An integrated dryness index based on geographically weighted regression and satellite earth observations.

Drought, characterized by the limited water availability in the atmosphere and hydrological systems, is one of the most destructive natural calamities. Defining droughts based on a single variable/index (e.g.

Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks.

Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns.

The edge of the petri dish for a nation: Water resources carrying capacity assessment for Iran.

Different methods have been proposed in population dynamics to estimate carrying capacity (K). This study estimates K for Iran, using three novel methods by integrating land and water limits into assessments based on Human Appropriated Net Primary Production (HANPP). The first method uses land suitability as the limiting resource.

A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models.

Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling.

Integration of synthetic aperture radar and optical satellite data for corn biomass estimation.

Efforts to use satellites to monitor the condition and productivity of crops, although extensive, can be challenging to operationalize at field scales in part due to low frequency revisit of higher resolution space-based sensors, in the context of an actively growing crop canopy. The presence of clouds and cloud shadows further impedes the exploitation of high resolution optical sensors for operational monitoring of crop development. The objective of this research was to present an option to facilitate greater temporal observing opportunities to monitor the accumulation of corn biomass, by integrating biomass products from Synthetic Aperture Radar (SAR) and optical satellite sensors.

Land cover mapping based on random forest classification of multitemporal spectral and thermal images.

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8's spectral and thermal imageries using a random forest (RF) classifier.