Search

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.

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.

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.

The Beijing-Tianjin-Hebei region is one of the most polluted areas in China, especially in terms of particulate matter pollution. To describe the aerosol distribution of such an area, the aerosol optical depth (AOD) with a 1 km spatial resolution was retrieved from MODIS data based on a new high-resolution aerosol retrieval algorithm with a priori land surface reflectance (LSR) database support (HARLS). Continuous AOD data from 2013 and 2014 retrieved by this algorithm were used to analyze the spatial and temporal distribution of the AOD in the above area. The AOD in the Beijing-Tianjin-Hebei region has obvious regional and seasonal variations. The AOD in the south and east is higher than in the north and west, and the AOD in spring and summer is higher than that in winter and autumn.

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 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.

To improve the spatial resolution of the global aerosol optical depth (AOD) distribution, a new method for AOD retrieval is proposed over land in this paper. A monthly Global Land Surface Reflectance Database (GLSRD) was constructed using the long time serious of MODIS surface reflectance product (MOD09A1) and used for the surface reflectance estimation over land for AOD retrieval. A seasonal Global Land Aerosol Type Database (GLATD) was also built based on the MODIS aerosol product (MOD04) and used for providing the aerosol types over land in AOD retrieval. Thus, the AOD global dataset with 1 km resolution was produced and was validated against with the AERONET ground-based AOD measurements located in the global 172 stations over land. Results showed that the AOD retrievals are highly consistent with AERONET AODs and showed an overall better precision over both dark and bright areas.

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.