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Animals disperse seeds in various ways that affect seed deposition sites and seed survival, ultimately shaping plant species distribution, community composition, and ecosystem structure. Some animal species can disperse seeds through multiple pathways (e.g., defecation, regurgitation, epizoochory), each likely producing distinct seed dispersal outcomes. We studied how seed traits (size and toughness) interact with disperser species to influence seed dispersal pathway and how this ultimately shapes the proportion of seeds deposited in various habitat types. We focused on three frugivorous species of duikers (African forest antelopes) in the Dja Faunal Reserve, a tropical rainforest in southern Cameroon. Duikers can both defecate and regurgitate seeds, the latter predominantly occurring during rumination at their bedding sites (or "nests"). We located duiker nests and dungs along 18 linear 1-km-transects to assess: (1) how seed traits affect the likelihood of dispersal via defecation versus regurgitation, (2) if defecated versus regurgitated seeds are deposited at different rates in different forest types (assessed by indigenous Baka), microhabitats, and forest structural attributes (measured by drone lidar), and (3) if these differ between three duiker species that vary in size and diel activity patterns. We found that duikers predominantly defecated small seeds (10 mm length), the latter including 25 different plant species. The three duiker species varied in their nesting habits, with nocturnal bay duikers (Cephalophus dorsalis) nesting in dense understory vegetation at proportions 3-4 times higher than Peter's and yellow-backed duikers (Cephalophus callipygus and Cephalophus silvicultor). As a result, bay duikers deposited larger regurgitated seeds at a higher rate in habitats with denser understory where lianas and palms predominate and near fallen trees. This directed regurgitation seed deposition likely plays an important and unique role in forest succession and structure. This study highlights the importance of ungulate seed dispersal by regurgitation, a vastly understudied process that could impact many ecosystems given the prevalence of ruminating ungulates worldwide.

Solar-induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra-hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf-level physiology and gross primary productivity under well-watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Abstract: Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Abstract: Hyperspectral remote sensing has the potential to map numerous attributes of the Earth’s surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in Californias water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Recent studies indicate that the Asian Water Tower (AWT) is at risk due to climate change, which can negatively impact water and food security in Asia. However, there is a lack of comprehensive information on lakes' spatial and temporal changes in this region. This information is crucial for understanding the risk magnitude and designing strategies. To fill this research gap, we analyzed 89,480 Landsat images from 1977 ± 2 to 2020 ± 2 to investigate the changes in the size of lakes recharged by the AWT. Our findings showed that out of the 209 lakes larger than 50 km2, 176 (84 %) grew during the wet season and 167 (81 %) during the dry season. 74 % of expanded lakes are located in the Inner Tibetan Plateau (TP) and Tarim basins. The lakes that shrank are found mainly in the Helmand, Indus, and Yangtze basins. Over the entire period, the area of shrinkage (55,077.028 km2 in wet season, 53,986.796 km2 in dry) markedly exceeded expansion (13,000.267 km2 in wet, 11,038.805 km2 in dry), with the drastic decline of the Aral Sea being a major contributor to shrinkage, accounting for 90 % of the total loss. From 1990 ± 2 to 2020 ± 2, alpine lakes mostly expanded, plain lakes mostly shrank, with the opposite trend from 1977 ± 2 to 1990 ± 2. Glacial loss and permafrost thawing under global warming in the Inner TP, Tarim Interior, Syr Darya, and Mekong basins were strongly correlated with lake expansion. However, permafrost discontinuities may prevent significant growth of lakes in the Indus and Ganges basins despite increased recharge. Our findings point to the prominence of the risk the lakes recharged by AWT face. Taking immediate action to manage these risks and adaptation is crucial as the AWT retreats and lake recharges are slowed.

WHOI assistant scientist Tom Bell stands with his drone on a crop of rockweed in Nahant, Mass. (Photo by Daniel Hentz, [c] Woods Hole Oceanographic Institution) IN COLLABORATION WITH HIS [...]

Strawberries in California are grown in specific coastal areas where land is scarce and climate change threatens future production. Strawberry growers are under pressure to adopt sustainable production strategies such as crop rotation, but this practice requires more land than back-to-back planting. The objectives of this research were to quantify the rate of crop rotation across the three main strawberry producing regions in California (Ventura, Santa Maria, and Monterey Bay), and evaluate geographic and edaphic influences on crop rotation. All strawberry fields in the main strawberry producing regions of California were identified by satellite imagery and manual inspection for the years 2017 through 2022. ArcGIS Pro was used to outline each strawberry field and compare among years to determine the period between successive strawberry plantings. Edaphic characteristics and shapefiles for surrounding fields were retrieved from public datasets. The three regions significantly differed in their rates of crop rotation. On average, 95, 52, and 25% of strawberry hectares were rotated each year in the Monterey Bay, Santa Maria, and Ventura regions, respectively. Shorter rotation lengths were associated with reduced distance from the ocean and soil with a higher percentage of sand. Based on 2 years of disease surveys, fields infested with Macrophomina phaseolina tended to be further inland than fields infested with Fusarium oxysporum f. sp. fragariae in the Monterey Bay region. This study determined that distance from the ocean and soil texture are associated with crop rotation lengths in California strawberry production. Enough land may be available in the Santa Maria and Monterey Bay regions for growers to lengthen crop rotations, but water quality, social networks, and financial considerations that were outside the scope of this study are likely to limit the ability for strawberry growers to maximize the duration of crop rotations.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Mountain shrublands are widespread habitats of the European Alps. Shrub encroachment into above treeline grazed lands profoundly modifies biodiversity and ecosystem functioning. Yet, mountain shrublands remain overlooked in vegetation distribution modeling because it is difficult to distinguish them from productive grasslands. Here, we used the pigment-sensitive spectral indices based on Sentinel-2 bands within a specific phenological window, to produce a high-resolution distribution map of mountain shrublands in the French Alps. We evaluated the performance of our classification using a large dataset of vegetation plots and found that our model is highly sensitive to Ericaceous species which constitute most of the dense alpine shrublands in the French Alps. Our analysis of topoclimatic and land use factors limiting the shrubland distribution at regional scale found that, consistent with the ecophysiology of shrubs, expansion is limited by a combination of water deficit and temperature. We discussed the past and current land-use implications in the observed distribution and put forward hypotheses combining climate and land-use trajectories. Our work provides a baseline for monitoring mountain shrub dynamics and exploring the response of shrublands to past and ongoing climate and land use changes.

Accurate information retrieval from multi-source and multi-resolution image data constitutes a foundation for knowledge discovery. Scene image classification in the remote sensing (RS) community using aerial very high resolution (VHR) images is one of the well-researched areas, which mostly utilise deep learning (DL)—based methods thanks to their remarkable classification performance. Nevertheless, existing DL-based methods still have a limited ability to capture precise spatial semantic information scattered toward the horizontal and vertical directions across such images at multiple scales and rotations. As such, we herein propose a novel approach, employing an innovative rotation invariant horizontal vertical pooled module (RIHVPM), to well-represent aerial VHR RS images for stable and improved classification performance. Notably, the proposed RIHVPM benefits from the multiple tensor rotations coupled with attention-enabled multiscale horizontal and vertical pooling operations for image representation. An experimental study on three benchmark datasets demonstrates competent and/or higher classification performance (AID: 96.44%, NWPU: 94.32% and UCM: 99.04%) and robustness/stability (minimum standard deviation of 0.001) of the proposed approach. [ABSTRACT FROM AUTHOR]

With the development and popularization of unmanned aerial vehicle (UAV) equipment, UAV-based remote sensing technology has developed rapidly. With UAV aerial photography platforms, the obtained low altitude remote sensing images of surfaces have high temporal and spatial resolution and can be used for monitoring and change analyses of surface geographic information. As it is impractical to carry complex and large load equipment on a UAV, it is desirable to develop embedded systems for relevant real-time processing with UAV remote sensing technology. This study combines the advantages of UAV remote sensing image processing and embedded development technology to develop a near real-time UAV remote sensing image change detection system, which realizes the fast, accurate, and automatic detection of variations in the target area. Finally, the feasibility, correctness, and practicability of the prototype system are demonstrated with experiments. [ABSTRACT FROM AUTHOR]

Joint sensing and communications systems have gained significant research interest by merging sensing capabilities with communication functionalities. However, few works have examined the case of multiple users. This work investigates a dual‐user joint sensing and communications system, focusing on the interference between the users that explores the optimal performance trade‐offs through a time‐division approach. Bi‐static radar setting is considered. Two typical strategies under this approach are studied: one in which both users follow the same order of communications and then sensing, and the other in which the tasks are performed in opposite order at two users. In each strategy, the sum rate and the detection probability are evaluated and optimized. The results show that the opposite order strategy offers superior performance to the same order strategy, and they also quantify their performance difference. This research highlights the potential benefits of time‐division strategies and multiple users in joint sensing and communications systems. [ABSTRACT FROM AUTHOR]

ABSTRACT The data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost‐intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS‐PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R2 = 0.78; RMSE = 0.28 m3/ha), the RF method yielded the highest model performance for BA (R2 = 0.70; RMSE = 2.53 m2/ha), and finally, the highest success for dq was obtained in the DT method (R2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images. [ABSTRACT FROM AUTHOR]

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation. [ABSTRACT FROM AUTHOR]

The Eastern Mausoleum of Qin State is a significant component of the Qin Dynasty's royal tombs, reflecting the social development level during the Warring States period (475 BC ~ 221 BC) in China. At the onset of our investigation, we mapped the site's boundaries utilizing Corona satellite imagery and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to identify archaeological features. Additionally, this study is the first to propose the use of thermal infrared band data from the SDGSAT-1 satellite to explore thermal archaeological traces, demonstrating their viability for archaeological site analysis. Subsequently, electromagnetic (EM) prospection was utilized to validate the presence of an ancient burial chamber passage. Landscape monitoring and analysis of the No.1 Mausoleum were performed using Corona and Google Earth images, revealing the accuracy of the Geomancy Theory of Chinese mausoleums through dynamic remote sensing of surface changes. Furthermore, our employment of space-to-ground observational modalities and resultant Digital Elevation Models (DEM) have been used to provided new insights into the application of Remote Sensing (RS) and Geomancy in archaeology, thereby emphasizing the pivotal role of site selection in heritage preservation. This research underscores the promise of synergistic space-ground observations in both exploiting the archaeological riches of cultural heritage sites and ensuring the enduring conservation of these irreplaceable patrimonial assets. [ABSTRACT FROM AUTHOR]

Mangrove forests are critical coastal ecosystems that provide great socio‐ecological services, which are also highly vulnerable to climate change, particularly to sea level rise (SLR). Here we assess changes in mangrove forests in four distinct river/tide/wave‐dominant large deltas along the Indo‐Pacific coast based on 1,336 remote sensing images by machine learning techniques. We find that mangroves are migrating seaward at a rate of 18% ± 12% m/yr, which can offset landward mangroves loss, 67% of which caused by land use conversion. The fact that mangroves are expanding seaward with accretion rates exceeding SLR suggests that climate change has not yet triggered substantial loss in deltaic mangrove forests. Assuming that present environmental conditions do not change and that sediment and organic deposition in the deltaic topsets match SLR rates, we project that 90% of deltaic mangrove forests may start to retreat after 132–194 years. Early inundation of mangroves will occur in wave‐dominated delta. Plain Language Summary: Mangrove forests provide significant ecological and societal services, and mitigation global warming. However, large‐scale loss in mangroves could be induced by anthropogenic drivers and sea level rise (SLR). Our study based on deltas along the Indo‐Pacific coast, highlight that mangroves are expanding seaward at a rate of 18% ± 12% m/yr, indicating that there is little impacts from SLR and has not been substantial loss in mangrove forests in these deltas so far. Mangrove expansion here can efficiently offset 67% landward mangrove losses indicates that our new model project that 90% of mangrove shorelines will may start retreating within 132–194 years. We conclude that favoring mangroves expansion seaward would enhance coastal protection and reduce the need of landward mangrove restoration. Key Points: Mangroves along typical deltas are expanding seaward of about 18% ± 12% m/yr, indicating that there is little impacts from sea level riseMangrove expansion here can efficiently offset 67% landward mangrove lossesNew model project that 90% of mangrove fringes may start retreating within 132–194 years, and wave‐dominated delta present early inundation [ABSTRACT FROM AUTHOR]

The magnitude of integrated water vapor (IWV) varies considerably in the spatial‐temporal domain, which demonstrates the significance of high‐spatiotemporal‐resolution IWV observations for atmospheric water vapor distribution monitoring, both locally and globally. Unlike previously published algorithms based on data fusion, an empirical retrieval algorithm is for the first time proposed to directly retrieve high‐temporal‐resolution IWV estimates from near‐infrared radiance observations of the non‐geostationary Ocean and Land Color Instrument (OLCI). The retrieval algorithm is developed based on an observed regression relationship between satellite‐based OLCI‐estimated transmittance and ground‐based Global Navigation Satellite System (GNSS)‐estimated IWV in the temporal domain. The results show that all newly retrieved IWV estimates have an overall good consistency with ground‐based IWV from additional GNSS‐sensed measurements, indicating the feasibility of the retrieval approach. The performance of the retrieval algorithm is acceptable and satisfactory when compared with that of IWV retrievals listed in previous studies. Plain Language Summary: Integrated water vapor (IWV) is the largest natural greenhouse component, which plays a crucially important role in weather, climate, and other related fields. Remote sensing of IWV from satellite‐based instruments provides a unique technique for monitoring atmospheric water vapor distribution at proper spatial and temporal resolutions in both local and global areas. However, non‐geostationary satellite‐retrieved IWV observations have much lower temporal resolutions compared to geostationary satellite‐sensed IWV measurements. The previously published improvements in the temporal resolution of non‐geostationary satellite‐retrieved IWV estimates are primarily performed based on data fusion approaches using reanalysis‐based high‐temporal‐resolution IWV data. We propose a feasible IWV retrieval algorithm for directly retrieving high‐temporal‐resolution IWV data from non‐geostationary Ocean and Land Color Instrument (OLCI)‐sensed near‐infrared radiance observations. For the first time, this study provides implications for the direct retrieval of high‐temporal‐resolution IWV estimates from non‐geostationary satellite measurements. The retrieval algorithm has significant potential to be applicable to other non‐geostationary OLCI‐like instruments, such as Medium Resolution Imaging Spectrometer (MERIS), Medium Resolution Spectral Imager (MERSI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Key Points: For the first time, a relationship between Ocean and Land Color Instrument and Global Navigation Satellite System is observed and definedAn empirical retrieval model is proposed to directly derive high‐temporal‐resolution water vapor from Ocean and Land Color Instrument dataThe newly retrieved water vapor has satisfactory performance when compared with additional Global Navigation Satellite System measurements [ABSTRACT FROM AUTHOR]

Water stress in the Amazon is exacerbated by rising temperatures and reduced moisture levels. However, understanding forest responses to increased aridity is hindered by limited in situ water potential observations in the Amazon. Remote sensing of water content has emerged as a promising metric. Vegetation Water Content (VWC) diurnal dynamics is hypothesized to reflect water stress responses. Conventional sensors' low sampling rates impede capturing and studying sub‐daily VWC dynamics. Leveraging Global Navigation Satellite System Reflectometry (GNSS‐R) with unprecedented sampling rates, this study reveals significant disparities in morning and evening VWCs in the Amazon, for example, by ≈ ${\approx} $1.1 and 1.0 kg/m2 ${\mathrm{m}}^{2}$ during the wet and dry seasons of 2019. A strong correlation (R=0.8) $(R=0.8)$ between Δ ${\Delta }$VWC (the difference between evening and morning VWCs) and vapor pressure deficit is observed in Amazonian peatland. This highlights the potential of VWC from innovative remote sensing techniques in elucidating water stress dynamics in critical ecosystems. Plain Language Summary: In the Amazon rainforest, rising temperatures and decreasing moisture levels are causing plants to experience more water stress. However, scientists have struggled to understand how the forests are responding to these drier conditions as direct measurements of plant moisture content do not provide sufficient coverage. Recently, researchers have started using satellites to measure water in plants, which could help us understand how they are coping with the lack of water. However, conventional sensors hardly offer measurements often enough to capture the daily changes in plant water levels. This study uses a novel satellite observation technique, Global Navigation Satellite System Reflectometry, that offers measurements with unprecedented frequency. It is found that there are significant differences in plants' water content in the morning compared to the evening in the Amazon, for example, by ≈ ${\approx} $1.1 and 1.0 kg/m2 ${\mathrm{m}}^{2}$ during the wet and dry seasons of 2019. This study reveals that the difference level responds significantly to environmental aridity. As a result, novel satellite methods could help us better understand how water stress is affecting the Amazon rainforest. Key Points: Global Navigation Satellite System Refractometry offers unprecedented sampling, unveiling Amazon's diurnal vegetation water contentVegetation water content generally peaks in mornings, fluctuating significantly throughout the dayAmazonian peatland's VWC diurnal cycle correlates strongly (R=0.8) $(R=0.8)$ with vapor pressure deficit, proposed as a water stress indicator [ABSTRACT FROM AUTHOR]

The three‐dimensional structure of the Tropical Cyclone's baroclinic wake is synthesized as an averaged baroclinic‐dominant response of the upper ocean. The resulting persisting sea surface depression can easily be monitored using the present‐day altimeter constellation. Following a semi‐empirical framework, these baroclinic wake signatures are linked to the inner core TC dynamic and the ocean stratification. To collect these fine‐scale parameters, spaceborne SAR instruments and Argo fleet are used, to precisely capture the maximum wind region and the irregularities of the ocean vertical structure. This combination of high‐resolution information is found paramount to fully capture the modulation of sea surface height anomalies, and its mean trend, especially for major hurricanes. Baroclinic signatures mostly range around 10–20 cm and peak at 40 cm. Deeper anomalies correspond to barotropic response, removed from the present analysis. Plain Language Summary: In the wakes of Tropical Cyclones (TCs), sea surface depressions of about 10 cm appear. These TC signatures are persistent enough to be easily monitored by the current fleet of altimeter instruments. A measured sea surface height anomaly integrates and reduces the air/sea interactions during the TC passage into a single observable metric. It mostly encodes the cyclonic wind forcing and the interior ocean state. A broad constellation of remote sensing and in‐situ instruments has been gathered to compile 200 cyclonic episodes, collecting a wide range of TC sizes, intensities, and translation speeds together with oceanic conditions. A synthetic relationship is found to robustly predict most observed sea surface height anomalies. Moreover, when high‐resolution information is available to estimate the ocean interior state and the TC radius of maximum winds. Such a diagnostic thus explains the dominant baroclinic ocean response to a TC passage, and, inversely, can be used to infer ocean stratification or forcing parameters in the absence of high‐resolution observations. Key Points: Residual sea surface height anomalies from the cold wakes of Tropical Cyclones are analyzed using a multi‐modal approach at global scaleA scaling law provides a robust interpretation of the baroclinic response to given ocean stratification and forcing conditionsHigh resolution measurements are found critical to correctly anticipate the SSHA amplitudes with localized pre‐storm ocean stratification [ABSTRACT FROM AUTHOR]

A lack of high‐density rootzone soil moisture (θRZ) observations limits the estimation of continental‐scale, space‐time contiguous θRZ dynamics. We derive a proxy of daily θRZ dynamics — active rootzone degree of saturation (SRZ) — by recursive low‐pass (LP) filtering of surface soil moisture (θS) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between SRZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θS from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated SRZ compares well against SMAP Level‐4 estimates and in‐situ θRZ, at the corresponding scale. The instantaneous hydrologic state (SRZ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMSRZ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics. Plain Language Summary: Rootzone soil moisture plays a vital role in agricultural, hydrological, and ecosystem processes. The available spaceborne satellites for monitoring soil moisture can only capture variability in a shallow soil layer at the surface, typically limited to the top 5 cm. Hence, spatiotemporally continuous estimation of rootzone soil moisture dynamics typically relies on soil moisture estimates from land‐surface models, which are subject to errors in the surface meteorological forcing data, process formulations, and model parameters. Some studies suggest that the rootzone soil moisture dynamics can be estimated by filtering the high‐frequency variability in the surface soil moisture. However, such "filters" require observed rootzone data (often unavailable at high spatial density) for calibration. This study uses the relationship between surface soil moisture and evaporative fraction derived using spaceborne observations from the Soil Moisture Active Passive mission and the Moderate Resolution Imaging Spectroradiometer to estimate rootzone soil moisture dynamics for the Contiguous U.S. at 9 km grid resolution. We further demonstrate that this approach can be extended into a near‐real‐time agricultural drought monitor to assess drought impacts on vegetation using surface soil moisture observations. Key Points: Terrestrial water‐energy coupling is used to parameterize low‐pass filter to estimate rootzone dynamics from surface soil moistureRootzone degree of saturation and water‐energy coupling thresholds are estimated using evaporative fraction and surface soil moistureSMAP‐based rootzone degree of saturation can used for operational, near‐real‐time agricultural drought monitoring over Contiguous U.S [ABSTRACT FROM AUTHOR]

Photodetectors with good polarization detection ability are promising in many applications, such as remote sensing imaging and environmental monitoring. However, the traditional polarization detection systems fall short in meeting integration demands of the integrated‐circuits field due to additional optical elements. The emerging 2D materials with in‐plane anisotropic structures provide a possible method to fabricate remarkable polarization detectors. Modulating the band structure by gate voltage is an important strategy for developing optoelectronic devices. Herein, a polarized photodetector based on PdSe2/MoS2 out‐of‐plane heterojunction is fabricated. Due to its unique out‐of‐plane heterostructure, the device exhibits excellent photoresponse characteristics and polarization sensitivity, including an excellent responsivity of 10.19A/W, an extremely high external quantum efficiency of 2429%, a fast rise/decay time of 68/192 µs, and a high photocurrent anisotropy ratio of 3.09. Based on the adjustment of the built‐in electric field through gate voltage, the performance of the device can be accordingly modulated. As the gate voltage increases from −30 to 30 V, the responsivity gradually increases from 7.5 to 13A/W and the detectivity increases from 1.53 to 2.63 × 109Jones. Finally, its olarization imaging ability is demonstrated at different polarization angles. The findings indicate that PdSe2/MoS2 devices exhibit significant potential for polarized photoelectric detection. [ABSTRACT FROM AUTHOR]

• A phase error correction method (IFOA-JMEC) for InIRA images is proposed. • Ionospheric scintillation and atmospheric turbulence are main errors source for InIRA. • Correcting spatial-variant Error in range by dividing subblocks and interpolating. • Background atmosphere has little influence on InIRA height measurement accuracy. • Phase screen models for ionospheric scintillation and atmospheric turbulence. The interferometric imaging radar altimeter (InIRA) is a newly developed microwave remote sensing system in recent years. It is of great significance to analyze the main sources of its height measurement error through simulation, and to develop the corresponding image phase error correction methods. This can provide basis and reference for the subsequent correction of the real images phase error. In this study, according to the interaction principle between atmospheric interference and InIRA radar electromagnetic waves, the influence of background ionosphere and background troposphere on height measurement error is analyzed by introducing ionosphere and troposphere delays into echo signals respectively. The impact of ionospheric scintillation and atmospheric turbulence on the height measurement error is analyzed by building multi-layer phase screen models. Then according to the analysis results, a phase error correction method for InIRA images combined by the improved fruit fly optimization algorithm (IFOA) and the joint minimum entropy criterion (JMEC) is proposed. The image is divided into several sub-images with negligible spatial variation of phase error according to the principle of first range and then azimuth. IFOA is used to search the optimal phase compensation values iteratively to compensate the phase of all sub-images. When the joint entropy of the sub-images reaches the minimum value, the phase error correction of the whole InIRA image is completed. Experimental results show that the proposed method can correct the phase of InIRA images with inconspicuous features and polluted by weak ionospheric scintillation and atmospheric turbulence relatively quickly. After correction, the height measurement error can be reduced from decimeter level to centimeter level. The correction accuracy and efficiency of the proposed method are superior to those of the method combined by particle swarm optimization algorithm and joint minimum entropy criterion (PSO-JMEC), the method combined by genetic algorithm and joint minimum entropy criterion (GA-JMEC), the method combined by phase gradient autofocus and Map Drift (PGA-MD), and improved PGA-MD algorithm. [ABSTRACT FROM AUTHOR]

Remote sensing ship image detection methods have broad application prospects in areas such as maritime traffic and fisheries management. However, previous detection methods relied heavily on a large amount of accurately annotated training data. When the number of remote sensing ship targets is scarce, the detection performance of previous methods is unsatisfactory. To address this issue, this paper proposes a few-shot detection method based on global and local feature aggregation. Specifically, we introduce global and local feature aggregation. We aggregate query-image global and local features with support features. This encourages the model to learn invariant features under varying global feature conditions which enhances the model's performance in training and inference. Building upon this, we propose combined feature aggregation, where query features are aggregated with all support features in the same batch, further reducing the confusion of target features caused by the imbalance between base-class samples and novel-class samples, improving the model's learning effectiveness for novel classes. Additionally, we employ an adversarial autoencoder to reconstruct support features, enhancing the model's generalization performance. Finally, the model underwent extensive experiments on the publicly available remote sensing ship dataset HRSC-2016. The results indicate that compared to the baseline model, our model achieved new state-of-the-art performance under various dataset settings. This model presented in this paper will provide new insights for few-shot detection work based on meta -learning. [ABSTRACT FROM AUTHOR]

In the derection of remote sensing (RS) image analysis, semantic segmentation, as an important technology, is of key significance for the identification and analysis of land surface cover types. In recent years, applying deep learning models to tasks such as road extraction, water distribution extraction, building classification and building segmentation from RS images has become an important research hotspot. Due to its limited receptive field, traditional convolutional neural networks (CNN) cannot effectively capture global context information. Transformer uses the multi-head self-attention mechanism to capture a wide range of information and can solve this problem well. Therefore, we proposed ST-MDAMNet based on Swin Transformer and combined with the multi-dimensional attention mechanism. First, a feature enhancement module (FAM) is introduced after each stage of the Swin Transformer encoder to effectively enhance the model's proficiency in identifying essential information. Secondly, a feature fusion module (FFM) is proposed to effectively fuse the multi-scale information of the encoder part. It further improves the expression ability of different dimensional features and effectively improves the detection effect of small targets. Ultimately, the fused features are input into the multi-dimensional attention module (MDAM) to carefully optimize the features, which greatly increases the effect of semantic segmentation of RS images. We demonstrate the effectiveness of each module through ablation experiments. Comparative experiments are completed on two publicly large-scale datasets, and the proposed method shows excellent results compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Utilizing imaging spectroscopy technology to identify methane super‐emitters plays a vital role in mitigating methane emissions in the Oil & Gas (O&G) sector. While earlier research has uncovered significant point‐source methane emissions from O&G production in the US and Canada, which are key regions with large methane emissions, a comprehensive post‐COVID‐19 survey has been notably absent. Here, we perform a detailed survey of methane super‐emitters across multiple basins of North America (Marcellus Shale, Haynesville/Bossier Shale, Permian Basin and Montney Shale) using the new Chinese Gaofen5‐01A/02 (GF5‐01A/02) satellite measurements during 2022–2023. We detect 139 individual methane plumes emanating from 122 point sources, with flux rates ranging from 519 to 16,071 kg hr−1. These emissions exhibit a highly skewed and heavy‐tailed distribution, constituting approximately 23% of the flux inversion with TROPOMI in the sample region, with a range of 13%–40%. Moreover, we observe a 66.7% reduction in methane emissions in Permian Basin during COVID‐19, followed by fluctuations until spring 2023. By summer 2023, methane emissions rebound to twice their previous magnitude (1.68 ± 0.58 Tg a−1). Using these point‐source surveys, we further quantify a regional methane emission of 2.69 ± 0.86 Tg a−1 in Permian Basin. This estimation closely aligns with top‐down inversions (2.22 ± 0.40 Tg a−1) from TROPOMI. The upscale estimation underscores the effectiveness of high‐resolution remote sensing measurements in improving bottom‐up emissions inventories and refining regional methane emission assessments. Our results highlight the potential climate benefits derived from regular monitoring and specific remediation efforts focused on relatively few strong point‐source emissions. Plain Language Summary: Reducing methane (CH4) leaks from Oil & Gas (O&G) production is crucial for abating climate change. However, detecting these abnormal CH4 emissions globally is challenging as they often occur unexpectedly. Satellite remote sensing with hyperspectral imaging spectrometer provides an effective approach for top‐down monitoring. These instruments produce CH4 plume maps, enabling the quantification of emissions. In this research, we conduct a comprehensive survey in major O&G basins of North America during 2022–2023 using the new Chinese Gaofen5‐01A/02 satellite. Through repeated observations by high‐resolution satellites, we capture CH4 emission dynamics in sample basins and quantify their contribution to regional methane budget. Our results demonstrate the value of high‐resolution satellite observations in reducing uncertainties in quantifying anthropogenic CH4 emission and supporting strategies for emission mitigation. Key Points: The new Chinese Gaofen5‐01A/02 hyperspectral satellite missions have great capability in methane mappingWe find substantial methane point‐source emissions in Permian Basin after COVID‐19 during 2022–2023Satellite‐based survey can effectively improve bottom‐up regional methane emissions inventories [ABSTRACT FROM AUTHOR]

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat--land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

In the context of hyperspectral image (HSI) analysis, a widely used feature extraction method, Principal Components Analysis (PCA) suffers from limitations such as wavelength bias and a lack of consideration for local spectral information. While various segmentation based PCA methods attempt to address these issues by incorporating local relationships, they still overlook band similarity beyond immediate neighbours. To address these challenges, this paper introduces a novel approach called dependency based segmented PCA (dPCA). This method employs hierarchical clustering-driven mutual information-based segmentation, facilitating more comprehensive feature extraction from HSI data. By utilizing this dependency-based segmentation, both global and local structures are effectively captured, providing enhanced details for classification tasks. The proposed dPCA is evaluated on four prominent HSI datasets in remote sensing for land use classification, and the experimental results underscore its superiority over conventional PCA, and other segmentation based PCA methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

Soil salinization presents a considerable risk to agricultural sustainability globally. Remote sensing technology facilitates extensive monitoring and assessment of soil salinization, thus providing technical support for its prevention and management. This study utilizes bibliometric methods to examine the attributes of publications, key research areas, and their evolutionary trends within the domain of soil salinization remote sensing evaluation, spanning 1999 to 2023, using data from the Web of Science Core Collection. Visualization is facilitated by CiteSpace software. The results indicate: (1) In terms of research trends, international investigations into remote sensing monitoring of soil salinization show an overall upward trend, with France, the United States and China being the largest contributors to this field of research. The Chinese Academy of Sciences is the institution that has published the most papers, and the journal that has published the most papers is Remote Sensing; (2) In terms of collaborative networks, research institutes and government organizations contribute to a large extent to institutional collaboration. However, most cooperation is currently internal; (3) Keyword analysis demonstrates that over the past 25 years, the research field of soil salinization has evolved from preliminary quantitative monitoring to more in-depth studies using intelligent analysis. The implementation of machine learning and new-generation remote sensing satellites significantly enhances the precision and efficiency of remote sensing monitoring, pointing towards new directions for solving future soil salinization issues. This study reviews the development history of the field of soil salinization remote sensing monitoring, highlights the current state of research, clarifies the focal research areas and evolutionary trends, and offers valuable references for global monitoring of soil salinization. [ABSTRACT FROM AUTHOR]

Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation. [ABSTRACT FROM AUTHOR]

Scene classification in panchromatic (PAN) remote sensing images is a challenging task due to arbitrary spatial arrangement of a variety of objects with complex background in the absence of RGB-channel information. In this paper, we propose an elevation-guided attention network (EGANet) for multimodal scene classification in panchromatic images by leveraging elevation information from digital elevation model (DEM). The proposed network helps to identify the potential regions containing prominent class-specific features in the panchromatic image scene with the attention of elevation features extracted from a convolution neural network (CNN). Then, elevation-guided features in panchromatic image scene are obtained by the correlation of these two modalities for effective scene classification. The efficacy of the proposed method is demonstrated on Cartosat-1 panchromatic remote sensing image datasets with a lot of variations in view-angle, occlusion, background, and illumination conditions. The experimental results show that our proposed EGANet achieves scene classification accuracy with an improvement of 5% in comparison with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop. Monomodal images cannot meet the needs of image analysis, which requires image fusion and stitching to process images for better image analysis. Image registration is an important prerequisite of image fusion and stitching. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multimodal image data. Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD (AM-PIIFD). This method extracts KAZE features in the scale space constructed by nonlinear diffusion filtering. It can effectively preserve the edge feature information while filtering out the noise. Then, the proposed AM-PIIFD feature descriptor is used to describe the multi-scale features. Finally, according to the consistency of the feature main orientation, most of the mismatches are removed, and the image alignment transformation is realized. The qualitative and quantitative comparisons with the other three advanced methods show that our method can achieve good performance in multimodal remote sensing image registration. [ABSTRACT FROM AUTHOR]