Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types

Influence of geographic origin and tissue type on the medicinal chemical compounds of Semiliquidambar cathayensis

Semiliquidambar cathayensis is widely used in traditional Chinese medicine owing its high concentrations of polyphenol, triterpenoidic acid, and flavonoids. This study aimed to explore the impact of geographical origin and tissue type on the contents of chemical compounds of S. cathayensis, as determined by colorimetric and chromatographic methods. Therefore, we quantitively evaluated chemical compounds found in the tissues of various organs of plants collected in six different regions. Overall, we found that geographical origin affected the content of medicinal compounds in S. cathayensis leaves, with plants from Jingzhou county showing the best therapeutic potential. However, no specific correlation was observed with latitude. It is noteworthy that the amount of paeoniflorin and other compounds can be used as biomarkers of geographical origin and tissue type. Most medicinal compounds accumulated mainly in the leaves, whereas ursolic and oleanolic acids accumulated in the roots. These results show that the comprehensive medicinal value of the leaves of S. cathayensis in Jingzhou county is the highest, but the root should be selected first to collect oleanolic acid and ursolic acid

Mixotrophic Cultivation Optimization of Microalga <i>Euglena pisciformis</i> AEW501 for Paramylon Production

Euglena, a flagellated unicellular protist, has recently received widespread attention for various high-value metabolites, especially paramylon, which was only found in Euglenophyta. The limited species and low biomass of Euglena has impeded paramylon exploitation and utilization. This study established an optimal cultivation method of Euglena pisciformis AEW501 for paramylon production under mixotrophic cultivation. The results showed that the optimum mixotrophic conditions were 20 °C, pH 7.0, and 63 μmol photons m−2∙s−1, and the concentrations of sodium acetate and diammonium hydrogen phosphate were 0.98 g L−1 and 0.79 g L−1, respectively. The maximal biomass and paramylon content were 0.72 g L−1 and 71.39% of dry weight. The algal powder contained more than 16 amino acids, 6 vitamins, and 10 unsaturated fatty acids under the optimal cultivation. E. pisciformis paramylon was pure β-1,3-glucan-type polysaccharide (the purity was up to 99.13 ± 0.61%) composed of linear glucose chains linked together by β-1,3-glycosidic bonds. These findings present a valuable basis for the industrial exploitation of paramylon with E. pisciformis AEW501

Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products

Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI retrieved from the multi-sensor dataset show good agreement with the field measurements. The correlation coefficient (R2) is 0.90, and the average root mean square error (RMSE) is 0.42. The network of multiple sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and meaningful temporal resolution

L‐Arginine‐Loaded Gold Nanocages Ameliorate Myocardial Ischemia/Reperfusion Injury by Promoting Nitric Oxide Production and Maintaining Mitochondrial Function

Abstract Cardiovascular disease is the leading cause of death worldwide. Reperfusion therapy is vital to patient survival after a heart attack but can cause myocardial ischemia/reperfusion injury (MI/RI). Nitric oxide (NO) can ameliorate MI/RI and is a key molecule for drug development. However, reactive oxygen species (ROS) can easily oxidize NO to peroxynitrite, which causes secondary cardiomyocyte damage. Herein, L‐arginine‐loaded selenium‐coated gold nanocages (AAS) are designed, synthesized, and modified with PCM (WLSEAGPVVTVRALRGTGSW) to obtain AASP, which targets cardiomyocytes, exhibits increased cellular uptake, and improves photoacoustic imaging in vitro and in vivo. AASP significantly inhibits oxygen glucose deprivation/reoxygenation (OGD/R)‐induced H9C2 cell cytotoxicity and apoptosis. Mechanistic investigation revealed that AASP improves mitochondrial membrane potential (MMP), restores ATP synthase activity, blocks ROS generation, and prevents NO oxidation, and NO blocks ROS release by regulating the closing of the mitochondrial permeability transition pore (mPTP). AASP administration in vivo improves myocardial function, inhibits myocardial apoptosis and fibrosis, and ultimately attenuates MI/RI in rats by maintaining mitochondrial function and regulating NO signaling. AASP shows good safety and biocompatibility in vivo. This findings confirm the rational design of AASP, which can provide effective treatment for MI/RI