A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔT$_{sb}$) on the daily mean LST (T$_{dm}$) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the T$_{dm}$. Several methods have been proposed for the estimation of the T$_{dm}$, yet they are becoming less capable of generating spatiotemporally seamless T$_{dm}$ across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless T$_{dm}$ products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔT$_{sb}$ significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the T$_{dm}$ estimated with the traditional averaging method yields a positive systematic ΔT$_{sb}$ of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr$^{–1}$), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr$^{–1}$. We consider the IADTC framework can guide the further optimization of T$_{dm}$ estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis

The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012

Along with rapid urbanization, nighttime activities from places, such as restaurants, pubs and bars, and theatres, have created enormous economic and social benefits. The nighttime economy (NTE), as a newly developed social phenomenon, has been used to describe economic activities at night. However, few studies have investigated urban nighttime economy and its relation to urbanization from nighttime light (NTL) data perspective. To fill this gap, this study proposed a nighttime light economy index (NLEI). The correlation analysis was performed between the NLEI and economic indicators at both the city and provincial levels in China from 1992 to 2012 using the DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) time series data. Results revealed that correlations between the NLEI and all kinds of economic indicators were statistically significant. It was observed that both the urbanization and nighttime economy levels increased greatly from 1992 to 2012 in China. Cities and provinces in east China displayed relatively higher annual growth rates of NLEI compared to those in southwest and northwest China. Based on the quadrant map of urbanization and nighttime economy levels, most of the provincial capitals and provinces in east China were in the advanced coordination pattern while those in west China in the low-level coordination pattern

Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level

The proliferation of impervious surfaces results in a series of environmental issues, such as the decrease of vegetated areas and the aggravation of the urban heat island effects. The mapping of impervious surface and its spatial distributions is of significance for the ecological study of urban environment. Currently, the integration of optical and synthetic aperture radar (SAR) data has shown advantages in accurately characterizing impervious surface. However, the fusion mainly occurs at the pixel and feature levels which are subject to influences of data noises and feature selections, respectively. In this paper, an innovative and effective method was developed to extract urban impervious surface by synergistically utilizing optical and SAR images at the decision level. The objective of this paper was to obtain an accurate urban impervious surface map based on the random forest classifier and the evidence theory and to provide a detailed uncertainty analysis accompanying the fused impervious surface maps. In this study, both the GaoFen (GF-1) and Sentinel-1A imagery were first used as independent data sources for mapping urban impervious surfaces. Then additional spectral features and texture features were extracted and integrated with the original GF-1 and Sentinel-1A images in generating impervious surfaces. Finally, based on the Dempster-Shafer (D-S) theory, impervious surfaces were produced by fusing the previously estimated impervious surfaces from different datasets at the decision level. Results showed that impervious surfaces estimated from the combined use of original images and features yielded a higher accuracy than those from the original optical or SAR data. Further validations suggested that optical data was better than SAR data in separating impervious surfaces from non-impervious surfaces. The fused impervious surfaces at the decision level had a higher overall accuracy than those produced independently by optical or SAR data. It was also highlighted that the fusion of GF-1 and Sentinel-1A images reduced the amount of confusions among the low reflectance of impervious surface and water, as well as for low reflectance of bare land. An overall accuracy of 95.33% was achieved for extracting urban impervious surfaces by fused datasets. The spatial distributions of uncertainties provided by the evidence theory displayed a confidence level of at least 75% for the impervious surfaces derived from the fused datasets

Controllable transition from finger-like pores to inter-connected pores of PLLA membranes

Poly (L-lactic acid) (PLLA) flat sheet membranes with infer connected pores were prepared via nun solvent induced phase separation process. Polyethylene oxide (KO) with molecule weight of 100 kD was used as additives. The morphology evolution from the partial to total inter-connected pores in cross section was accomplished by adjusting the FED concentration. Besides, the effect of different molecule weight of polyethylene glycol (PEG), high coagulation intensity, different mass fraction ratios of FED to PLLA and the casting solution exposure time in air on morphologies of PLLA membranes were also investigated respectively. Finally, as a hemodialysis membrane, the water permeability, solute clearance rate and mechanical properties were detei mined. it was shown that PLLA membrane with highly inter-connected pores exhibited a higher water flux of 225 L/hm(2) and tensile strain of 83%. The clearance of the as-prepared membranes to urea and lysozyme is 77% and 33% respectively, while keeping BSA rejection with 90%. (C) 2015 Elsevier B.V. All rights reserved

Enhancing antibacterial performances of PVDF hollow fibers by embedding Ag-loaded zeolites on the membrane outer layer via co-extruding technique

Ag-loaded zeolites were synthesized and embedded into the outer layer of poly(vinylidene fluoride) (PVDF) dual-layer hollow fiber membrane (D) using a dry-jet wet-spinning co-extruding technique. The distribution of the Ag-loaded zeolites in the outer layer was confirmed by field emission scanning electron microscopy (FE-SEM) and energy-dispersive X-ray spectroscopy (EDX). It was found that the D contained only 54% of the amount of Ag-loaded zeolites that were distributed throughout the cross section of PVDF single-layer hollow fiber membrane (S), as calculated by thermogravimetric analysis (TGA). However, D showed excellent antibacterial efficiency and resistance to bacterial adhesion because of the higher concentration of Ag+ in the outer layer of the membrane. The surface morphologies, pure water flux, mean pore size, pore size distribution, and thermal stability of both PVDF membranes were also investigated. (C) 2014 Elsevier Ltd. All rights reserved

Fouling-resistant and adhesion-resistant surface modification of dual layer PVDF hollow fiber membrane by dopamine and quaternary polyethyleneimine

Poly(vinylidene fluoride) (PVDF) dual layer hollow fiber membrane was modified by coating dopamine and grafting polyethyleneimine, then the hydrophilic and antibacterial PVDF membrane was obtained through quaternization reactions. The attenuated total reflectant Fourier transform infrared spectra (ATRFTIR) and X-ray photoelectron spectroscopy (XPS) were applied to confirm the successful modification. The water contact angle measurement suggested that the membrane hydrophilicity was significantly improved. Morphological changes of membranes were characterized by field emission scanning electron microscope (FE-SEM). The pure water flux before and after BSA contamination was measured and the flux recovery rate for the modified membrane was as high as 94%. The antibacterial tests showed that the hydrophilic and antibacterial PVDF membrane owned excellent anti-bacterial efficiency and resistance to bacterial adhesion. The average pore size, mechanical property and zeta potential were also investigated. All the results demonstrated this facile method can enhance the hydrophilicity and antibacterial ability of PVDF dual layer hollow fiber membrane, and would have application in water treatment. (C) 2015 Elsevier B.V. All rights reserved

Combining Spatiotemporally Global and Local Interpolations Improves Modeling of Annual Land Surface Temperature Cycles

Annual temperature cycle (ATC) models are widely used to characterize temporally continuous land surface temperature (LST) dynamics within an annual cycle. However, the existing ATC models ignore the spatiotemporally local correlations among adjacent LST pixels and are inadequate for capturing the complex relationships between LSTs and LST-related descriptors. To address these issues, we propose an improved ATC model (termed the ATC_GL), which combines both the spatiotemporally global and local interpolations. Using the random forest (RF) algorithm, the ATC_GL model quantifies the complex relationships between LSTs and LST-related descriptors such as the surface air temperature, normalized difference vegetation index, and digital elevation model. The performances of the ATC_GL and several extensively used LST reconstruction methods were compared under both clear-sky and overcast conditions. In the scenario with randomly missing LSTs, the accuracy of the ATC_GL was 2.3 K and 3.1 K higher than that of the ATCE (the enhanced ATC model) and the ATCO (the original ATC model), respectively. In the scenario with LST gaps of various sizes, the ATC_GL maintained the highest accuracy and was less sensitive to gap size when compared with the ATCH (the hybrid ATC model), Kriging interpolation, RSDAST (Remotely Sensed Daily Land Surface Temperature), and HIT (Hybrid Interpolation Technique). In the scenario of overcast conditions, the accuracy of the ATC_GL was 1.0 K higher than that of other LST reconstruction methods. The ATC_GL enriches the ATC model family and provides enhanced performance for generating spatiotemporally seamless LST products with high accuracy