Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Dosimetric Effects of Thermoplastic Immobilizing Devices on Surface Dose

Thermoplastic immobilizing masks have dosimetric effects on the patient’s skin dose. The thermoplastic percentage depth dose (PDD), equivalent thickness of water for the masks and surface doses were determined. The surface dose factors due to the thermoplastic mask was found to be 1.7949, 1.9456, 2.0563, 2.1967, 2.3827, 2.5459 and 2.6565 for field sizes of 5 × 5, 8 × 8, 10 × 10, 12 × 12, 15 × 15, 18 × 18 and 20 × 20 cm2 respectively which shifted the percentage depth dose curve to lower values. The physical thermoplastic thickness was measured to be between 2.30 and 1.80 mm, and the equivalent thicknesses of water, de, were determined to be between 1.2 and 1.00 mm. This meant that, as the mask thickness decreased, its water equivalent thickness also decreased. The presence of the mask material increased the skin dose to a factor of 1%. The thermoplastic mask factor was also found to be 0.99.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.

Water storage variations of the Yangtze,Yellow,and Zhujiang river basins derived from the DEOS Mass Transport (DMT-1) model

In general, China is short of water resources and some regions even experience a shortage of daily water supply. This could threaten the stability and economic development of the nation. A study on the water storage variations is especially important for the water management and storage prediction in three largest river basins of China, namely, Yangtze, Yellow, and Zhujiang, where the most dense population and leading economic regions are located. The satellite gravity mission GRACE （Gravity Recovery and Climate Experiment） provides an opportunity to macroseopically identify water （or mass） variations in the Earth＇s system with a spatial resolution of 300-400 km and a temporal resolution of about one month. We use the first release of the DEOS （Delft Institute of Earth Observation and Space Systems） Mass Transport （DMT-1） model based on GRACE data to analyze water storage changes in the three river basins. The DMT-1 model consists of monthly solutions, which are computed using an innovative methodology. The methodology includes, in particular, the application of a statistically optimal Wiener-type filter based on full varianee-covariance matrices of noise and signal. This results in particularly sharp mass variation maps. Taking one monthly solution as an example, we compare the results derived from the DMT-1 model with ones produced with the standard post-processing scheme based on a combination of the de-striping and Gaussian filtering. The comparison shows that the DMT-1 model outperforms the other models and is suitable for the analysis of the mass changes in river basins. A subset of the DMT-1 solutions in the interval between February 2003 and May 2008 is used to estimate the secular trends and seasonal variations for the three river basins. The estimated trends show that the water storage of the Yellow River basin does not have significant changes, while the Zhujiang and Yangtze river basins have a large and statistically significant water storage increase. The estimation of seasonal variations demonstrates that the water storage variations in Yangtze and Zhujiang river basins are almost in the same phase. The amplitude of variations in the Zhujiang River basin is larger than that in Yangtze. No clear annual variations are observed in the Yellow River basin. The observed water storage variations generally coincide with the observations and conclusions presented in the hydrological reports of the Chinese Ministry of Water Resources