Frontiers in Remote Sensing | New and Recent Articles

Detection of Basal Stem Rot (BSR) caused by Ganoderma boninense remains difficult in oil palm disease management because spectral and physiological changes in the canopy accompany infection well before palms enter terminal decline. This study developed a pipeline combining UAV‐borne hyperspectral imagery at 5 cm spatial resolution, deep learning-based instance segmentation, machine learning class…

A crucial indicator of hydrodynamic conditions, sediment transport systems, and geomorphic evolution is riverbed sediment. Hydraulic engineering, channel upkeep, and ecological management all depend on accurate sediment classification. Because of their intricate geomorphology and dynamic hydrodynamic forces, riverine systems show more spatial heterogeneity than comparatively stable marine habitat…

Phytoplankton Primary Production supports most of the marine ecosystem and is highly sensitive to changing environmental pressures. There is much debate about whether marine primary production is increasing or decreasing and what environmental parameters may be driving these changes. We analysed a 21-year time-series of net primary production (NPP) computed from Ocean Colour Climate Change Initia…

The interaction between Land Surface Temperature (LST) and albedo plays a crucial role in regulating surface energy dynamics and environmental variability. This study presents the first comprehensive, nation-scale diagnostic analysis of the LST-albedo relationship in Iran, using daily MODIS MCD43A4 and MOD11A1 datasets spanning 8035 days from 1 January 2001, to 31 December 2022. Data preprocessin…

The lower Yalong River Basin is an important clean-energy base in China and an ecologically fragile mountain basin. Under rapid cascade hydropower development, the relationship between hydropower development stages and long-term watershed carbon storage changes remains unclear. This study assessed carbon storage dynamics in the lower Yalong River Basin from 1986 to 2021 using Landsat imagery, ran…

Agroforestry practices are one of the major pillars of Natural Resources Management (NRM), offering substantial benefits by improving environmental conditions, socio-economy, soil health, biodiversity, and climate resilience. Despite multi-dimensional benefits, robust data for regional planning and effective implementation are lacking, particularly in combined with existing land management practi…

Wetland carbon sink is considered to be one of the most important components of the global carbon cycle. Space-borne remote sensing serves as a vital data source for the classification and carbon sink estimation of wetland. However, inadequate spatial resolution often impedes the accurate classification of different vegetation types, substantially affecting the precision of carbon sink assessment…

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…