Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

Sehgal, Vinay Kumar; Chakraborty, Debasish; Sahoo, Rabi Narayan

doi:10.1016/j.inpa.2016.04.001

Cited by 38 publications

(38 citation statements)

References 47 publications

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“…Indeed, a study estimating water-related variables (leaf and canopy water content) from forest, shrubs, and grassland found that the PROSAIL model inversion performance was rather limited across sites without precise in-situ knowledge [133]. For crops, most studies found that PROSAIL achieved reasonably accurate simulations, e.g., [41,83,110,111,137,147]. Compared to a three-dimensional dynamic maize model, the PROSAIL model performance was only slightly decreased [81].…”

Section: Biophysical and Biochemical Variablesmentioning

confidence: 99%

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

et al. 2018

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Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

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Section: Biophysical and Biochemical Variablesmentioning

confidence: 99%

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

et al. 2018

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“…Similar performance of selected plant functional traits was observed in previous studies (e.g. Casas et al 2014;Darvishzadeh et al 2008;Sehgal et al 2016).…”

Section: Discussionsupporting

confidence: 87%

“…To find the solution to the inversion for a given canopy spectrum for each estimated reflectance spectrum of the LUT, the RMSE between measured and estimated spectra was calculated according to: ∑ where is a measured reflectance at wavelength λ and is an estimated reflectance at wavelength λ in the LUT, and n is the number of wavelengths (Darvishzadeh et al 2008). To enhance the consistency of the estimated variables, we used the mean value of the best 100 simulations as the final parameter combination (Sehgal et al 2016).…”

Section: Retrieval Of Plant Functional Traits From Hyperspectral Imagmentioning

confidence: 99%

Remotely sensing the species of individual trees

Shi¹

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This PhD journey has been an amazing, precious and unforgettable period in my life. My sincerest appreciation to all who have contributed to this thesis and supported me throughout my PhD journey. First and foremost, I would like to express my sincere thanks to my promotor, Prof. Andrew K. Skidmore. Andrew, thank you for your valuable guidance, insightful advice, and continuous support. The discussion we had taught me how to view scientific questions from different perspectives and gradually I became more and more confident when you challenge me. Thanks to your encouragement and trust, I dare to think bigger and explore the topic more freely. I am very grateful for your patient guidance and critical comments during my PhD research. You always remind me the importance of "critical thinking" and "think out of the box", which I will benefit from not only for my PhD research, but also for the rest of my life. Thank you for recommending me to spend a period of my PhD journey in DLR, Germany, which greatly enriched my PhD and life experience.

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“…To derive vegetation attributes from hyperspectral remote sensing data, empirical (statistical) [4,10,11], physically based (using radiative transfer models (RTMs)) [12][13][14][15] or hybrid approaches can be used [16,17]. In the physically based approach, the interaction between solar radiation and vegetation is represented by RTMs based on physical laws [16,18]. RTMs can be utilized in either the forward or inverse (backward) mode.…”

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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Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

Cited by 38 publications

References 47 publications

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Remotely sensing the species of individual trees

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

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