A spectral directional reflectance model of row crops

Zhao, Feng; Gu, Xingfa; Verhoef, W.; Wang, Qiao; Yu, Tao; Liu, Qiang; Huang, Huaguo; Qin, Wei; Chen, Liangfu; Zhao, Haitao

doi:10.1016/j.rse.2009.09.018

Cited by 54 publications

(38 citation statements)

References 49 publications

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“…Moreover, for practical applications of remote sensing technique, canopy-level ChlF model can be simulated in order to interpret the canopy fluorescence signal from the airborne and space-borne observations. With the fast development of the vegetative canopy models based on the radiative transfer theory [8,[31][32][33] and the computer simulation methods [34], coupling the leaf-level ChlF model (e.g., FluorMODleaf) with a canopy-level ChlF models can become a promising tool for the growth status monitoring of crops in precision agriculture.…”

Section: Potential and Limitations Of Applying Model Inversion For Thmentioning

confidence: 99%

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

et al. 2015

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Abstract:In this study, backward and forward fluorescence radiance within the emission spectrum of 640-850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the OPEN ACCESSRemote Sens. 2015, 7 14180 fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R 2 ) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10 −6 W· m −2 · sr −1 · nm −1 , respectively, for backward fluorescence, and 0.92 and 3.84 × 10 −6 W· m −2 · sr −1 · nm −1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.

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Section: Potential and Limitations Of Applying Model Inversion For Thmentioning

confidence: 99%

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

et al. 2015

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“…Squared correlation coefficients for model validation (R 2 _VAL), root mean square error (RMSE) and normalized root mean square error (NRMSE, %) were used to calculate the fitness between the estimated and observed values [19] and to evaluate the overall performance of model. In general, all data calculations were implemented with IDL 8.0.…”

Section: Field Datamentioning

confidence: 99%

“…GO modeling approaches are relatively straightforward and can accurately simulate the angular variations of canopy reflectance as a function of row orientation, crop dimension, soil background and shadow effects, due to different Sun-viewing geometries [19,20]. However, this approach ignores the surface reflection at the shaded area that results from multiple scattering and the non-Lambertian reflection of the vegetation-soil system, and it is therefore of limited use for estimating the structure of the crop architecture and, therefore, needs to be improved by integrating RT models.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, this method is based on analytical approximations and includes the connection between the biophysical variables and the canopy reflectance. Zhao et al [19] developed an optical RT model of row crops based on a novel mathematical treatment of the four-stream SAIL model. The performance of the model was evaluated against field measurements of winter wheat, as well as with an established 3D computer simulation model.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat

et al. 2014

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Rice and wheat are mainly planted in a row structure in China. Radiative transfer models have the potential to provide an accurate description of the bidirectional reflectance characteristics of the canopies of row-planted crops, but few of them have addressed the problem of row-planted structures. In this paper, a new 4SAIL-RowCrop model for row-planted rice and wheat canopies was developed by integrating the 4SAIL model and the Kimes geometric model. The Kimes model and the Kimes-Porous geometric optics (GO) module were used to simulate different scene component proportions. Spectral reflectance and transmittance were subsequently calculated using the 4SAIL model to determine the reflectance of crucial scene components: the illuminated canopy, illuminated background and shadowed background. The model was validated by measuring the reflectance of rice and wheat cultivars at different growth stages, planting densities and nitrogen fertilization rates. The directional and nadir reflectance simulated by the model agreed well with experimental data, with squared correlation coefficients of 0.69 and 0.98, root mean square errors of 0.013 and 0.009 and normalized root mean square errors of 15.8% and 12.4%, respectively. The results indicate that the 4SAIL-RowCrop model is suitable for simulating the spectral reflectance of the canopy of row-planted rice and wheat.

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“…It is a parameter determined at the canopy scale, and is an important biophysical parameter in atmosphere-ecosystem interaction, ecophysiology, hydrological and ecological models, and global climate change studies [2] . Quantitative evaluation of LAI can provide information for the evaluation of crop growth, forecast for crop yield, and the possibility of irrigation scheduling information based on remote sensing data [3] . LAI is functionally linked to the canopy structure and canopy spectral reflectance.…”

Section: Introductionmentioning

confidence: 99%

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

Wang¹,

Zhang²,

Lin³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

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A spectral directional reflectance model of row crops

Cited by 54 publications

References 49 publications

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Development of a Novel Bidirectional Canopy Reflectance Model for Row-Planted Rice and Wheat

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

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