The challenge of joint detection and tracking of multiple extended targets (ETs) arises in many radar applications, especially for extended stealth targets (ESTs) with small signal-to-noise ratio (SNR). Recently, the multi-Bernoulli (MB) filter-based random matrix model (RMM) has been proposed for tracking ellipsoidal ETs. The MB-RMM filter depends on the measurements (position and range rate), which

An advanced algorithm is introduced to enhance the efficiency in measuring vegetation parameters using L-band polarimetric synthetic aperture radar interferometry data. In this method, the combination of the eigenvalue decomposition technique and the coherence matrix optimization method is to achieve higher accuracy for ground phase estimation. Then an exhaustive search method based on the optimal

The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional

In sea clutter background, spaceborne or airborne surveillance radar with a high-speed moving platform is confronted with the problem of clutter spreading. This poses a considerable challenge for maritime radar systems when detecting slowly moving targets of low signal-to-noise ratio. However, the space-time decorrelation characteristics caused by the internal motions of sea clutter under different

The quantification of mineral resources refers to the fractional contribution of endmembers at the pixel level, namely, fraction cover mapping of mineralogy. Over a large area, the mineral deposit occurs generally in a limited number either on a host rock or any geologic structure. In remote sensing, the purity of mineral’s spectra is usually perturbed either because of the weathering effect or the

Imaging, feature extraction, and recognition of targets with micromotion by retrieving their three-dimensional micromotion trajectories (3-D MMTs) have attracted a lot of interest in recent years. We propose a method for retrieving the 3-D MMT of a rotating target based on the interferometric phases obtained using a wideband interferometric radar system with three antennas positioned in L-shape. First

Recently, a C-band dual-polarization Doppler weather radar (C-DWR) was installed at Thumba (8.537°N, 76.865°E), a coastal station in the southern peninsula of India known as “Gateway of Indian Summer Monsoon.” The C-DWR operates in the frequency range of 5.6 to 5.65 GHz with a peak transmitting power of 250 kW at 0.004 duty ratio and employs the state-of-the-art technology for both transmitting and

The use of remote sensing data has become very useful to generate statistical information about society and its environment. In this sense, land use and land cover classification (LULC) are tasks related to determining the cover on the Earth’s surface. In the decision-making process, this kind of information is relevant to handle in the best way how the information about events such as earthquakes

Hyperspectral classification is a widely discussed problem in the remote sensing field. Many researchers have reported good results of hyperspectral classification. However, when applied to the real world, the strong demand for labeled data for hyperspectral classification will be a big obstacle. To address this problem, researchers have explored few-shot learning and semisupervised methods in a variety

Crop monitoring using synthetic aperture radar requires an understanding of how dynamic crop features influence radar response. We use crop parameters from the decision support system for agrotechnology transfer (DSSAT) model, a dynamic crop growth model, as inputs to the Michigan microwave canopy scattering (MIMICS) model, a radiometric model, to simulate radar scattering from selected wheat, rice

This study analyzes the response changes of the Medium Resolution Spectral Imager II (MERSI-II), which is an optical instrument on the Fengyun-3D satellite, during different periods since liftoff. The study was based on the Dunhuang radiometric calibration site. The solar reflective channels, assessed using the reflectance-based method, showed a degradation of MERSI-II. The degradation rate of change

Signal parameter estimation is a common process in the sphere of radar signal processing. For the widely used chirp signal, the estimation of pulse width is essential in applications of signal reconnaissance, passive radar, anti-jamming, and others. An efficient pulse width estimation method for multi-component chirp signal is proposed. Different from the conventional pulse width estimation methods

The short-wave infrared (SWIR) bands (5 to 7, 26) of Terra Moderate Resolution Imaging Spectroradiometer (MODIS), which are colocated with the midwave infrared bands (20 to 25) on the short- and mid-wave infrared focal plane assembly, have a known issue related to 5.3-μm out-of-band (OOB) thermal leak and electronic crosstalk that was identified prelaunch. As a result, a crosstalk correction algorithm

Surface subsidence caused by mining is difficult to monitor. Traditional field measurements are time-consuming and labor intensive. Small baseline subset (SBAS) technique is a long-term sequence method based on the principles of radar interferometry that can be used to effectively survey small ground surface deformations. This technique has been successfully applied to urban subsidence in previous

A well-performed difference map is very important for the change detection of remote sensing images. However, due to the influence of the lighting conditions and the change of the sensor, the difference maps often have low contrast between changed and unchanged pixels, which makes it difficult for subsequent cluster analysis. A coupled distance metric learning (CDML) model is proposed to solve the

In consideration of within-class endmember variability, it is realistic to use multiple endmembers to model a pure class. We propose an advanced multi-endmember unmixing algorithm based on twin support vector machines (UTSVM), which derives the abundances based on the distances from the mixed pixels to each classification hyperplane. Unmixing uncertainty, an issue often neglected in multi-endmember

Our work aimed to evaluate the use of visible–near-infrared (Vis–NIR) spectroscopy for predicting the production of leaf dry mass (LDM), as well as macro- and micronutrients contents of soybean leaves grown after application of limestone-mining coproducts. The treatments were arranged within a triple factorial scheme (6 × 2 × 2 + 2) and placed into pots in a greenhouse. We evaluated the following factors:

Hyperspectral image (HSI) analysis is a growing area in the community of remote sensing, particularly with images exhibiting high spatial and spectral resolutions. Multiple kernel learning (MKL) has been proposed and found to classify HSIs efficiently owing to its capability for handling diverse feature fusion. However, constructing base kernels, selecting key kernels, and adjusting their contributions

Infrared (IR) sounders, onboard satellites in low Earth orbit, are high spectral resolution interferometers capable of providing very accurate IR radiance measurements using thousands of channels. Though their temporal resolution is a relatively low 12 h, these data can be used to produce high vertical resolution temperature and humidity profiles. Imagers, often aboard satellites in geostationary Earth

This erratum corrects an error in the description of a span of years.

This erratum explains a correction to a figure.

As one of the most threatening invasive alien species to mangroves in China, Spartina alterniflora (S. alterniflora) has broadly existed along the Chinese tropical and subtropical coasts. Monitoring S. alterniflora with remote sensing is urgent and requisite for scientific invasive plant control and management. However, given the spectral similarity between S. alterniflora and other wetland types,

We recapitulate the approaches of sensible heat flux (H) estimation, which is a critical parameter in the remote sensing (RS)-based evapotranspiration (ET) models. We propose a classification scheme for the ET models considering their distinctions in approaches for the estimation of H. Adhering to the proposed classification scheme, the theoretical backgrounds of H estimation in the single-source and

The first VIIRS instrument is aboard the Suomi National Polar-orbiting Partnership satellite. The instrument has 14 reflective solar bands (RSBs) to passively collect photons reflected from the Earth surface in the design wavelengths from 412 to 2250 nm. The instrument uses a solar diffuser (SD) to radiometrically calibrate its RSBs. When lit by the Sun through an attenuation screen (the SD screen)

In part I (Lei et al., J. Appl. Remote Sens., Vol. 14, Issue 4), we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance

Modern synthetic aperture radars (SAR), e.g., space SARs for remote sensing of the Earth, use signals with linear frequency modulation and signals with phase-code shift keying (PCSK) coded by M-sequence (MS) as probing signals. Utilization of PCSK-signals permits an essential improvement of the radar image quality at the stage of its compression on azimuthal coordinate. Polyphase (p-phase where p is

Tropospheric signals are considered as one of the most important performance limitations to compute the deformations caused by earthquake, subsidence, volcano, and so on using interferometric synthetic aperture radar (InSAR) technique. Various correction methods have been proposed to reduce the effect of these signals in displacement fields in previous research works. Different types of correction

Nearshore bars are important coastal features that influence beach–dune dynamics and protect the coast from high-energy wave events. Historically, in situ methods were used to study beach–dune dynamics and nearshore bars. Video monitoring systems, such as Argus, have been the favored remote sensing approach for studying nearshore bars dynamics and evolution. These systems are costly to install and

Hyperspectral unmixing is one of the most important procedures for remote sensing image processing. The multilayer non-negative matrix factorization (MLNMF)-based method has been widely used for hyperspectral unmixing due to its good performance for highly mixed data with multiple-decomposition structure. However, few works consider the spatial information in the image, which may enhance the performance

Hyperspectral unmixing is an important task in the analyses and applications of hyperspectral images. Recently, the autoencoder network has been intensively studied to unmix hyperspectral image, recovering the material signatures and their corresponding abundance maps from the hyperspectral pixels. However, the autoencoder network cannot get a unique solution since the loss function is nonconvex. In

Accurate retrieval of top of the atmosphere (TOA) radiance is a sustained research work in ocean color remote sensing. Spectral shapes of sensor input and sensor spectral response (SRF) function may significantly affect the achievable accuracy. A model is presented here to quantify the effects of spectral shapes. We also proposed an innovative method to retrieve the TOA spectral radiance that accounts

Light detection and ranging (Lidar) is considered the most advanced technology to assess forest aboveground biomass (AGB). Currently, this technology is shared by different sensors ranging from ground [terrestrial laser scanning (TLS)], airborne [aerial laser scanning (ALS)] up to spaceborne ones, which entail different spatial scales. However, few studies tested the simultaneous and combined use of

This guest editorial introduces the Special Section on Instrument Calibration and Product Validation of GOES-R.

Extending the temperature measurement range and sustaining the system performance are important requirements in Rayleigh lidars. While replacing hardware and increasing the power aperture product are ways of achieving these objectives, the data analysis approach is another means for achieving the same. A method to retrieve atmospheric temperatures using the penalized maximum likelihood method after

Evapotranspiration (ET) is a critical component of the water cycle on a global scale. Changes in leaf area index (LAI) influence ET changes. Satellite products are among the effective methods of estimating ET and LAI at local, regional, and global scales. Therefore, the purpose of our study was to evaluate three global remote sensing products of ET and two products of LAI and investigate their simultaneous

A synthetic aperture radar (SAR) target classification method is proposed by selecting deep features learned by convolutional neural network (CNN) using the nonlinear correlation information entropy (NCIE). The deep feature maps are learned from original SAR images by CNN. Considering the possible indiscrimination and redundancy, all the deep feature maps from different layers are analyzed by NCIE

The Advanced Baseline Imager (ABI) is a passive imaging radiometer on-board National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellites-R (GOES-R) series. Its bands 7 to 16 are categorized as infrared (IR) bands, sampling within a spectral range of 3.9 to 13.3 μm in mid-wave infrared (MWIR) and long-wave infrared (LWIR) regions. ABI provides variable

Artificial illumination identification within images is a useful tool for many applications. Performing such identification allows for an estimation of the illumination source spectrum, which in turn can be used for additional applications ranging from spectral detection and exploitation to statistics about nighttime light usage. Illumination identification has been performed in laboratory settings

Hyperspectral imaging provides a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, processing hyperspectral images (HSIs) is usually computationally expensive due to the great amount of both spatial and spectral data they incorporate. We present a low-cost convolutional neural network designed for HSI classification. Its architecture

We evaluated the opportunities and performance of a new type of systematic pixel sharp calibration site for airborne hyperspectral mineral mapping in the environment of an operational copper deposit in Mongolia. The calibration site was designed to be used for estimation of sensitivity and quantification of key minerals in individual pixels in specific geological scenarios. The layout of the calibration

Morphological and triangular irregular network (TIN) ground filters require setting up different parameters to achieve high accuracy for different terrains. A proposed morphologically iterative TIN (MIT) ground filter only requires maximum building size in the processing of raw light detection and ranging (LiDAR) data. This approach applies morphological and TIN densification in an iterative way for

With the development of remote sensing technology, several spatial sources for measuring precipitation have become available: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data are of particular interest. The CHIRPS product was used, evaluated, and compared within the Tensift basin located in west-central Morocco, for the period 1990 to 2017, using 11 meteorological stations.

Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the

Drawing a spectrally homogeneous region of interest in a remotely sensed image is a common task for an image analyst when performing, for instance, atmospheric correction or end-member selection. Manually selecting a homogeneous sample of pixels can be tedious and error prone due to the limits of human perception and data visualization. I present a region shrinkage method that automates the extraction

The surveillance of large areas to ensure local security requires remote sensors with high temporal and spatial resolution. Captive balloons with infrared and visible sensors, like ALTAVE captive balloon system, can perform a long-term day–night surveillance and provide security of large areas by monitoring people and vehicles, but it is an exhaustive task for a human. In order to provide a more efficient

Evapotranspiration (ET) quantification improves the comprehension of the water, heat, and carbon interactions and the feedback to the climate, which is essential for global change research. We aimed to model ET using artificial neural networks (ANNs) based on Landsat-8 and reanalysis data from the National Centers for Environmental Prediction over the grasslands of the Pampa biome. The output variable

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion.

Ordinary multispectral (MS) image change detection (CD) techniques have not excavated the inherent information of MS images. Deep learning methods are a type of effective method that grabs the changed and unchanged information by deep feature extraction. However, a larger number of labeled samples are difficult to obtain for CD in MS images. An unsupervised MS image CD method is proposed based on a

Water vapor is an important greenhouse gas. The atmospheric water vapor content is monitored by remote methods, which require accurate spectroscopic information about H2O absorption line parameters. The problems of accuracy of accounting the selective water vapor absorption in the simulation of atmospheric transfer of solar radiation are considered in this work. The atmospheric spectra are simulated

The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland

In coastal waters, colored dissolved organic matter (CDOM) is often present in moderate to high concentrations due to input from nearby landmasses and rivers. CDOM absorbs light sharply in the UV-blue region, causing waters to appear yellow or brown. This has a significant effect on optical and biogeochemical processes in coastal waters, as it inhibits the penetration of light within the water. It

The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from (Moderate

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island

Impervious surfaces (ISs) are significant indicators of the overall environmental health of watersheds. Monitoring the IS spatial patterns of watersheds is helpful for improving water management and regional pollution assessment. A random forest regression (RFR) approach was developed to estimate the subpixel IS percentage (ISP) from Chinese GF-5 hyperspectral imagery in 30 m pixels in the Nansi Lake

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been successfully operating on orbit since November 28, 2017. The day–night band (DNB) onboard NOAA-20 VIIRS is a panchromatic channel covering wavelengths from 0.5 to 0.9 μm, capable of observing the Earth scene in visible/near-infrared spectral range at a spatial resolution of 750 m. The DNB operates at low-, mid-, or high-radiometric

We propose a method for detecting multiple scatterers (targets) in the elevation direction for synthetic aperture radar tomography. The proposed method can resolve closely spaced targets through a two-step procedure. In the first step, coarse detection is performed with a successive cancellation scheme in which possible locations of targets are marked. Then, in the second step, by searching in the

LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables

Optical trapezoid model (OPTRAM) as a method has been proposed to retrieve soil moisture from remote sensing data. It is based on the assumption that a trapezoidal shape would be derived from plotting of vegetation index (VI) versus shortwave-infrared transformed reflectance (STR) data assuming a linear relationship between VI and STR. A literature review and the present study indicate that the relationship

The accurate estimation of global chlorophyll-a (Chla) concentration from the large remote sensing data in a timely manner is crucial for supporting various applications. Moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used earth observation data sources, which has the characteristics of global coverage, high spectral resolution, and short revisit period. So the estimation