Advances in Environmental Remote Sensing

Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 10⁵ tonne CO₂e y⁻¹ and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 10⁵ tonne CO₂e y⁻¹) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales. Full article

Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms. Full article

The research on regional aerosol optical properties is of great significance for exploring climate regulation mechanisms and controlling atmospheric pollution. Based on the solar radiation observation platform, a three-month optical observation of atmospheric aerosols was conducted in Wuhan, China. The daily and monthly variation characteristics of aerosol optical depth (AOD₅₅₀), Angstrom parameter (α_440–870), and turbidity coefficient (β) were revealed, and the interrelations between the three optical parameters were fitted. Then, the potential relationships between atmospheric particulate matter (PM_2.5, PM₁₀) with AOD₅₅₀ and β were discussed. The results show that the average values of AOD₅₅₀, α_440–870, and β in this case study are 0.42, 1.32, and 0.20, respectively. The frequency distribution patterns of the three optical parameters are all unimodal. AOD₅₅₀ has a good linear correlation system with β, and the Pearson correlation coefficient reaches 0.94, while its correlation with α_440–870 is not significant. The daily variation in AOD₅₅₀ and β both show an increasing trend, and their monthly increases are more than 50%. However, the daily variation in α_440–870 is relatively stable, and the fitted line is a nearly horizontal line with no significant monthly variation. The fluctuation of particulate matter concentration affects the aerosol optical properties to some extent, among which β has a prominent effect on the response to the change in PM_2.5 concentration with a linear correlation coefficient of 0.861. As the concentration of particulate matter increases, the proportion of fine particulate matter in the atmosphere increases monthly, and the ratio of PM₁₀ to PM_2.5 concentrations decreases from 1.8:1 to 1.2:1. Atmospheric pollution conditions are frequent during this observation period, mainly at mildly turbid levels. Atmospheric turbidity shows an increasing trend month by month, and the concentration of particulate matter increases rapidly. The response of atmospheric aerosol optical properties to the changes in fine particulate matter concentration is significant, and controlling the particulate matter content in the atmosphere is an effective means to mitigate aerosol pollution. Full article

Remote sensing (RS) has revolutionized field data collection processes and provided timely and spatially consistent acquisition of data on the terrestrial landscape properties. This research paper investigates the relationship between Wind Erosion (WE) and Remote Sensing (RS) techniques. By examining, analyzing, and reviewing recent studies utilizing RS, we underscore the importance of wind erosion research by exploring indicators that influence the detection, evaluation, and modeling of wind erosion. Furthermore, it identifies research gaps particularly in soil erodibility estimation, soil moisture monitoring, and surface roughness assessment using RS. Overall, this research enhances our understanding of WE and RS and offers insights into future research directions. To conduct this study, we employed a two-fold approach. First, we utilized a non-systematic review approach by accessing the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database. Subsequently, we conducted a systematic review of the relevant literature on wind erosion and remote sensing in the core collection of the Web of Science (WoS) database. Additionally, we employed the VOSviewer bibliometric software to generate a cooperative keyword network analysis, facilitating the advancements and identifying emerging areas of WE and RS research. With a non-systematic review, we focused on examining the current state and potential of remote sensing for mapping and analyzing following indicators of wind erosion modelling: (1) soil erodibility; (2) soil moisture; (3) surface roughness; (4) vegetation cover; (5) wind barriers; and (6) wind erosion mapping. Our study highlights the widespread utilization of freely available RS data, such as MODIS and Landsat, for WE modeling. However, we also acknowledge the limitations of high resolution sensors due to their high costs. RS techniques offer an efficient and cost-effective approach for mapping erosion at various scales and call for a more comprehensive and detailed assessment of soil erosion at regional scales. These findings provide valuable guidance for future research endeavors in this domain. Full article

This paper addresses the accuracy of estimates for the contemporary level of global thunderstorm activity found from the synchronous records of integrated Schumann resonance (SR) intensity at two high-latitude observatories in the Northern and Southern hemispheres. The results are based on numerical simulations of electromagnetic fields in the frequency band of the global (Schumann) resonance in the Earth–ionosphere cavity characterized by a realistic conductivity profile in the middle atmosphere. The credible distribution is used for global thunderstorm activity in space and time. The paired observatory locations are considered either at the geographic poles or at Svalbard and the Antarctic Peninsula. The seasonal variations in the spatial distribution of global thunderstorms are adopted from the OTD satellite observations. The diurnal variations imply the spatial and temporal distribution of lightning strokes measured by the WWLLN network for an arbitrarily chosen date of 18 January 2022. The results obtained suggest that simultaneous records of the integrated SR intensity at Svalbard and in Antarctica provide errors below 3% in the diurnal variations of global thunderstorm activity with a temporal resolution of 10 min. The seasonal changes in global thunderstorm intensity are estimated with an error of ~10%. Since the level of global thunderstorm activity varies by a factor of two on the both time scales, the estimates confirm the appropriate accuracy of the estimate of thunderstorm activity from the concurrent measurements at the high-latitude SR observatories in the Arctic and Antarctic. Full article

Aerial photographs of the European Alps usually only reach back to the middle of the 20th century, which limits the time span of corresponding studies that quantitatively analyse long-term surface changes of proglacial areas using georeferenced orthophotos. To the end of the Little Ice Age, this leads to a gap of about 100 years. Using digital monoplotting and several historical terrestrial photographs, we show the quantification of surface changes of a Little Ice Age lateral moraine section until the late second half of the 19th century, reaching a total study period of 130 years (1890–2020). The (initial) gully system expands (almost) continuously over the entire study period from 1890 to 2020. Until 1953, the vegetation-covered areas also expanded (mainly scree communities, alpine grasslands and dwarf shrub communities), before decreasing again, especially between 1990 and 2003, due to large-scale erosion within the gully system. Furthermore, our results show that the land-cover development was impacted by temperature and precipitation changes. With the 130-year study period, we contribute to a substantial improvement in the understanding of the processes in the proglacial by analysing the early phase and thus the immediate response of the lateral moraine to the ice exposure. Full article

The source region of the Yellow River (SRYR) is an important water conservation and farming area in China. Under the dual influence of the natural environment and external pressure, ecological patches in the region are becoming increasingly fragmented, and landscape connectivity is continuously declining, which directly affect the landscape patch pattern and SRYR sustainable development. In the SRYR, morphological spatial pattern analysis (MSPA) and landscape index methods were used to extract ecologically important sources. Based on the minimum cumulative resistance model (MCR), Linkage Mapper was used to generate a potential corridor, and then potential stepped stone patches were identified and extracted by the gravity model and betweenness centrality to build an optimal SRYR ecological network. The distribution of patches in the core area of the SRYR was fragmented, accounting for 80.53% of the total grassland area. The 10 ecological sources based on the landscape connectivity index and 15 important corridors identified based on the MCR model were mainly distributed in the central and eastern regions of the SRYR. Through betweenness centrality, 10 stepped stone patches were added, and 45 planned ecological corridors were obtained to optimize the SRYR ecological network and enhance east and west connectivity. Our research results can provide an important reference for the protection of the SRYR ecosystem, and have important guiding significance and practical value for ecological network construction in ecologically fragmented areas. Full article

Advancement in remote sensing platforms, sensors, and technology has significantly improved the assessment of hard-to-access areas, such as mountains. Despite these improvements, Africa lags in terms of research work published. This is of great concern as the continent needs more research to achieve sustainable development. Therefore, this study applied a bibliometric analysis of the annual production of publications on the application of remote sensing methods in mountainous environments. In total, 3849 original articles between 1973 and 2021 were used, and the results indicate a steady growth in publications from 2004 (n = 26) to 2021 (n = 504). Considering the source journals, Remote Sensing was the top-ranked, with 453 total publications. The University of the Chinese Academy of Sciences was the highest-ranking affiliation, with 217 articles, and China produced the highest number of publications (n = 217). Keywords used between 1973 and 1997, such as “Canada”, “alps”, and “GIS”, metamorphosed into “remote sensing” between 1998 and 2021. This metamorphosis indicates a change in the areas of interest and an increase in the application of remote sensing methods. Most studies were conducted in the Global North countries, and a few were published in low-impact journals within the African continent. This study can help researchers and scholars better understand the progress and intellectual structure of the field and future research directions in the application of remote sensing methods in mountainous environments. Full article

The occurrence of natural disasters as a consequence of accidental hazardous chemical spills remains a concern. The inadequate, or delayed, initial response may fail to mitigate their impact; hence, imminent monitoring of responses in the initial stage is critical. Classical contact-type measurement methods, however, sometimes miss solvent chemicals and invoke risks for operators during field operation. Remote sensing methods are an alternative method as non-contact, spatially distributable, efficient and continuously operatable features. Herein, we tackle challenges posed by the increasingly available UAV-based hyperspect ral images in riverine environments to identify the presence of hazardous chemical solvents in rivers, which are less investigated in the absence of direct measurement strategies. We propose a referable standard procedure for a unique spectral library based on pre-scanning hyperspectral sensors with respect to representative hazardous chemicals registered on the national hazardous chemical list. We utilized the hyperspectral images to identify 18 types of hazardous chemicals injected into the river in an outdoor environment, where a dedicated hyperspectral ground imaging system mounted with a hyperspectral camera was designed and applied. Finally, we tested the efficiency of the library to recognize unknown chemicals, which showed >70% success rate. Full article

Remote sensing is the technique of acquiring data from the earth’s surface from sensors installed on satellites or on manned or unmanned aircrafts. Its use is common in dozens of sectors of science and technology, agriculture, atmosphere, soil, water, land surface, oceans and coasts, snow and ice, and natural disasters, among others. This article focuses on an in-depth literature review of some of the most common and promising disciplines, which are asbestos–cement roof identification, vegetation identification, the oil and gas industry, and geology, with the aim of having clarity on the trends in research on these issues at the international level. The most relevant problems in each sector have been highlighted, evidencing the need for future research in the area in light of technological advances in multi- and hyperspectral sensors and the availability of satellite images with more precise spatial resolution. A bibliometric analysis is proposed for each discipline and the network of related keywords is discussed. Finally, the results suggest that policymakers, urban planners, mine, and oil and gas companies should consider remote sensing as primary tool when planning comprehensive development strategies and in field parameter multitemporal analysis. Full article

The heterogeneity of the fractured-basalt and interbedded-sediment aquifer along the eastern margin of the Columbia Plateau Regional Aquifer System has presented challenges to resource managers in quantifying recharge and estimating sustainable withdrawals. Previous studies indicated recharge pathways in alluvial sediments atop a mountain–front interface upgradient of the basalt flows. In this sedimentary zone, six seismic stations were deployed for one year to detect velocity changes in low-frequency seismic waves that could be correlated to changes in groundwater recorded by a well transducer near the center of the seismic station network. Waveforms in the 1−5 Hz range were recorded at each station to determine changes in wave velocities between station pairs and correlate these velocity changes to changes in groundwater levels. The velocity–groundwater relation allowed for estimation of daily groundwater levels beneath the seismic station network. Existing hydrogeologic information was used to estimate hydraulic gradients and hydraulic conductivities, which allowed for the calculation of the daily volume of recharge passing beneath the seismic stations and into the confined aquifer system. The daily recharge volumes across the seismic station network were summed for comparison of the total annual recharge calculated from the change in seismic wave velocities (154,660 m³) to a flow model calculation of recharge based on areal precipitation and infiltration (26,250 m³). The 6× greater recharge estimated from the seismic wave velocity changes for this portion of the recharge zone is attributed to preferential pathways of high hydraulic conductivity and greater depth associated with paleochannels beneath the seismic station network. Full article

GaoFen-5 (02) (GF5-02) is a new Chinese operational satellite that was launched on 7 September 2021. The Directional Polarimetric Camera (DPC) is one of the main payloads and is mainly used for the remote sensing monitoring of atmospheric components such as aerosols and water vapor. At present, the DPC is in the stage of on-orbit testing, and no public DPC precipitable water vapor (PWV) data are available. In this study, a PWV retrieval algorithm based on the spectral characteristics of DPC data is developed. The algorithm consists of three parts: (1) the construction of the lookup table, (2) the calculation of water vapor absorption transmittance (WVAT) in the band at 910 nm, and (3) DPC PWV retrieval. The global PWV results derived from DPC data are spatially continuous, which can illustrate the global distribution of water vapor content well. The validation based on the Aerosol Robotic Network (AERONET) PWV data shows that the DPC PWV data have accuracy similar to that of Moderate-resolution Imaging Spectroradiometer (MODIS) PWV data, with coefficient correlation of determination ($R^2$), mean absolute error (MAE), and relative error (RE) of 0.32, 0.30, and 0.93 using the DPC and 0.23, 0.36, and 0.96 using the MODIS, respectively. The results show that our proposed DPC PWV retrieval algorithm is feasible and has high accuracy. By analyzing the errors, we found that the calibration coefficients of the DPC in the 865 nm and 910 nm bands need to be updated. Full article

Based on the optimal estimation (OE) theory and information content analysis method, we discuss the ability to include the multi-angle satellite ultraviolet polarization channel to retrieve the aerosol layer height (ALH) for ten typical aerosol types in the China region. We also quantitatively evaluate the effects of polarization measurements and the number of viewing angles on ALH retrieval under different conditions (aerosol model, aerosol optical depth, etc.). By comparing the different degree of freedom for signal (DFS) results of ALH caused by the theoretical retrieval error changes in different microphysical parameters in the aerosol and surface model, we identify the key factors affecting ALH retrieval. The results show that the extended ultraviolet band provides important information for ALH retrieval and is closely related to the scattering and absorption characteristics of aerosol models. The polarization measurements in fine mode reduce the posterior error of ALH retrieval by 6–39%; however, this is relatively small for coarse mode. In particular, when it is applied to the transported dust and background dust aerosol types, the posterior error is only reduced by 1–8% after adding polarization measurements. For these two aerosol types with weak absorption at the ultraviolet band, increasing the number of angles observed in addition to increasing the polarization channel will more effectively improve ALH inversion. Compared with other aerosol and surface model parameters, the retrieval errors for the total volume column, effective variance, real part of the complex refractive index, and surface reflectance are the main factors affecting ALH retrieval. Therefore, reducing the theoretical retrieval error of these parameters will be helpful. Full article

The planetary boundary layer height is a very important parameter in the atmosphere because it determines the range where the most effective dispersion processes take place, and it serves as a medium for the vertical transport of heat, moisture, and pollutants. The accurate estimation of boundary layer height (BLH) is vital for air pollution prediction. In this paper, the BLH estimated by AD-Net was compared with that from the ECMWFs over East Asia from September 2015 to August 2018. A continuous 24 h BLH estimation from AD-Net generally matched with the aerosol vertical structures. Diurnal and seasonal variation and spatial variation of BLH can also be shown, suggesting the good performance of AD-Net BLH. The comparison of seasonal mean BLH between AD-Net and ECMWFs was conducted at 20 lidar sites. On average, there was an underestimation of the ECMWFs, mostly in summer and winter. A significant disagreement between AD-Net and the ECMWFs was noted, especially over coastal areas and mountain areas. In order to investigate the difference between them, two BLHs were compared under different land cover types and climate conditions. In general, the BLH of the ECMWFs was less than that of AD-Net over most of the land cover types in summer and winter. The smallest differences (0.26 km) existed over water surfaces in winter compared with AD-Net, and the largest underestimation (1.42 km) occurred over grassland surfaces in summer. Similarly, all the BLHs of the ECMWFs were lesser than those of AD-Net under different climatological conditions in summer and winter. The mean difference between AD-Net BLH and ECMWFs BLH was 1.05, 0.71, and 0.48 km for arid regions, semi-arid and semi-wet regions, and wet regions, respectively. The largest underestimation occurred over arid regions in winter, with a value of 1.42 km. The smallest underestimation occurred over wet regions, with a value of 0.27 km. The present research provides better insight into the BLH performance in the ECMWFs reanalysis data. The new continuous PBL dataset can be used to improve the model parameterization of PBL and our understanding of the atmospheric transport of pollutants which affect air quality and human health. Full article

The Giant Panda (Ailuropoda melanoleuca) is a flagship species for endangered wildlife conservation and is a specific relic species in China. Its habitat conservation has received widespread attention around the world. Since 2010, the phenomenon of an aging labor force gradually appeared within the Giant Panda Nature Reserve and its surrounding communities. Under the new labor force structure, households’ resource utilization behavior has had different characteristics, which has led an evolution in giant panda habitats. This study is based on a questionnaire and geographic data. It reveals the internal mechanisms of households’ resource utilization behavior impacting giant panda habitat patterns under the ongoing trend of labor force aging. The study shows that labor force aging has promoted rising ecological niche widths and falling ecological niche overlaps. These could drive a growth in giant panda habitat globally. From a spatial perspective, nature reserves with lower comprehensive ecological niche widths and higher ecological niche overlaps face greater conflict between conservation and development. However, the phenomenon of labor force aging mitigates these ecological conflicts to a certain extent. Full article

The rapid socio-economic development of the metropolitan area has led to the continuous deterioration of the ecological environment. This leads to intense competition and conflict between different spatial use types. Spatial conflict research is essential to achieve ecological-economic coordination and high-quality development. However, existing studies lack comprehensive and direct ecological-economic spatial conflicts, especially those on the spatial-temporal evolution and potential drivers of spatial conflict. In this study, we identified the ecological-economic spatial conflicts in the Nanjing metropolitan area in 2010, 2015, and 2020. This study used the random forest to analyze the factors that influenced the change of spatial conflict. Results show that: (1) From 2010 to 2020, the ecological-economic spatial conflict in the Nanjing metropolitan area changed significantly. (2) Land use change has an important effect on spatial conflicts, which are easily triggered by uncontrolled urban expansion, but ecological land can mitigate spatial conflicts. (3) Relevant driving factors of spatial conflicts show multi-level features, so the development of conflict reconciliation countermeasures needs to be tailored to local conditions. This study provides a significant foundation for the high-quality development of the Nanjing metropolitan area and provides a reference for the planning and management of the territorial space. Full article

A methodological approach to refining the identification and mapping of fluvial terraces has been applied, combining geomorphological field surveys with the computation and assessment of different morphometric parameters (local, statistical, and object-oriented), derived from a high-resolution digital terrain model (DTM) obtained from a LiDAR survey. The mid-sector floodplain of the Misa River basins was taken as a valid example of the main river valleys draining the northern Marche Apennines (Italy) and was considered an ideal site to test a combination of different geomorphological techniques for enhancing fluvial terraces’ detection and mapping. In this area, late Pleistocene–Holocene fluvial terraces are well exposed, and their geomorphological and geochronological characteristics have largely already been studied. However, a reliable distinction of the different Holocene terrace levels, including a detailed geomorphological mapping of different terrace features, is still lacking due to the very complex terrace geometry and the lack of good-quality deposit outcrops. Land-surface quantitative (LSQ) analysis has been coupled with the available outcomes of previous studies and ad-hoc geomorphological field surveys to enhance the identification and mapping of fluvial terraces. The results of this work provided information for the discernment of terrace remnants belonging to the full-glacial fill terrace generation (late Pleistocene) as well as reconstruction of the terrace top–surface, and can be used to distinguish the inner terrace limits coinciding with the margin of the floodplain. It has also been possible to identify and delimit the late Pleistocene terrace from a staircase of three younger strath terraces formed during the Holocene. The results of this study demonstrated that the investigation of fluvial landforms, at different scales, can strongly benefit from the integration of field surveys and quantitative geomorphic analysis based on high-resolution digital topographic datasets. In particular, the integration of LSQ analysis with ground-truth geomorphological data can be dramatically helpful for the identification and mapping of fluvial terraces. Full article

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography. Full article

The occlusion of cloud layers affects the accurate acquisition of ground object information and causes a large amount of useless remote-sensing data transmission and processing, wasting storage, as well as computing resources. Therefore, in this paper, we designed a lightweight composite neural network model to calculate the cloud amount in high-resolution visible remote-sensing images by training the model using thumbnail images and browsing images in remote-sensing images. The training samples were established using paired thumbnail images and browsing images, and the cloud-amount calculation model was obtained by training a proposed composite neural network. The strategy used the thumbnail images for preliminary judgment and the browsing images for accurate calculation, and this combination can quickly determine the cloud amount. The multi-scale confidence fusion module and bag-of-words loss function were redesigned to achieve fast and accurate calculation of cloud-amount data from remote-sensing images. This effectively alleviates the problem of low cloud-amount calculation, thin clouds not being counted as clouds, and that of ice and clouds being confused as in existing methods. Furthermore, a complete dataset of cloud-amount calculation for remote-sensing images, CTI_RSCloud, was constructed for training and testing. The experimental results show that, with less than 13 MB of parameters, the proposed lightweight network model greatly improves the timeliness of cloud-amount calculation, with a runtime is in the millisecond range. In addition, the calculation accuracy is better than the classic lightweight networks and backbone networks of the best cloud-detection models. Full article

Land use segmentation is a fundamental yet challenging task in remote sensing. Most current methods mainly take images as input and sometimes cannot achieve satisfactory results due to limited information. Inspired by the inherent relations between land cover and land use, we investigate land use segmentation using additional land cover data. The topological relations among land cover objects are beneficial for bridging the semantic gap between land cover and land use. Specifically, these relations are usually depicted by a geo-object-based graph structure. Deep convolutional neural networks (CNNs) are capable of extracting local patterns but fail to efficiently explore topological relations. In contrast, contextual relations among objects can be easily captured by graph convolutional networks (GCNs). In this study, we integrated CNNs and GCNs and proposed the CNN-enhanced HEterogeneous Graph Convolutional Network (CHeGCN) to incorporate local spectral-spatial features and long-range dependencies. We represent topological relations by heterogeneous graphs which are constructed with images and land cover data. Afterwards, we employed GCNs to build topological relations by graph reasoning. Finally, we fused CNN and GCN features to accomplish the inference from land cover to land use. Compared with other homogeneous graph-based models, the land cover data provide more sufficient information for graph reasoning. The proposed method can achieve the transformation from land cover to land use. Extensive experiments showed the competitive performance of CHeGCN and demonstrated the positive effects of land cover data. On the IoU metric over two datasets, CHeGCN outperforms CNNs and GCNs by nearly 3.5% and 5%, respectively. In contrast to homogeneous graphs, heterogeneous graphs have an IoU improvement of approximately 2.5% in the ablation experiments. Furthermore, the generated visualizations help explore the underlying mechanism of CHeGCN. It is worth noting that CHeGCN can be easily degenerated to scenarios where no land cover information is available and achieves satisfactory performance. Full article

During industrial operations and in confined places, carbon monoxide (CO) may collect in harmful proportions if ventilation is insufficient or appliances are not properly maintained. When the concentration of CO is too high, it might result in suffocation, coma, or even death. The detection of tiny concentrations of CO plays an important role in safe production. Due to the selective absorption of specific wavelengths of light by gas molecules, lasers have a wide range of applications in the field of gas detection. In this paper, a tunable diode laser absorption spectroscopy (TDLAS) system for CO detection was constructed using an interband cascaded laser (ICL) with a central wavelength of 4.625 μm. The modulated signal generated by the FPGA module was output to the laser controller to modulate the laser. The signal received by the detector was input to the FPGA module. After lock-in amplification, the second harmonic signal of high frequency modulation was output. Several concentrations of CO that were dispersed via static gas distribution were identified. A CO detection system with an open optical path was constructed, and the detection distance was about 8 m. The minimum detectable concentration is around 10.32 ppmm. The concentration of CO in the open optical path was 510.6 ppmm, according to the calibration of the detected concentration. The remote detection system based on TDLAS using an ICL can be used to monitor CO in the open optical path. Full article

The dynamic variation in the water surfaces of the river networks within the Qinghai-Tibet Plateau affects the water resource availability for downstream ecosystems and human activities. Small rivers (with a river width less than 30 m) are an important component of this network, but are difficult to map in the Qinghai-Tibet Plateau. Firstly, the width of most rivers is very narrow, at around 20 m, which appears as only one or two pixels in Sentinel-2 images and thus is susceptible to salt-and-pepper noise. Secondly, local mountain shadows, cloud shadows, and snow pixels have spectral characteristics similar to those of rivers, leading to misclassification. Therefore, we propose an automated small river mapping (ASRM) method based on Sentinel-2 imagery to address these two difficulties. A preprocessing procedure was designed to remove the salt-and-pepper noise and enhance the linear characteristic of rivers with specific widths. A flexible digital elevation model (DEM)-based post-processing was then imposed to remove the misclassifications caused by mountain shadows, cloud shadows, and snow pixels. The ASRM results achieved an overall accuracy of 87.5%, outperforming five preexisting remote sensing-derived river network products. The proposed ASRM method has shown great potential for small river mapping in the entire Qinghai-Tibet Plateau. Full article

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km). Full article

High-energy particles such as neutron act as serious threats to electronic equipment on board aircraft via Single Event Effects (SEE), but atmospheric neutron flux profile which could cover civil aviation altitude is rarely observed. To address the representative of atmospheric radiation data in SEE analysis, we propose a new method of detecting atmospheric neutron profile for civil aviation altitude. Using the sounding balloon carrying one nuclear radiometer, the radiation dose could be observed with high accuracy. Subsequently, the profile of atmospheric neutron flux can be derived on the basis of the conversion equation between radiation dose and the neutron flux. We implement two experiments, and the results show that this low-cost method could reliably obtain the vertical distribution of atmospheric neutron and might be integrated into SEE analysis of civil aircraft design. Full article

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications. Full article

The DPC is a multiangle sensor that detects atmospheric parameters. However, the retrieval of high-precision and high-spatial-resolution aerosol products from the DPC remains a great challenge due to the ill-posed nature of the problem. Thus, a novel aerosol optical depth (AOD) retrieval algorithm was proposed using visible surface reflectance relationships (VISRRs). The VISRR algorithm accounts for the surface anisotropy and needs neither a shortwave infrared band nor a surface reflectance database that can retrieve AOD over dark and bright land cover. Firstly, moderate-resolution imaging spectroradiometer (MODIS) surface reflectance (MYD09) products were used to derive the preceding surface reflectance relationships (SRRs), which are related to surface types, scattering angle, and normalized difference vegetation index (NDVI). Furthermore, to solve the problem of the NDVI being susceptible to the atmosphere, an innovative method based on an iterative atmospheric correction was proposed to provide a realistic NDVI. The VISRR algorithm was then applied to the thirteen months of DPC multiangle data over the China region. AOD product comparison between the DPC and MODIS showed that they had similar spatial distribution, but the DPC had both high spatial resolution and coverage. The validation between the ground-based sites and the retrieval results showed that the DPC AOD performed best, with a Pearson correlation coefficient (R) of 0.88, a root mean square error (RMSE) of 0.17, and a good fraction (Gfrac) of 62.7%. Then, the uncertainties regarding the AOD products were discussed for future improvements. Our results revealed that the VISRR algorithm is an effective method for retrieving reliable, simultaneously high-spatial-resolution and full-surface-coverage AOD data with good accuracy. Full article

Long-term sea surface salinity (SSS) in the East China Sea (ECS) was estimated based on Ocean Color Climate Change Initiative (OC-CCI) data using machine learning during the summer season (June to September) from 1997 to 2021. Changjiang diluted water (CDW) in the ECS propagates northeastward and forms longitudinally-oriented ocean fronts. To determine the CDW’s distribution, three fronts were investigated: (1) a CDW front based on chlorophyll-a concentration (Chl), SSS, and sea surface temperature (SST); (2) a CDW front based on sea surface density (SSD); and (3) a CDW front for nutrient distribution. The Chl fronts matched well with the SSS fronts, suggesting that Chl variation in the ECS is highly correlated with the CDW. Furthermore, the SSD fronts spatially matched well with nitrogen concentration. Sea level anomaly (SLA) variation with SSD was also detected, indicating that CDW had sufficiently large effects on SLA so that they may be detectable by altimeter measurements. This result suggests that the influence of steric height changes and the inflow from rivers are significant in the ECS. Additionally, the continuous long-term SSD developed in this study enables researchers to detect the CDW front and its influence on the ECS marine environment. Full article

As one of the most sensitive areas to global environmental change, especially global climate change, the Qinghai–Tibet Plateau is an ideal area for studying global climate change and ecosystems. There are few studies on the analysis of the vegetation’s driving factors on the Qinghai–Tibet Plateau based on large-scale and high-resolution data due to the incompetence of satellite sensors. In order to study the long-term vegetation spatiotemporal pattern and its driving factors, this study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to improve the spatial resolution of the GIMMS NDVI3g (8 km) data of the Qinghai–Tibet Plateau in 1990 and 1995 based on the MODIS NDVI (500 m) data. The research on the spatiotemporal pattern and driving factors of vegetation on the Qinghai–Tibet Plateau from 1990 to 2015 was carried out afterward, with combined data including topographic factors, annual average temperature, and annual precipitation. The results showed that there was a strong correlation between the actual MODIS NDVI image and the fused GIMMS NDVI3g image, which means that the accuracy of the fused GIMMS NDVI3g image is reliable and can provide basic data for the accurate evaluation of the spatial and temporal patterns of vegetation on the Qinghai–Tibet Plateau. From 1990 to 2015, the overall vegetation coverage of the Qinghai–Tibet Plateau showed a degrading trend at a rate of −0.41%, and the degradation trend of vegetation coverage was the weakest when the slope was ≥25°. Due to the influence of the policy of returning farmland to forests, the overall degradation trend has gradually weakened. The significant changes in vegetation in 2010 can be attributed to the difference in the spatial distribution of climatic factors such as temperature and precipitation. The area with reduced vegetation in the west was larger than the area with increased vegetation in the east. The effects of temperature and precipitation on the distribution, direction, and degradation level of vegetation coverage were varied by the areal differentiation in different zones. Full article

Sichuan Province is an important ecological barrier in the upper reaches of the Yangtze River. Therefore, it is critical to investigate the temporal and spatial changes, as well as the driving factors, of ecosystem service values (ESVs) in Sichuan Province. This paper used land use data from 2000, 2005, 2010, 2015, and 2020 to quantify the spatiotemporal changes in the ESVs in Sichuan Province. Correlation coefficients and bivariate spatial autocorrelation methods were used to analyze the trade-offs and synergies of ESVs in the city (autonomous prefecture) and grid scales. At the same time, we used a Geographical Detector model (GDM) to explore the synergies between nine factors and ESVs. The results revealed that: (1) In Sichuan Province, the ESVs increased by 0.77% from 729.26 × 10⁹ CNY in 2000 to 741.69 × 10⁹ CNY in 2020 (unit: CNY = Chinese Yuan). Furthermore, ecosystem services had a dynamic degree of 0.13%. Among them, the ESVs of forestland were the highest, accounting for about 60.59% of the total value. Among the individual ecosystem services, only food production, environmental purification, and soil conservation decreased in value, while the values of other ecosystem services increased. (2) The ESVs increased with elevation, showing a spatial distribution pattern of first rising and then decreasing. The high-value areas of ESVs per unit area were primarily distributed in the forestland of the transition area between the basin and plateau; The low-value areas were distributed in the northwest, or the urban areas with frequent human activities in the Sichuan Basin. (3) The tradeoffs and synergies between multi-scale ecosystems showed that ecosystem services were synergies-dominated. As the scale of research increased, the tradeoffs between ecosystems gradually transformed into synergies. (4) The main driving factors for the spatial differentiation of ESVs in Sichuan Province were average annual precipitation, average annual temperature, and gross domestic product (GDP); the interaction between normalized difference vegetation index (NDVI) and GDP had the strongest driving effect on ESVs, generally up to 30%. As a result, the distribution of ESVs in Sichuan Province was influenced by both the natural environment and the social economy. The present study not only identified the temporal and spatial variation characteristics and driving factors of ESVs in Sichuan Province, but also provided a reference for the establishment of land use planning and ecological environmental protection mechanisms in this region. Full article

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼10.8 $\mathsf{\mu }$m). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 min forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD. Full article

Inland water is an important part of the Earth’s water cycle. Mapping inland water is vital for understanding surface hydrology and climate change. Spaceborne global navigation satellite systems reflectometry (GNSS-R) has been proven to be an effective technique to detect inland water bodies. This paper proposes a new method to map inland water bodies using the delay-Doppler map (DDM) measurements provided by the GNSS-R platform Cyclone GNSS (CYGNSS). In this new method, we develop a refined power ratio to identify the coherence in DDM caused by the inland water. Processed with an image segmentation method, the refined power ratio is then applied to discriminate the permanent inland water bodies from the land. Using CYGNSS data over the Amazon Basin and the Congo Basin in 2020, we successfully generated water masks with a spatial resolution of 0.01°. Compared with the reference optical water masks, the overall detection accuracy in the Amazon Basin is 94.48% and the water detection accuracy is 92.23%, and the corresponding accuracies in the Congo Basin are 96.12% and 93.16%, respectively. Compared with the previous DDM power-spread detector (DPSD) method, the new method’s false alarms and misses in the Amazon Basin are reduced by 17.1% and 9.1%, respectively, while the false alarms and misses in the Congo Basin are reduced by 10.2% and 22%, respectively. Moreover, our method is proven to be useful for detecting short-term flood inundation. Full article

The Qinghai–Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration. Full article

The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satellite imagery. However, accuracy can be hampered by atmospheric phenomena that can drastically change climatic conditions in tropical regions, affecting the spectral properties of the vegetation. In this sense, we studied the accuracy of palm plantation mapping by using features from different regions of the electromagnetic spectrum and a data fusion approach, and then compared the changes in accuracy over the years 2016, 2017, and 2018 (two of them with reported climatic anomalies). Optical-based maps obtained higher accuracy than thermal- and microwave-based maps, but they were the most affected by inter-annual climate variability (error margin between 5 and 10%), while thermal-based maps were the least affected (error margin between 8 and 9%). Data fusion combinations improved accuracy and reduced dissimilarities between years (e.g., phenology-based map accuracy changed by up to 20.8%, while phenology fused with microwave features changed by up to 6.8%). We conclude that inter-annual climate variability on land-cover mapping should be considered, especially if the outputs will be used as input in future studies. Full article

In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data over the Arctic Ocean during 94 Spring campaigns between 2009 and 2019. The ultimate objective of this analysis is to better understand sea-ice topography to improve the estimation of the sea-ice freeboard for nadir-looking altimeters. We first introduce the use of an exponentially modified Gaussian (EMG) distribution to fit the surface elevation probability density function (PDF). The characteristic function of the EMG distribution can be integrated in the modeling of radar altimeter waveforms. Our results indicate that the Arctic sea-ice elevation PDF is dominantly positively skewed and the EMG distribution is better suited to fit the PDFs than the classical Gaussian or lognormal PDFs. We characterize the elevation correlation characteristics by computing the autocorrelation function (ACF) and correlation length (CL) of the ATM measurements. To support the radar altimeter waveform retracking over sea ice, we perform this study typically on 1.5 km ATM along-track segments that reflect the footprint diameter size of radar altimeters. During the studied period, the mean CL values range from 20 to 30 m, which is about 2% of the radar altimeter footprint diameter (1.5 km). Full article

Mapping and monitoring coral reef benthic composition using remotely sensed imagery provides a large-scale inference of spatial and temporal dynamics. These maps have become essential components in marine science and management, with their utility being dependent upon accuracy, scale, and repeatability. One of the primary factors that affects the utility of a coral reef benthic composition map is the choice of the machine-learning algorithm used to classify the coral reef benthic classes. Current machine-learning algorithms used to map coral reef benthic composition and detect changes over time achieve moderate to high overall accuracies yet have not demonstrated spatio-temporal generalisation. The inability to generalise limits their scalability to only those reefs where in situ reference data samples are present. This limitation is becoming more pronounced given the rapid increase in the availability of high temporal (daily) and high spatial resolution (<5 m) multispectral satellite imagery. Therefore, there is presently a need to identify algorithms capable of spatio-temporal generalisation in order to increase the scalability of coral reef benthic composition mapping and change detection. This review focuses on the most commonly used machine-learning algorithms applied to map coral reef benthic composition and detect benthic changes over time using multispectral satellite imagery. The review then introduces convolutional neural networks that have recently demonstrated an ability to spatially and temporally generalise in relation to coral reef benthic mapping; and recurrent neural networks that have demonstrated spatio-temporal generalisation in the field of land cover change detection. A clear conclusion of this review is that existing convolutional neural network and recurrent neural network frameworks hold the most potential in relation to increasing the spatio-temporal scalability of coral reef benthic composition mapping and change detection due to their ability to spatially and temporally generalise. Full article

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan–Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities. Full article

Soil pollution in coal mining areas is a serious environmental problem in China and elsewhere. In this study, surface and vertical profile soil samples were collected from a coal mine area in Dazhu, Southwestern China. Microscopic observation, concentrations, chemical speciation, statistical analysis, spatial distribution, and risk assessment were used to assess heavy metal pollution. The results show that the weathering of coal-bearing sandstone and mining activities substantially contributed to soil pollution. The concentrations of Fe, Ni, Cu, Zn, Mn, Cd, Hg, and Pb exceeded their background values. Cd caused the most intense pollution and was associated with heavily–extremely contaminated soils. The residual fraction was dominant for most metals, except Cd and Mn, for which the reducible fraction was dominant (Cd: 55.17%; Mn: 81.16%). Zn, Ni, Cd, and Cu presented similar distribution patterns, and Hg and As also shared similar distribution characteristics. Factor 1 represented anthropogenic and lithologic sources, which were affected by mining activities; Factor 2 represented anthropogenic sources, e.g., fertilizers and traffic pollution; and Factor 3 represented the contribution of metals from soil-forming parent material. More than half of the study area had high pollution risk and was not suitable for vegetable cultivation. Full article

Cloud droplets size distribution (DSD) is one of the significant characteristics for liquid clouds. It plays an important role for the aerosol–droplet–cloud mechanism and variation in cloud microphysics. However, the minuscule sampling space is insufficient for the observation of whole DSD when using high-magnification optical systems. In this paper, we propose an observation method for cloud droplets ranging from 2 to 16 μm, by which the balance relationship between sampling space and optical magnification is realized. The method combines an in-line digital holographic interferometer (DHI) with the optical magnification of 5.89× and spatial stitching technique. The minimum size in DSD is extended to 2 μm, which improves the integrity of size distribution. Simultaneously, the stability of DSD is enhanced by increasing the tenfold sampling volume of cloud droplets. The comparative experiment between the in-line DHI and fog monitor demonstrates that the DSD obtained by this method is reliable, which can be used for the analysis of microphysical parameters. In the Beijing Aerosol and Cloud Interaction Chamber (BACIC), the observation results show that the size of cloud droplets follows the Gamma distribution, which is consistent with the theoretical DSD. The results of cloud microphysical parameters indicate that each pair of parameters has a positive correlation, and then the validity of observation method is confirmed. Additionally, the high-concentration aerosol condition significantly mitigates the effect of random turbulence and enhances the robustness of the microphysical parameter data. Full article

Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems. Full article

Ultra−high spatial resolution, which can bring more detail to ground observation, is a constant pursuit of the modern space−borne synthetic aperture radar. However, the exact imaging in this case has always been a complex technical problem due to its complicated imaging geometry and signal structure. To achieve those applications’ strict requirements, a novel ultra−high resolution imaging algorithm based on an accurate high−order 2−D spectrum is presented in this paper. The only first two Doppler parameters needed as range models in the defective spectrum are replaced by a polynomial range model, which can derive coefficients from the relative motion between the radar and the targets. Then, the new spectrum is calculated through the Lagrange inversion formula. Based on this, the novel imaging algorithm is elaborated in detail as follows: The range high−order term of the spectrum is compensated completely, and the range chirp rate space variance is eliminated by the cubic phase term. Two steps of range cell migration correct are applied in this algorithm before and after the range compression; one is the traditional linear chirp scaling method, and another is the interpolation to correct the quadratic range cell migration introduced by the range chirp rate equalization. The simulation results illustrate that the proposed algorithm can handle the exact imaging processing with a 0.25 m resolution around the azimuth and range in 2 km $\times$ 6 km, which validates the feasibility of the proposed algorithm. Full article

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%). Full article