Leaf pigment content retrieval is an essential research field in remote sensing. However, retrieval studies on anthocyanins are quite rare compared to those on chlorophylls and carotenoids. Given the critical physiological significance of anthocyanins, this situation should be improved. In this study, using the reflectance, partial least squares regression (PLSR) and Gaussian process regression (GPR) were sought to retrieve the leaf anthocyanin content. To our knowledge, this is the first time that PLSR and GPR have been employed in such studies. The results showed that, based on the logarithmic transformation of the reflectance (log(1/R)) with 564 and 705 nm, the GPR model performed the best (R²/RMSE (nmol/cm²): 0.93/2.18 in the calibration, and 0.93/2.20 in the validation) of all the investigated methods. The PLSR model involved four wavelengths and achieved relatively low accuracy (R²/RMSE (nmol/cm²): 0.87/2.88 in calibration, and 0.88/2.89 in validation). GPR apparently outperformed PLSR. The reason was likely that the non-linear property made GPR more effective than the linear PLSR in characterizing the relationship for the absorbance vs. content of anthocyanins. For GPR, selected wavelengths around the green peak and red edge region (one from each) were promising to build simple and accurate two-wavelength models with R² > 0.90.

Chlorophyll is of great physiological and ecological significance. Leaf and canopy chlorophyll contents can be retrieved from remotely sensed data based on vegetation indices (VIs). However, the impacts of canopy structure and soil remain open problems. VIs are typically calculated from spectral reflectance. In this study, we also constructed and examined VIs based on canopy scattering coefficients ( W _λ ) from spectral invariants theory. Based on extensive leaf and canopy radiative transfer simulations, linear regression and artificial neural network models were built with reflectance-based and W _λ -based VIs to retrieve leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). The results showed that the canopy structure and soil significantly affected the retrievals. W _λ can effectively suppress the impacts of the leaf angle distribution (LAD) but not the leaf area index (LAI). The W _λ , estimated as the ratio reflectance/directional area scattering factor (DASF), contained a large error when the soil effect was strong. The W _λ -based VIs did not yield very accurate results in LCC estimation but exhibited higher accuracy for CCC estimation compared to reflectance-based VIs. Of all the VIs investigated, the best VI was D99 (( R ₈₅₀ - R ₇₁₀ )/( R ₈₅₀ - R ₆₈₀ )) for LCC and Wmul ( W ₇₄₉ × W ₉₅₆ ) for CCC. Compared to D99 for LCC, Wmul for CCC was less accurate, and the accuracy varied more among canopies with different LADs. The main reason was that CCC equals LCC multiplied by LAI, but LCC and LAI impact VIs in a similar manner.

Leaf pigment content retrieval is negatively affected by specular reflectance. To alleviate this effect, some specific techniques that take specular reflectance or specular effects into account have been proposed. In this study, continuous wavelet transform (CWT) and specific techniques including some vegetation indices (VIs), radiative transfer (RT), and hybrid models, were examined and compared in the nadir and near the mirror-like direction, with a 30° incident zenith angle. Results show that the RT and hybrid models appeared to be ill-posed, and they were not applicable at this high-incident zenith angle (>20°). Most VIs effectively alleviated the specular disturbance in the forward 35° direction, and comparable accuracy was obtained between the two viewing directions. Multiple linear regression (MLR), derivative transformation, and CWT were effective for specular interference alleviation. The MLR-based methods (reflectance, derivatives, etc., as the independent variables and pigment content as the response) generally obtained higher retrieval accuracies than the VIs. With MLR-based methods, the retrieval was more accurate for chlorophylls than for carotenoids. CWT plus MLR (MLR on wavelet coefficients) was the most prominent among all the methods, and it generally obtained the highest accuracy. The results are 2.68 and 0.88 μg/cm² for chlorophylls and carotenoids, respectively, in the nadir direction, and 2.42 and 0.86 μg/cm² in the forward 35° direction, with reflectance or the first derivative input for CWT. In the retrieval, wavelet coefficients at the optimal decomposition scale may achieve a balance in corresponding to fine, and broad absorption features, and the overall reflectance properties.

PET image reconstruction direct from list-mode data can eliminate the storage of empty sinogram bins, preserve all the precision and accuracy of the large amount data of PET scanners. However, the traditional list-mode reconstruction methods, such as list-mode ML-EM algorithm always suffer from high level of noise due to the ill-conditioning of the PET reconstruction problem. In this paper, we proposed a novel deep learning based method for list-mode reconstruction. We first adopted a domain transfer function to convert the sensor domain data to image domain, then the Residual Attention Dense U-net was used to learn the reconstruction.The proposed RADU-net is based on the RDU-net structure, where the Attention Gate module is integrated into.Instead of concatenating the left side feature to the right side directly, the attention gated module takes advantage of the high level feature to guide the low level feature to realize the attention mechanism before doing the concatenation. Realistic PET acquisitions of 25 digital PET brain phantoms were simulated, generating noisy list-mode data, used for evaluation. Quantification results show that the proposed listmodeCNN can outperform the U-net, list-mode ML-EM as well as TV regularized ML-EM in terms of SSIM and PSNR.