Abstract. Our aim was to evaluate a spatiotemporal image-fusion model (STI-FM) for enhancing the temporal resolution (i.e., from 16 to 8 days) of Landsat-8 surface reflectance images by utilizing the moderate-resolution imaging spectroradiometer (MODIS) images, and assess its applicability over a heterogeneous agriculture dominant semiarid region in Jordan. Our proposed model had two major components: (i) establishing relationships between two 8-day MODIS composite images acquired at two different times (i.e., time 1 and time 2); and (ii) generating synthetic Landsat-8 surface reflectance images at time 2 as a function of Landsat-8 images available at time 1 and the relationship constructed in the first component. We evaluated the synthetic images with the actual Landsat-8 images and observed strong relations between them. For example: the coefficient of determination (r 2 ) was in the range: (i) 0.72 to 0.82; (ii) 0.71 to 0.79; and (iii) 0.78 to 0.83; for red, near-infrared (NIR), and shortwave infrared (SWIR 2.2 μm ) spectral bands, respectively. In addition, root mean square error (RMSE) and absolute average difference (AAD) values were: (i) in between 0.003 and 0.004, and 0.0002, respectively, for red band; (ii) 0.005 and 0.0003, respectively, for NIR band; and (iii) 0.004 and in between 0.0001 and 0.0002, respectively, for SWIR 2.2 μm band. The developed method would be useful in understanding the dynamics of environment issues (e.g., agriculture drought and irrigation management), which require both relatively high spatial (i.e., 30 m) and high temporal resolution (i.e., 8 days) images.

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The objective of the study was to develop a remote sensing (i.e., Landsat-8 and MODIS)-based agricultural drought indicator (ADI) at 30-m spatial resolution and 8-day temporal resolution and also to evaluate its performance over a heterogeneous agriculture dominant semi-arid region in Jordan. Firstly, we used principal component analysis (PCA) to evaluate the correlations among six commonly used remote sensing-derived agricultural drought related variables. The variables included normalized difference water index (NDWI), normalized difference vegetation index (NDVI), visible and shortwave drought index (VSDI), normalized multiband drought index (NMDI), moisture stress index (MSI), and land surface temperature (LST). Secondly, we integrated the relatively less correlated variables (that were found to be NDWI, VSDI, and LST) to generate four agricultural drought categories/conditions (i.e., wet, mild drought, moderate drought, and severe drought). Finally, we evaluated the ADI maps against a set of 8-day ground-based standardized precipitation index values (i.e., SPI-1, SPI-2, …, SPI-8) by use of confusion matrices and observed the best results for SPI-4 (i.e., overall accuracy and Kappa-values were 83% and 76%, respectively) and SPI-5 (i.e., overall accuracy and Kappa-values were 85% and 78%, respectively). The results demonstrated that the method would be valuable for monitoring agricultural drought conditions in semi-arid regions at both a reasonably high spatial resolution (i.e., 30-m) and a short time period (i.e., 8-day).

This paper aims to explore the spatiotemporal pattern of traffic accidents using five years of data between 2015 and 2019 for the Irbid Governorate, Jordan. The spatial pattern of traffic-accident hotspots and their temporal evolution were identified along the internal and arterial roads network in the study area using spatial autocorrelation (Global Moran I index) and local hotspot analysis (Getis–Ord Gi*) techniques within the GIS environment. The study showed a gradual increase in the reported traffic accidents of approximately 38% at the year level. The analysis of traffic accidents at the severity level showed a distinguished spatial distribution of hotspot locations. The less severe traffic accidents (~95%) occurred on the internal road network in the Irbid Governorate’s towns where the highest traffic volume exist. The spatial autocorrelation analysis and the Getis–Ord Gi* statistics with 99% of significance level showed clustering patterns of traffic accidents along the internal and the arterial road network segments. Between 2015 and 2019, a notable evolution of the traffic-accident hotspots clusters was pronounced. The results can be used to guide traffic managers and decision makers to take appropriate actions for enhancing the hotspot locations and improving their traffic safety status.