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A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals. [ABSTRACT FROM AUTHOR]

This review article delves into the latest advancements and transformative impacts of remotely sensed methods in measuring water depth and underwater topography. It particularly focuses on topobathymetric data acquisition which is crucial for numerous applications including navigation, flood control, coastal erosion, sea-level rise predictions, and mapping, modelling, and monitoring of offshore resources. Traditionally, measuring depths, distribution, and volumes of submerged resources has been challenging due to time, cost, labor, and reliance on sophisticated, mostly airborne technologies. These conventional methods often struggled in remote, hazardous, or shallow water areas. Recent advances in Light Detection and Ranging (LiDAR) technology, especially its miniaturization and integration with unoccupied aerial systems (UAS), have significantly improved the collection of three-dimensional bathymetric data. This progress presents effective solutions for previously faced challenges. Despite these advancements, there's a need for a thorough assessment of the state of LiDAR-based topobathymetric mapping and the impact of the increasing accessibility of UAS in this field. Our systematic review scrutinizes topobathymetric LiDAR technology and research, centering on acquisition platforms and methods in coastal and riverine environments for mapping, planning, monitoring, and inspections. We document the spread of topobathymetric LiDAR research over time, geographically, across disciplines, and in various publication outlets. Utilizing the VOSviewer analytical tool, we conducted a bibliometric keywordbased network analysis, evaluating the distribution of topobathymetric citations, and their bibliographic coupling, co-citation, and co-authorship relationships. Our findings reveal the innovative yet emerging state of UAS applications in topobathymetry and the ongoing reliance on LiDAR technology for 3-D underwater data acquisition. This study provides a fundamental assessment of current trends and developments in coastal and riverine bathymetric applications, highlighting the evolving landscape of remotely sensed methods in underwater topography. [ABSTRACT FROM AUTHOR]

Abstract Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling. This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics. We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study). Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools.

Abstract The flexibility of UAV‐lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX − 1LR over a 300 × 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies. We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vegetation structural complexity (VSC)—the three‐dimensional distribution of plants within an ecosystem—is an important ecological trait. To date, research has focused primarily on the effects of VSC on ecological patterns and processes, but comparatively little is known about what drives variation in VSC.Recent advances in active remote sensing technology, particularly light detection and ranging and radio detection and ranging, have allowed the measurement of VSC at unprecedented spatial scales and resolutions. Out of this and earlier work has emerged evidence that VSC is typically associated with greater ecosystem functioning (especially microclimate regulation, productivity, faunal diversity and habitat provisioning), making restoration of vegetation complexity a potentially powerful restoration tool.Recent studies of VSC across natural and experimental gradients of plant diversity have also revealed that more diverse plant communities tend to be more structurally complex. However, the shape and generality of this relationship—and the mechanism(s) by which phytodiversity might contribute to structural complexity—remain poorly understood.Here, we review how active remote sensing has facilitated recent VSC research and shaped our understanding of the relationship between vegetation complexity and ecosystem function. We then present a theoretical framework for the relationship between phytodiversity and VSC based on classic biodiversity‐ecosystem functioning principles. Finally, we evaluate the evidence for the notion that diverse plant assemblages tend to be more structurally complex and explore the shape of the relationship between phytodiversity and VSC using data from 13 recent remote sensing studies.Synthesis. The relationship between phytodiversity and VSC appears to be almost universally positive. Preliminary evidence further suggests that the most common relationships between phytodiversity and VSC are linear or saturating, indicating that the extent of functional redundancy between species varies across plant communities and ecosystems. In contrast, we find little evidence for exponential or negative relationships between plant diversity and VSC, suggesting that even modest increases in plant diversity could markedly increase structural complexity. Additional investigations of phytodiversity‐VSC relationships are necessary to establish whether the observed positive relationships are causal (and, if so, in which direction) and to clarify the potential impact of plant community restoration on structural complexity and broader ecosystem function. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three‐dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling.This cross‐journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne, UAV borne and spaceborne) to address ecological topics.We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study).Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy‐based approaches to direct and more data‐science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science‐based on‐demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large‐scale mapping and monitoring of wildlife dynamics, among other topics that can take advantage of multitemporal/time series data. Nevertheless, access to demanding and costly very high‐resolution data sources may still be maintained and optimized by establishing international and public–private partnered data pools. [ABSTRACT FROM AUTHOR]

The flexibility of UAV‐lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest.To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX − 1LR over a 300 × 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover.Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies.We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas. [ABSTRACT FROM AUTHOR]

To provide references for the design of the lab's upcoming prototype of the compact spaceborne lidar with a high-repetition-rate laser (CSLHRL), in this paper, the detection signal of spaceborne lidar was simulated by the measured signal of ground-based lidar, and then, the detection capability of spaceborne lidar under different atmospheric conditions was evaluated by means of the signal-to-noise ratio (SNR), volume depolarization ratio (VDR) and attenuated color ratio (ACR). Firstly, the Fernald method was used to invert the optical parameters of cloud and aerosol with the measured signal of ground-based lidar. Secondly, the effective signal of the spaceborne lidar was simulated according to the known atmospheric optical parameters and the parameters of the spaceborne lidar system. Finally, by changing the cumulative laser pulse number and atmospheric conditions, a simulation was carried out to further evaluate the detection performance of the spaceborne lidar, and some suggestions for the development of the system are given. The experimental results showed that the cloud layer and aerosol layer with an extinction coefficient above 0.3 km−1 could be easily obtained when the laser cumulative pulse number was 1000 and the vertical resolution was 15 m at night; the identification of moderate pollution aerosols and thick clouds could be easily identified in the daytime when the laser cumulative pulse number was 10,000 and the vertical resolution was 120 m. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cloud thermodynamic phase is a crucial parameter to understand the Earth's radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘ C, followed by that for MP at −13 ∘ C. Between 0 and −27 ∘ C, the occurrence of SC and MP dominates IC, while below −27 ∘ C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites. [ABSTRACT FROM AUTHOR]

Using the CloudSat and CALIPSO active remote sensing dataset from 2006 to 2017, this study investigates the spatial and temporal distribution along with vertical distribution of cloud macrophysical characteristics in six study areas over China mainland and east coast (15°–55°N, 70°–140°E). The results show that the cloud top height and cloud base height have significant regional, seasonal and zonal variations. The regional differences in the seasonal average vertical distribution of cloud cover are especially large, which are particularly reflected by the differences of heights with peak value of cloud cover, the curve characteristics of cloud cover with height, and the standard deviation of cloud cover. The cloud spacing, which is the distance between two cloud layers for cloud system with layers no less than 2, is found greater in the ocean area than in the land area and is greater in the low‐latitude area than in the high‐latitude area. Moreover, with the increase of the number of cloud layers in the cloud system, the cloud spacing decreases gradually. It is worth noting that the frequency distribution of cloud layers is the same as that of cloud spacing. By dividing clouds into eight types according to CloudSat data, the occurrence frequency of different cloud types shows great differences, ranking from high to low as high cloud > altostratus > altocumulus > stratus > stratocumulus > cumulus > nimbostratus > deep convective cloud. Altostratus mainly occurs in northern regions, and high cloud mostly occurs in Tibetan Plateau, southern China mainland and eastern ocean. The vertical distributions of cloud macrophysical characteristics over the six typical study areas have been further demonstrated by constructing Pc‐τ diagram. [ABSTRACT FROM AUTHOR]

Using Scatterometer-based backscatter data, the spatial and temporal melt dynamics of Antarctic ice shelves were tracked from 2000 to 2018. We constructed melt onset and duration maps for the whole Antarctic ice shelves using a pixel-based, adaptive threshold approach based on backscatter during the transition period between winter and summer. We explore the climatic influences on the spatial extent and timing of snowmelt using meteorological data from automatic weather stations and investigate the climatic controls on the spatial extent and timing of snowmelt. Melt extent usually starts in the latter week of November, peaks in the end of December/January, and vanishes in the first/second week of February on most ice shelves. On the Antarctic Peninsula (AP), the average melt was 70 days, with the melt onset on 20 November for almost 50% of the region. In comparison to the AP, the Eastern Antarctic experienced less melt, with melt lasting 40–50 days. For the Larsen-C, Shackleton, Amery, and Fimbul ice shelf, there was a substantial link between melt area and air temperature. A significant correlation is found between increased temperature advection and high melt area for the Amery, Shackleton, and Larsen-C ice shelves. The time series of total melt area showed a decreasing trend of −196 km2/yr which was statistical significant at 97% interval. The teleconnections discovered between melt area and the combined anomalies of Southern Annular Mode and Southern Oscillation Index point to the high southern latitudes being coupled to the global climate system. The most persistent and intensive melt occurred on the AP, West Ice Shelf, Shackleton Ice Shelf, and Amery Ice Shelf, which should be actively monitored for future stability.

ICESat-2's Advanced Topographic Laser Altimeter System (ATLAS) has emerged as a useful tool for calculating attenuation signals in natural surface waters, thus improving our understanding of particulates from open-ocean plankton to nearshore suspended terrigenous sediments. While several studies have employed methods based on Beer's Law to derive attenuation coefficients (including through a machine-learning approach), a rigorous test of specific tuning parameters and processing choices has not yet been performed. Here we present comprehensive sensitivity tests of noise removal, choice of bin sizes, surface-peak exclusion, and beam pairing across four contrasting marine environments as well as solar background removal at an additional site to quantify the impacts of these processing choices on the derived photon-based attenuation coefficient K dph. Ultimately, calculated K dph values were not statistically sensitive to choices of horizontal bin sizes, vertical bin sizes, and surface exclusion depths with ranges of 500–2000 m, 0.25–1.0 m, and 0.5–1.0 m, respectively. Use of strong-beam data is recommended because weak-beam data introduce additional noise, though in open-ocean waters where photon counts are sparse, it may be desirable to include weak-beam data. In a daytime/nighttime data comparison, daytime data were found to be usable, though removal of the solar background increased the K dph estimates by ∼27–64%. A robust solution for removing afterpulses remains elusive, though a gaussian decomposition scheme was attempted. It did not, however, yield statistically different K dph values relative to the uncorrected dataset. Detailed information about processing choices and a suggested workflow for ocean applications are provided. Together the results pave the way for expanded K dph analyses of global datasets (including turbid coastal waters). • Study evaluates the impact of processing choices on K dph and offers a workflow. • Many processing terms have little impact on K dph , including afterpulse removal. • Using strong-beam data is recommended and using daytime data is possible. [ABSTRACT FROM AUTHOR]

Potential of constraining semiempirical model with physically based scatter model simulations has long been recognized. This study contributes to this topic through the assessment of backscattering coefficient (σo) simulations and soil moisture retrieval using the water cloud model (WCM) constrained by a discrete scattering model (i.e., Tor Vergata) under both frozen and thawed soil conditions. The WCM is coupled with Oh (hereafter “WCM+Oh”) and Dubois (WCM+Dubois) surface scattering models, respectively. The soil permittivity is obtained using the four-phase dielectric mixing model. One year of C-band copolarized σo observations are collected by a ground-based scatterometer deployed in the seasonally frozen Tibetan meadow ecosystem. It is found that: the calibrated Tor Vergata (hereafter “TVG”) model simulates well the seasonal dynamics and magnitudes of scatterometer measurements, and the simulated scattering components and vegetation transmissivity agree well with the seasonal vegetation dynamics; the total scattering simulated by the TVG constrained WCMs shows a good consistency with the scatterometer measurements, and the simulated soil and vegetation scattering components are in line with the TVG simulations; and the retrieved soil moisture based on the constrained WCMs captures well the seasonal variability noted in the in situ measurements. An additional experiment is performed to calibrate the WCMs directly, and the results show that the calibrated WCMs achieve comparable results with the calibrated TVG model and the constrained WCMs in terms of the total σo and soil moisture retrieved. However, the direct calibration of the WCMs leads to unrealistic characterization of individual soil and vegetation scattering contributions, of which an underestimation of the vegetation contribution at VV polarization is most notable. These findings demonstrate that usage of a physically based scatter model to constrain semiempirical models leads to results that provide a more robust representation of reality, which is needed for developing worldwide soil moisture monitoring from active microwave remote sensing.

The issue of natural oil seepage is one of the challenges in the Kuwaiti marine environment. Many observations have been made about the existence of a natural oil seepage near Qaruh Island, which is a small island located in the northwest of the Arabian Gulf. This study aims to take advantage of active remote sensing (radar imagery) in monitoring this phenomenon, both spatially and statistically. One hundred eleven images taken from Sentinel-1 in the period from September 2014 to September 2017 were analyzed. Thirty-four oil slicks were detected, with a ratio of one caused every 3 days. Additionally, the spatial central tendency measures were identified. The results showed that the average extension of the phenomenon was 68.2 km2, while it reached a maximum of 225.8 km2, with a minimum of 12.5 km2—a range of 213.3 km2. The mean and median centers are located to the southeast of Qaruh Island at a direction of about 148° and 150° and distance of 9.2 km and 7 km, respectively. The directional distribution cycles clarify that the phenomenon was directed to the northwest and southeast. The accuracy of Sentinel-1 data has been verified by Landsat 8 images in true and false colors. Thus, this study provides a geostatistical view that helps to improve the spatial understanding of this phenomenon. [ABSTRACT FROM AUTHOR]

Based on the products of joint CloudSat and CALIPSO observations from 2006 to 2017, this study investigates the global cloud macro-physical characteristics, including the spatial differences in cloud height, cloud thickness, cloud type, and cloud phase, along with the spatial-temporal variations and the land-sea discrepancy of the cloud vertical structure variables according to the study areas. The results show that the cloud frequency is higher over oceans than over land globally, but the opposite is true for cloud systems with more than two layers. Seasonal variability of global-average total cloud fraction is small, but large among different latitudinal bands. The global spatial distributions of cloud top height (CTH) and cloud base height (CBH) are similar, showing larger cloud heights over the land than over the ocean. The low clouds are mostly located in the central subtropical region, with CTH among 2.5–7.5 km, While the high clouds are mainly distributed in the equatorial region with CTH about 10–15 km. Meanwhile, the regional and seasonal variability of cloud geometric thickness (CGT) in all study areas are small, and the average thickness of higher clouds is slightly larger than that of lower clouds. By categorizing the clouds into eight types, it was found that the frequency of cirrus (Ci) and stratocumulus (Sc) is higher, followed by altocumulus (Ac), altostratus (As), nimbostratus (Ns) and cumulus (Cu), and the frequency of stratus (St) and deep convective clouds (DC) is the lowest. In addition, the occurrence frequency of clouds in phases was ranked from high to low as ice clouds, liquid clouds and mixed-phase clouds. Ice clouds are concentrated in the tropics and mid-latitudes, and located almost symmetrically around the equator during Mar-Apr-May (MAM) and Sep-Oct-Nov (SON). Liquid clouds are mainly distributed around 30°S and 30°N with large seasonal variability, and their frequency is higher over the ocean than over the land. Mixed-phase clouds are concentrated in the middle and high latitudes, with much less occurring in the equatorial regions. • Cloud frequency is higher over ocean than over land globally, but is opposite for clouds with layers >2. • The cloud height (CTH and CBH) is higher over land than over ocean. • The global-mean cloud fraction varies greatly among different latitudinal bands. • The frequency of Ci and Sc is the highest, while that of St and DC is the lowest. • The frequency of cloud phases is ice clouds >liquid clouds >mixed-phase clouds. [ABSTRACT FROM AUTHOR]

Aim: Light Detection And Ranging (LiDAR) is a promising remote sensing technique for ecological applications because it can quantify vegetation structure at high resolution over broad spatial extents. Using country‐wide airborne laser scanning (ALS) data, we test to what extent fine‐scale LiDAR metrics capturing low vegetation, medium‐to‐high vegetation and landscape‐scale habitat structures can explain the habitat preferences of threatened butterflies at a national extent. Location: The Netherlands. Methods: We applied a machine‐learning (random forest) algorithm to build species distribution models (SDMs) for grassland and woodland butterflies in wet and dry habitats using various LiDAR metrics and butterfly presence–absence data collected by a national butterfly monitoring scheme. The LiDAR metrics captured vertical vegetation complexity (e.g., height and vegetation density of different strata) and horizontal heterogeneity (e.g., vegetation roughness, microtopography, vegetation openness and woodland edge extent). We assessed the relative variable importance and interpreted response curves of each LiDAR metric for explaining butterfly occurrences. Results: All SDMs showed a good to excellent fit, with woodland butterfly SDMs performing slightly better than those of grassland butterflies. Grassland butterfly occurrences were best explained by landscape‐scale habitat structures (e.g., open patches, microtopography) and vegetation height. Woodland butterfly occurrences were mainly determined by vegetation density of medium‐to‐high vegetation, open patches and woodland edge extent. The importance of metrics generally differed between wet and dry habitats for both grassland and woodland species. Main conclusions: Vertical variability and horizontal heterogeneity of vegetation structure are key determinants of butterfly species distributions, even in low‐stature habitats such as grasslands, dunes and heathlands. The information content of low vegetation LiDAR metrics could further be improved with country‐wide leaf‐on ALS data or surveys from drones and terrestrial laser scanners at specific sites. LiDAR thus offers great potential for predictive habitat distribution modelling and other studies on ecological niches and invertebrate–habitat relationships. [ABSTRACT FROM AUTHOR]

Numerous organisms depend on the physical structure of their habitats, but incorporating such information into ecological niche analyses has been limited by the lack of adequate data over broad spatial extents. The increasing availability of high‐resolution measurements from country‐wide airborne laser scanning (ALS) surveys – a light detection and ranging (LiDAR) technology – now provides unprecedented opportunities for characterizing habitat structure. Here, we use country‐wide ALS data in combination with presence–absence observations of birds from a national monitoring scheme in the Netherlands to quantify niche filling, niche overlap and niche separation of three closely‐related wetland birds (great reed warbler, Eurasian reed warbler and Savi's warbler). We developed a workflow to derive LiDAR metrics capturing different aspects of vertical and horizontal vegetation structure and used a principal component analysis (PCA), niche equivalency and niche similarity tests to analyse the fine‐scale breeding habitat niches of these warbler species in the Netherlands. The widespread Eurasian reed warbler almost completely filled the available wetland habitat space (93%) whereas the two other species showed considerably less niche filling (64% and 74%, respectively). Substantial niche overlap occurred among all species, but each species occupied a distinct part of the habitat space. The great reed warbler mainly occurred in tall and vertically complex wetland vegetation and was absent in areas with large proportions of reedbeds. The Eurasian reed warbler occupied all parts of the wetland habitat space, whereas the Savi's warbler mainly occurred in large homogenous reedbeds with low vegetation height. Our results demonstrate that broad‐scale ecological niche analyses can incorporate the fine‐scale 3D habitat preference of species with unprecedented detail (e.g. 10 m resolution), and thus go much beyond quantifying the climate niche and 2D habitat information from land cover maps. This is important to identify habitat features and priorities for biodiversity conservation in wetlands and other habitats. [ABSTRACT FROM AUTHOR]

NASA has been conducting several studies for quantifying and monitoring atmospheric CO2 including the Orbiting Carbon Observatory space-based missions. To complement CO2 passive sensing, the National Research Council recommended active remote sensing techniques, which are valuable for validating current space-based measurements and potential future missions. Recently, a 2- $\mu \text{m}$ triple-pulse integrated path differential absorption (IPDA) lidar was developed for simultaneous and independent measurements of atmospheric CO2 and H2O. This instrument was operated at fixed wavelengths, precisely selected to avoid mutual interferences. Focusing on optimized CO2 measurements, this instrument has been updated to operate in double-pulse mode. The objective is to demonstrate high-precision and high-accuracy column CO2 measurements using tunable on-line wavelength suitable for adaptive targeting. Statistical analysis of long record CO2 field measurement and retrieval results in 1.92-ppm accuracy, equivalent to 0.44% systematic error, and 1.66-ppm precision, equivalent to 0.39% random error. Referring to the R30 CO2 absorption line, this was achieved using an on-line laser frequency offset of 1 GHz, a 5.2% target reflectivity, and 10-s average. The record indicated a better than 99% data success rate. Tuning the on-line frequency offset to 0.5 GHz results in 1.11- and 0.33-ppm measurement accuracy and precision, equivalent to 0.26% and 0.08% systematic and random errors, respectively, obtained using 13.6% target reflectivity. Range measurements indicate 0.7-m precision and 0.2-m accuracy. These results demonstrate the reliability of atmospheric CO2 measurements with high precision and high accuracy, using the pulsed 2- $\mu \text{m}$ IPDA lidar. [ABSTRACT FROM AUTHOR]

The detection and identification of materials from a distance is highly desirable for applications where accessibility is limited or there are safety concerns. Raman spectroscopy can be performed remotely and provides a very high level of confidence in detection of chemicals through vibrational modes. However, the remote Raman detection of chemicals is challenging because of the very weak nature of Raman signals. Using a remote Raman system, we performed fast remote detection of various solid and liquid chemicals from 1752 m during afternoon hours on a sunny day in Hawaii. Remote Raman systems with kilometer target range could be useful for chemical detection of volcanic gases, methane clathrate icebergs or fire ice, toxic gas clouds and toxic waste, explosives, and hazardous chemicals. With this successful test, we demonstrate the feasibility of developing future mid-size remote Raman systems suitable for long range chemical detection using helicopters and light airplanes. [ABSTRACT FROM AUTHOR]

The capability of an airborne 2-μm integrated path differential absorption (IPDA) lidar for high-accuracy and high-precision active remote sensing of weighted-average column dry-air volume mixing ratio of atmospheric carbon dioxide (XCO2) is demonstrated. A test flight was conducted over the costal oceanic region of the USA to assess instrument performance during severe weather. The IPDA targets CO2 R30 absorption line using high-energy 2-μm laser transmitter. HgCdTe avalanche photodiode detection system is used in the receiver. Updated instrument model included range correction factor to account for platform attitude. Error budget for XCO2 retrieval predicts lower random error for longer sensing column length. Systematic error is dominated by water vapor (H2O) through dry-air number density derivation, followed by H2O interference and ranging related uncertainties. IPDA XCO2 retrieval results in 404.43 ± 1.23 ppm, as compared to 405.49 ± 0.01 ppm from prediction models, using consistent reflectivity and steady elevation oceanic surface target. This translates to 0.26% and 0.30% relative accuracy and precision, respectively. During gradual spiral descend, IPDA results in 404.89 ± 1.19 ppm as compared model of 404.75 ± 0.73 ppm indicating 0.04% and 0.23% relative accuracy, respectively. Challenging cloud targets limited retrieval accuracy and precision to 2.56% and 4.78%, respectively, due to H2O and ranging errors.

The societal benefits of understanding climate change through the identification of global carbon dioxide sources and sinks led to the recommendation for NASA's Active Sensing of Carbon Dioxide Emissions over Nights, Days, and Seasons space-based mission for global carbon dioxide measurements. For more than 15 years, the NASA Langley Research Center has developed several carbon dioxide active remote sensors using the differential absorption lidar technique operating at 2-μm wavelength. Recently, an airborne double-pulsed integrated path differential absorption lidar was developed, tested, and validated for atmospheric carbon dioxide measurement. Results indicated 1.02% column carbon dioxide measurement uncertainty and 0.28% bias over the ocean. Currently, this technology is progressing toward triple-pulse operation targeting both atmospheric carbon dioxide and water vapor—the dominant interfering molecule on carbon dioxide remote sensing. Measurements from the double-pulse lidar and the advancement of the triple-pulse lidar development are presented. In addition, measurement simulations with a space-based IPDA lidar, incorporating new technologies, are also presented to assess feasibility of carbon dioxide measurements from space. [ABSTRACT FROM AUTHOR]

Estimating pesticide spray drift, which is a part of total drift loss, is complex as airborne pesticide concentrations are low and depend on multiple factors. The aim was to measure and compare vertical profiles of spray drift generated by different sprayers using Open Path Fourier-Transform-Infra-Red (OP-FTIR) spectrometer. Field tests included three types of commercial agricultural sprayers. The OP-FTIR was placed at the edge of an apple orchard with the line of sight parallel to tree rows. The OP-FTIR and its reflector were mounted on platform lifts to allow measurements at 4 heights: 3 (canopy height), 4, 5, and 6 m above ground. The sprayers sprayed water within the three tree rows closest to the OP-FTIR as well as outside each tree row in order to estimate the spray drift as function of distance with and without tree interference. The results of the experiments showed that, under the meteorological conditions prevailing, there were substantial differences between the sprayers in terms of spray drift of droplets with diameter > 5 μm. Additionally, the results showed that spray drift can be reduced substantially (by up to 50%) by using a tree-line barrier or a buffer zone. [ABSTRACT FROM AUTHOR]

Using the data collected over the Southern Great Plains ARM site from 2006 to 2010, the surface Active Remote Sensing of Cloud (ARSCL) and CloudSat-CALIPSO satellite (CC) retrievals of total cloud and six specified cloud types [low, mid-low (ML), high-mid-low (HML), mid, high-mid (HM) and high] were compared in terms of cloud fraction (CF), cloud-base height (CBH), cloud-top height (CTH) and cloud thickness (CT), on different temporal scales, to identify their respective advantages and limitations. Good agreement between the two methods was exhibited in the total CF. However, large discrepancies were found between the cloud distributions of the two methods at a high (240-m) vertical grid spacing. Compared to the satellites, ARSCL retrievals detected more boundary layer clouds, while they underestimated high clouds. In terms of the six specific cloud types, more low- and mid-level clouds but less HML- and high-level clouds were detected by ARSCL than by CC. In contrast, the ARSCL retrievals of ML- and HM-level clouds agreed more closely with the estimations from the CC product. Lower CBHs tended to be reported by the surface data for low-, ML- and HML-level clouds; however, higher CTHs were often recorded by the satellite product for HML-, HM- and high-level clouds. The mean CTs for low- and ML-level cloud were similar between the two products; however, the mean CTs for HML-, mid-, HM- and high-level clouds from ARSCL were smaller than those from CC. [ABSTRACT FROM AUTHOR]

One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (s0). The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r2 = 0.88). In turn, when effective LAI (LAIeff) has been taken into account, the parameters of the scattering canopy model are better estimated (r2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values.

The goal of this review is to present leading examples of current methodologies for extracting forest characteristics from full-waveform LiDAR data. Four key questions are addressed: (i) does full-waveform LiDAR provide advantages over discrete-return laser sensors; (ii) will full-waveform LiDAR provide valid results in support of forest inventory operations and allow for a decrease in ground sampling efforts; (iii) is the use of full-waveform LiDAR data cost effective; and (iv) what is the scope of the applied methods (i.e., is full-waveform LiDAR accurate for different forest compositions, structures, and densities, and is it sensitive to leaf-off/leaf-on conditions)? Key forest structure characteristics can be estimated with significant accuracy using full-waveform metrics, although methodologies and their corresponding accuracies differ. For example, some processing methods are valid at the plot scale, whereas other procedures perform well at the regional scale; to be effective, certain LiDAR data analyses require a minimum point density, whereas other methods perform well using large-footprint sensors. Therefore, it is important to match processing methods with the appropriate scale and scope. The aim of this paper is to provide the forest research community and remote sensing technology developers with an overview of existing methods for inferring key forest characteristics, including their applicability and performance.

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces.

Soil subsurface moisture content, especially in the root zone, is important for evaluation the influence of soil moisture to agricultural crops. Conservative monitoring by point-measurement methods is time-consuming and expensive. In this paper we represent an active remote-sensing tool for subsurface spatial imaging and analysis of electromagnetic physical properties, mostly water content, by ground-penetrating radar (GPR) reflection. Combined with laboratory methods, this technique enables real-time and highly accurate evaluations of soils' physical qualities in the field. To calculate subsurface moisture content, a model based on the soil texture, porosity, saturation, organic matter and effective electrical conductivity is required. We developed an innovative method that make it possible measures spatial subsurface moisture content up to a depth of 1.5 m in agricultural soils and applied it to two different unsaturated soil types from agricultural fields in Israel: loess soil type (Calcic haploxeralf), common in rural areas of southern Israel with about 30% clay, 30% silt and 40% sand, and hamra soil type (Typic rhodoxeralf), common in rural areas of central Israel with about 10% clay, 5% silt and 85% sand. Combined field and laboratory measurements and model development gave efficient determinations of spatial moisture content in these fields. The environmentally friendly GPR system enabled nondestructive testing. The developed method for measuring moisture content in the laboratory enabled highly accurate interpretation and physical computing. Spatial soil moisture content to 1.5 m depth was determined with 1-5% accuracy, making our method useful for the design of irrigation plans for different interfaces. [ABSTRACT FROM AUTHOR]

Active and passive microwave remote sensing has been used for monitoring the soil moisture and snow water equivalent. In the interactions of microwaves with bare soil, the effects are determined by scattering of electromagnetic waves by random rough surfaces. In the interactions of microwaves with terrestrial snow, the effects are determined by volume scattering of dense media characterized by densely packed particles. In this paper, we review the electromagnetic full-wave simulations that we have conducted for such problems. In volume scattering problems, one needs many densely packed scatterers in a random medium sample to simulate the physical solutions. In random rough surface scattering problems, one needs many valleys and peaks in the sample surface. In random media and rough surface problems, the geometric characterizations of the media and computer generations of statistical samples of the media are also challenges besides electromagnetic computations. In the scattering of waves by soil surfaces, we consider the soil to be a lossy dielectric medium. The random rough surface is characterized by Gaussian random processes with exponential correlation functions. Surfaces of exponential correlation functions have fine-scale structures that cause significant radar backscattering in active microwave remote sensing. Fine-scale features also cause increase in emission in passive microwave remote sensing. We apply Monte Carlo simulations of solving full 3-D Maxwell's equations for such a problem. A hybrid UV/PBTG/SMCG method is developed to accelerate method of moment solutions. The results are illustrated for coherent waves and incoherent waves. We also illustrate bistatic scattering, backscattering, and emissivity which are signatures measured in microwave remote sensing. For the case of scattering by terrestrial snow, snow is a dense medium with densely packed ice grains. We have used two models: densely packed particles and bicontinuous media. For the case of densely packed particles, we used the Metropolis shuffling method to simulate the positions of particles. The particles are also allowed to have adhesive properties. The Foldy–Lax equations of multiple scattering are used to study scattering from the densely packed spherical particles. The results are illustrated for the coherent waves and incoherent waves. For the case of bicontinuous media, the method developed by Cahn is applied to construct the interfaces from a large number of stochastic sinusoidal waves with random phases and directions. The volume scattering problem is then solved by using CGS–FFT. We illustrate the results of frequency and polarization dependence of such dense media scattering. [ABSTRACT FROM PUBLISHER]

Traditional ground-based air sampling measurements of air quality have blind monitoring areas in the junctions between provinces, cities and urban and rural areas, and they lack the ability of vertical monitoring. Stereoscopic hyperspectral remote sensing techniques provide a promising strategy for improving our understanding of air pollution. Satellite and ground based hyperspectral remote sensing techniques have been demonstrated to have unparalleled technical advantages in monitoring the horizontal and vertical distributions of air pollutants compared to other monitoring techniques. However, to unveil the complex evolutions and processes of the atmospheric environment, the current stereoscopic hyperspectral remote sensing techniques still face several technical bottlenecks, such as a limited temporal resolution in horizontal space, a limited stereoscopic spatial resolution, the limited types of trace gases, the impact of cloud coverage, and the difficulty in nighttime monitoring. The new technical requirements mainly include the following changes: (1) from horizontal and vertical to grid-stereoscopic monitoring; (2) from kilometer to meter resolutions; and (3) from once a day to full-time monitoring with a high temporal resolution. In this article, we systematically review the recent advances in satellite- and ground-based hyperspectral remote sensing techniques, including China's first hyperspectral satellite GF-5, hardware, algorithms, and applications. Moreover, we discuss the broad application prospects of the unmanned aerial vehicle hyperspectral remote sensing monitoring system, the active hyperspectral remote sensing monitoring system, and machine learning in air pollution monitoring in the future. We recommend using the expected multi-means joint hyperspectral stereoscopic remote sensing monitoring mode to assist the effective monitoring and regulation of air pollution in the future. [ABSTRACT FROM AUTHOR]