The destruction created by cyclones depends upon their intensity. Ocean wind is the crucial parameter to understand and forecast the intensity of cyclones. During cyclones, due to dynamic ocean conditions and the limited data availability, a neural network with the regularization approach is used to retrieve cyclonic wind (CW) speed. Several optimization algorithms are compared to get a robust neural

The Earth’s surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly

Urbanization at the expense of the natural environment has been increasing in Turkey in recent years. The toll it takes on the ecosystem has an adverse impact on local weather systems and natural resources. These rapid ecosystem changes are mostly observed in the western parts of Turkey. For a reliable prediction of sustainable development planning, the normalized difference vegetation index (NDVI)

Cloud thermodynamic phase (CTP) classification is a vital piece of information for determining how clouds affect climate and electromagnetic wave propagation. We describe a passive shortwave infrared (SWIR) three-channel polarimeter for CTP remote sensing. This instrument adds polarimetric sensitivity to the traditional radiance ratio method that capitalizes on the difference in spectral absorption

In recent years, many target detection methods for hyperspectral images based on sparse representation (SR) theory have been proposed and achieved good results. However, these methods still have some deficiencies. Specifically, these methods usually give the same weight to the pixels in the neighborhood when they use the spatial information, and obtain the spectral average features to replace the spectral

Because of its high altitude, the Qinghai-Tibetan Plateau (QTP) serves as a de facto ecological barrier between China and Southeast Asia. Studying the phenological characteristics of vegetation on QTP and their response to climate change can help us to understand how climate change can impact on highland terrestrial ecosystems and how to improve predicting highland vegetation phenology. Using long-term

Terrestrial laser scanning technology has developed rapidly, and substantial data have been accumulated in dynamic forest monitoring. Point cloud data of standing trees not only provide tree parameters but also show three-dimensional tree structure. Selection of the key parameters from the point cloud data is a prerequisite for volume estimation of standing trees. Our study collected three phases of

A wide variety of swarm intelligence algorithm-based approaches have been recently developed for selecting the near-optimal bands from hyperspectral images (HSIs). However, such methods [including the nondominated sorting genetic algorithm (NSGA)] for HSIs pixel classification are limited by the lack of effective initialization and directional evolution. This research proposed a successive projections

Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are

Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net

In the remote sensing image processing field, cloud and snow detection for high-resolution sensors and cloud and snow morphology in different latitudes of the world is challenging. A deep learning training model (Softmax) was developed to improve the accuracy of cloud and snow identification from Gaofen-1 and Pakistan Remote Sensing Satellite-1 images. First, more than 1800 scenes remote sensing images

We report results of lidar studies of the atmospheric aerosols present above the city of Sofia during four episodes of wintertime Saharan dust intrusions in the last decade. The optical and microphysical properties of the detected aerosols, as well as the aerosol layering and dynamics, are retrieved and characterized. By combining lidar results with in situ PM10 concentration measurement data and air-transport

Livestock in the Amazon is mainly limited by the lack of forage biomass production in the dry season. The sensitivity of pasture to drought depends on pasture management and on certain natural factors that enable the grass cover to stay green for longer under low rainfall. The sustainability of livestock production systems depends on identifying these natural factors to make better use of their forage

This erratum clarifies issues with several figures in the paper.

In the final feature map obtained using a convolutional neural network for remote sensing image segmentation, there are great differences between the feature values of the pixels near the edge of the block and those inside the block; ensuring consistency between these feature values is the key to improving the accuracy of segmentation results. The proposed model uses an edge feature branch and a semantic

Crop segregation in optical remote sensing cannot always be easily achieved among similar species since their spectral response can be similar. We aimed to distinguish fields of two C4 gramineae with similar plant structure and crop calendar, maize (Zea mays) and sorghum (Sorghum bicolor), in an agricultural area of the state of Guanajuato, in central México. One hundred and forty-three fields of these

The semantic segmentation of optical satellite remote sensing images is more challenging than that of natural images, owing to the considerable differences in the texture, shape, topology, and scale of ground features in different areas and the coexistence of dense and sparse arrangements. To alleviate these difficulties and ensure a high accuracy, a semantic segmentation framework named the self-attention

Hyperspectral images (HSIs) contain rich spectral information and spatial information. How to apply these two information types and fully combine the correlation between them remains a challenge worthy of further research and discussion. In this study, a multi-branch-multi-scale residual fusion network (MB-MS-RFN) for HSI classification is proposed. First, a 3D multi-branch-multi-scale convolution

For their high maneuverability, unmanned aerial vehicles (UAVs) are widely used in object detection, including the detection of ships. However, object detection in aerial images taken by UAV remains a challenge due to the arbitrary shooting perspectives and small proportion of targets. Existing anchor-based detectors, whose performance could be easily affected by the aspect ratios and scales of anchor

Staggered synthetic aperture radar (SAR), which operates with pulse repetition interval (PRI) variation, deals with the blind range problem caused by constant PRI in conventional high-resolution wide-swath SAR imaging. Due to the variation of PRI, there appear periodic loss of pulses and nonuniform sampling in azimuth. However, the exiting reconstruction methods mostly correct range curvature after

Activation functions play increasingly important roles in deep learning. The activation functions give the neural network the ability to learn complex patterns by introducing non-linearity. Complex-valued activation functions for complex-valued neural networks are extended from real-valued activation functions like sigmoid, tanh, rectified linear unit, and exponential linear unit. Recently, the most

We focus on the range migration (RM) and Doppler frequency migration (DFM) corrections in the long-time coherent integration, and a fast detection method based on two-dimensional trilinear autocorrelation function is proposed for the maneuvering target with jerk motion. This proposed method can integrate the echoes’ energy into peaks in a three-dimensional parameter space coherently and estimate the

Coastal monitoring is an essential feature for the sustainable management of naturally vulnerable areas; however, data acquisition is a tedious task. We aim to identify an efficient method of automatic shoreline monitoring based on high water level detection using very high-resolution Pleiades images and taking as the pilot zone the Mostaganem coastline (Algeria). Through a comparative study between

Deep learning opened new possibilities for remote sensing image analysis using multiple neural nets layers. We introduce a hybrid pixel-based model that allows improving the unsupervised training with stacked autoencoders (SAE) by inserting convolutional neural networks (CNN) in the encoding and decoding steps. Inclusion of the convolution in the encoding and decoding steps allows a feature-based description

The two-dimensional (2D) resolution is poor due to the narrow transmitting bandwidth and the limited observation angle in monostatic ISAR imaging. A multiradar fusion imaging method based on fast linearized Bregman iteration (FLBI) algorithm is proposed to improve the 2D resolution of the ISAR imaging. First, the sparsity of the ISAR imaging echo data is exploited to establish the multiradar fusion

Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote

Vehicle detection is an important method for understanding high-resolution remote sensing images. Deep convolutional neural network (DCNN)-based methods have improved many computer vision tasks and have achieved state-of-the-art results in many object detection datasets. Object detection of remote sensing images has been radically changed by the introduction of DCNN. Considering correlation between

Deramp space-time adaptive processing (Deramp-STAP) algorithm has been validated in its effectiveness in reducing the required channels to enable ground moving target indication (GMTI) for multi-channel synthetic aperture radar (SAR) systems. However, the parameter estimation for the moving target, including the critical radial velocity, is not accurate enough, and the radial velocity is always approximated

Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have

A time-series filtering algorithm named the best slope temporal segmentation filtering (BSTS) algorithm was developed to improve the estimation accuracy of above-ground biomass (AGB) of Pinus densata Mast. In this algorithm, the best slope was used to interrupt the time series. Following interruptions, linear regression was employed to simulate each segment of time series. By splicing the time-series

Sparsity and low rankness are essential properties of real-world data. The sparsity attribute mentions that the data contain very few non-zero elements. On the other hand, the low-rank property characterizes redundancy in the data. The concept of sparsity and low rankness is central to numerous signal processing tasks such as signal inversion, data compression, and noise removal. With the advent of

A technique that utilizes quasi-deep convective clouds (qDCC) for the calibration assessment of the thermal emissive bands (TEB) on remote sensing instruments has been proven viable. The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi-National Polar-orbiting Partnership (S-NPP) and NOAA-20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) TEB calibration uses a

Surface subsidence and its secondary effects caused by underground coal mining continually threaten the safety and property of residents in mining areas. The commonly used total station and leveling approach is time-consuming and laborious, and the spatial range is limited. In this study, an FEIMA D2000 quadrotor unmanned aerial vehicle (UAV) was used to conduct 4-cm / pixel photogrammetry in four

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid

Wetlands for the entire state of Pennsylvania (119 , 282 km2) were mapped using object-based image analysis (OBIA). The OBIA combined vector output from logistic statistical models informed by hydrogeologic variables (e.g., hydrology facets, soils, and climate) with LiDAR-derived terrain variables, high-resolution aerial orthoimagery, and land-cover data through a data-fusion approach. Wetland presence

In the field of offshore oil slicks at the sea surface, radar or optical imagery can provide much useful information. Regarding optical imagery, detection relies on the differences between the reflectance of water alone and oil-covered water. As soon as the thickness of the oil layer is large enough to induce a spectral difference between the reflectance of water and oil-covered water, spectral indices

Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets

Undaria pinnatifida (Harvey) Suringar, native to north-western Asia, is a prolific invasive seaweed species that has established across much of New Zealand, competing and co-existing with native seaweed species. Remote sensing could be used to map both invasive and native seaweeds in New Zealand. The aim of this study is to evaluate the hyperspectral differences (and the wavelengths at which they differ)

The efficient and rapid extraction of information about the growth period of the Zanthoxylum bungeanum maxim is one of the prerequisites for analyzing and mastering its growth trend. The 5-cm high-resolution visible-light red, green, blue (RGB) image of the Zanthoxylum bungeanum growing period is obtained based on the low-altitude aerial photography of a quad-rotor unmanned aerial vehicle (UAV) platform

Control of orbital angular momentum (OAM) offers the potential for increases in control and sensitivity for high-performance microwave systems. EM waves with properties dependent on spatial distribution are said to be “structured.” Control of OAM in microwave systems is an example of a wave structure that exploits EM degrees of freedom, which most conventional systems do not use. OAM is characterized

The global interaction of CO2 flux was highly dynamic under seasonal and inter-annual variability. Thus, precise estimation of gross primary productivity (GPP) and net photosynthesis (PsnNet) under seasonal variations was important to understand the global carbon cycle and ecosystem response to climate variability. Considering this significance, in our paper, we have conducted a comparative investigation

Terra and Aqua MODIS have successfully operated for more than 20 and 18 years, respectively, and far exceeded their designed lifetimes of 6 years. MODIS has 36 spectral bands, among which 16 are the thermal emissive bands (TEB) covering a wavelength range from 3.75 to 14.24 μm. Observations from the MODIS TEB have been used to generate a number of data products, such as surface/cloud/atmospheric temperatures

Detecting and monitoring surface water has received much attention in recent decades. Surface water is one of the most critical water resources for both human and ecological systems. Remote sensing technology has made it possible to have accurate and frequent updates of surface water. We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR

Windthrows induced by strong winds pose a major threat to both transport infrastructure and road users. Therefore, an exposure analysis of trees along the federal trunk road network was carried out and exemplarily applied for the federal state of North Rhine-Westphalia, Germany. The aim of this project was the development of a GIS-based method on the basis of freely accessible high-resolution airborne

The Orbita hyperspectral satellite (OHS) is the first commercial hyperspectral satellite in China that completed launching and networking. It can collect world-class hyperspectral data and obtain aerial hyperspectral imagery with 32 bands covering the spectrum range from 400 to 1000 nm at a 10-m resolution, which are of great significance for the quantitative analyses of remote sensing and refined

Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative

Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery

Urban agglomerations (UA) are the fastest growing regional types during recent years, especially in developing countries. Monitoring and evaluating the eco-environment quality of UA is significant for sustainability. The previous remote sensing models of urban eco-environment are generally based on the vegetation-impervious surface-soil framework, which neglects the air quality in urban areas. We constructed

Stellar map denoising and centroid positioning, which directly determine the postpositioning accuracy of star trackers, are key technologies in stellar map processing. Due to the influence of a complex starry sky background, there is often a large amount of noise in stellar maps, which makes it difficult to accurately locate the stellar centroid. A stellar map processing method based on dark channel

The lack of data on water consumption by agriculture in the context of climate change led the authors to estimate the water balance of the Bekaa Valley. Plots were mapped according to the normalized difference vegetation index profiles of spring and summer crop with the Sentinels-2 images. Surface energy balance model python (PYSEBAL) estimated the seasonal evapotranspiration (ETseason) and net irrigation

Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aerial systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of our research are to (1) evaluate three supervised

Surface roughness is a crucial parameter for the estimation of soil moisture (SM). The present study attempted to optimize the surface roughness parameter (h) for the estimation of SM from Soil Moisture Active Passive (SMAP) using tau–omega (τ-ω) model and also downscaled the estimated SM product using a polynomial regression relation among Normalized Difference Vegetation Index (NDVI), land surface

We propose the convolutional neural networks approach for detecting urban slums from very high resolution (VHR) satellite images. Slums are becoming an inevitable and growing phenomenon in cities of the global south, whose locations are inappropriate in official statistics and maps. Thus the automatic detection and identification of slums provides vibrant information to decision-makers for formulating

Mapping the refined land-cover classification mapping (RLCM) is a primary and essential strategy for evaluating the ecological change and understanding the ecosystem services. A common problem during the generation of RLCM is a scale mismatch between remote sensing (RS) data and field quadrat, which leads to inaccuracy of the classification result. A multi-scale transformation method was developed

Deforestation and forest degradation are two important contributors to atmospheric emissions of carbon. To claim carbon credit for conservation plus enhancement of existing forest carbon stock under reducing emissions from deforestation and forest degradation (REDD+) program, a post-Kyoto climate change mitigation initiative, developing countries need to assess the level of respective forest biomass-carbon

The coffee crop spectral behavior identification throughout its cycle can contribute to its development monitoring under pest incidence. We aim to identify coffee development through time signatures of enhanced vegetation index (EVI), as well as to evaluate the use of seasonal autoregressive integrated moving average (SARIMA) models to identify coffee trees spectrum-time patterns under different irrigation

Road extraction as a significant role in traffic management, road monitoring, and autodriving cars have been important research topics in recent years. A deep learning method based on U-net and spatially squeeze and exciting channelwise (SE) is proposed for recognizing road. The SE attention block reweights feature maps from U-net layers and highlights only useful channels. We added this block to U-net

Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years’ crop maps using previously labeled data. We propose vector dynamic time warping (VDTW)