Sky cover is a unique parameter because its quantification is subject to the perspective of the observer or characteristics of the observing instrumentation. Forecasting sky cover provided a professional challenge to operational meteorologists seeking to offer a refined forecast beyond numerical weather prediction guidance along the path of totality resulting from a solar eclipse traversing North America on August 21, 2017. A routine analysis with which to monitor subtle trends in sky cover and compare sky cover forecasts is also not widely available. This contribution reviews 1-h gridded forecasts of sky cover from the United States National Weather Service (NWS) on the eclipse day and compares them with hourly satellite and surface sky observations for an area of interest over the southeastern United States. An inconsistency between the real-time mesoscale analysis (RTMA) and the NWS National Digital Forecast Database is revealed during the eclipse totality. A satellite-to-satellite comparison of the adjusted average cloud top emissivity over this same area reveals how resolution and algorithm improvements to next-generation satellite imagers may alter the RTMA of total cloud cover in the latest era of Geostationary Operational Environmental Satellites (GOES), starting with the GOES-16 Advanced Baseline Imager.

The use and importance of synthetic aperture radar (SAR) data for flood area mapping studies have been proved beyond the doubts as SAR signals are able to penetrate the thick formation of clouds and are able to receive the reflected signals of surface objects even during extreme weather conditions. At the same time, the accuracies of an SAR image-based flood area mapping model has a direct relationship with the frequency of the source SAR signal, the polarization mode that has been used, and the incidence angle by which the disaster region has been sensed. In addition to this, while evolving SAR image-based flood area mapping models, it is a must to understand the response of the inundated regions of the different geographical regions, as well as the response of the same geographical region during different climatic and seasonal periods as the same object, produces different signatures in varying situations. As of this date, there is no single article that can synchronize such information, which is widely disseminated across various research publications. This article mainly focuses on gathering and reviewing such vital information as well as bringing out the details about the physical basis of the interaction of the radar signal with various water surfaces of different geographical conditions, the response of inundated regions under different frequency, polarization, and incidence angle. Such information is mainly used to understand the difficulties that arise when mapping the inundated regions using the SAR image. In the end, the significant observations of the literature reviews are highlighted, which is very useful for young researchers who are interested in building flood area application models using different sets of SAR data.

Guest editorial introduces the Special Section on Recent Advances in Earth Observation Technologies for Agrometeorology and Agroclimatology

Based on the fifth version MOD09A1 product of NASA LP DAAC (Land Processes Distributed Active Center) from 2001 to 2016, an enhanced vegetation index (EVI) time series dataset in the Qinling Mountains was reconstructed; it was used to extract the plant phenological parameters of the region by employing the maximal slopes of the alteration method and threshold method. The results show that the Whittaker filter method was better than other methods for the reconstruction of shrubland, farmland, and forest in the Qinling Mountains. Based on the reconstructed EVI, the characteristics of vegetation coverage, the start of growth season (SOG), the end of growth season (EOG), and the length of growth season (LOG) in the Qinling Mountains were all analyzed. There was an increasing trend of vegetation coverage in most regions (about 89.93% of the monitored areas) over the Qinling Mountains in the past decades, and the average phenological distribution pattern in the Qinling Mountains was closely related to the local hydrothermal conditions. From the high altitude mountainous areas to the farming areas, the SOG was gradually postponed, the EOG was gradually earlier, and the LOG gradually shortened. Furthermore, the time series variation of SOG, EOG, and LOG from 2001 to 2016 in the Qinling Mountains was also studied. The variation showed that the SOG shifted earlier, which was more prominent in high-altitude areas, while, for some southern and northern foothills altitude below 500 m and a few areas of the eastern Funiu Mountains, the SOG was delayed. The EOG shifted later, which was more apparent in the northern Qinling foothills and mid- to low-altitude areas of the eastern ranges, and the LOG was extended. Finally, the studies of correlation analysis between the plant phenology and a temperature threshold of 10°C showed that global warming was the major factor affecting the phytophenology of the Qinling Mountains, and the effects were concentrated mainly in the 1000- to 2000-m zones of the southern and northern Qinling Mountains. Moreover, the northern Qinling foothills showed a greater response to accumulated temperature than the southern foothills, and the low-altitude area of the eastern Funiu Mountains exhibited the lowest correlation with the accumulated temperature.

Leaf water content (LWC) is an essential constituent of plant leaves that determines vegetation health and its productivity. An accurate and on-time measurement of water content is crucial for planning irrigation, forecasting drought, and predicting woodland fire. The retrieval of LWC from visible to shortwave infrared (VSWIR: 0.4 to 2.5  μm) has been extensively investigated but little has been done in the mid- and thermal-infrared (MIR and TIR: 2.50 to 14.0  μm) windows of electromagnetic spectrum. This study is mainly focused on retrieval of LWC from MIR and TIR, using genetic algorithm (GA) integrated with partial least square regression (PLSR). GA fused with PLSR selects spectral wavebands with high predictive performance, i.e., yields high adjusted-R2 and low root-mean-square error (RMSE). In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R2 of 0.93 and RMSE cross validation equal to 7.1%. This study also demonstrated that MIR is more sensitive to the variation in LWC as compared to TIR. However, the combined use of MIR and TIR spectra enhances the predictive performance in retrieval of LWC. The integration of GA and PLSR not only increases the estimation precision by selecting the most sensitive spectral bands but also helps in identifying the important spectral regions for quantifying water stresses in vegetation. The findings of this study will allow the future space missions (like HyspIRI) to position wavebands at sensitive regions for characterizing vegetation stresses.

This study explored three techniques for estimating the soil salt content from Landsat data. First, the 127 items of in situ measured hyperspectral reflectance data were collected and resampled to the spectral resolution of the reflectance bands of Landsat 5 and Landsat 8, respectively. Second, 12 soil salt indices (SSI) summarized from previous literature were determined based on the simulated Landsat bands. Third, 127 measurement groups with Landsat bands and SSI were randomly divided into training (102) and testing subgroups (25). Three techniques including partial least square regression (PLSR), support vector machine (SVM), and deep learning (DL) were used to establish a soil salinity model using SSI and the simulated Landsat bands as independent variables (IV), respectively. Results indicated that PLSR with SSI performed best for both simulated Landsat 5 and Landsat 8 data. Compared with PLSR, SVM underestimated soil salt content, whereas DL obtained centralized simulations and failed to capture the lower and upper observations. We recommend the PLSR model with SSI as IV to estimate soil salt content because it can identify >66% moderate-to-high-saline soils, which indicates its great potential for soil salt monitoring in arid or semiarid regions.

Mapping and monitoring cropland areas and distributions from remote sensing data could provide early warning information of threats to the global and regional food security. Winter wheat is traditionally the most cultivated food crop in China, and the Huang-Huai-Hai (HHH) plain is an important winter wheat production base. Due to a long latitudinal distance, the same winter wheat growth stage delays from the north to the south of the plain. Influenced by the monsoon climate, isolines of the winter wheat phenology are oriented in a northeast–southwest direction, which is similar to that of the temperature distribution. In this paper, Moderate Resolution Imaging Spectroradiometer-enhanced vegetation index (EVI) was applied to estimate the winter wheat planting information on the HHH plain, using a model built according to seasonal change of the winter wheat EVI. The result shows that the average accuracy of the estimation was 75.4% with a standard deviation of 26.1%, when the impacts from the phenology delay and the monsoon climate were not considered. When winter wheat phenology delay was considered with and without the influences from the monsoon climate, the accuracy was 93.2% with a standard deviation of 6.1% and 84.7% with a standard deviation of 11.0%, respectively. The accuracy increased evidently. Therefore, both the phenology delay and monsoon climate impacts should be taken into consideration when estimating the winter wheat planting information in a large monsoon climate region.

Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. Synthetic aperture radar (SAR) backscatter is strongly affected by the volumetric soil moisture content, thus providing the potential to derive spatially distributed soil moisture information. Archives of historic SAR data exist, in which the use is limited by the lack of corresponding ground truth measurements. This study analyzes the potential of using a soil moisture index (SMI) with high spatial resolution to assess the soil moisture status in the absence of ground truth data. The index method is applied to agricultural areas in the catchment of the river Rur in Germany. The SMI was evaluated using antecedent precipitation and the wetting and drying behavior. The spatial patterns of the SMI were analyzed using semivariograms. This study confirms the applicability of a high resolution soil moisture index for monitoring near-surface soil moisture changes, to analyze soil moisture patterns, and indicates the possibility to complement antecedent precipitation as an input to hydrological models.

As an important component of the Earth’s ecosystem, soil moisture plays a vital role in the global water cycle and serves as an important parameter in the study of hydrology, meteorology, and agroecology. Based on the energy balance theory of underlying surface, the atmospheric temperature data recorded by an automatic weather station as well as unmanned aerial vehicle (UAV)-borne thermal infrared and multispectral remote-sensing data were used to establish inversion models of relative soil moisture at different depths based on remote-sensing UAV data and date from near-ground quadrats, respectively. Spatial differences and data accuracy verification were then performed using the 2017 spring wheat moisture data as a control. The results showed that: (1) the relative moisture of farmland soil can be effectively estimated using the proposed soil moisture inversion model. In terrestrial ecosystems, the ratio of actual to potential evapotranspiration, which is often used to characterize potential drought levels, is linearly correlated to soil moisture at different depths; (2) during the inversion of farmland soil moisture, the UAV-based observation method is superior to the near-ground quadrat observation method in both efficiency and accuracy. In addition, the relative soil moisture estimation model based on UAV data has a high accuracy, with R2 reaching 0.629, and a root mean square error (RMSE) of <0.100; and (3) the number and size of quadrats are important factors affecting the inversion accuracy. The data collected by the UAVs covered a wide range and had high spatial matching degree at the field scale. Especially, during estimation of the relative moisture of surface soil (0 to 10 and 0 to 20 cm), the linear fitting between the inversion model based on UAV data and the measured value was optimal. The error was minimal (RMSE  <  0.07) and R2 was >0.714, so this method is more suitable for estimating and dynamically monitoring relative soil moisture of farmland at the field scale.

The comparison of anomaly detection capabilities within hyperspectral imagery (HSI) often relies on anecdotal evidence from a small collection of truth-labeled images. This can lead to situations where the performance may not extend to real-world operating conditions. To partially remedy this, we propose an approach for measuring the sensitivity of an algorithm to the characteristics of the anomalies present. This approach, known as Mahalanobis masking, creates incrementally more difficult to detect anomalies by decreasing their Mahalanobis distance from the background of the image. The sensitivity of an algorithm to changes in the observed anomalies extends the performance evaluation beyond the limited available truth-labeled images. We present both qualitative and quantitative approaches to algorithm evaluation. In a case study, the value of exposing algorithms to a wider range of anomalies is displayed highlighting previously undiscovered algorithm instabilities.

Low-altitude unmanned aerial vehicles (UAV) are widely used to acquire aerial photographs, some of which are oblique and have a large angle of view. Precise, automatic registration of such images is a challenge for conventional image processing methods. We present an affine scale-invariant feature transform (ASIFT)-based method that can register UAV oblique images at a subpixel level. First, we used the ASIFT algorithm to collect initial feature points. Positions of the feature points on corresponding local images were then corrected using the weighted least square matching (WLSM) method. Mismatching points were discarded and a local transform model was estimated using the adaptive normalized cross correlation algorithm, which also provides initial parameters for WLSM. Experiments show that sufficient feature points are collected to successfully register, to the subpixel level, UAV and other images with large angle-of-view variations and strong affine distortions. The proposed method improves the matching accuracy of previous UAV image registration methods.

Magnetic anomaly detection (MAD) is a technique applied in searching, localizing, and even tracking (if the target is moving) a ferromagnetic target of interest. Due to the complexity of the ambient magnetic field, almost all of the detection methods need a filter to be a preprocessing procedure. A typical way is passing the measured signal through a fixed frequency band that contains the frequency of the target signal. However, the target signal’s frequency is mostly determined by the movement velocity of the magnetometer and the distance from magnetometer to target. Namely, in different cases, the targets’ frequencies are different. We analyze the target model and represent the target signal with a single scalar variable. Through projecting the three-dimensional space into a two-dimensional plane, we lastly transform the target signal into a superposition of three sinusoids. Based on it, we propose a method to estimate the frequency band adaptively. Furthermore, we present an adaptive filtering method based on wavelet transform and take some simulation tests to prove that the proposed method has better performance compared with traditional filters.

This paper proposed an object-based multiscale method for synthetic aperture radar (SAR) image change detection based on the statistical model. Rather than the pixel-based analysis conducted in the traditional way, the object-based image analysis was employed to take a collection of pixels as the unit of analysis, which reduced small spurious changes and was less strict relative to registration. In addition, a multiscale concept was adopted to exhibit the inherent multiscale characteristics of the target. To achieve object-based, multiscale change detection results, the multidate segmentation was performed on two temporal SAR images and extended to a set of suitable scales. Then, the Edgeworth series expansion was employed to estimate the probability density function, and the Kullback–Leibler divergence was adopted to calculate the distance between pairs of pixel collections. Next, the divergence index maps were divided into changed and unchanged classes to obtain change detection results for each scale. Finally, the subresults were combined to obtain a more accurate detection result. The experimental results obtained using real data demonstrated the effectiveness of the proposed method.

Multinomial logistic regression (MLR) is an effective classifier in spatial–spectral-based hyperspectral image (HSI) classification. However, in some typical scenarios, such as Gaussian regularized MLR (GSMLR) and Laplacian graph regularized MLR (LPMLR), it hits a large   (  cd  )    ×    (  cd  )   linear system during the regressors learning procedure that is unbearable in both space and time complexity (c is the number of classes and d is the length of feature). Even if using middle-sized features, it often runs out of memory. To this end, we propose two exact divide-and-conquer (DC) algorithms, DC-GSMLR and DC-LPMLR, to reduce the computation complexity. Both decompose the regressors learning problem into a series of equivalent smaller subproblems, each of which can be solved in closed form. Unlike the approximation ones available, they provide exact merged solutions instead. With the same accuracy, DC-LPMLR and DC-GSMLR only need to solve c  +  1 and 2 d  ×  d linear systems, respectively, significantly reducing the peak memory usage by almost O  (  c  )   and O  (  c2  /  2  )   times. For time, experiments on two popular HSI datasets indicate considerable speedup ratio as high as one or two orders of magnitude, showing the practicability in real applications.

The quality of multiresolution segmentation directly influences the accuracy of high-resolution remote sensing image classification using object-oriented analysis technology. However, a perfect segmentation scale optimization method has not yet been developed. Using the fact that the optimal segmentation scale of high-resolution remote sensing images is closely related to the complexity of the objects on the image, we propose an approach for calculating the optimal segmentation scale based on the scene complexity of an image. First, we calculate the scene complexity of high-resolution remote sensing images using Watson’s vision model. Then, we analyze the relationship between the image scene complexity and the optimal segmentation scale based on the model calculation. Optimal segmentation scales are found to be related to the scene complexity of high-resolution remote sensing images by an exponential function, allowing direct calculation of the optimal segmentation scale based on the fitted formulas and the image scene complexity. Finally, we propose a multilevel segmentation strategy to increase the object targeting in the optimal segmentation scale. The optimal segmentation scale calculation method proposed here is simple to perform and has a broad range of potential applications.

For hyperspectral images, most traditional semisupervised classification algorithms ignore spatial information. An effective hyperspectral spatial–spectral semisupervised classification algorithm based on teaching–learning-based optimization (S3C-TLBO) is proposed. In the algorithm, two aspects were used to expand the training labeled set: first, the spatial neighborhood samples of the existing labeled samples with high confidence were labeled to expand the labeled training samples. Second, the margin sampling (MS) combined with improved TLBO was exploited to quickly select the unlabeled samples near the classification hyperplanes from the unlabeled sample set. In addition, spatial neighborhood information was incorporated into kernel function to train a spatial–spectral support vector machine, which was used to determine the category of the test sample comprehensively. The image noise points were removed by smoothing the classification result with neighborhood information. Two sets of experiments on two hyperspectral datasets showed that the proposed algorithm can produce greater classification accuracy and reduce noise points by organically using the combinative spatial information. In particular, the S3C-TLBO can produce better classification result in few labeled samples, which is a major difficulty in hyperspectral image classification.

A slope three-layer scattering model (STSM) for retrieving forest height in mountain forest region using L-band polarimetric synthetic aperture radar interferometry (PolInSAR) data is proposed in this paper. The proposed model separates the vertical structure of forest into three layers: canopy, tree trunk, and ground layer, which account for the effect of topography for forest height calculation in a sloping forest area. Compared to the conventional two-layer random volume over ground model, the STSM improves substantial for modeling of actual mountain forest, allowing better understanding of microwave scattering process in sloping forest area. The STSM not only enables the accuracy improvement of the forest height estimation in sloping forest area but also provides the potential to isolate more accurately the direct scattering, double-bounce ground trunk interaction, and volume contribution, which usually cannot be achieved in the previous forest height estimation methods. The STSM performance is evaluated with simulated data from PolSARProSim software and ALOS/PALSAR L-band spaceborne PolInSAR data over the Kalimantan areas, Indonesia. The experimental results indicate that forest height could be effectively extracted by the proposed STSM.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of the maneuvering target with small-angle measurements is expected, which can be achieved by using the radar operating at a higher central frequency. However, because the defocus induced by the spatially variant phase error becomes significant with the resolution improvement and the central frequency increasing, the global phase correction is invalid. Moreover, due to the scattering characteristics of aspect-dependent scatterers and small-angle measurements, we can rarely obtain the rough outline of the maneuvering target. To deal with these difficulties, we propose an ISAR imaging method for the maneuvering target with distributed high resolution radars. To mitigate the spatially variant phase error, we correct phase errors in patches based on the weighted least-squares algorithm, which makes the image well-focused. Then, the ISAR imaging method with distributed radars is presented to obtain the fusion image, with the object to capture enough scatterers to represent the maneuvering target. The fusion image could provide more details. Real data have verified the effectiveness of the proposed method.

Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects.

Compared with conventional single-sensor inverse synthetic aperture radar (ISAR), multisensor ISAR technique can reduce the integration time and improve the resolution of the image effectively. One solution is investigated for multisensor ISAR imaging and phase adjustment with sparse measurements via the compressed sensing (CS) framework. In most previous research of CS-based radar imaging, the target is modeled as a few strong scatterers that randomly distributed in the imaging plane and only the image domain sparsity assumption is used in image reconstruction, which will result in the degradation of image quality, especially when the measurements are limited. In practical ISAR imaging, some strong scatterers facing the radar always form flat regions in high-resolution radar imaging. Therefore, the dependence and redundancy of these scatterers can also be exploited in target reconstruction. We utilize this information and propose a multiplatform ISAR imaging method that combines the image domain sparsity and edge-preserving total variation to improve the image quality with sparse measurements. Meanwhile, an iterative minimization approach is also used to process the phase adjustment with the discontinuous echo data. Experimental results show the effectiveness of the proposed method.

Signals from Costas-coded frequency-hopped radars exhibit an approximate “thumbtack” ambiguity function and prevent range-Doppler coupling. Therefore, these radars provide high-resolution range and velocity performance. However, the radar signals are highly sensitive to Doppler frequency shifts, and peak divergence arises due to Doppler mismatches when the target is moving. To solve these problems, we thoroughly analyze the effect of velocity on range profiles. Then, we propose a method of motion compensation based on particle swarm optimization using improved waveform entropy as the objective function. The target velocity is accurately determined through an iterative search. After motion compensation, the focus of the target range profile is notably improved. Simulation results confirm that the proposed method has a low computational complexity and retrieves accurate velocity estimates even under low signal-to-noise ratio and high target speeds, suggesting its adaptability and robustness for real-world applications.

In-orbit verification of the coregistration of channels in a scanning microwave or infrared radiometer can in principle be done during normal in-orbit operation, using the regular events of lunar intrusion in the instrument cold space calibration view. A technique of data analysis based on best fit of data across lunar intrusions has been used to check the mutual alignment of the spectral channels of the microwave humidity sounder (MHS) instrument. MHS is a cross-track scanning radiometer in the millimeter-wave range flying on EUMETSAT and NOAA polar satellites, used operationally for the retrieval of atmospheric parameters in numerical weather prediction and nowcasting. This technique does not require any special operation or maneuver and only relies on the analysis of data from the nominal scanning operation. The coalignment of sounding channels and window channels can be evaluated by this technique, which would not be possible using Earth landmarks, due to the absorption effect of the atmosphere. The analysis reported shows an achievable accuracy below 0.5 mrad against a beam width at 3 dB and spatial sampling interval of about 20 mrad. In-orbit results for the MHS instrument on Metop-B are also compared with the prelaunch instrument characterization, showing a good correlation.

This paper addresses the optimization problem of frequency division phase-coded (FDPC) waveforms for improving the detection performance in multiple-input multiple-output over-the-horizon radars (MIMO-OTHR). A sparse frequency division complementary chaotic modulation (SFDCCM) waveform is proposed and analyzed. First, the basic structure of the FDPC waveform is presented, and its MIMO ambiguity function is derived to provide guidance on further waveform optimization. It turns out that the modulation codes and carrier frequencies are two key factors for good waveform performance. Then, the chaotic sequences are introduced due to their unique advantages, and an adaptive clonal selection algorithm is utilized to obtain a pair of complementary chaotic sequences to suppress the range sidelobes. The optimized complementary pair is modulated on different carrier frequencies. As the available spectrum of the MIMO-OTHR is discontinuously distributed, the carrier frequencies are generally sparse and should be properly selected according to the real-time frequency spectrum monitoring (RTFSM) system. Finally, the SFDCCM is proposed based on the FDPC structure. The pulse compression results of the SFDCCM are compared with typical waveforms. Four cases of carrier frequency selection in the SFDCCM and the corresponding doppler tolerance are analyzed. Simulation results validate the superiority of the proposed waveform in this paper.

Considering the high computational burden of typical space–time adaptive processing (STAP) based on sparse representation (SR) (SR-STAP) method, a reduced-dimension (RD) SR-STAP method using simplified time–time (STT) transform spectrum is proposed to overcome this issue. First, the STT transform spectrum formula of clutter on cell under test (CUT) is deduced and the main energy of CUT in the STT transform domain is extracted. Second, to design the RD matrix, an adjustable RD threshold is defined, which is used to make a comparison with STT transform spectrum energy. Third, the RD SR dictionary is constructed to estimate the clutter spatial–temporal spectrum. Numerical simulation results demonstrate that the proposed sparse representation based on simplified time-time-STAP method reduces the computational burden significantly and has a highly similar clutter suppression performance compared with the typical SR-STAP.

Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth’s surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms—LBP and TRW-S—perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm).

Remote sensing images with high-spatial and high-temporal (HSHT) resolution are difficult to be consistently achieved with a single polar orbit satellite because of the trade-off between spatial and temporal resolution. However, blending algorithms have been developed to synthesize HSHT images from low-spatial but high-temporal resolution images and high-spatial but low-temporal resolution images. One example is a widely used weight-function-based method known as the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Similar-pixels play a key role in the prediction accuracy of this model, and their identification is influenced by the method used, the number of classes, and the moving window size. However, influence of these parameters on the final prediction accuracy has not been thoroughly examined. Therefore, this study assesses the accuracy of ESTARFM when similar-pixels are identified by two separate methods using different numbers of classes and moving window sizes. The results indicated that the class-image-based method outperformed the threshold-based method in most cases and the increase of moving window size generally improved the accuracy for both methods. However, the improvement was minor and accuracy degraded in some cases if the moving window was too large. In addition, the ESTARFM program was optimized in this study, and its computational efficiency was improved compared with previous studies.

A variational model is proposed to despeckle the multiplication noise in synthetic aperture radar (SAR) images to provide a high-quality interpretation of SAR data. The model consists of the data fidelity term and Beltrami regularization term. The former ensures the convexity of the model and avoids the nonlinear image transformation. The latter restrains the staircase-like artifacts caused by total variation regularization and effectively preserves the geometric structure of the image. The despeckled image is formulated as an optimization solution of the energy functional and the primal-dual algorithm is adopted to efficiently find the global minimum. Experiments on both synthetic and real SAR data demonstrate that, compared with five state-of-the-art methods, the proposed method achieves competitive results, both visually and quantitatively.

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help mitigate false positives. A material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms is presented. The algorithms utilize unwhitened radiance data and Nelder–Meade numerical optimization to estimate the temperature, humidity, and ozone levels of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian information criteria for model selection. The resulting identification product includes an optimal atmospheric profile and a full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing the model parameters to improve identification is also presented. Several examples are provided using modeled data at several noise levels.

A vehicle detection algorithm is presented for synthetic aperture radar (SAR) images. This method formulates the detection mission within a sparse representation (SR) fusion frame. A set of residuals, for one specific feature, is first generated by performing the sparse reconstructions over dictionaries associated with the available set of possible targets. They are then normalized and further formed into a single residual sequence. After the collection of all residual sequences for all types of features, a linear fusion strategy is applied to the sequences to infer an optimal target estimate. As the final decision is made based on the residual fusion related with the concatenation of multiple features, this algorithm exhibits strong discriminative powers with respect to target confirmation. Moreover, a merging technique is developed to integrate a more accurate region for each vehicle. The test results based on real scene data show that the presented method is superior to some state-of-the-art alternatives.

Differential interferometry synthetic aperture radar (D-InSAR) is susceptible to time and space coherence and atmospheric delay in the monitoring process, resulting in a significant reduction. Permanent scattering interferometry (PS-InSAR) technology can effectively improve the temporal and spatial resolution of deformation monitoring and the precision of the solution, reduce the time and space coherent effect, and weaken the error caused by the atmospheric delay. Due to the above advantages, PS-InSAR is a powerful means to obtain the information of ground subsidence monitoring. This study selected the Beijing urban land subsidence area as the research area, used the ASAR data for 30 scenes of 2012 to 2015. The interference of the permanent scattering technique and the stamps method were used to evaluate the phase unwrapping and remove the terrain, atmospheric, and orbit error, finally to obtain the interference measurement result of the time series in Beijing area. This paper preliminarily verified the correctness of the interference measurement results, and further analyzed the dynamic change and spatial distribution trend of the urban land subsidence rate in Beijing, provided the reliable and detailed observation data for predicting the urban deformation disaster, and provided the guidance basis for the prevention of the urban land subsidence deformation.

An early warning of crop losses in response to weather fluctuations helps farmers, governments, traders, and policy makers better monitor global food supply and demand and identifies nations in need of aid. This paper discusses the utility of vegetation health (VH) indices, derived from the advance very high-resolution radiometer (AVHRR) and visible infrared imaging radiometer suite (VIIRS), as a proxy for modeling Australian wheat from the National Oceanic and Atmospheric Administration (NOAA) operational afternoon polar-orbiting satellites. These models are used to assess wheat production and to provide an early warning of drought-related losses. The NOAA AVHRR- and VIIRS-based VH indices were used to model wheat yield in Australia. A strong correlation (≥0.7) between wheat yield and VH indices was found during the critical reproductive stage of development (enhanced crops sensitivity to weather), which starts 2 to 3 weeks before and ends 2 to 3 weeks after wheat heading. The results of modeling and independent testing proved that the VH indices (especially those estimating thermal and health conditions) are a good proxy providing 1 to 2 months before harvest yield prediction (with 3% to 6% error). With the new generation of NOAA-20 operational polar-orbiting satellites, launched in November 2017, the VH method will be improved considerably both in an advanced crop/pasture prediction, spatial resolution, and accuracy.

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-a concentration data from the 2007 National Lake Assessment (NLA) (lake N  =  1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-a retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-a variation. Multiple linear regression could only explain 39.8% of chlorophyll-a variation. The chlorophyll-a concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-a data from the NLA, chlorophyll-a measurements with our Landsat TM/ETM+ model had almost the same correlation with lake’s total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-a have value for ecological assessments and managing algal problems in lakes.

In order to explore the effect of land uses on urban heat island (UHI), the correlation between land-use pattern and land surface temperature (LST) is quantitatively analyzed separately in the urban and suburban areas. In the case of the Changsha–Zhuzhou–Xiangtan city group of China, the LST is retrieved by the Landsat-5 TM image acquired on September 2009, and the land-use composition and configuration are described by five landscape metrics: the percentage of landscape, edge density, mean patch area, the mean Euclidean nearest-neighbor distance, and Shannon’s evenness index. Pearson’s correlation coefficients and an ordinary least squares multiple linear regression model are utilized to evaluate the correlation between each landscape metric and LST, respectively, and to select the optimal combination of landscape metrics to describe the LST at an optimal scale in the whole and the urban/suburban parts of the study area. According to the conclusions, it is suggested that, in order to restrain the aggravating UHI and its influence, all land uses should be reasonably distributed, and the proportion of critical land-use types should be optimized.

This paper proposes a method of semiautomatic right-angle building extraction from very high resolution (VHR) aerial images, based on graph cuts with star shape constraint and regularization. In the proposed method, first, the image block containing the target building is obtained by a seed line from humans. Next, the image block is preprocessed by bilateral filtering and then the simple linear iterative clustering oversegmentation method was used to segment the image into small regions. Then regions that contain the pixels belonging to the seed line are merged as the foreground area, and the background area is automatically composed by annular regions near the imagery boundary. With the obtained foreground and background areas, the graph cuts model with the star shape prior is used to acquire the building object. Finally, we defined a method of detecting the building orientation and proposed a regularizing building method based on the corners and the building orientation. The experiments performed on two VHR aerial images demonstrate the accuracy and stability of the proposed method.

With ∼175  Mha (38% of which is in the Cerrado biome), pastures are the main land use forms in Brazil. Aiming at improving pasture utilization, in this study we combined field and satellite data to assess the biophysical characteristics and productivity patterns of the Cerrado pasturelands. From October 2013 to September 2014, field data of total and green biomass and percent green cover (%GC) were collected in two study areas located in the municipality of Uberlândia, Minas Gerais State. These data were extrapolated to the entire Cerrado biome through regression equations involving normalized vegetation index values derived from Landsat-8 Operational Land Imager (OLI) and Terra MODIS sensors for the year 2014. Total green biomass in the growing season was used to estimate potential livestock intensification in terms of animal unit (AU) per hectare. Average green biomass values ranged from 1482  kg ha  −  1 in August (dry season peak) to 2878  kg ha  −  1 in March (end of the rainy season), whereas %GC ranged from 49% in August to 66% in January and April 2014. Considering the estimated availability of green forage, cattle stocking rate in the Cerrado could increase, on average, from 1.11 to 2.56  AU ha  −  1.

Shadow is an important concern in aerial and satellite remotely sensed imagery. Consequently, shadow detection is important for many image processing and analysis operations. Among shadow detection methods, there exist methods that apply a single threshold to a shadow index to determine the shadow cover. Shadowed pixels are affected by shadow and land cover/use, therefore ignoring the land cover/use effect would lead to the wrong analysis of these pixels. The effects of land cover/use and shadow strength on eight color space-based shadow indices have been investigated. Eight shadow indices have been applied to an aerial color image. Subsequently, samples of aerial color images were collected for eight land covers/uses from shadowed and nonshadowed regions, using three shadow-strength classes: strong, medium, and weak shadow. The performance of each shadow index for separating shadowed and nonshadowed samples was examined using a threshold applied separately for each land cover/use and shadow class. All applied shadow indices showed high sensitivity to land cover/use and shadow strength. The results showed that ignoring different shadow strengths (considering the different shadow classes as a single shadow class) leads to some confusion for many color space-based shadow indices and also that a single threshold cannot separate the shadow pixels from the nonshadow pixels. All applied shadow indices show sensitivity to the land cover/use and shadow strength.

The Deepwater Horizon (DWH) oil blowout in the Gulf of Mexico (GoM) led to the largest offshore oil spill in U.S. history. The accident resulted in oil slicks that covered between 10,000 and upward of 40,000  km2 of the Gulf between April and July 2010. Quantifying the actual spatial extent of oil over such synoptic scales on an operational basis and, in particular, estimating the oil volume (or slick thickness) of large oil slicks on the ocean surface has proven to be a challenge to researchers and responders alike. This challenge must be addressed to assess and understand impacts on marine and coastal resources and to prepare a response to future spills. We estimated surface oil volume and probability of occurrence of different oil thicknesses during the DWH blowout in the GoM by combining synoptic measurements (2330-km swath) from the satellite-borne NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and near-concurrent, much narrower swath (∼5  km) hyperspectral observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A histogram-matching approach was used to transfer AVIRIS-derived oil volume to MODIS pixel-scale dimensions, after masking clouds under both sun glint and nonglint conditions. Probability functions were used to apply the transformation to 19 MODIS images collected during the DWH event. This generated three types of MODIS oil maps: maps of surface oil volume, maps of relative oil thickness with four different classes (i.e., 0  μm, <0.08  μm, 0.08 to 8  μm, and >8  μm), and maps of probability distributions of different thicknesses. The results were compared with satellite-based synthetic aperture radar measurements and evaluated with concurrent aerial photographs. Although the methods may not be ideal and the results may contain large uncertainties, the current attempt suggests that coarse-resolution optical remote sensing observations can provide estimates of relative oil thickness/volume for large oil slicks captured by satellites.

A high-resolution optical system is designed for a remote sensing satellite camera in low earth orbit, with an entrance pupil diameter and effective focal length of 110 and 840 mm, respectively. The modulation transfer function of the camera is equal to 0.8 at a Nyquist frequency and the image quality is close to the diffraction limit. In the next step, stray light is analyzed and a baffle with 13 internal vanes is designed. Results show that stray lights can be completely eliminated at angles greater than 2.5 deg, whereas at angles less than 2.5 deg the intensity of stray lights is reduced up to 100,000 times of its original intensity.

We propose the use of temporal series of remote-sensing images (RS) for the characterization of the dynamics of the crop canopy throughout the growing and development cycle. Crop phenology, meteorological data, and normalized difference vegetation index (NDVI) were obtained during the period 2008 to 2016 for commercial fields planted with wheat. Three temporal scales based on the number of days, the growing degree-days (GDD), and the reference evapotranspiration (ETo) were analyzed for the intercomparison of the growing cycles. The use of the accumulated value of ETo as the reference scale for the temporal evolution of NDVI allowed for a better analysis of the differences among the fields. This scale also improves the estimation of the duration of the cycles and the prediction of flowering and physiological maturity. The analysis of the accumulated NDVI indicated that flowering occurs during the middle of the growing cycle and that the accumulated NDVI in the vegetative and reproductive phases is similar if the growing cycle is analyzed in terms of ETo or GDD. In addition, the estimation of the green-up based on RS data allows for the definition of the beginning of the growing period for this crop even in the absence of planting dates data.

The accurate extraction of building roofs from airborne light detection and ranging (LiDAR) point clouds plays an important role in many applications, such as digital building modeling and disaster assessment. However, this remains a challenging task because of the diversity of building roof structures, irregular distributions of LiDAR points, and mutual disturbances of neighboring points. Most of the existing methods show little capability to detect inconspicuous roofs, i.e., roofs with small sizes or fuzzy boundaries. We present a coarse-to-fine method to accurately extract roofs from airborne LiDAR point clouds. This method first iteratively extracts large roofs by three successive steps with dynamically adjusted parameters during its “coarse” stage, and then extracts small roofs from the remained points using an improved random sample consensus method during the “fine” stage. Experimental results show that the method can significantly improve the accuracy of roof extraction by robustly identifying most of the inconspicuous roofs in LiDAR point clouds.

Hyperspectral infrared remote sensing can provide the information about temperature and humidity of the atmosphere at high vertical resolution and high accuracy. To assimilate its radiances directly, we must correct biases between the observed radiances and the simulated ones from the model first guess, caused by systematic error of radiances and by the radiative transfer model and assimilating system. The method used for bias correction was developed for global models, and its adaptation to regional models raises further questions. This study is based on coupling the mesoscale numerical model weather research and forecast and gridpoint statistical interpolation assimilation system, using adaptive variational bias correction (VarBC) to the scan angle and air-mass factor, and investigates the characteristics of bias correction coefficients for the regional model. It was found that advanced infrared sounder (AIRS) channels located in the 15-μm CO2 absorption band had large scan bias, and that its nadir bias had a time dependence, which is probably due to the bias from the radiative transfer model. By contrast, other channels had small scan bias and weak time dependence. In air-mass bias correction, predictors of zenith and temperature lapse rate had huge oscillations due to variations in data coverage from the regional models. The effect of this scheme on correction in a regional model was verified via the histogram analysis of innovation. The verification showed that correction on most of the channels got satisfactory results except for several land surface channels. The corrected histogram satisfied the requirement of an unbiased normal distribution. In a typhoon forecast experiment, the influence of radiance bias correction on forecast result was tested. It showed that, compared with parameters from the global model, regional radiance correction parameters study improved the prediction of the typhoon 72-h forecast.

Different management modes can influence land-use and land-cover changes (LULCC), resulting in changes in ecosystem service values (ESV). However, few studies compared the differences in ESV under different management modes. The objective was to analyze the impact of different management modes on ESV. Multisource remote sensing images were used to obtain the LULCC data for Fujin City over four periods (1986, 1995, 2005, and 2015). We modified the equivalent value factors method by crop yield to estimate the ESV in different years. Statistics show that paddy increased from 59.2 to 1341.8  km2 in the agricultural region, whereas it increased from 60.1 to 2236.3  km2 in the reclamation region during 1986 to 2015. The total ESV decreased by 49.0% and 43.4%, respectively, in the agricultural region and reclamation region. The agricultural products function exhibited a larger increase and the freshwater supply function exhibited a greater decrease in the reclamation region than in the agricultural region. Two regions can learn from each other and carry out innovations in land management approaches to promote common developmental goals.

The monitoring of river ice development is a crucial guidance indicator to establish safe crossings along river ice covers. This is the first study, based on our knowledge, to understand the interactions between river ice cover structures and radar signals and to further monitor ice development using C-band synthetic aperture radar images. The study was applied to the Slave River, Canada, using the Freeman–Durden decomposition of quad-pol C-band Radarsat-2 FQ14W images and ice core crystallography analysis. Results demonstrate that the combination of volume and surface scattering can be used to monitor ice cover development that cannot be interpreted from single polarization images, such as Radarsat-2 spotlight images used in this study. These results indicate that the decomposed quad-pol Radarsat-2 images can provide a more effective guide than the single-pol Radarsat-2 SLA images to select safe ice transportation routes. This decomposition approach can be extended to other snow and ice covered rivers.

Ingesting microwave sounder radiances from SAPHIR of Megha–Tropiques has been attempted. A local ensemble transform Kalman filter assimilation algorithm is adopted to ingest radiances directly into the ARW–WRF model. The forward radiative transfer calculations were surrogated with an artificial neural network (ANN) based on the fast radiative transfer model. Raining pixels from the observations were removed using a threshold test on the observed brightness temperatures. Following this, corrections of both scan and air mass biases were accomplished using a predictor-based approach. The bias characteristics of each channel were calculated from ab initio clear-sky profiles from European reanalysis Interim reanalysis data. The vertical localization functions required for the radiance observations were chosen to be similar to the weighting function of the respective channel. The overall performance of the SAPHIR radiance assimilation in terms of the average error over the forecast period showed a positive impact on the cyclone track prediction when compared with the control run and the best track data from the Indian Meteorological Department. The effect of assimilation is observed to improve the minimum sea-level pressure values, whereas the improvements in the maximum sustainable wind speed are not significant. An assimilation experiment was set up to ingest channel-wise radiances independently, and it was concluded that the assimilation of channel 5 radiances results in the least error in the track forecast. The effect of using ensembles generated by initial perturbations in (i) temperature and (ii) both temperature and humidity was studied. The ensembles generated from perturbations in both humidity and temperature resulted in a better 72-h track compared with perturbation of only one of them. The overall performance of the assimilation of all the six channels for both 48- and 72-h forecast lead times showed a considerable improvement against the control run without any assimilation. Furthermore, the results show a degradation of the forecast of cyclone track in the first 24 h. The sensitivity toward channel-wise radiances showed a positive impact on the precipitation forecast when compared with global precipitation mission rainfall estimates. Threat and bias scores were used for quantitative assessment of precipitation, which indicated improvements in skill after assimilating all six channel radiances from SAPHIR. Finally, a sequential assimilation experiment was set up, and the improvements in the analysis fields were computed.

Sea ice can cause some of the most prominent marine disasters in polar and high latitude regions, and remote sensing technology provides an important means to detect such hazards. The accuracy of sea ice detection depends on the number and quality of labeled samples, but because of environmental conditions in sea ice regions, acquisition of labeled samples can be time-consuming and labor-intensive. To solve this problem, we propose a combined active learning (AL) and semisupervised learning (SSL) classification framework for sea ice detection. At first, we acquire most informative and representative samples by AL; then labeled samples acquired by AL are used as the initial labeled samples for SSL, in this framework, we not only choose the most valuable samples but also use the large number of unlabeled samples to enhance the classification accuracy. In AL phase, we use two different sampling strategies: uncertainty and diversity. In the SSL phase, we utilize a sampling function integrating AL to acquire semilabeled samples, and we use a transductive support vector machine as a classification model. We analyze three remote sensing images (hyperspectral and multispectral) and conduct detailed comparative analyses between the proposed method and others. Our proposed method achieves the highest classification accuracies (89.9734%, 97.4919%, and 89.7166%) in both experiments. These results show that the proposed method exhibits better overall performance than other methods and can be effectively applied to sea ice detection using remote sensing.

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

The integration of Landsat 8 OLI and ASTER data is an efficient tool for interpreting lead–zinc mineralization in the Huoshaoyun Pb–Zn mining region located in the west Kunlun mountains at high altitude and very rugged terrain, where traditional geological work becomes limited and time-consuming. This task was accomplished by using band ratios (BRs), principal component analysis, and spectral matched filtering methods. It is concluded that some BR color composites and principal components of each imagery contain useful information for lithological mapping. SMF technique is useful for detecting lead–zinc mineralization zones, and the results could be verified by handheld portable X-ray fluorescence analysis. Therefore, the proposed methodology shows strong potential of Landsat 8 OLI and ASTER data in lithological mapping and lead–zinc mineralization zone extraction in carbonate stratum.

The identification and mapping of crops are important for estimating potential harvest as well as for agricultural field management. Optical remote sensing is one of the most attractive options because it offers vegetation indices and some data have been distributed free of charge. Especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI) with blue, green, red, and near-infrared-1 bands at 10 m; red edge 1 to 3, near-infrared-2, and shortwave infrared 1 and 2 at 20 m; and 3 atmospheric bands (band 1, band 9, and band 10) at 60 m, offer some vegetation indices calculated to assess vegetation status. However, sufficient consideration has not been given to the potential of vegetation indices calculated from MSI data. Thus, 82 published indices were calculated and their importance were evaluated for classifying crop types. The two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2% to 92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3% to 92.0% of overall accuracies were confirmed. Furthermore, stacking contributed to higher overall accuracies (90.2% to 92.2%), and significant differences were confirmed with the results of SVM and RF. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types.

The identification of ginkgo is of great importance for the intelligent use and management of ginkgo resources. A texture and spectrum time series analysis (TeSTA) model was used to identify and map ginkgo trees as the vegetation spectral and textural properties change regularly during the growing period. The texture and spectrum time series were constructed from temporal remotely sensed images and then projected into a multidimensional measurement space in which the vector difference was used to determine the threshold to identify ginkgo trees. Using this approach, ginkgo trees in Beijing’s Olympic Forest Park were extracted using six Gaofen-2 satellite images. In order to assess the accuracy of this method, two other classification methods were used for ginkgo identification. One method uses only the spectrum time series and the other method uses the support vector machine (SVM) algorithm. The results showed that the TeSTA model performed better than the SVM and spectrum time series model with lower commission errors and higher user’s accuracy. Thus, the TeSTA model can be used for identifying ginkgo trees and providing an important tool for forestry management.

Change-detection analysis using bitemporal satellite imagery is a reliable method for providing and assessing information about flood-induced changes over a wide area in a timely and cost-effective manner. Accurate radiometric normalization between bitemporal imagery is a critical component in the application of change-detection techniques to flood mapping because the accuracy of the change detection is directly affected by the quality of radiometric normalization. A methodology based on multivariate alteration detection (MAD) is introduced as an approach that enables reliable radiometric normalization of bitemporal very high-resolution (VHR) images for detecting flood-induced changes. The method uses a weighting function to adaptively identify weights based on open water features, which are estimated by the normalized difference water index, in the computation of the covariance matrices of the MAD transform. To quantitatively evaluate and test the performance of the proposed method, a comparison is made between it and the iteratively reweighted (IR)-MAD method based on statistical tests and the accuracy of flood change detection. Change vector analysis- and MAD-based change-detection methods were used for the comparison of the proposed and IR-MAD methods. Experimental results on KOMPSAT-2 bitemporal VHR images prove that the proposed method produced better results than the IR-MAD method in the statistical tests and also increased the overall accuracy of flood change detection by 1.8% and 12.6% for the two study sites.

Geological mapping usually requires field visits, but the processing and interpretation of hyperspectral satellite images can also be very beneficial for this task. Microwave or radar remote sensing can obtain surface morphologies using synthetic aperture radar (SAR) images and reduce the duration of field visits dramatically by discriminating geological units based on lithology and texture. This requires the surface roughness to be modeled against microwave signal backscattering. The integral equation model (IEM) is the most well-known rough scattering model, in which surface roughness is calculated using the roughness height statistical parameter (rms-height); however, this study uses an improved IEM based on power-law geometry. The roughness map of the Anaran anticline (located between Dehloran and Ilam in Iran) using TerraSAR images is computed and classified to generate a morphological map. Calculating the roughness map requires training the IEM model and the formation of the look-up-table for pure lithological sites. In situ microtopography measurement was performed on seven different sites containing the main lithologies in the study area, using total station surveying, to train the mathematical model and compare and evaluate the results. Comparing this roughness map with ground-truth data at test sites indicates that computations using the new IEM model results in a misclassification of <10  %   of the samples in the map. This error is acceptable, indicating that the new model could, in many cases, reduce the duration of field visits.

A pan-sharpening method using joint and dual bilateral filters (DBFs) has been proposed. This approach is based on a consistent combination of large- and small-scale features obtained from the decomposition of high spectral resolution multispectral (MS) and high spatial resolution panchromatic (PAN) images. In the decomposition process, MS and PAN images are used to extract the features using joint and DBFs, respectively. These filters accommodate the relationship between MS and PAN images and decompose them into a base layer (large-scale) and a detail layer (small-scale). Since the joint bilateral filter (JBF) preserves the edges of an auxiliary image, it is used for decomposition of MS images where different layers are estimated using the PAN image as an auxiliary image. Similarly, different layers of the PAN image are obtained from a DBF which preserves the edges of both (MS and PAN) input images. This process is further extended to multistage decomposition to obtain a bilateral image pyramid. The base and detail layers of both MS and PAN images obtained at various stages are combined using a weighted sum. Finally, the estimated weighted sum of detail layer (small-scale) of the PAN image is fused separately to the weighted base layers (large-scale) of the MS images. Performance of the proposed method is evaluated by conducting the experiments on degraded as well as undegraded datasets, captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The noise rejection capabilities of the proposed method are also tested by conducting experiments on the noisy data. The results are compared with the widely popular methods and the recently proposed fusion approaches based on a bilateral filter. Along with qualitative evaluation, the quantitative performance of the proposed fusion technique has also been verified by estimating different measures for degraded and undegraded experiments. The experimental results and quantitative measures demonstrate that the proposed method performs better in degraded and undegraded conditions along with noisy situations when compared to other state-of-art methods.

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect >10,000  s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Radiofrequency (RF) through-the-wall mapping (TWM) employs techniques originally applied in x-ray computerized tomographic imaging to map obstacles behind walls. It aims to provide valuable information for rescuing efforts in damaged buildings, as well as for military operations in urban scenarios. This work defines a finite-element method (FEM) based framework to allow fast and accurate simulations of the reconstruction of floor blueprints, using very high-frequency signals. To the best of our knowledge, this is the first use of FEM in a TWM scenario. This framework allows quick evaluation of different algorithms without the need to assemble a full test setup, which might not be available due to budgetary and time constraints. Using this, the present work evaluates a collection of reconstruction methods under a parallel-beam acquisition geometry for different spatial sampling rates, number of projections, antenna gains, and operational frequencies. This work also proposes a multisensor circular acquisition geometry, which is expected to reduce acquisition time and thus enable the detection of floor layout changes (e.g., the opening and closing of a door). Considering all the drawbacks associated with such a complex problem, a robust and reliable computational setup based on a flexible method, such as FEM, can be very useful.

This article aims to analyze the accuracy of the latest versions of burned area (BA) products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) series (MCD45A1 v051, MCD64A1 v051, and MCD64A1 v006), quantifying their progression in the annual/seasonal mapping of fire scars on a regional scale over tropical savanna environments. To this end, a validation was processed comparing each product with a BA reference covering the period of 2001 to 2016, available for one of the largest savanna vegetation enclaves located in the southern Brazilian Amazon, the Campos Amazônicos Savanna Enclave. Three indices derived from cross-tabulated error matrices were used in the comparison [commission error, omission error (Oe), and Dice coefficient]. Pareto boundaries were also computed to explore the influence of the spatial resolution of the MODIS products on detecting BAs. The overall assessment found a clear decrease in Oes, varying from 0.797 and 0.499 in the MCD45A1 v051 and MCD64A1 v051, respectively, to a value of 0.280 in the MCD64A1 v006. Improvements obtained with MCD64A1 v006 in the middle and late dry season periods were significant. The results validate the higher performance of MCD64A1 v006 and confirm it as a useful instrument for the regional mapping of BAs over tropical savanna areas.

Increasing demand for food in East Africa has created a shift to utilize wetlands for food production. Lack of spatial information hampers sustainable use of the Kilombero Valley floodplain. We take advantage of multispectral data from RapidEye, Landsat-8, and Sentinel-2 to derive high temporal resolution maps along three hydrological zones of the Kilombero Valley, assess seasonal land cover dynamics, and relate these dynamics to groundwater measurements. The depth of groundwater increases from June and declines from December, consistent with the end and the beginning of the rainy season, respectively. Bare land cover over the study area was 45% to 57% and increases to 62% to 69% as the season shifts from rainy to dry seasons while vegetation coverage, which was 34% to 47%, decreased to 25% to 27%. During the dry season, 68% to 81% of the total vegetation is within the riparian zone indicating the hydrological conditions favor plant growth. Vegetation growth in the fringe and middle zones mainly relies on precipitation whereas that in the riparian zone relies on saturation from the river. Our findings exemplify the relationship between seasonal land cover change and hydrological conditions and contribute to improved understanding of the spatial–temporal land cover dynamic in the Kilombero floodplain, required for planning sustained use of the wetland.

The deep learning methods have recently been successfully explored for hyperspectral image classification. However, it may not perform well when training samples are scarce. A deep transfer learning method is proposed to improve the hyperspectral image classification performance in the situation of limited training samples. First, a Siamese network composed of two convolutional neural networks is designed for local image descriptors extraction. Subsequently, the pretrained Siamese network model is reused to transfer knowledge to the hyperspectral image classification tasks by feeding deep features extracted from each band into a recurrent neural network. Indeed, a deep convolutional recurrent neural network is constructed for hyperspectral image classification by this way. Finally, the entire network is tuned by a small number of labeled samples. The important characteristic of the designed model is that the deep convolutional recurrent neural network provides a way of utilizing the spatial–spectral features without dimension reduction. Furthermore, the transfer learning method provides an opportunity to train such deep model with limited labeled samples. Experiments on three widely used hyperspectral datasets demonstrate that the proposed transfer learning method can improve the classification performance and competitive classification results can be achieved when compared with state-of-the-art methods.

Accurate and timely regional estimates of agricultural production are key for decision makers. This study aims to understand how different machine learning techniques impact soybean yield estimation in extracting maximum information from remotely sensed MODIS enhanced vegetation index (EVI) that is constrained by phenology. Specifically, a methodology is developed for incorporating phenological information aligned with EVI acquisition for each pixel and selecting the most significant predictors out of 36 predictors using feature selection. These predictors were then used in four machine learning algorithms (MLA) to obtain soybean yield estimates for observed farms in the Paraná State, Brazil. The optimal MLA was then implemented for the whole state to obtain regional soybean yield. The gradient boosting model (GBM) with all 36 predictors performed well with a mean difference of 3.5  kg ha  −  1, an RMSD of 373  kg ha  −  1, and Willmott’s d of 0.85, however, the random forest (RF) algorithm using five optimal EVI predictors presented similar results, but with considerably less computational time. Both GBM and RF provided higher regional yields compared to the officially reported yields by 1775  ×  103 and 2059  ×  103  metric tons, respectively. The RF with five EVI predictors provided the best results for regional soybean estimations, considering the accuracy and computational performances.

Airborne remote sensing approaches to natural gas leak detection have recently become a viable alternative to traditional in situ surveillance methods. However, there have been few formal studies addressing the advantages and disadvantages of the various kinds of instruments typically employed for this purpose. This investigation compares the theoretical performances of differential absorption lidar systems operating near 1.65 and 3.3  μm. The random errors affecting these instruments’ respective retrievals were simulated over a range of aircraft altitudes and observed natural gas concentrations. It was found that the 3.3-μm system is capable of measuring smaller leaks with less error than the 1.65-μm system but only when flying at lower altitudes. The noise floors of the 1.65- and 3.3-μm instruments simulated in this particular analysis are ∼1.4 and ∼0.1  ppm m, respectively. However, when flying at altitudes >∼220  m or observing leaks with concentrations >∼500  ppm m, the 1.65-μm system exhibits better precision than the 3.3-μm system. These results demonstrate that it may be more appropriate to employ one instrument over the other depending on the surveillance scenario at hand.

Impacts of the extreme 2013 to 2014 drought on vegetation canopy cover in the Big Sur region of central coastal California were assessed using a combination of satellite image analysis and in situ measurements of soil moisture. Landsat and moderate resolution imaging spectroradiometer satellite images were analyzed and compared across six ecosystems representative of the predominant vegetation types of the region at the U. S. Forest Service’s Brazil Ranch study site. Results showed that differing spatial densities of woody plants among these ecosystems corresponded to the observed changes in the normalized difference vegetation index (NDVI) over the period of 2010 to 2016, with the herbaceous-dominated coastal terrace, open shrub, and ridge-top grassland ecosystems declining 30% in greenness during the extreme drought period of 2012 to 2014. Landsat images from the month of peak annual NDVI confirmed this as a statistically significant difference in the loss of green cover in open shrub and grassland ecosystems during the 2012 to 2014 drought, compared to the closed shrubland and redwood-dominated ecosystems that were relatively unaffected at the study site.

Changes in the plant community composition due to biological invasion result in extensive changes to the ecosystem structure and functioning. Encroachment of Chir pine (Pinus roxburghii) in Banj oak (Quercus leucotrichophora) habitats in the western Himalaya is a well-established phenomenon and in due course eliminates the latter completely. Monitoring the impact of Chir pine incursion into Banj oak habitats is important for conservation and management of the oak ecosystems in western Himalaya. A remote-sensing-based classification method has been developed to precisely map and measure the potentially invaded forest areas in part of the western Himalaya. Timely identification of location and extent of invaded pine areas provides the opportunity to control the invasion. Temporal Landsat 8 OLI satellite data were used for temporal vegetation index database. Fuzzy-based, possibilistic C-means classifier is a soft classification technique used to identify the oak habitats and its coexistence with Chir pine. The outputs were validated using field data. A total of 33.16  ±  10.01  km2 was estimated as the mixed class showing coexistence of Banj oak and Chir pine. The overall accuracy was found to be 0.86  ±  0.06 from the error matrix based on the estimated area proportion. In addition to providing a method for mapping coexistence of Banj oak and Chir pine, this study also provides information on the potential extent of the incursion of Chir pine in Banj oak forests in the western Himalaya. The critical information on potential area under invasion provides forest managers to take essential steps to minimize the incursion.

In dryland systems, the flood pulse is the driving force in system dynamics but is highly variable in flow volume and landscape inundation features. Remote sensing can provide critical information fundamental to evaluating and forecasting flow behavior and population vulnerability; however, training and classifying an extensive time series of images is labor intensive, limiting the usefulness of these approaches in evaluating flood pulse dynamics and landscape interactions. Here, we provide an alternative approach that relies on only one set of “pooled” training samples for time series image classification and analysis. We test this approach by mapping the flood pulse in a time series of moderate resolution imaging spectroradiometer (MODIS) images from the Chobe River Basin of Botswana for the years 2014 to 2016. MODIS MOD09A1 images collected during the flooding season (February to July) were converted to Kauth–Thomas components, then sampled to form a training pool. Images were then classified using these pooled training samples. Results indicated that classification accuracies obtained using pooled training were statistically indistinguishable from classifications obtained from conventional training. Application of the method to another year’s data (2013) also yielded comparably accurate results, suggesting that the training pool method remains robust when applied to image data other than that used to create the training pool.

This is an erratum correcting the paper “Comparing the theoretical performances of 1.65- and 3.3-μm differential absorption lidar systems used for airborne remote sensing of natural gas leaks.”