No abstract

No abstract

Environmental applications require accurate air temperature (Tair) datasets with different temporal and spatial resolutions. Existing methods generally improve the estimation accuracy of Tair using environmental variables as auxiliary data to overcome problems related to sparse metrological stations. However, these data are always fixed and do not comprehensively explain the variations in Tair values at all temporal and spatial scales. Moreover, these methods seldom consider the spatial heterogeneity of relationships between Tair and auxiliary data. This heterogeneity is often caused by several factors, such as land type, topography, and climate. This study proposes an estimation method to produce maximum, minimum, and mean Tair (Tmax, Tmin, and Tmean) datasets at different temporal and spatial resolutions using satellite‐derived digital elevation model data and both nighttime and daytime land surface temperature data as auxiliary data. The method is based on the assumption that the relationships between Tair and the chosen auxiliary data vary spatially. These relationships were further explored using geographically weighted regression with adaptive bi‐square kernel function. The derived relationships were used to construct a Tair estimation model. Monthly Tair data with 5‐km resolution and 8‐day Tair data with 1‐km resolution were produced for 2010. The results show that the proposed method can accurately represent the variations in Tair; the R2 values were in the range of 0.95–0.99 for the monthly Tair data and 0.93–0.99 for the 8‐day Tair data. The root mean square errors (RMSEs) for the monthly and 8‐day Tmax, Tmin, and Tmean data of the year 2010 were 1.29 and 1.45 °C, 1.24 and 1.29 °C, and 0.8 and 1.2 °C, respectively. These results were compared with those from other estimation methods, specifically the estimation of Tair based on multiple linear regression (EATMLR) and regression kriging (EATRK). The proposed method was found to produce RMSEs that were 25–26% smaller than EATMLR and 34–42% smaller than EATRK.

The objective of this research is to investigate the potential of nighttime light images, acquired with Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS), in evaluating global armed conflicts. To achieve this purpose, we assessed the relationship between armed conflicts and the satellite-observed nighttime light variation over 159 countries through annual composites of the nighttime light images. Firstly, a light ratio index was developed to reduce the data inconsistency of annual nighttime light images during 1992-2010. Then 12 countries were selected as examples for a primary investigation, and we found the outbreak of a war can reduce the light and the ceasefire can increase the light from the remote sensing images, which indicates armed conflict events always have significant impact on the nighttime light. Based on this assertion, a nighttime light variation index (NLVI) was developed to quantify the variation of the time series nighttime light. Then using conditional probability analysis, the probability of a country suffering from armed conflicts increases with increase of NLVI. Particularly, when the NLVI value is in a very high level as defined, 80% of the countries have experienced armed conflicts. Furthermore, using correlation analysis, the number of global armed conflicts is highly correlated with the global NLVI in temporal dimension, with a correlation coefficient larger than 0.77. In summary, the potential of nighttime light images in armed conflict evaluation is extended from a regional scale to a global scale by this study.

Spatially downscaling satellite precipitation products have been performed on annual and monthly precipitation. Accurate downscaling on daily precipitation remains a challenge due to the limitation of the downscaling assumption, the large spatial discontinuity of daily precipitation, and the relatively poor quality of satellite‐derived daily precipitation product. In this study, an integrated downscaling‐fusion framework was proposed and used to downscale satellite‐derived daily precipitation. First, a spatio‐temporal downscaling scheme is applied to produce preliminary downscaled daily precipitation. The accuracy of the derived preliminary results is then boosted by merging with daily gauge observations using an ensemble fusion method. The performance of the proposed framework was tested and evaluated by downscaling the Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG) daily precipitation data from 0.1 to 0.01° over eastern and central China for the period of 2015–2016. The results showed that (a) the downscaling scheme accurately mapped the spatio‐temporal variation in daily precipitation, and the preliminary downscaled results perfectly maintained the accuracy of the original IMERG data; (b) the fused results were much more accurate than the original IMERG data, decreasing the root‐mean‐square errors (RMSEs) by 22, 10, and 18% at daily, monthly, and annual timescales, respectively, for the whole period; and (c) the fused daily precipitation data considerably strengthened the detection of rain/no rain area compared with the original IMERG daily precipitation data, with a 17% reduction in the inconsistency index.