Table of Contents

Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students’ comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing. Full article

In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60 ${}^{\circ }$ simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of ${10}^4$ and ${10}^3$ for summer and winter, respectively. This is encouraging for the processing of the future SWOT data. Full article

Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. Atmospheric correction for remote-sensing images in these areas has not been good. In this paper, we proposed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial-resolution imagery in arid areas with bright surfaces. Land surface in arid areas is usually bright and stable and the variation of atmosphere in these areas is also very small; consequently, the land-surface characteristics, specifically the bidirectional reflectance distribution factor (BRDF), can be retrieved easily and accurately using time series of satellite images with relatively lower spatial resolution like the Moderate-resolution Imaging Spectroradiometer (MODIS) with 500 m resolution and the retrieved BRDF is then used to retrieve the AOD from MHSR images. This algorithm has three advantages: (i) it is well suited to arid areas with bright surfaces; (ii) it is very efficient because of employed lower resolution BRDF; and (iii) it is completely automatic. The derived AODs from the Multispectral Instrument (MSI) on board Sentinel-2, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Gao Fen 1 Wide Field Viewer (GF-1/WFV), Gao Fen 6 Wide Field Viewer (GF-6/WFV), and Huan Jing 1 CCD (HJ-1/CCD) data are validated using ground measurements from 4 stations of the AErosol Robotic NETwork (AERONET) around the world. Full article

In Global Navigation Satellite System (GNSS) positioning, gross errors seriously limit the validity of Kalman filtering and make the final positioning solutions untrustworthy. Thus, the detection and correction of gross errors have become indispensable parts of Kalman filtering. Starting by defining an incremental Chi-square method of recursive least squares, this paper extends this definition to Kalman filtering to detect gross errors, explains its nature and its relation with the currently adopted Chi-square variables of Kalman filtering in model and data spaces, and compares them with the predictive residual statistics. Two robust Kalman filtering models based on an incremental Chi-square method (CI-RKF) were established, and the one with a better incremental Chi-square component was selected based on a static accuracy evaluation experiment. We applied the selected robust model to the GNSS positioning and the GNSS/inertial measurement unit (IMU) / visual odometry (VO) integrated navigation experiment in an occluded urban area at the East China Normal University. We compared the results for conventional Kalman filtering (CKF) with a robust Kalman filtering constructed using predictive residual statistics and an Institute of Geodesy and Geophysics (IGGШ) weight factor, abbreviated as “PRS-IGG-RKF”. The results show that the overall accuracy of CI-RKF in GNSS positioning was improved by 22.68%, 54.33%, and 72.45% in the static experiment, and 12.30%, 7.50%, and 16.15% in the kinematic experiment. The integrated navigation results indicate that the CI-RKF fusion method increased the system positioning accuracy by 66.73%, 59.59%, and 59.62% in one of the severe occlusion areas, and 42.04%, 59.04%, and 52.41% in the other. Full article

Chang’E-5 will be China’s first sample−return mission. The proposed landing site is at the late-Eratosthenian-aged Rümker region of the lunar nearside. During this mission, a driller will be sunk into the lunar regolith to collect samples from depths up to two meters. This mission provides an ideal opportunity to investigate the lunar regolith temperature variation, which is important to the drilling program. This study focuses on the temperature variation of lunar regolith, especially the subsurface temperature. Such temperature information is crucial to both the engineering needs of the drilling program and interpretation of future heat-flow measurements at the lunar landing site. Based on the real-time illumination, and particularly the terrain obscuration, a one-dimensional heat equation was applied to estimate the temperature variation over the whole landing region. Our results confirm that while solar illumination strongly affects the surface temperature, such effect becomes weak at increasing depths. The skin depth of diurnal temperature variations is restricted to the uppermost ~5 cm, and the temperature of regolith deeper than ~0.6 m is controlled by the interior heat flow. At such a depth, China’s future lunar exploration is adequate to measure the inner heat flow, considering the drilling depth will be close to 2 m. Full article

The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human–computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels. Full article

Current scene classification for high-resolution remote sensing images usually uses deep convolutional neural networks (DCNN) to extract extensive features and adopts support vector machine (SVM) as classifier. DCNN can well exploit deep features but ignore valuable shallow features like texture and directional information; and SVM can hardly train a large amount of samples in an efficient way. This paper proposes a fast deep perception network (FDPResnet) that integrates DCNN and Broad Learning System (BLS), a novel effective learning system, to extract both deep and shallow features and encapsulates a designed DPModel to fuse the two kinds of features. FDPResnet first extracts the shallow and the deep scene features of a remote sensing image through a pre-trained model on residual neural network-101 (Resnet101). Then, it inputs the two kinds of features into a designed deep perception module (DPModel) to obtain a new set of feature vectors that can describe both higher-level semantic and lower-level space information of the image. The DPModel is the key module responsible for dimension reduction and feature fusion. Finally, the obtained new feature vector is input into BLS for training and classification, and we can obtain a satisfactory classification result. A series of experiments are conducted on the challenging NWPU-RESISC45 remote sensing image dataset, and the results demonstrate that our approach outperforms some popular state-of-the-art deep learning methods, and present high-accurate scene classification within a shorter running time. Full article

Thirty infrasound sensors have been operated over Japan since 2015. We developed the irregular array data processing in order to detect and estimate the parameters of the arrival source waves by using infrasound data related to the sequence of the volcanic eruption at Mt. Shinmoedake in March 2018. We found that the apparent velocity at the ground was equal to the acoustic velocity at particular reflection levels. The results were confirmed through a comparison of the findings of the apparent velocity with a wave propagation simulation on the basis of the azimuth, infrasound time arrivals, and the state of the atmospheric background using global atmospheric models. In addition, simple ideas for estimating horizontal wind speeds at certain atmospheric altitudes based on infrasound observation data and their validation and comparison were presented. The calculated upper wind speed and wind observed by radiosonde measurements were found to have a qualitative agreement. Propagation modeling for these events estimated celerities in the propagation direction to the sensors that were consistent with the tropospheric and stratospheric ducting. This study could inspire writers, in particular, and readers, in general, to take advantage of the benefits of infrasound wave remote-sensing for the study of the Earth’s atmospheric dynamics. Full article

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%. Full article

Parameters of geomorphological characteristics are critical for research on yardangs. However, which are low-cost, accurate, and automatic or semi-automatic methods for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R² = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R² = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23%with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138). Full article

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers. Full article

Surface currents in the Alboran Sea are characterized by a very fast evolution that is not well captured by altimetric maps due to sampling limitations. On the contrary, satellite infrared measurements provide high resolution synoptic images of the ocean at high temporal rate, allowing to capture the evolution of the flow. The capability of Surface Quasi-Geostrophic (SQG) dynamics to retrieve surface currents from thermal images was evaluated by comparing resulting velocities with in situ observations provided by surface drifters. A difficulty encountered comes from the lack of information about ocean salinity. We propose to exploit the strong relationship between salinity and temperature to identify water masses with distinctive salinity in satellite images and use this information to correct buoyancy. Once corrected, our results show that the SQG approach can retrieve ocean currents slightly better to that of near-real-time currents derived from altimetry in general, but much better in areas badly sampled by altimeters such as the area to the east of the Strait of Gibraltar. Although this area is far from the geostrophic equilibrium, the results show that the good sampling of infrared radiometers allows at least retrieving the direction of ocean currents in this area. The proposed approach can be used in other areas of the ocean for which water masses with distinctive salinity can be identified from satellite observations. Full article

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (Ψ_PD) and stomatal conductance (g_s), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R² ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R² ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management. Full article

Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling. Full article

The accuracy of global tropospheric empirical models depends on the model expression and the modeling data sources. Although the current temporal resolution of available models is usually one day, it is anticipated that this will be improved in the future. To achieve compatibility with future high temporal-resolution data sources, this study develops a new global tropospheric correction model, the Wuhan-University Global Tropospheric Empirical Model (WGTEM). Evaluation of WGTEM model expression determines that it has better precision than other models, and this is attributed to its ability to consider diurnal variations in meteorological parameters and the double-peak daily variation in air pressure, which are not concerned in other models. The external accuracy of the WGTEM was evaluated after modeling with the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim products, and results show its accuracy exceeds that of the current ITG model and its Zenith Tropospheric Delay (ZTD) performance is also superior. Full article

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates
global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ
observations. The SSTs have uncertainty information provided with them and an ice concentration
(IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output
each day. The system is run in near real time to produce data for use in applications such as
numerical weather prediction. Data production is monitored routinely and outputs are available from
the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an
operational product, the OSTIA system is continuously under development. For example, since the
original descriptor paper was published, the underlying data assimilation scheme that is used to
generate the foundation SST analyses has been updated. Various publications have described these
changes but a full description is not available in a single place. This technical note focuses on the
production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive
description of the current system configuration. Full article

For years, Light Detection and Ranging (LiDAR) technology has been considered as a challenge when it comes to developing efficient software to handle the extremely large volumes of data this surveying method is able to collect. In contexts such as this, big data technologies have been providing powerful solutions for distributed storage and computing. In this work, a big data approach on geospatial processing for massive aerial LiDAR point clouds is presented. By using Cassandra and Spark, our proposal is intended to support the execution of any kind of heavy time-consuming process; nonetheless, as an initial case of study, we have focused on fast ground-only rasters obtention to generate digital terrain models (DTMs) from massive LiDAR datasets. Filtered clouds obtained from the isolated processing of adjacent zones may exhibit errors located on the boundaries of the zones in the form of misclassified points. Usually, this type of error is corrected through manual or semi-automatic procedures. In this work, we also present an automated strategy for correcting errors of this type, improving the quality of the classification process and the DTMs obtained while minimizing user intervention. The autonomous nature of all computing stages, along with the low processing times achieved, opens the possibility of considering the system as a highly scalable service-oriented solution for on-demand DTM generation or any other geospatial process. Said solution would be a highly useful and unique service for many users in the LiDAR field, and one which could get near to real-time processing with appropriate computational resources. Full article

Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO₂ concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO₂ dry air mole fraction (XCO₂) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO₂ changes during growing season for 13 years. We derived a relationship between XCO₂ and surface net CO₂ fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO₂ flux enhancement based on satellite observed XCO₂ changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO₂ emissions, we estimated an enhanced XCO₂ drawdown during the GP of −0.070 to −0.084 ppm yr⁻¹. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO₂ to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord. Full article

Among all the forensic applications in which it has become an important exploration tool, ground penetrating radar (GPR) methodology is being increasingly adopted for buried landmine localisation, a framework in which it is expected to improve the operations efficiency, given the high resolution imaging capability and the possibility of detecting both metallic and non-metallic landmines. In this context, this study presents landmine detection equipment based on multi-polarisation: a ground coupled GPR platform, which ensures suitable penetration/resolution performance without affecting the safety of surveys, thanks to the inclusion of a flexible ballistic shielding for supporting eventual blasts. The experimental results have shown that not only can the blanket absorb blast-induced flying fragments impacts, but that it also allows for the acquisition of data with the accuracy required to generate a correct 3D reconstruction of the subsurface. The produced GPR volume is then processed through an automated learning scheme based on a Convolutional Neural Network (CNN) capable of detecting buried objects with a high degree of accuracy. Full article

As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance R_rs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as R_rs (λ), transparency, water colour, water temperature, and turbidity. Full article

On 20 April 2013, a moment magnitude (Mw) 6.6 earthquake occurred in the Lushan region of southwestern China and caused more than 190 fatalities. In this study, we use geodetic data from nearly 30 continuously operating global positioning system (GPS) stations, two periods of leveling data, and interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation of the Lushan earthquake. By using the Helmert variance component estimation method, a joint inversion is performed to estimate source parameters by using these GPS, leveling, and InSAR data sets. The results indicate that the 2013 Lushan earthquake occurred on a blind thrust fault. The event was dominated by thrust faulting with a minor left-lateral strike–slip component. The dip angle of the seismogenic fault was approximately 45.0°, and the fault strike was 208°, which is similar to the strike of the southern Longmenshan fault. Our finite fault model reveals that the peak slip of 0.71 m occurred at a depth of ~12 km, with substantial slip at depths of 6–20 km. The estimated magnitude was approximately Mw 6.6, consistent with seismological results. Furthermore, the calculated static Coulomb stress changes indicate that the 2013 Lushan earthquake may have been statically triggered by the 2008 Wenchuan earthquake. Full article

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan. Full article

Cloud screening in satellite imagery is essential for enabling retrievals of atmospheric and surface properties. For climate data record (CDR) generation, cloud screening must be balanced, so both false cloud-free and false cloudy retrievals are minimized. Many methods used in recent CDRs show signs of clear-conservative cloud screening leading to overestimated cloudiness. This study presents a new cloud screening approach for Advanced Very-High-Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery based on the Bayesian discrimination theory. The method is trained on high-quality cloud observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The method delivers results designed for optimally balanced cloud screening expressed as cloud probabilities together with information on for which clouds (minimum cloud optical thickness) the probabilities are valid. Cloud screening characteristics over 28 different Earth surface categories were estimated. Using independent CALIOP observations (including all observed clouds) in 2010 for validation, the total global hit rates for AVHRR data and the SEVIRI full disk were 82% and 85%, respectively. High-latitude oceans had the best performance, with a hit rate of approximately 93%. The results were compared to the CM SAF cLoud, Albedo, and surface RAdiation dataset from AVHRR data–second edition (CLARA-A2) CDR and showed general improvements over most global regions. Notably, the Kuipers’ Skill Score improved, verifying a more balanced cloud screening. The new method will be used to prepare the new CLARA-A3 and CLAAS-3 (CLoud property dAtAset using SEVIRI, Edition 3) CDRs in the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. Full article

Tree spatial distribution patterns such as random, regular, and clustered play a crucial role in numerical simulations of carbon and water cycles and energy exchanges between forest ecosystems and the atmosphere. An efficient approach is needed to characterize tree spatial distribution patterns quantitatively. This study aims to employ increasingly available aerial laser scanning (ALS) data to capture individual tree locations and further characterize their spatial distribution patterns at the landscape or regional levels. First, we use the pair correlation function to identify the categories (i.e., random, regular, and clustered) of tree spatial distribution patterns, and then determine the unknown parameters of statistical models used for approximating each tree spatial distribution pattern using ALS-based metrics. After applying the proposed method in both natural and urban forest sites, our results show that ALS-based tree crown radii can capture 58 %- 77 % (p < 0.001) variations of visual-based measurements depending on forest types and densities. The root mean squared errors (RMSEs) of ALS-based tree locations increase from 1.46 m to 2.51 m as the forest densities increasing. The Poisson, soft-core, and hybrid-Gibbs point processes are determined as the optimal models to approximate random, regular, and clustered tree spatial distribution patterns, respectively. This work provides a solid foundation for improving the simulation accuracy of forest canopy bidirectional reflectance distribution function (BRDF) and further obtain a better understanding of the processes of carbon and water cycles of forest ecosystems. Full article

The temporal and spatial variation in submesoscale eddies in the coastal region of Lianyungang (China) is studied over a period of nearly two years with high-resolution (0.03°, about 3 km) observations of surface currents derived from high-frequency coastal radars (HFRs). The centers and boundaries of submesoscale eddies are identified based on a vector geometry (VG) method. A color index (CI) representing MODIS ocean color patterns with a resolution of 500 m is used to compute CI gradient parameters, from which submesoscale features are extracted using a modified eddy-extraction approach. The results show that surface currents derived from HFRs and the CI-derived gradient parameters have the ability to capture submesoscale processes (SPs). The typical radius of an eddy in this region is 2–4 km. Although no significant difference in eddy properties is observed between the HFR-derived current fields and CI-derived gradient parameters, the CI-derived gradient parameters show more detailed eddy structures due to a higher resolution. In general, the HFR-derived current fields capture the eddy form, evolution and dissipation. Meanwhile, the CI-derived gradient parameters show more SPs and fill a gap left by the HFR-derived currents. This study shows that the HFR and CI products have the ability to detect SPs in the ocean and contribute to SP analyses. Full article

Landscape processes fluctuate over time, influencing the intra-annual dynamics of ecosystem services. However, current ecosystem service assessments generally do not account for such changes. This study argues that information on the dynamics of ecosystem services is essential for understanding and monitoring the impact of land management. We studied two regulating ecosystem services (i. erosion prevention, ii. regulation of water flows) and two provisioning services (iii. provision of forage, iv. biomass for essential oil production) in thicket vegetation and agricultural fields in the Baviaanskloof, South Africa. Using models based on Sentinel-2 data, calibrated with field measurements, we estimated the monthly supply of ecosystem services and assessed their intra-annual variability within vegetation cover types. We illustrated how the dynamic supply of ecosystem services related to temporal variations in their demand. We also found large spatial variability of the ecosystem service supply within a single vegetation cover type. In contrast to thicket vegetation, agricultural land showed larger temporal and spatial variability in the ecosystem service supply due to the effect of more intensive management. Knowledge of intra-annual dynamics is essential to jointly assess the temporal variation of supply and demand throughout the year to evaluate if the provision of ecosystem services occurs when most needed. Full article

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region. Full article

In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire’s effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management. Full article

To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation. Full article

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities. Full article