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Topic Review
The Existing Remote Sensing Index Resources
Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. 
  • 317
  • 18 Feb 2024
Topic Review
Rise in Mid-Tropospheric Temperature Trend over the Tibet
The Hindu Kush-Himalayan region (HKH), situated at high altitudes (~5 km above sea level), and the adjoining Indo-Gangetic plains (IG plains, ~0–250 m above sea level) are notably responsive to climatic shifts due to their geographic location and intricate topography. Ongoing research reveals that climate change's consequences and linked alterations in water resources—comprising glacial/snow meltwater and rainfall—hold diverse impacts on ecosystems, agriculture, industries, and inhabitants within this area. This investigation delved into a 45-year span of data (1978–2022) derived from Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU) instruments provided by Remote Sensing Systems (RSS Version 4.0). The goal was to scrutinize changes in mid-tropospheric temperature (TMT, 3–7 km altitude) and lower tropospheric temperature (TLT, 0–3 km altitude) concerning annual/monthly trends and anomalies. A noteworthy rise in mid-tropospheric temperatures (0–3 km altitude) across the HKH region, with increases of 1.49 °K in Tibet, 1.30 °K in the western Himalayas, and 1.35 °K in the eastern Himalayas over the 45-year timeframe. By contrast with an earlier 30-year period study (1979–2008), the present study observed a substantial percentage change of TMT trends for the high-altitude areas, including Tibet, the western Himalayas, and the eastern Himalayas—approximately 310%, 80%, and 170%, respectively. Conversely, the neighboring plains (western and eastern IG plains) exhibited negligible or considerably lower percent alterations (0% and 40%, respectively) over the past 14 years.
  • 315
  • 21 Aug 2023
Topic Review
Specific Sensors in Downwind Fire and Smoke Detection
Wildfires have played an increasing role in wreaking havoc on communities, livelihoods, and ecosystems globally, often starting in remote regions and rapidly spreading into inhabited areas where they become difficult to suppress due to their size and unpredictability. In sparsely populated remote regions where freshly ignited fires can propagate unimpeded, the need for distributed fire detection capabilities has become increasingly urgent.
  • 311
  • 27 Sep 2023
Topic Review
Autonomous Navigation Framework for Holonomic Mobile Robots
Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties.
  • 309
  • 13 Nov 2023
Topic Review
Remote Sensing Image Feature Learning Approaches
Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. There is an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively.
  • 305
  • 21 Jun 2023
Topic Review
Tectonic Geodesy Synthesis of the North Aegean Region
Satellite geodesy, an indispensable modern tool for determining upper-crust deformation, can be used to assess tectonically active structures and improve our understanding of the geotectonic evolution in tectonically active regions. A region fulfilling these criteria is the North Aegean, part of the Eastern Mediterranean. It is one of the most tectonically, and hence, seismically, active regions worldwide, which makes it ideal for applying a satellite geodesy investigation. 
  • 300
  • 11 Sep 2023
Topic Review
Muddy Waters Mapping Using Machine Learning
The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. 
  • 299
  • 12 Oct 2023
Topic Review
Recent Advances in Dielectric Properties-Based Soil Water Content Measurements
Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized based on mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC.
  • 292
  • 31 Oct 2024
Topic Review
Detection Principles of Mars Orbital Radars
The planet Mars, which is in the habitable zone of the solar system and is one of the closest planets to Earth, stands in contrast to Venus; therefore, it has attracted much attention and exploration as the most likely planet for future human colonization. 
  • 266
  • 07 Feb 2024
Topic Review
PVTv2 for Deep Hash Remote Sensing Image Retrieval
For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully express the complexity of the image information. Due to the large volume of remote sensing images, retrieval requires extensive memory and time. Researchers propose an end-to-end deep hash remote sensing image retrieval model (PVTA_MSF) by fusing multi-scale features based on the Pyramid Vision Transformer network (PVTv2).
  • 254
  • 16 Oct 2023
Topic Review
Height Estimation with Aerial Images
Height estimation is a key component of 3D scene understanding and has long held a significant position in the domains of remote sensing and computer vision. Initial research predominantly focused on stereo or multi-view image matching. With the advent of large-scale depth datasets, research focus has shifted. The effort is centered on estimating distance information from monocular 2D images using supervised learning. Monocular height estimation approaches can be generally categorized into three types: methodologies based on handcrafted features, methodologies utilizing convolutional neural networks (CNN), and methodologies based on attention mechanisms.
  • 244
  • 26 Jan 2024
Topic Review
Urban Flood Monitoring Technology
Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. General flooding and overflow modeling was performed based on a model that utilizes the flow of water and its geographical characteristics. More detailed geographical data and drainage network information are utilized in urban regions.
  • 242
  • 27 Nov 2023
Topic Review
MSGFNet for Remote Sensing Image Change Detection
Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion.
  • 236
  • 08 Feb 2024
Topic Review
Deep Learning for Land Use
Image super-resolution (SR) techniques can improve the spatial resolution of remote sensing images to provide more feature details and information, which is important for a wide range of remote sensing applications, including land use/cover classification (LUCC). Convolutional neural networks (CNNs) have achieved impressive results in the field of image SR, but the inherent localization of convolution limits the performance of CNN-based SR models. 
  • 222
  • 24 Nov 2023
Topic Review
Merits and Challenges in Geographic-Information-Systems and Remote-Sensing Application
Geographic Information Systems (GIS) and remote sensing (RS) have evolved into distinct disciplines within geospatial technology, each with well-established theoretical foundations and methodologies. They now encompass various methodologies and software tools(ArcGIS Pro, GRASS GIS, QGIS, ENVI, ERDAS Imagine etc.), expanding their applications in spatial data collection, measurement, analysis, storage, management, display, dissemination, and deployment. GIS has moved beyond just creating digital maps. It has evolved into a comprehensive framework for integrating, storing, analyzing, and presenting geospatial data. RS involves observational and investigative activities in the environmental realm. By continuously monitoring the surface environment, GIS and RS integrate temporal and spatial dimensions, enhancing our understanding of the natural world and the human–nature relationship. This understanding is crucial for recognizing, managing, and preserving archaeological and cultural heritage (ACH).
  • 220
  • 05 Jan 2024
Topic Review
Flood Segmentation in Post-Disaster High Resolution Aerial Images
Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. 
  • 186
  • 11 Oct 2023
Topic Review
Early Crop Mapping Using Dynamic Ecoregion Clustering
Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. Early crop mapping provides valuable information for crop management, such as predicting yield, monitoring crop growth, and identifying areas with high production potential.
  • 179
  • 10 Nov 2023
Topic Review
Semi-Supervised Learning for Forest Cover Mapping
Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. 
  • 172
  • 28 Sep 2023
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