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Background Phototherapy (PT) is the standard treatment of neonatal unconjugated hyperbilirubinemia. The irradiance footprint, i.e., the illuminated area by the PT device with sufficient spectral irradiance, is essential for PT to be effective. Irradiance footprint measurements are not performed in current clinical practice. We describe a user-friendly method to systematically evaluate the high spectral irradiance (HSI) footprint (illuminated area with spectral irradiance of ≥30 μW cm−2 nm−1) of PT devices in clinical practice. Materials and methods Six commercially available LED-based overhead PT devices were evaluated in overhead configuration with an incubator. Spectral irradiance (µW cm−2 nm−1) and HSI footprint were measured with a radiospectrometer (BiliBlanket Meter II). Results The average measured spectral irradiance ranged between 27 and 52 μW cm−2 nm−1 and HSI footprint ranged between 67 and 1465 cm2, respectively. Three, two, and one PT devices out of six covered the average BSA of an infant born at 22, 26–32, and 40 weeks of gestation, respectively. Conclusion Spectral irradiance of LED-based overhead PT devices is often lower than manufacturer’s specifications, and HSI footprints not always cover the average BSA of a newborn infant. The proposed measurement method will contribute to awareness of the importance of irradiance level as well as footprint measurements in the management of neonatal jaundice. Impact While a sufficient spectral irradiance footprint is essential for PT to be effective, some PT devices have spectral irradiance footprints that are too small to cover the entire body surface area (BSA) of a newborn infant. This study introduces a user-friendly, accessible method to systematically evaluate the spectral irradiance level and footprint of PT devices. This study supports awareness on the role of the spectral irradiance footprint in the efficacy of PT devices. Irradiance footprint can be easily measured during phototherapy with the proposed method.

Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, frozen-/thawed-layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow depth and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel distributed temperature profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of ±0.1 ∘C and enables improving it to ±0.015 ∘C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 s time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow depth and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

Extracting building footprints from remotely sensed imagery has long been a challenging task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes of rooftops, omission of small buildings, and varying scale of buildings hinder existing automated models for extracting sharp building boundaries. Different reasons account for these challenges. In convolutional neural network-based methods, the down-sampling operation loses spatial details of the input images; and small buildings are omitted from the high-level features. The sheltering trees and adjacent objects shadowing may cause errors since semantic information cannot be effectively preserved. Moreover, the insufficient use of multi-scale building features causes blurry edges in the predictions for buildings with complex shapes. To address these challenges, we propose a novel coarse-to-fine boundary refinement network (CBR-Net) that accurately extracts building footprints from remote sensing imagery. Unlike the existing semantic segmentation methods that directly generate building predictions at the highest level, we designed a module that progressively refines the building prediction in a coarse-to-fine manner. In this way, the advantages of both the high-level and low-level features can be retained. We also present a novel boundary refinement (BR) module that enhances the ability of the CBR-Net model to perceive and refine building edges. The BR module refines building prediction by perceiving the direction of each pixel in a remotely sensed optical image to the center of the nearest object to which it might belong. The refined results are used as pseudo labels in a self-supervision process that increases model robustness to noisy labels or obstructions. Experimental results on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset, demonstrate the effectiveness of the proposed method. In evaluation tests, CBR-Net outperformed other state-of-the-art algorithms on the three datasets by maintaining both the continuous entities and accurate boundaries of buildings. The source code of the proposed CBR-Net is available at https://github.com/HaonanGuo/CBRNet .

Detection of cloud contaminated field of views (FOV) from satellite hyperspectral infrared sounders is essential for numerical weather prediction. A new cloud detection model is developed for the cross-track infrared sounder (CrIS) using the artificial deep neural network (DNN) technique. The truth cloud information used is from another instrument of Visible Infrared Imaging Radiometer Suite (VIIRS) deployed on the same platform of CrIS. The training data set is built from CrIS-VIIRS collocated measurements randomly selected from different months to represent different atmospheric and surface conditions. Then, we use the VIIRS cloud mask collocated within the CrIS footprint to train the CrIS spectra for cloud detection. Specifically, the CrIS spectra were transformed into their principal components (PCs), with only the top 75 PCs used as the predictors rather than the entire CrIS channels, for the purpose of better regression and convergence during the training process and faster prediction. Results were examined globally by the considered truth derived from the VIIRS cloud mask. Generally, the spatial distribution of the proposed CrIS cloud detection result agrees with that from the VIIRS, with a high model accuracy of 93%. Further analysis indicates that the proposed CrIS cloud detection result is slightly better over daytime than nighttime with the accuracy values of 94% versus 91%. The ocean areas have a higher cloud detection accuracy than continental land with accuracy values of 95% versus 88%. In addition, sometimes the DNN model would recognize the thin cloud as clear sky, as their spectra are very similar to each other. False detected pixels are also found over snow- or ice-covered and desert areas. This is possibly due to the VIIRS cloud mask that has a relatively low accuracy over these areas.

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Characterizing the subpixel heterogeneity within satellite pixels is a key issue in validation. Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation, including spatial heterogeneity, representativeness errors, and representative area with subpixel size, were analyzed and discussed. Furthermore, this paper discussed the optimal subpixel size to assess spatial heterogeneity within a coarse pixel and the optimal footprint of in situ measurements for building dense and sparse validation networks. Spatial heterogeneity decreases with the increase of subpixel size. And a reduction about 10% can be obtained from 5 m×5 m to 150 m×150 m subpixel size, depending on the degree of spatial heterogeneity within the typical satellite pixels. And the sensitiveness of spatial heterogeneity to subpixel size decreases gradually with the increasing of subpixel size. Ideally, spatial heterogeneity should be assessed using maps with pixel sizes corresponding to the footprint of in situ measurements. Regarding the deployment of future validation networks, the footprint of in situ sites should be designed at least larger than 25 m for dense networks. And much larger footprints (e.g., 100 m) are preferred in designing sparse networks. The representativeness error is not fully related to subpixel sizes because it is affected by many factors. The findings are also transferable to model evaluation when comparing model grid values to local observations.

A NOAA-20 (N20) Advanced Technology Microwave Sounder (ATMS) active geolocation (GEO) test was performed around January 2018 with 24 preselected coastline crossing scenes. After a comprehensive analysis of ATMS stare data and the corresponding Visible Infrared Imaging Radiometer Suite (VIIRS) data, the ATMS pitch, roll, and yaw pointing angle errors are found from the nadir perpendicular, the nadir oblique shallow angle, and the off-nadir perpendicular coastline crossing data, respectively. In this study, we first determine the ATMS radiometric coastline crossing time by using the ATMS radiometric count data. Since the coastline can be located anywhere within one ATMS field of view (FOV) from the passive (regular scanning) GEO data, depending on the scan starting time, it is not a valid assumption for the inflection point being the same as the coastline location. Consequently, using the passive GEO data to validate the sensor’s on-orbit pointing angle performance is limited. After finding the ATMS radiometric coastline crossing time from the ATMS data, we compare it with the VIIRS effective time stamp. Specifically, the VIIRS M3, M4, and M5 (true color) and M15 and M16 bands (thermal) data have a much smaller footprint size. Using the differences of the ATMS and VIIRS (effective) coastlines crossing times, the N20 ATMS pitch, roll, and yaw pointing angle errors are found to be −0.09°, −0.24°, and 0.28°, respectively. To determine ATMS GEO properly, these on-orbit pointing errors need to be corrected, adding to the ATMS sensor data record (SDR) processing coefficient table, and passed on to the operational GEO processing code.

For operational weather forecasting and nowcasting, a refresh rate (RR) of 10 min and a better-than-4-km footprint size for Infrared (IR) bands is desired. Such imaging capability is only available from geostationary orbit (GEO) satellites, such as ABI onboard geostationary operational environmental satellites (GOES)-16/-17, but mainly limited to the tropics and mid-latitudes (referred to as GEO-like imaging capability). For high latitudes such as the Alaskan region, the IR footprint size from ABI/GOES-17 is worse than 6 km, limiting the application over the region. Tundra satellites, with a nonzero inclination angle and a nonzero eccentricity, have longer dwell times near the apogee than the perigee and can be used to monitor the high latitudes and polar regions. This study investigates Tundra satellites’ imaging capability by assuming an ABI-like instrument with the same IR spatial resolution of $56~\mu $ rad onboard. For regional applications, a constellation of two Tundra satellites may provide GEO-like imaging capability for a large domain. This useful domain is further improved when combined with a GEO, i.e., two $Tundra + GOES-17$ . For global applications, a constellation of three Tundra satellites may provide GEO-like imaging capability for high latitudes (improved capability over GEO) and polar regions (unprecedented capability) in both hemispheres. The additional capability from Tundra satellites in tropics and mid-latitudes makes the global space-based meteorological observing system more robust and resilient. While a constellation of three $Tundra + 3$ GEOs may provide global GEO-like imaging capability, having more than three GEOs may be desired by agencies/countries, allowing for improved spatial resolutions over their sub-point locations.

Satellite stereo mapping, together with laser altimetry, can be used to obtain three-dimensional geospatial information. Spaceborne laser altimeter can provide high-accuracy elevation information; however, due to the lack of detailed intensity information, its planimetric accuracy is usually worse than the ranging accuracy. The Chinese Gaofen-7 (GF-7) satellite, which was designed for civilian mapping application, was launched on November 3, 2019. The GF-7 satellite’s main payloads are a laser altimeter system (with footprint camera) and a dual-linear charge-coupled device (CCD) mapping camera. According to the pixel coordinate of the laser footprint in the stereo image, the laser altimeter together with the footprint camera can provide planimetric geodetic coordinates for the control points of a higher accuracy than the other traditional satellite laser altimeters, and represents a new technology for satellite mapping. In this article, a laser footprint planimetric location method for the GF-7 satellite is proposed. The method is designed based on the main payload characteristics of GF-7 and the working modes of the laser altimeter by the combined use of subpixel phase correlation image matching and four types of laser spot center detection methods. The planimetric positioning accuracies of the laser spots in urban, suburban, farmland, forest, mountainous, and ice sheet areas were also analyzed. The experimental results show that the accuracy of planimetric location relative to stereo image for the laser footprint is 0.3–1.0 m (except for ice sheets ~12 m) when the footprint camera works under the synchronous mode, and 0.2–0.4 m when the footprint camera works under asynchronous mode (AM).

LiDAR footprint, defined as the illumination area of LiDAR sensor on the ground, is the fundamental unit that the sensor collects information from. The design of footprint size crucially influences the acquired LiDAR signals. For large-footprint full-waveform LiDAR, a well-designed footprint size is indispensable to acquire accurate and complete vertical profiles of scene targets. The methods that design the footprint size are increasingly needed to satisfy various application requirements. In this study, an analytical method to designing the footprint size is proposed for forest and topography applications. It is established based on a mixture Gaussian model and the designed footprint size ensures the signals of vegetation and ground can be completely extracted. Experiment results with our method show that the footprint size is preferably in the range of 10.6–25.0 m for forest application, while it is less than 32.3 m for topography application. The intersection of the two sets satisfies both applications. Furthermore, a series of sensibility studies were performed to analyze the influence of multiple key parameters to the optimal footprint size, including the scene characteristics, instrumental configurations, and application requirements. This study provides a theoretical basis for the design of future large-footprint full-waveform laser altimeters.

The Earth-observing Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite regularly performs on-orbit radiometric calibration of its reflective solar bands (RSBs), primarily through observations of an onboard sunlit solar diffuser (SD). The on-orbit change of the SD bidirectional reflectance distribution function (BRDF) value, quantified by a numerical factor called the H-factor, is determined by the onboard SD stability monitor. Our previous study showed that the H-factor is solar angle- and view direction-dependent. In this study, we determine the dependence of the H-factor on the detector SD view footprint location. We fit an empirical model to the NASA Collection 1 SNPP VIIRS Level 1B (L1B) spectral reflectance difference across the detectors in an RSB over uniform Earth scenes of the Libya 4 desert and deep convective clouds (DCCs). We apply the model predicted SD-positional-dependent H-factor to calibrate the RSBs. Under this new calibration scheme, the original unreal striping is removed from the homogeneous Libya 4 desert and the DCC images, as well as the original unreal striping from the Dunhuang desert image. The SD-positional-dependent H-factor has been used to calculate the SNPP VIIRS RSB radiometric correction factor for the NASA Collection 2.0 SNPP VIIRS L1B products.

Urban dynamic temporal analysis is essential for the policymaker, natural resources management plan, decision-makers, and scientific purposes. Satisfactory urban management strategies depend on consistent plotting of land use land cover changes. Remote sensing procedures in temporal change detection bring reliability to the decision taker for wide-cover management plans. Two temporal datasets of images were processed to estimate the urban dynamics in current research. SAR microwave data and MSI optical data were acquired from the European Space Agency in 2016 and 2020. For each dataset, preprocessing and data-specific processing procedures were followed to estimate the Urban Footprint within the study area. The urban coherence technique was applied to SAR data, and the change detection technique based on supervised classification was applied on MSI data to estimate the Urban Footprint. The accuracy assessment was estimated using ground truth points (150) as well as training sites (30). The results indicated that the urban expansion in Jeddah city was increased to be nearly 8% using optical data; however, it was increased to be less than 3% using SAR data. Irrespective of MSI data accuracy assessment (83.19%), SAR data showed more consistent Urban Footprinting (92.35%). Therefore, the urban coherence of the coastal city of Jeddah is reliably emphasized using SAR data over MSI data which is a decision-making significance to manage the natural resources within comparable arid environments.

The detection and location capability of the International Monitoring System for small seismic events in the continental and oceanic regions surrounding the Sea of Japan is determined mainly by three primary seismic arrays: USRK, KSRS, and MJAR. Body wave arrivals are coherent on USRK and KSRS up to frequencies of around 4 Hz and classical array processing methods can detect and extract features for most regional signals on these stations. We demonstrate how empirical matched field processing (EMFP), a generalization of frequency-wavenumber or f-k analysis, can contribute to calibrated direction estimates which mitigate bias resulting from near-station geological structure. It does this by comparing the narrowband phase shifts between the signals on different sensors, observed at a given time, with corresponding measurements on signals from historical seismic events. The EMFP detection statistic is usually evaluated as a function of source location rather than slowness space and the size of the geographical footprint valid for EMFP templates is affected by array geometry, the availablesignal bandwidth, and Earth structure over the propagation path. The MJAR arrayhas similar dimensions to KSRS but is sited in far more complex geology which results in poor parameter estimates with classical f-k analysis for all signals lacking energy at 1 Hz or below. EMFP mitigates the signal incoherence to some degree but the geographical footprint valid for a given matched field template on MJAR is very small. Spectrogram beamforming provides a robust detection algorithm for high-frequency signals at MJAR. The array aperture is large enough that f-k analysis performed on continuous AR-AIC functions, calculated from optimally bandpass-filtered signals at the different sites, can provide robust slowness estimates for regional P-waves. Given a significantly higher SNR for regional S-phases on the horizontal components of the 3-component site of MJAR, we would expect incoherent detection and estimation of S-phases to improve with 3-component sensors at all sites. Given the diversity of the IMS stations, and the diversity of the methods which provide optimal results for a given station, we advocate the development of seismic processing pipelines which can process highly heterogeneous inputs to help associate characteristics of the incoming signals with physical events.

Building extraction based on high-resolution remote sensing imagery has been widely used in automatic surveying and mapping. However, few methods have been developed for building instance extraction, i.e., extracting each building’s footprint separately, which is required in a number of applications, such as the smallest unit of a cadastral database. In building instance extraction, there are two challenges: 1) buildings with various scales exist in the imagery and 2) precise building footprints are difficult to extract due to the blurry boundaries. In this article, to solve these problems, a multiscale U-shaped convolutional neural network building instance extraction framework with edge constraint (EMU-CNN) for high-spatial-resolution remote sensing imagery is proposed. The proposed framework consists of three components: 1) a multiscale fusion U-shaped network (MFUN); 2) a region proposal network (RPN); and 3) an edge-constrained multitask network (ECMN). First, in the proposed method, the MFUN includes three parallel branches to learn multiple building features with different scales. The RPN then detects the positions of the building instances, even for buildings that are connected with each other. Moreover, according to the instance positions, the ECMN is proposed to extract a precise mask and suppress overfitting. The experiments conducted on a self-annotated data set and two public data sets (the ISPRS Vaihingen semantic labeling contest data set and the WHU aerial image data set) show that the EMU-CNN method can achieve excellent performance and shows great robustness at different scales.

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ( $R^{2}$ ) of 0.77–0.84 ( $p ) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2 among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for $R^{2}$ and decreased by 4.2–8.3 W/m2 for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

The land on which we live has a tight environmental system, and the elements of life in it must be preserved, as pollution rates have increased in general, including water pollution, and the environmental problems it causes due to population inflation. The development of modern science and technology can contribute greatly to preserving the components of the environmental balance. It was scientifically found that each body has a radiative value that distinguishes it from other bodies and this is called the thermal fingerprint of objects, and therefore the radiation signature of a body is what distinguishes it from other bodies. Accordingly, from here it is possible to reveal what these objects are without direct contact and know their components from distance. This study addresses the aspect of monitoring the spectral footprint of polluted water, especially the waste of hospitals and factories, which causes great damage to the beaches and must be monitored first-hand through the use of a thermal monitoring device and conducting field examinations directly. The study area was determined near one of the wastes of Baghdad hospitals on one side of the Tigris River and compared with the values of the thermal spectral footprint taken from satellites that carry thermal sensors The results showed the convergence of the measured values with the values taken from the thermal sensors, which confirms the accuracy of the thermal images in the (10-11) beam of the satellite

Extracting and identifying building boundaries from high-resolution images have been a hot topic in the field of remote sensing for years. Various methods including geometric, radiometric, object based and edge detection were previously deliberated and implemented in different studies in the context of building extraction. Nevertheless, the reliability of extraction process is mainly subject to user intervention. The current study proposes a new automatic morphology-based approach for extracting buildings using high-resolution satellite images of Al-Hudaybiyah region in the city of Makkah as a case study. The proposed technique integrates the support vector machine for extracting buildings that have bright and dark roofs. The appropriateness of this method has been examined by means of various indicators for example completeness, correctness and quality. Preliminary findings will illustrate the precision and accuracy of the used machine learning algorithm. Research results can provide a generic indicator to assist the planning authorities in achieving better urban planning processes taking into account all potential environmental, social and urban demands and requirements.

Terrestrial laser scanners (TLS) are used for a variety of applications, e.g., surveying, forestry, cultural heritage preservation, mining, topographic mapping, urban planning, forensics etc. This technology has made a huge shift in 3D spatial data collection due to much faster speed compared to other techniques. In the absence of guiding principles for positioning TLS relative to an object, surveyors collect data at maximum arrangements of scanning geometry elements due to fear of incomplete data of TLS. In 3D spatial data acquisition, positional accuracy and Level of Detail (LOD) are major considerations and are dependent on laser incident angle, footprint size, range and resolution. Mathematical models have been developed relating range, incident angle and laser footprint size for different surface configurations. These models can be used to position TLS to collect data at required positional accuracy and LOD. Models have been verified by deriving one model from other surface models by changing parameters. Effects of incident angle and footprint size have been studied mathematically and experimentally on a natural sloping surface. From the results, surveyors can plan the positioning of the scanner so that data is collected at the required accuracy and LOD.

Building footprint information is one of the key factors for sustainable urban planning and environmental monitoring. Mapping building footprints from remote sensing images is an important and challenging task in the earth observation field. Over the years, convolutional neural networks have shown outstanding improvements in the building extraction field due to their ability to automatically extract hierarchical features and make building predictions. However, as buildings are various in different sizes, scenes, and roofing materials, it is hard to precisely depict buildings of varied sizes, especially in large areas (e.g., nationwide). To tackle these limitations, we propose a novel deep-supervision convolutional neural network (denoted as DS-Net) for extracting building footprints from high-resolution remote sensing images. In the proposed network, we applied deep supervision with an extra lightweight encoder, which enables the network to learn representative building features of different scales. Furthermore, a scale attention module is designed to aggregate multiscale features and generate the final building prediction. Experiments on two publicly available building datasets, including the WHU Building Dataset and the Massachusetts Building Dataset, show the effectiveness of the proposed method. With only a 0.22-M increment of parameters compared with U-Net, the proposed DS-Net achieved an IoU of 90.4% on the WHU Building Dataset and 73.8% on the Massachusetts Dataset. DS-Net also outperforms the state-of-the-art building extraction methods on the two datasets, indicating the effectiveness of the proposed deep supervision and scale attention.

The demand for leaf area index (LAI) retrieval from spaceborne full-waveform LiDAR increases due to its direct sampling of the three-dimensional forest structure at a near-global scale. However, the nonrandomness (i.e., clumping effect) of canopy composition limits the reliability of LAI derived from two common methods. They either assume a homogeneous scene in the footprint or just correct for the large gaps-induced between-crown clumping. The clumping in the crown is still an unaddressed issue. We proposed a method to compensate occlusion (i.e., lower canopy layers are occluded by the upper canopy in the process of LiDAR measurement), through which the vertical canopy profile can be resolved from the waveform. Further, we developed a method of deriving relative path length distribution that can reflect the heterogeneity of the canopy from the occlusion-corrected waveform. In addition to correcting the between-crown clumping, we corrected the within-crown clumping further using the derived relative path length distribution, based on path length distribution (PATH) theory. We used simulated waveform data with known LAI and GLAS data with corresponding field-measured LAI to test the performance of our and the other two common LAI retrieval methods. Results show that the errors of our approach are the lowest (with an error generally below 10% and the maximum error below 20%, compared with up to 69% and 47% for the other two methods), and it is relatively stable in various scenes. This study demonstrated the potential of improving LAI retrieval through full utilization of full-waveform data.

In the last 15 years, mapping technology has become a necessity in smart cities planning. And 2D are starting to be augmented by 3D maps. 3D Maps are already used in the cartographic field, to create a three-dimensional view of the terrain and buildings. In this paper we address the concept of 3D Maps and we compare two methods to generate such maps. In this study two 3D maps were built, one using photogrammetric 3D stereo-restitution and one using automatic extrusion from a LiDAR point cloud and a set of 2D vector polygons. Upon comparison of these maps, we have concluded that the accuracy of the two maps is very similar and it depends very much on the input data and we have observed that creating a precise 3D map in photogrammetric environment takes much longer than the one built using the LiDAR point cloud. As 3D maps become the future of mapping, there is a continuous need for more accurate and complete field data to be collected and processed. Once more detailed field data becomes available, a clear conclusion on which of the methods provide us with a more accurate 3D map could be drawn. The evolution of 3D mapping is rapidly growing together with the applications developed to use it, especially in surveying and material monitoring. The key to future development of smart cities in based on better designs and infrastructures, and 3D mapping technology is a vital instrument to assist such a development.

Compensating for the effects of an acquisition footprint can be one of the most daunting problems when using seismic attributes for quantitative interpretation. This is especially true for unconventional plays because they are on land with accompanying irregular acquisition geometries. Additionally, in such plays, the physical property changes are often small, making the seismic amplitude fidelity critical. We have developed a methodology that integrates a 1D elastic prestack synthetic model with 3D acquisition geometry to accurately model the seismic footprint produced by irregular or insufficient sampling of primary reflectivity. The stacked amplitude response of the modeled survey is then used to mitigate the poststack footprint on the field seismic. Modeling and removing this element of the acquisition footprint quantitatively improve the interpretive value of the mapped seismic amplitudes. In our study area, correlation between seismic amplitudes and well control increased from an [Formula: see text] of 0.053 before correction to an [Formula: see text] of 0.629 after. Our approach is especially effective in situations in which the spatial frequency of the footprint overlaps that of the geologic signal. Geological feature: Acquisition related seismic amplitude artifacts Seismic appearance: Smoothly varying amplitude changes Alternative interpretations: Bed thickness variation Features with similar appearance: Carbonate porosity Formation: Niobrara Formation, mixed chalks and marlstones Age: Upper Cretaceous Location: Wattenberg Field, Denver Basin, north central Colorado Seismic data: Joint acquisition between Anadarko Petroleum and Colorado School of Mines, Reservoir Characterization Project Analysis tools: Elastic prestack seismic modeling

Digital elevation models (DEMs) are of fundamental importance for a large variety of scientific and commercial applications. Many geoscience studies require the most precise and current information about the Earth’s topography. Independent quality assessments of these DEMs are crucial to their appropriate use in land process studies, as inputs to models, and for detection of topographic change. The Ice, Cloud and land Elevation Satellite (ICESat) provided globally-distributed elevation data of high accuracy that demonstrated to be well-suited for evaluating continental DEMs after appropriate editing (Carabajal and Harding, 2005; Carabajal and Harding, 2006; Carabajal et al., 2010 and 2011; Carabajal and Boy, 2016). ICESat-2, launched on September 15th, 2018, provides an opportunity to develop a dataset suitable for Geodetic Ground Control. With increased coverage, ICESat-2/ATLAS features 6 laser beams with 532 nm wavelength, using photon counting technologies. With a nearly polar orbit, altimetry from ICESat-2 is available for latitudes reaching up to 88 degrees, on a 91-day repeat orbit with monthly sub-cycles. ICESat-2’s footprint size is ∼17 m, at 10 kHz pulse repetition frequency, or 0.75 m along track. Its pointing control is 45 m, with a pointing knowledge of 6.5 m, and a single photon precision of 800 ps. Sophisticated data processing techniques on the ground, optimized by surface type, produce high quality estimates of topography. We illustrate the use of ICESat-2 altimetry to assess DEM’s accuracy using ATL08 release 002 elevations (Land and Vegetation) products (Neuenschwander and Pitts, 2019), showing comparable results to those using ICESat-derived Geodetic Ground Control.

The GaoFen-7 (GF-7) satellite is successfully launched on November 3, 2019, and its laser altimeter system is officially and firstly employed as the main payload for earth observations in China, which includes two sets of laser altimeters and laser footprint cameras. The Laser Footprint Image (LFI) is used to capture laser spots on the ground. In order to make up for the shortcomings of high cost field work for the traditional laser altimeter ground detector-based calibration method, this paper proposes a novel laser altimeter calibration method based on LFI. Firstly, the spaceborne laser calibration model and the Laser Footprint Camera (LFC) geolocation model are established. Secondly, the image coordinates of laser spot centroid are extracted from LFI, and the ground location of is obtained by ray intersecting with the reference Digital Surface Model (DSM). Finally, the centroid of laser spot is considered as Ground Control Point (GCP), and the pointing bias of GF-7 laser altimeter is calibrated by the Least Squares Estimation (LSE). The ALOS Global Digital Surface Model “ALOS World 3D-30m” (AW3D30) is used to evaluate the elevation accuracy of GF-7 laser altimeter before and after the calibration. The results indicate that elevation accuracy of the GF-7 laser altimeter is improved significantly after calibration. The proposed method can be effectively applied for high-frequency geometric calibration of GF-7 laser altimeter.

Unmanned aerial vehicle laser scanning (ULS) has recently become available for operational mapping and monitoring (e.g. for forestry applications or erosion studies). It combines advantages of terrestrial and airborne laser scanning, but there is still little proof of ULS accuracy. For the detection and monitoring of small-magnitude surfaces changes with multitemporal point clouds, an estimate of the level of detection (LOD) is required. The LOD is a threshold applied on distance measurements to separate real surface change (e.g. due to erosion or deposition by geomorphic processes) from errors. This paper investigates key components of the error budget for two ULS point clouds acquired for erosion monitoring at a grassland site in the Alps. In addition to the registration error and effects of the local surface roughness, we assess the positional uncertainties of each point that result from laser footprint effects, which are a function of the scanning geometry (including range, incidence angle and beam divergence). By removing erroneous points with an increasingly stricter point error criterion, we illustrate that the positional point errors strongly affect the LOD and discuss how this type of error can be mitigated. Moreover, our experimental results with three different surface classes (bare earth and rock, buildings and grassland) show that the level of detection tends to be slightly better for areas with bare earth and rock than for grass-covered areas (due to their roughness). For all these surface types reliable distance measurements are possible with sub-decimetre levels of detection.

Aquatic ecosystems are major sources of greenhouse gases (GHGs). Robust measurements of natural GHG emissions are vital for evaluating regional to global carbon budgets and for assessing climate feedbacks of natural emissions to improve climate models. Diffusive and ebullitive (bubble) transport are two major pathways of gas release from surface waters. To capture the high temporal variability of these fluxes in a well-defined footprint, we designed and built an inexpensive device that includes an easily mobile diffusive flux chamber and a bubble counter all in one. In addition to automatically collecting gas samples for subsequent various analyses in the laboratory, this device also utilized a low-cost carbon dioxide (CO2) sensor (SenseAir, Sweden) and methane (CH4) sensor (Figaro, Japan) to measure GHG fluxes. Each of the devices was equipped with an XBee module to enable local radio communication (DigiMesh network) for time synchronization and data readout at a server controller station on the lakeshore. The software of this server controller was operated on a lowcost computer (Raspberry Pi), which has a 3G connection for remote control and monitor functions from anywhere in the world. This study shows the potential of a low-cost automatic sensor network system for studying GHG fluxes on lakes in remote locations. Funding Agencies|US National Science Foundation (NSF) (MacroSystems Biology, EF)National Science Foundation (NSF) [1241937]; US National Science Foundation (NSF) (Northern Ecosystems Research for Undergraduates program, NSF REU site EAR) [1063037]; Swedish Research Council (VR)Swedish Research Council [2012-00048, 2016-04829]; European Research Council (ERC)European Research Council (ERC) [725546]; VINNOVAVinnova [2015-03529]

Building footprint maps are useful for urban planning, infrastructural development, population estimation and disaster management. With the availability of very-high resolution satellite imagery, r...

GPS interferometric reflectometry (GPS-IR) is a technique that can be used to measure snow accumulation on ice sheets. The footprint of the method (∼1000 m2) is larger than that of many other in situ methods. A long-term comparison with hand measurements yielded an accuracy assessment of 2 cm. Depending on the placement of the GPS antenna, these data are also sensitive to firn density. The purpose of this short note is to make public GPS-IR measurements of snow accumulation for four sites in Greenland, compare these records with in situ sensors, and make available open-source GPS-IR software to the cryosphere community.

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (>312 km2).

Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

Urbanization has attracted wide and active interests due to the impact on regional sustainable development. As an important indicator of urbanization, impervious surface area (ISA) should be accurately monitored. In scenario of identifying ISA by supervised classification from satellite images, the training samples are usually labeled manually, which is highly labor-intensive and time-consuming. High-resolution nighttime light image provides a unique footprint of human activities and settlements which are strongly correlated with ISA. In view of this, a novel ISA training sample selection method is proposed by integrating the JL1-3B high-resolution nighttime light imagery and Sentinel-2 time series imagery, and the random forest is applied to classify ISA from Sentinel-2 imagery. The quality of the automatically selected samples was quantitatively validated. There were over three study areas, and the overall classification accuracies were above 97%, showing reliable and robust performance. Compared with conventional methods, the proposed approach achieves satisfactory results in separating bare land from ISA. This study provides a data fusion way which can automatically generate sufficient and high-quality training samples for ISA mapping, and suggests that high-resolution nighttime imagery could demonstrate a promising potential for urban remote sensing.

Determining the accuracy of lake water levels calculated based on Ice, Cloud, and land Elevation Satellite (ICESat) data mainly relies on identifying lake water footprints (LWFs), which are obtained using an overlay analysis of lake water masks (LWMs) and ICESat tracks. However, most previous studies that have conducted a buffer analysis based on LWMs have set the buffer size subjectively without providing a detailed explanation for this or conducting a system analysis. In this study, the effects of using inside and outside buffers to obtain LWMs for seven lakes are analyzed. The Modified Normalized Difference Water Index (MNDWI) was applied to extract LWMs from Thematic Mapper (TM) images. The boxplot was used to remove footprints with abnormal elevations, and then the average of the remaining footprints was calculated as the ICESat water level. To compare with the in situ measured data, the root mean square error (RMSE) was used for accuracy evaluation. Results show the following: (1) for Yamzhog Yumco, which is a narrow lake, the altimetry accuracy is higher when using the outside buffer than for the inside buffer or with no buffer, and the highest accuracy is obtained with an outside buffer of approximately 100 m. (2) For other relatively wide lakes, such as Lake Michigan, Lake Erie, Lake Huron, Lake Ontario and Lake Superior, the inside buffer method does not always improve altimetry accuracy, and this result differs from those presented previously. (3) For different lakes, the range of change in altimetry accuracy is affected by the number of LWFs. This study is of value for use in studies that apply ICESat altimetry data to obtain changes in lake water levels, especially for relatively narrow lakes, and the results imply that the altimetry accuracy can be improved by using the outside buffer.

Spaceborne full-waveform LiDAR has shown unique advantages in measuring global surface elevation. Laser footprints generally have decimeter-level vertical accuracy, meeting the requirement of ground elevation control points. In contrast, the footprint horizontal geolocation accuracy is in the meter to ten-meter levels. Although previous researches attempted to locate the footprint horizontal coordinate based on the digital surface model (DSM), the applicability and performance of the DSM-based positioning method in evaluating the footprint geolocation accuracy should be rigorously assessed before large-scale applications. Therefore, this study practices the DSM-based footprint positioning method over several study sites with various land covers and different laser campaigns. The footprint geolocation accuracy of the ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), the first Earth observation full-waveform LiDAR satellite, is evaluated by the DSM-based method. Results indicate that the DSM-based positioning method is only suitable for areas with significant height features, but not applicable in areas with high spatial correlation. The derived footprint geolocation accuracy (8.19-m horizontal shifting with 4.19-m standard deviation) is relatively reliable in urban site with relatively high spatial heterogeneity. This study helps make better use of the DSM-based footprint positioning method and design calibration experiments of full-waveform LiDAR satellites.

In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this article, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) The deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) The combined approach can significantly improve the peak detection rate; the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind-Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind-Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.

The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without ground control points of the two line-array stereo mapping cameras. This article details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory, and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry, and other industries.

The Global Ecosystem Dynamics Investigation (GEDI) LiDAR provides new spaceborne vegetation canopy structural information including relative canopy height products defined with respect to 25 m diameter footprints. The GEDI geolocation requirement is that each 25 m footprint center is horizontally georeferenced to within 10 m (1σ), assuming normally distributed geolocation errors with a 0 m mean and a 10 m standard deviation. The impact of this geolocation uncertainty on the reliability of forest canopy height estimation is examined considering Airborne Laser scanner (ALS) and GEDI data acquired in 2014 and 2019 respectively. A total of 445 GEDI footprints acquired over 2000 ha of unforested and tropical secondary forest in the western Democratic Republic of the Congo with vegetation heights ranging from 1 m to 42 m are considered. Airborne true color 10 cm imagery and an ALS derived canopy height model are examined to contextualize the results. GEDI waveforms are simulated from the ALS data at the reported locations of the GEDI footprints and used to derive h ˆ 95 , h ˆ 85 , h ˆ 75 relative heights that define the canopy height relative to the ground below which 95%, 85% and 75% of the simulated cumulative waveform energy is returned. A Monte Carlo simulation is undertaken, moving the centers of each GEDI footprint with 300 randomly generated position errors modelled using the GEDI geolocation uncertainty (0 m mean, 10 m standard deviation), and each time simulating the GEDI waveform from the ALS data. Relative heights are extracted from the 300 simulated GEDI waveforms and their variation, defined by the 25th and 75th percentiles, and the interquartile range (IQR) (75th - 25th percentiles), are quantified to provide insights into the impact of the GEDI geolocation uncertainty on forest canopy height retrieval. The IQR accounts for 50% of the variation in the forest canopy height due to GEDI geolocation uncertainty. High IQR values, greater than or comparable to the relative height derived from the ALS data at the GEDI reported footprint location are shown to occur where the footprint covered or was adjacent to spatially heterogeneous canopies, including canopies with small forest stands, holes in the vegetation canopy, and forest edges. This is a concern for the use of GEDI data acquired over these conditions which are prevalent in many forest systems. The impact of GEDI geolocation uncertainty on tropical forest change monitoring is demonstrated by comparing the GEDI h 95 product footprint values (sensed in 2019) with simulated h ˆ 95 values derived from the ALS data (sensed in 2014) at the GEDI product reported footprint location and at the 300 shifted footprint locations. GEDI footprints where five-year canopy height changes, and not changes due to artefacts associated with the GEDI geolocation uncertainty or the GEDI simulator, are attributed conservatively. Differences among the six algorithm setting group GEDI h 95 product relative height values are evident and influential on the change attribution. PlanetScope 3 m imagery sensed in 2019 are examined to provide qualitative evidence that support the efficacy of the approach for forest height reduction monitoring. The simulation approach described in this study provides a route to determine if forest canopy height change found by comparing multi-temporal data (for example, GEDI with previously collected ALS data or GEDI data) is significant relative to errors imposed by the GEDI geolocation. The treatment of change is simple, and recommendations for improvements to detect more subtle change are made. The study was undertaken using the Release 1.0 GEDI data and suggests, pending planned geolocation improvement, the need to accommodate for GEDI geolocation uncertainty, particularly over canopies that are spatially fragmented or that have heterogeneous three dimensional structure at scales comparable to the 25 m GEDI footprint dimension.

Water depth estimation models based on optical satellite images often require ground-truth data for supervised training procedures. However, in the South China Sea (SCS), the ground truth data are limited or outdated. Therefore, it is challenging to derive reasonable water depths around islands or coral reefs without prior knowledge. ICESat-2 is a space-borne LiDAR satellite launched in September 2018 that provides geolocated height at the laser footprint on a global scale and opens an opportunity for bathymetric mapping in regions normally inaccessible. The combination of ICESat-2 data with Sentinel-2 optical images is developed to extend the application of satellite-derived bathymetry (SDB). Three SDB algorithms, including ratio transform (RT), multiple linear regression, and classification-based (CB) algorithms, are applied to investigate the water-depth retrieval capabilities. Five islands located in different parts of the SCS are selected, analyzed, and evaluated with the support of Google Earth Engine. Comparing the ICESat-2 water depth profiles against airborne LiDAR data, the statistical indexes of R2 and RMSE reached 0.99 and 0.31 m, respectively. This demonstrates the suitability of using ICESat-2 data as a reliable data source in shallow water. On the other hand, the CB model is used to address the issue of heterogeneity by dividing the target islands into groups based on spectral characteristics. The results show that an integration of ICESat-2 and Sentinel-2 imageries can achieve R2 at 0.75 to 0.95 and RMSE at 0.66 to 1.87 m with the deepest pixels at 19 to 32 m across these five islands. We conclude that the ICESat-2/Sentinel-2 synergy scheme is capable of overcoming current limitations in various regions and thus can fill the gaps in bathymetry charts.

The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined, this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F1 measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way.

International audience; The Soil Moisture and Ocean Salinity (SMOS) satellite has been providing global data for more than 11 years. The algorithm to retrieve surface soil moisture (SM) and vegetation optical depth (VOD) from Brightness Temperatures (TB) has constantly evolved as our understanding of the instrument and of the responses to various ecosystems increased. The evolution of the radiative transfer modelling used in the inversion process is in part based upon ground measurements done on experimental sites. We present the particular case of an L-band radiometer placed on top of a 800- meter-high cliff. This design allows us to study large and various scenes when compared to classical experiments where a radiometer is placed 10–20 m above a surface. With our set up, the footprints cover areas larger than 300 m in diameter, covering thus several land use types and the footprint can be directed towards different dominant land classes (forest, crops, urban etc…). This paper presents the whole set up consisting of in situ measurements (soil moisture and surface temperatures) and L-band radiometry acquired by the LEWIS instrument at high incidence angles. We perform SM/VOD retrievals from brightness temperatures using various configurations which are compared to the in situ observations. We test in particular a 2-VOD retrieval approach that consists in deriving one SM for the entire scene but two VOD, one for agricultural/low vegetation and a VOD for the forest parts, which improves the performance of the SM/VOD retrievals.

Corner cube retro‐reflectors (CCRs), passive optical components, are used to independently evaluate the geolocation accuracy and effective laser footprint diameter of NASA's laser altimetry mission, ICESat‐2, at two specific study sites: White Sands Missile Range in New Mexico and along a segment of the 88°S line of latitude in Antarctica. The CCR methodology provides ICESat‐2 the ability to monitor these altimeter performance metrics throughout the mission lifetime as an indicator of the health of the instrument and the quality of the observations for science applications. The results using this technique reveal a mean geolocation accuracy of the ICESat‐2 measurements of 3.5 m ± 2.1 m, meeting the mission requirement of 6.5 m. For those instances where multiple CCRs are illuminated, the mean effective laser footprint diameter is 10.9 m ± 1.2 m, with the variability assumed to be due to the influence of atmospheric conditions, but warrants further investigation.

The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts or land surveying, occasionally assisted by semi-automatic tools. In the last decade, deep learning-based approaches have demonstrated their capabilities to extract these elements automatically and accurately from remote sensing imagery. The building footprint and road network detection problem can be considered a multi-class semantic segmentation task, that is, a single model performs a pixel-wise classification on multiple classes, optimizing the overall performance. However, depending on the spatial resolution of the imagery used, both classes may coexist within the same pixel, drastically reducing their separability. In this regard, binary decomposition techniques, which have been widely studied in the machine learning literature, are proved useful for addressing multiclass problems. Accordingly, the multi-class problem can be split into multiple binary semantic segmentation sub-problems, specializing different models for each class. Nevertheless, in these cases, an aggregation step is required to obtain the final output labels. Additionally, other novel approaches, such as multi-task learning, may come in handy to further increase the performance of the binary semantic segmentation models. Since there is no certainty as to which strategy should be carried out to accurately tackle a multi-class remote sensing semantic segmentation problem, this paper performs an in-depth study to shed light on the issue. For this purpose, open-access Sentinel-1 and Sentinel-2 imagery (at 10 m) are considered for extracting buildings and roads, making use of the well-known U-Net convolutional neural network. It is worth stressing that building and road classes may coexist within the same pixel when working at such a low spatial resolution, setting a challenging problem scheme. Accordingly, a robust experimental study is developed to assess the benefits of the decomposition strategies and their combination with a multi-task learning scheme. The obtained results demonstrate that decomposing the considered multi-class remote sensing semantic segmentation problem into multiple binary ones using a One-vs-All binary decomposition technique leads to better results than the standard direct multi-class approach. Additionally, the benefits of using a multi-task learning scheme for pushing the performance of binary segmentation models are also shown. Christian Ayala was partially supported by the Goverment of Navarra under the industrial PhD program 2020 reference 0011-1408-2020-000008. Mikel Galar was partially supported by Tracasa Instrumental S.L. under projects OTRI 2018-901-073, OTRI 2019-901-091 and OTRI 2020-901-050, and by the Spanish MICIN (PID2019-108392GB-I00 / AEI / 10.13039/501100011033).

High precision on-orbit geometric calibration technology is the key to obtaining high precision satellite laser altimeters. Different from the traditional full-waveform laser altimeter, the single-photon laser altimeter has the characteristics of high repetition frequency and small footprint. These characteristics put forward new requirements for on-orbit geometric calibration. Aiming at the characteristics of the single-photon laser altimeter, this paper proposes a pointing angle and range calibration algorithm based on a Corner Cube Retro-Reflectors (CCRs). The algorithm determines the position of the CCR closest to the center of the footprint based on the photon signal returned by the CCR deployed on the ground, and unifies the natural ground and the laser footprint and establishes an on-orbit geometric calibration model. Through 8 sets of control experiments, the system errors of 30 second,60 second and 90 second are added to the pointing angle, and the system errors of -3m,-4m,-6m and -9m are added to the range. After using this algorithm for calibration, The average elevation deviation is decreased from more than 86m to less than 1m. The results show that the CCR-based pointing angle and range calibration algorithm can better restore the added system error, and can effectively improve the data accuracy of the laser altimeter.

Natural and anthropogenic infrasound may travel vast distances, making it an invaluable resource for monitoring phenomena such as nuclear explosions, volcanic eruptions, severe storms, and many others. Typically, these waves are captured using pressure sensors, which cannot encode the direction of arrival—critical information when the source location is not known beforehand. Obtaining this information therefore requires arrays of sensors with apertures ranging from tens of meters to kilometers depending on the wavelengths of interest. This is often impractical in locations that lack the necessary real estate (urban areas, rugged regions, or remote islands); in any case, it requires multiple power, digitizer, and telemetry deployments. Here, the theoretical basis behind a compact infrasound direction of arrival sensor based on the acoustic metamaterials is presented. This sensor occupies a footprint that is orders of magnitude smaller than the span of a typical infrasound array. The diminutive size of the unit greatly expands the locations where it can be deployed. The sensor design is described, its ability to determine the direction of arrival is evaluated, and further avenues of study are suggested.

Hydrosat's VanZyl-1 demonstration payload is representative of an emerging class of small satellite payloads, with a goal to enable infrared measurements relevant to Earth science applications in a small size, weight, and power (SWaP) footprint. In order to optimize the performance of an infrared instrument within the confines of a hosted payload allocation or other SmallSat form factor, careful attention must be paid to the radiometric calibration sources that will be available on orbit. This paper explores the state of the art in compact blackbody sources and alternative on-board calibration sources, as well as established practices for vicarious calibration such as oceans or the lunar surface. These options will be summarized in terms of high-level calibration goals including relative detector calibration, absolute response calibration, and reference target radiometric knowledge over time.