Frontiers in Remote Sensing | New and Recent Articles

Deep learning (DL) has significantly advanced pattern recognition and hyperspectral image (HSI) classification owing to its strong capability for hierarchical feature representation. However, existing DL-based HSI classification methods are often limited by scarce labeled samples, high parameter complexity, and the difficulty of learning discriminative features from high-dimensional spectral-spat…

The systematic acquisition of high-resolution observations over the years by the Sentinel-2 and Landsat-8 missions, combined with machine learning, offers new opportunities to improve agricultural yield estimation modeling at the field level. Such models require a large amount of reference data for appropriate calibration without overfitting and an independent validation. This research leverages …

Monitoring shoreline dynamics in rapidly engineered coastal settings is essential for evidence-based coastal planning, particularly in the Arabian Gulf where reclamation, nourishment, waterfront construction, marina development, and destination-scale infrastructure continuously reshape land–water boundaries. This study quantified shoreline change and mapped relative erosion hazard around Saadiyat…

Satellite image analysis is essential for remote sensing analysis. Two types of data are captured via satellite: Synthetic Aperture Radar (SAR) imagery (which has structure) and multispectral imagery (which contains spectral information), so complementary data may present unique challenges because noise, image resolution, and image angles differ across modalities. This paper describes the creatio…

Strait of Hormuz is a climatically sensitive marine transition zone in which interplay of complex air sea interactions, monsoonal forcing, and land ocean thermal contrasts produces a strong impact on the variability of environment in the region. The long term climate behavior of such confined coastal systems is a difficult issue to assess because of the nonlinear connections between the atmospher…

The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This study investigates the use of multispectral aerial and satellite imagery for wildfire management through an assessment of current literature an…

Accurate and time-sensitive spatial ecological information is essential for biodiversity policies and environmental planning, yet existing remote sensing (RS) classification workflows may struggle to integrate ecological semantics and prior domain knowledge, limiting their interpretability and performance for statutory habitat assessments such as Biodiversity Net Gain (BNG). In this study we expl…

Climate variability and agricultural expansion are fundamentally reshaping ecosystem functions (EFs) in drylands. Xinjiang Region, a vast and typical dryland area in China, also faces dual pressures of natural and anthropogenic disturbances. Numerous local studies in Xinjiang have quantified individual EFs using biophysical models. However, research remains limited on the region-wide spatial dist…

Floods have become more unpredictable and erratic due to the influence of extreme hydroclimatic events. Therefore, obtaining near-real-time, accurate flood inundation maps for such events is essential for effective flood emergency response, which can be achieved by leveraging remotely sensed data. This study integrated high-resolution remote sensing data to enhance flood inundation mapping in a d…

Hyperspectral unmixing aims to decompose each pixel in a hyperspectral image into a set of constituent endmembers and their corresponding abundances. Recent deep learning based approaches have demonstrated strong performance in capturing both spectral and spatial features. However, obtaining reliable per-pixel abundance ground truth in real hyperspectral scenes is generally infeasible, which moti…

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by trai…

Nutrient limitations can significantly impact the ecosystem services provided by the savanna biome, potentially leading to degradation and reduced grazing capacity if not detected in time. A key indicator of growth-limiting nutrients is the Nitrogen to Phosphorus (N:P) ratio. However, grass foliar phosphorus content has rarely been studied in African savannas, especially using remote sensing appr…

In the regime-shifting Arctic, organic carbon export from river watersheds is expected to rise due to changes in hydrological regimes and permafrost thawing, affecting coastal and shelf biogeochemistry. Ocean color remote sensing enables monitoring inaccessible areas like the Beaufort Sea, improving our knowledge of coastal dynamics and land-to-ocean transport of Chromophoric Dissolved Organic Ma…

Monitoring urban expansion in arid regions is complicated by the spectral similarity between impervious surfaces and bare soil. Although machine learning classifiers on platforms like Google Earth Engine (GEE) offer effective solutions, their performance in these environments has not been systematically benchmarked. This study addresses this gap by comparing five supervised ML algorithms—Random F…

Since the 1990s, Doerffer and Schiller have been developing physics-based neural network algorithms for analyzing ocean colour in satellite imagery of optically complex coastal waters. At its core, the approach uses neural networks to solve the inversions in various aspects of solar radiative transfer in both the atmosphere and water, including atmospheric correction, towards the estimation of in…

Graphical AbstractInfographic illustrating research themes in unidentified underwater biological sounds, featuring animal silhouettes surrounded by arrows pointing to images representing identifying, finding, understanding, and using sounds, along with maps, graphs, and labeled research categories and references.

Hurricanes Ian and Nicole hit Mosquito Lagoon, Florida in the Fall of 2022 and since then, the ecosystem has greatly shifted. Prior to these storm events, seagrass in Mosquito Lagoon was almost non-existent due to poor ecosystem conditions but made a rapid recovery in 2023. To study this change, a Random Forest Classification was implemented using Harmonized Landsat Sentinel imagery semi-monthly …

IntroductionRapid urbanization and industrialization in Xi’an have precipitated a sharp conflict between spatial expansion and environmental conservation, necessitating a rigorous spatiotemporal assessment of regional ecological quality.MethodsThis study evaluates the eco-environmental quality of Xi’an’s main urban area from 2021 to 2024 by synergizing the Remote Sensing Ecological Index (RSEI) w…