Evaluation of PROSAIL inversion for retrieval of chlorophyll, leaf dry matter, leaf angle, and leaf area index of wheat using spectrodirectional measurements

Lunagaria, M. M.; Patel, H. R.

doi:10.1080/01431161.2018.1524608

Cited by 37 publications

(16 citation statements)

References 56 publications

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“…The fill factor, the phase function and the scattering albedo of sand particles are needed for Hapke model [28]. The leaf area, leaf inclination distribution, reflectance and transmittance of leaf, soil reflectance and illumination condition are needed for SAIL model [29]. However, these parameters are not always available.…”

Section: Methodsmentioning

confidence: 99%

A Flight Direction Design Method for Airborne Spectral Imaging Considering the Anisotropy Reflectance of the Target in Rugged Terrain

Zhao

Cui

Jia

2019

Sensors

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An excellent mission plan is the prerequisite for the acquisition of high quality airborne hyperspectral images which are useful for environmental research, mining etc. In order to minimize the radiance non-uniformity caused by the anisotropic reflectance of targets, the flight direction is mostly designed on the solar azimuth or 180° from it for whiskbroom and pushbroom imagers. However, the radiance to the observer is determined not only by the reflectance of the target, but also by the terrain, the illumination direction and the observation direction. So, the flight direction which is defined to minimize radiance non-uniformity might change with the terrain. In order to find the best flight direction for rugged terrain, we firstly analyze the causes of the effect of terrain on radiation non-uniformity based on the radiative transfer process. Then, the flight direction design method is proposed for composite sloping terrain. Tested by digital and physical simulation experiments, the radiance non-uniformity is minimized when the aircraft flies in the designated direction. Finally, a workflow for flight direction planning and optimizing is summarized, considering the flight mission planning techniques and the workflow of remote sensing missions.

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Section: Methodsmentioning

confidence: 99%

A Flight Direction Design Method for Airborne Spectral Imaging Considering the Anisotropy Reflectance of the Target in Rugged Terrain

Zhao

Cui

Jia

2019

Sensors

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“…The model is easily invertible and consists of leaf reflectance model PROSPECT and canopy radiative transfer model SAIL. In this special issue, Lunagaria and Patel (2019) demonstrate the use of the PROSAIL inversion model for retrieval of chlorophyll, leaf dry matter, leaf angle, and leaf area index for wheat, derived from field goniometer measurements. The PROSAIL model was inverted for all reflectance spectra for 54 different view angles and different wheat phenophases.…”

Section: Inversion Modeling For Retrieving Crop Biophysical Parametersmentioning

confidence: 99%

Remote sensing of agriculture – South/Southeast Asia research initiative special issue

Vadrevu

Dadhwal

Gutman³

et al. 2019

International Journal of Remote Sensing

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“…Model inversion methods are generally subdivided into three sub-categories: numerical optimization approach (e.g., Bacour et al, 2002; Eon et al, 2019; Lunagaria and Patel, 2019), look-up table approach (e.g., Weiss et al, 2000; X. Xu et al, 2019; Zhu et al, 2019), machine learning approach including use of neural networks (e.g., Bacour et al, 2006; García-Haro et al, 2018; Upreti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, this problem can be alleviated by using prior knowledge to strengthen constraints on individual variables or between variables. The simplest way is to define the lower and upper values between which the target trait can be retrieved from based on prior information, for example, field measurement data was used for defining input parameter range in the study of Lunagaria and Patel (2019). M. Xu et al (2019) indirectly introduced constraints between leaf chlorophyll content and LAI by establishing a 2-dimensional matrix-based relationship between leaf chlorophyll content and two vegetation indices (VIs) for VI-based look-up table inversion, which resulted in a better estimation precision than using individual VI.…”

Section: Introductionmentioning

confidence: 99%

Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning

Chen

Zheng

et al. 2021

Preprint

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A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. This can be addressed by using radiative transfer models (RTMs) to generate training dataset representing "real-world" data in situations with varying crop types and growth status as well as various observation configurations. However, this approach can lead to "ill-posed" problems related to assumptions in the sampling strategy and due to uncertainty in the model, resulting in unsatisfactory inversion results for retrieval of target variables. In order to address this problem, this research investigates a practical way to generate higher quality "synthetic" training data by integrating a crop growth model (CGM, in this case APSIM) with an RTM (in this case PROSAIL). This allows control of uncertainties of the RTM by imposing biological constraints on distribution and co-distribution of related variables. Subsequently, the method was theoretically validated on two types of synthetic dataset generated by PROSAIL or the coupling of APSIM and PROSAIL through comparing estimation precision for leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter (Cm) and leaf water content (Cw). Additionally, the capabilities of current deep learning techniques using high spectral resolution hyperspectral data were investigated. The main findings include: (1) Feedforward neural network (FFNN) provided with appropriate configuration is a promising technique to retrieve crop traits from input features consisting of 1 nm-wide hyperspectral bands across 400-2500 nm range and observation configuration (solar and viewing angles), leading to a precise joint estimation for LAI (RMSE=0.061 m2 m-2), Cab (RMSE=1.42 μg cm-2), Cm (RMSE=0.000176 g cm-2) and Cw (RMSE=0.000319 g cm-2); (2) For the aim of model simplification, a narrower range in 400-1100 nm without observation configuration in input of FFNN model provided less precise estimation for LAI (RMSE=0.087 m2 m-2), Cab (RMSE=1.92 μg cm-2), Cm (RMSE=0.000299 g cm-2) and Cw (RMSE=0.001271 g cm-2); (3) The introduction of biological constraints in training datasets improved FFNN model performance in both average precision and stability, resulting in a much accurate estimation for LAI (RMSE=0.006 m2 m-2), Cab (RMSE=0.45 μg cm-2), Cm (RMSE=0.000039 g cm-2) and Cw (RMSE=0.000072 g cm-2), and this improvement could be further increased by enriching sample diversity in training dataset.

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Evaluation of PROSAIL inversion for retrieval of chlorophyll, leaf dry matter, leaf angle, and leaf area index of wheat using spectrodirectional measurements

Cited by 37 publications

References 56 publications

A Flight Direction Design Method for Airborne Spectral Imaging Considering the Anisotropy Reflectance of the Target in Rugged Terrain

A Flight Direction Design Method for Airborne Spectral Imaging Considering the Anisotropy Reflectance of the Target in Rugged Terrain

Remote sensing of agriculture – South/Southeast Asia research initiative special issue

Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning

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