With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R 2 V = 0.73, RMSE V = 2.78 g·kg and RPD V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

Web Map Tile Services (WMTS) are widely used in many fields to quickly and efficiently visualize geospatial data for public use. To ensure that a WMTS can successfully fulfill users' expectations and requirements, the performance of a service must be measured to track latencies and bottlenecks that may downgrade the overall quality of service (QoS). Traditional synthetic workloads used to evaluate WMTS applications are usually generated by repeated static URLs, through randomized requests, or by an access log replay. These three methods do not take request characteristics and users' behaviors into consideration, while access logs are not available for systems still under development. Thus, the evaluation outcomes obtained by these methods cannot represent the real performance of online WMTS applications.In this article a new workload model named HELP (Hotspot/think-timE/Length/Path) is proposed to measure the performance of a prototype WMTS. This model describes how users browse a WMTS map and statistically characterizes complete map navigation behaviors. Then, the HELP model is implemented in HP LoadRunner and used to generate a synthetic workload to evaluate the target WMTS. Experimental results illustrate that the performance representation of the HELP workload is more accurate than that of the other two models, and how a bottleneck in the target system was identified. Additional statistical analysis of request logs and "hotspots" visualizations further validate the proposed HELP workload.

Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.