Economic development
The Space and Upper Atmosphere Research Commission (SUPARCO) has announced the successful deployment and operational readiness of Pakistan's advanced remote sensing satellite, marking a significant milestone in the country's space research capabilities. Launched on July 31, 2025, from China's Xichang Satellite Launch Center (XSLC)
Climate change
Researchers from Polytechnic University Milan in Milan, Italy have developed a new tool, GlacioTools, to simplify geospatial data processing for glacier studies. The tool, designed for QGIS, automates various tasks such as in-situ survey reporting, surface displacement and velocity map generation, and GCP documentation. This integration is expected to enhance
Economic development
Agricultural researchers from the University of Athens have recently published a study in the Remote Sensing journal, exploring the potential of precision irrigation in managing crop production. The study, which was funded by the Eu Regional Development Fund, finds that precision irrigation can significantly reduce crop water needs, minimizing over-irrigation
Climate change
Investigations into accurate biomass estimation for forest management and climate change mitigation have led to new research on the application of LiDAR data from Unmanned Aerial Vehicles (UAVs) in estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. Funded by the Portuguese
Climate change
Researchers from Isfahan University of Technology have discovered a strong correlation between human activities and groundwater storage changes in the Lake Urmia Basin, Iran. Using an integrated approach of remote sensing and Google Earth Engine, the study analyzed the effects of built-up areas, cropland, and surface water on groundwater storage
Economic development
Research conducted at Wuhan University introduces a novel approach to monitoring vertical urban growth using photogrammetry remote sensing and spatial information sciences. The Stack On-Off model approach efficiently monitors vertical urban growth in Wuhan, China, using Sentinel-1 data from 2015 to 2024. The study highlights the importance of understanding urban
Climate change
Malawi has made a significant step towards enhancing climate resilience and disaster preparedness by piloting the Platform for Real-time Impact and Situation Monitoring (PRISM) tool in five drought-prone districts. The tool is designed to integrate multiple data sources to deliver real-time insights into drought conditions and their potential impacts on
Climate change
A recent study from Mudanjiang Normal University, published in the Water journal, highlights the importance of remote-sensing technology in assessing coastal-ecosystem health. The research emphasizes the potential of remote-sensing technology in facilitating large-scale, long-time-series observations, making it a key tool for coastal-ecosystem health assessment. Despite its potential, the technology still
Machine learning
Researchers from the Chinese Academy of Sciences have proposed a novel paradigm for evapotranspiration estimation in data-sparse regions through the synergistic integration of data-driven and knowledge-based models. The study, which has been peer-reviewed, utilizes a physics-constrained hybrid model that incorporates Penman-Monteith-derived physical knowledge into a time series transformer. The framework
Machine learning
Research conducted by the University of Oviedo has demonstrated the efficacy of a machine learning model in detecting eutrophication in reservoirs using remote sensing data. The study, published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, utilized satellite imagery to monitor 20 reservoirs from
Machine learning
Researchers from King Saud University have developed an innovative solution to accurately assess the extent and quality of green spaces in urban areas. The team, led by Meshal Alfarhood, used live satellite imagery and advanced deep learning techniques to identify trees and measure green density in vegetated areas. Their methodology
Machine learning
Scientists at the Water Research and Technology Center in Tunisia have proposed an integrated approach combining convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to enhance the automatic detection of geological lineaments from satellite imagery. This research study reveals that Sentinel-1, with its 10-meter radar resolution, detects
