dimensional Radiative Transfer Model to Retrieve the Species Composition of a Mixed Forest Stand from Canopy Reflected Radiation

Левашова, Н. Т.; Lukyanenko, D. V.; Mukhartova, Yu. V.; Olchev, Alexander

doi:10.3390/rs10101661

Cited by 7 publications

(3 citation statements)

References 55 publications

(96 reference statements)

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“…The Soil–Leaf–Canopy (SLC) model [ 66 ], a combination of the leaf model PROSPECT-4 [ 67 ], the canopy model 4SAIL2 [ 66 ], and the soil model Hapke [ 68 ] were used in this study to predict the LAI, CCC, and fCover of potato crops (heterogeneous and discontinuous crop). The SLC model does not have many input parameters to optimize, as compared to other complex 3D models (e.g., DART [ 69 ]), which are mainly used when considering a spatially heterogeneous plant canopy [ 70 ]. Moreover, the fCover variable is directly quantified as a model output, compared to other RTMs (e.g., PROSAIL [ 71 ]).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

et al. 2021

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Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

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Section: Methodsmentioning

confidence: 99%

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

et al. 2021

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“…For row crops (heterogeneous and discontinuous crop) such as potato, the SLC (Soil-Leaf-Canopy model [22], which is an extended version of the PROSAIL model, seems to be more suitable because it can accommodate for both (homogeneous and heterogeneous) canopy structures. Moreover, compared with more complex 3D models, the SLC model is more simple and requires less information on canopy structure [23]; it can directly retrieve the fCover variable from two basic input variables-LAI and vertical crown cover (Cv) [24].…”

Section: Introductionmentioning

confidence: 99%

Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion

et al. 2019

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Look-up table (LUT)-based canopy reflectance models are considered robust methods to estimate vegetation attributes from remotely sensed data. However, the LUT inversion approach is sensitive to measurements and model uncertainties, which raise the ill-posed inverse problem. Therefore, regularization options are needed to mitigate this problem and reduce the uncertainties of estimates. In this study, we introduce a new method to regularize the LUT inversion approach to improve the accuracy of biophysical parameters (leaf area index (LAI) and fractional vegetation cover (fCover)). This was achieved by incorporating known variable correlations that existed at the test site into the LUT approach to correlate the model variables of the Soil–Leaf–Canopy (SLC) model using the Cholesky decomposition algorithm. The retrievals of 27 potato plots obtained from the regularized LUT (LUTreg) were compared with the standard LUT (LUTstd), which did not consider variable correlations. Different solutions from both types of LUTs (LUTreg and LUTstd) were utilized to improve the quality of the model outputs. Results indicate that the present method improved the accuracy of LAI estimation, with the coefficient of determination R2 = 0.74 and normalized root-mean-square error NRMSE = 24.45% in LUTreg, compared with R2 = 0.71 and NRMSE = 25.57% in LUTstd. In addition, the variability of LAI decreased in LUTreg (5.10) compared with that in LUTstd (12.10). Hence, our results give new insight into the impact of adding the correlation between variables to the LUT inversion approach to improve the accuracy of estimations. In this study, only two correlated variables (LAI and fCover) were examined; in subsequent studies, the full correlation matrix based on the Cholesky algorithm should be explored.

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“…While homogeneous one-dimensional RT models are successful in describing the propagation of light in the turbid medium of phytoelements, the 3D nature of complex and heterogeneous vegetation covers can have a significant effect on light interception [6]. The Monte Carlo (MC) ray-tracing technique is one of the major approaches used to solve the RT equation for realistic 3D architectures.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Canopy Structural and Radiative Transfer Parameters to Reconstructed Maize Structures Based on Terrestrial LiDAR Data

Ali

Zhao

et al. 2021

Remote Sensing

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The maturity and affordability of light detection and ranging (LiDAR) sensors have made possible the quick acquisition of 3D point cloud data to monitor phenotypic traits of vegetation canopies. However, while the majority of studies focused on the retrieval of macro scale parameters of vegetation, there are few studies addressing the reconstruction of explicit 3D structures from terrestrial LiDAR data and the retrieval of fine scale parameters from such structures. A challenging problem that arises from the latter studies is the need for a large amount of data to represent the various components in the actual canopy, which can be time consuming and resource intensive for processing and for further applications. In this study, we present a pipeline to reconstruct the 3D maize structures composed of triangle primitives based on multi-view terrestrial LiDAR measurements. We then study the sensitivity of the details with which the canopy architecture was represented for the computation of leaf angle distribution (LAD), leaf area index (LAI), gap fraction, and directional reflectance factors (DRF). Based on point clouds of a maize field in three stages of growth, we reconstructed the reference structures, which have the maximum number of triangles. To get a compromise between the details of the structure and accuracy reserved for later applications, we carried out a simplified process to have multiple configurations of details based on the decimation rate and the Hausdorff distance. Results show that LAD is not highly sensitive to the details of the structure (or the number of triangles). However, LAI, gap fraction, and DRF are more sensitive, and require a relatively high number of triangles. A choice of 100−500 triangles per leaf while maintaining the overall shapes of the leaves and a low Hausdorff distance is suggested as a good compromise to represent the canopy and give an overall accuracy of 98% for the computation of the various parameters.

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Application of a Three-Dimensional Radiative Transfer Model to Retrieve the Species Composition of a Mixed Forest Stand from Canopy Reflected Radiation

Cited by 7 publications

References 55 publications

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Introduction of Variable Correlation for the Improved Retrieval of Crop Traits Using Canopy Reflectance Model Inversion

Sensitivity Analysis of Canopy Structural and Radiative Transfer Parameters to Reconstructed Maize Structures Based on Terrestrial LiDAR Data

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