Frontiers in Remote Sensing | New and Recent Articles

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…

Managing within-field variability in cotton fields for precision nitrogen (N) management is difficult. The development of multispectral sensors and image data analytics offers a solution to addressing the issue. During the Kharif (monsoon) season of 2021 and 2022, field experiments were conducted at the ICAR-Central Institute for Cotton Research, Regional Station, Coimbatore, Tamil Nadu, involvin…

Deforestation and forest degradation are the main threats to biodiversity and carbon stocks in tropical forests. Advances in optical and SAR satellite sensors have enabled the development of real-time monitoring of deforestation on a global scale. SAR is particularly appealing in tropical areas due to its insensitivity to cloud cover. However, the automatic detection of small disturbed areas (suc…

Crop type mapping is crucial for agricultural land cover monitoring and decision-making. State-of-the-art methods developed using recent Sentinel satellite data have already demonstrated their ability to accurately map crop types. However, crop type mapping for the pre-Sentinel era remains challenging due to the limited availability of higher spatial- and temporal-resolution data. This study addr…

Quantifying how urban heat islands (UHIs) influence regional electricity consumption remains challenging because station-based indicators and prescribed heating/cooling degree-day (HDD/CDD) thresholds often fail to capture intra-urban thermal heterogeneity and nonlinear demand responses during extremes. This study addresses these limitations by combining machine learning with MODIS thermal remote…

Recent advances in mobile laser scanning (MLS) have enabled rapid three-dimensional data acquisition for urban tree monitoring, providing an alternative to traditional terrestrial laser scanning (TLS) and photogrammetric approaches. However, the high cost of commercial handheld mobile laser scanning (HMLS) systems limits their routine use in urban green-space inventories. This study evaluates the…