Search

Aerosol Optical Depth (AOD) is one of the important parameters to characterize the physical properties of the atmospheric aerosol, which is used to describe the extinction characteristics of the aerosol, and also to calculate the aerosol content, to assess the degree of air pollution and to study aerosol climate effect. To study the historical change of aerosol in long-time series, the advanced very high resolution radiometer (AVHRR) data earliest used for aerosol research was used in this study. Due to the lack of shortwave infrared (SWIR) (center at 2.13 µm) of the sensor, the relationship between the blue and red bands with SWIR cannot be provided, and the visible band used to calculate the normalized difference vegetation index (NDVI) contains the wavelength range of red and green, it is very difficult to calculate the accurate land surface reflectance (LSR). Therefore, based on the Dense Dark Vegetation algorithm (DDV), we propose to introduce mature MODIS vegetation index products (MYD13) to correct AVHRR NDVI, to support the estimation of AVHRR LSR, determine the relationship between corrected AVHRR NDVI and visible band LSR, and to carry out aerosol retrieval. The results show that about 63% of the data are within the error line, and there is a consistent distribution trend in the inter-comparison validation with MODIS aerosol products (MYD04).

The crude β-xylosidase from Aureobasidium pullulans CBS 135684 was immobilized on the surface of the modified spent expanded perlite via (3-aminopropyl) triethoxy-silane and glutaraldehyde cross-linking reaction. Over 80% of the initial free enzyme activity was detected on the bound enzyme. The immobilized β-xylosidase (204.7 ± 6.5 U g-1 of perlite) exhibited transxylosylation activity when sugars from black liquor xylan hydrolysate and a variety of alcohols (C1-6) were used as donor and acceptor molecules, respectively, yielding a number of alkyl xylosides. The synthesized hexyl xyloside exhibited some interesting properties comparable with those of a commercial hexyl glucoside, including an antioxidant activity (IC50 = 8.9 ± 2.0 mg mL-1), wetting time (43 s), and critical micelle concentration value (252 mmol L-1). Optimization for hexyl xyloside synthesis was performed via statistical analysis using a Box and Behnken design. The maximum predicted yield of hexyl xyloside (499.0 ± 1.1 mg g-1 xylan hydrolysate) was obtained with 10% (w/v) xylan hydrolysate, 10 U g-1 immobilized β-xylosidase, and 13% (v/v) hexanol at 70 °C, pH 6.0 for 4 h, and it was validated by the following experimental yield (498.9 ± 4.8 mg g-1 xylan hydrolysate). The maximum production yield remained relatively stable after the immobilized enzyme was reused for 12 cycles with a 471.5 ± 15.1 mg g-1 hexyl xyloside yield (94.4% of the first cycle). Thus, the immobilized β-xylosidase has a high potential as a robust catalyst for application in alkyl xyloside production.

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.78 and −2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of −0.26 and −1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of −1.60 and −1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.

JSON is a very popular data format in many applications in Web and enterprise. Recently, many data analytical systems support the loading and querying JSON data. However, JSON parsing can be costly, which dominates the execution time of querying JSON data. Many previous studies focus on building efficient parsers to reduce this parsing cost, and little work has been done on how to reduce the occurrences of parsing. In this paper, we start with a study with a real production workload in Alibaba, which consists of over 3 million queries on JSON. Our study reveals significant temporal and spatial correlations among those queries, which result in massive redundant parsing operations among queries. Instead of repetitively parsing the JSON data, we propose to develop a cache system named Maxson for caching the JSON query results (the values evaluated from JSONPath) for reuse. Specifically, we develop effective machine learning-based predictor with combining LSTM (long shortterm memory) and CRF (conditional random field) to determine the JSONPaths to cache given the space budget. We have implemented Maxson on top of SparkSQL. We experimentally evaluate Maxson and show that 1) Maxson is able to eliminate the most of duplicate JSON parsing overhead, 2) Maxson improves end-to-end workload performance by 1.5–6.5×.

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

Pyroligneous acids (PA) from hardwood, softwood, and bamboo significantly disinfected encephalomyocarditis virus (EMCV). Twenty-five kinds of phenolic derivatives in the PAs were identified and quantified. The total amounts of phenolic compounds in bamboo PA is higher than those in the PAs from softwood and hardwood. Phenol, 2-methoxyphenol, 2-methoxy-4-methylphenol, and 2-methoxy-4-ethylphenol are the most abundant compounds in the PAs examined. The activities of all the phenolic compounds against the encephalomyocarditis virus were assessed. The number of phenolic hydroxyl groups significantly affects the antiviral activity, and catechol and its derivatives exhibit higher viral inhibition effects than other phenolic derivatives. In addition, substituents affect the antiviral activity of the compounds. Phenolic compounds with a methyl group show higher activities than with a methoxyl group (e.g., 2-methylphenol > 2-methoxyphenol). Moreover, the relative position of functional groups also plays a key role...

Sand and dust storms (hereafter, "sandstorms") not only damage the ecological environment in northern and northwestern China but also influence the economic and social development of the affected regions and constitute a threat to human health. This study focuses on monitoring sandstorms and analyzing the sandstorm migration process in northern and northwestern China. These sandstorms are characterized by their high frequency occurrences, strong dust intensity, long durations and highly destructive effects. The dry climate conditions and low degree of vegetation coverage in this region increase the difficulty of sandstorm monitoring. This paper proposes a remote sensing monitoring method for sandstorms in northern and northwestern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and the radiation characteristics of the research region and of the sandstorms. A strong sandstorm that occurred on April 23–25, 2014, is analyzed to illustrate the proposed monitoring method. Information on the sandstorm is validated and analyzed through visual interpretation and comparison with Meteorological Information Comprehensive Analysis and Process System (MICAPS) ground measurements. The spatial distribution of the sandstorm is highly consistent with the true-color MODIS data. The comparison of the results of the remote sensing monitoring of the sandstorm with the MICAPS measurements yields a high coincidence rate of 96.3%. Additionally, the migration process of the sandstorm can be clearly recognized in 6 MODIS images captured during the 3-day sandstorm. Based on the above results, we conclude that the proposed method can be used for dynamic remote sensing monitoring of sandstorms in northern and northwestern China.

In this paper, in order to produce long term fully global land surface temperature (LST) product, the generalized split-window (GSW) algorithm and dual-window (DW) algorithm was used to retrieve LST from different geostationary (GEO) satellite data, including the FY-2E/4A, MTSAT-2/Himawari-8, MSG2, and GOES13/15. First, the coefficients of the GSW and DW algorithm were obtained from a simulation database constructed using the MODTRAN 5.2 and the SeeBor V5.0 atmospheric profile database. Second, the emissivity was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset (GED). Finally, the LST results of FY-4A AGRI and Himawari-8 AHI were retrieved and cross-validated. The results show that the LST algorithms developed in this work are capable of generating accurate high temporal resolution LST retrieval from global GEO satellite data.

In this study, we present an operational algorithm to retrieve the land surface temperature (LST) from MODIS thermal infrared data using physically retrieved emissivity product. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. First, the emissivity in the MODIS two split-window channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset. Then, the LST was retrieved using a modified generalized split-window algorithm. The MuSyQ MODIS LST product and the C6 MxD11 LST product were evaluated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces.

In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018) (http://www.robustvision.net/index.php). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: (a) How to extract more discriminative features for different scenes (compared to a single scene)? (b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.

The pyrolysis product, wood vinegar (WV), from Japanese larch exhibited strong antiviral activity against the encephalomycarditis virus (EMCV). Catechol, 3-methyl-, 4-methyl-, 4-ethyl-, and 3-methoxycatechol, and 2-methyl-1,4-benzenediol were identified as the major antiviral compounds. The viral inhibition ability of these compounds was affected by the structure and position of the substituent group attached to the aromatic skeleton. The IC50 of catechol was 0.67 mg mL−1 and those of its derivatives were

In this paper we study the problem of monocular relative depth perception in the wild. We introduce a simple yet effective method to automatically generate dense relative depth annotations from web stereo images, and propose a new dataset that consists of diverse images as well as corresponding dense relative depth maps. Further, an improved ranking loss is introduced to deal with imbalanced ordinal relations, enforcing the network to focus on a set of hard pairs. Experimental results demonstrate that our proposed approach not only achieves state-of-the-art accuracy of relative depth perception in the wild, but also benefits other dense per-pixel prediction tasks, e.g., metric depth estimation and semantic segmentation.

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

Cloud detection is a key processing step before extracting information of earth surface from the earth observation data. Lots of schemes have been developed for cloud detection, static threshold method is the main method that is widely used in cloud detection. However, for the huge difference between different land objects, it is much difficult to find a proper threshold to detect the cloudy pixel from clear sky, especially, when the land covered by the thin or broken cloud. Therefore, a dynamic threshold cloud detection algorithm was proposed in this paper to improve the cloud detection. A priori monthly surface reflectance database was constructed using MODIS surface reflectance products and used to estimate the surface reflectance for dynamic threshold determination. Dynamic thresholds were determined by the simulation relationships between the apparent reflectance and the surface reflectance under clear conditions with 6S model. MODIS and Landsat 8 OLI data were selected to perform the experiments. Results showed that this new algorithm demonstrated better detection results of different cloud types over different land types.