No abstract

Integrating spatial analysis with the supply and demand of biocapacity is critical for the sustainable development of regional eco-economic systems. Previous studies have focused on the temporal analysis of biocapacity at broad geographical scales, but lacked the systematic spatial realization at fine scales. An improvement is proposed of this conventional methodology of the ecological footprint by incorporating landuse data derived from high-resolution remote-sensing images into the calculation of biocapacity supply at regional, provincial and county levels in Northwestern China in 2000. The spatial heterogeneity and its effect on the biocapacity supply were systematically revealed for this region. First, the biocapacity supply declined from the east (the Guanzhong Basin and the Loess Plateau) to the middle (the Qaidam Basin and the Turpan Basin), and turned to rise from the middle to the west (the northwest of the Xinjiang Uygur Autonomy). Second, although the gap between biocapacity supply and demand resulted in a small ecological deficit at the regional level, a large ecological deficit was observed at the provincial and county levels, highlighting an unsustainable situation for some of the sub-regions. Importantly, a power law relationship was unveiled between the biocapacity supply and population density, suggesting that (i) the biocapacity supply as a critical indicator could reflect the intensity of human exploitation on local biophysical resources and (ii) humans tend to have a preference to inhabit those areas with high biological productivity. These results provide opportunities to enhance policy development by central and local governments as part of the long-term Great Western Development Strategy of China.

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.