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 a prime number) probing signal with zero autocorrelation zone has been synthesized, which signal represents a sequence of p PCSK-pulses coded by complementary sequences with additional frequency shift-keying of the subpulses of pulses. A comparative analysis of the correlation characteristics of the synthesized three-phase signal with a PCSK-signal coded by ternary MS has been performed. Total correlation characteristics of the signal ensemble used in the mode of the radar aperture synthesis have been also analyzed. The analysis has revealed that in case of a mismatch by Doppler frequency, the level of side lobes of autocorrelation function (ACF) of the synthesized signal is comparable with the level of side lobes of ACF of the PCSK-signal coded by MS. The total ACF of the ensemble consisting on p² signals has zero side lobes along the entire time axis τ. This makes it possible to use the signal in remote sensing radars to improve the quality of radar images.

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 two-source RS-based ET models are discussed in brief, along with their unique characteristics. We addressed the role of critical parameters that influenced the H computation under each model and presented the related progress in the research. The importance of data assimilation techniques, as well as the application of unmanned aerial vehicles for the uninterrupted estimation of turbulent heat flux, are discussed in the context of single-source and two-source models. The influence of scale on the validation of the models and the impact of the aggregation methods are discussed. We compared the performance of the popular ET models for the estimation of H, utilizing the information obtained from peer-reviewed articles. The limitations related to the RS datasets in terms of spatial and temporal resolution and the scope of alleviating the shortcomings using the future satellite missions are discussed. We conclude by pointing toward the current challenges and the prospective domain of research, which needs to be addressed critically in the future.

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 Lidar to estimate AGB, linking ground measurements up to satellite observations. To fill this gap, we performed a study in two Mediterranean forest types [i.e., mountainous beech (Fagus sylvatica) and black pine (Pinus nigra subsp. laricio)] with contrasting structures (i.e., broadleaf versus needleleaf forests), where field inventory, TLS, ALS, and the recent spaceborne Global Ecosystem Dynamics Investigation (GEDI) data were simultaneously acquired. A three-step procedure was followed, which involved (i) the validation of AGB estimates obtained from TLS against reference values obtained from conventional field inventory; (ii) the calibration and validation of AGB estimates derived from ALS against TLS measurements, and (iii) the calibration and validation of AGB estimates derived from GEDI against mapped AGB values obtained from ALS. Our main results indicated that TLS provides consistent measurements of AGB as compared with field measurements (R² ranged between 0.6 and 0.9 and root-mean-square error ranged between 29% and 49%), indicating its potential as ground reference for airborne Lidar observations. The combined availability of ground, airborne, and spaceborne observations is suitable to link ground measurements up to satellite observations. Differences in Lidar performance between needleleaf and broadleaf forests are also considered and discussed.

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 maintain, and a limited number of stations are available. Our study introduces a lower cost alternative to study these coastal features. A rule-based classification object-based image analysis approach is introduced to identify nearshore bars (and their characteristics, such as bar length, width, count, and distance from the wet/dry line) from very-high-resolution (VHR) multispectral imagery. Nearshore bars were successfully identified and characterized as single and multiple bar systems. Our study demonstrates that VHR satellite remote sensing is a viable alternative with comparative results to video monitoring systems. Their capability of acquiring high-resolution imagery in large spatial extent is superior in continuous observation and characterization of nearshore bars. The proposed approach provides information at a more relevant spatiotemporal scale at which these systems evolve, which is helpful to coastal scientists and managers.

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 methods are used to estimate the tropospheric signal on InSAR observations. For this purpose, meteorological data derived from ERA-Interim (ERA-I) data, Weather Research and Forecasting (WRF) model, and Advanced Synthetic Aperture Radar/ENVISAT acquisitions are used. ERA-I reanalysis data and a locally run WRF model are also used to compute the tropospheric corrections with integral of the air refractivity method, which is called integration method. Also, the ability of ray tracing techniques to reduce the effect of the tropospheric signal in unwrapped interferogram is compared with integration method. To carry out a comprehensive study, the effects of correction methods are studied in two different areas. The results of the ray tracing methods have a significant difference with the results obtained from integration method and are more efficient when the weather condition between two satellite acquisitions is more different. The results show that the three-dimensional ray tracing method can reduce the root-mean-square error of the results up to 4.8 cm compared to the integration methods.

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, such as mud covered by algae and the optical image coverage gaps due to cloud and tidal inundation in coastal areas, accurate and timely mapping of S. alterniflora is challenging. Using the extended Jeffries–Matusita distance (J_Bh), we first explored the best time window for detecting S. alterniflora with satellite data in Zhangjiang Estuary, Fujian, China. Then we presented a hierarchical classification framework to alleviate the spectral confusion problem, combining cost-free Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral image time series on the Google Earth Engine platform. Specifically, we integrated the inundation frequency map derived from the SAR time series, elevation, and slope criteria to calculate the potential areas of S. alterniflora and mangroves, then used the random-forest classification algorithm to identify S. alterniflora, and finally refined the classified map with a yearlong water mask. The optimal time windows of one month, two months, and three months identified by J_Bh were January, November and January, and November, January, and August, respectively; we got the high classification accuracies with corresponding overall accuracies of 99.35%, 99.63%, and 99.63%, respectively. The results suggested classification accuracy could be improved with a wider temporal window, but would saturate with 3-month imagery. The generated 10-m mangrove and S. alterniflora maps of 2017 and 2018 clearly showed the relatively stable spatial pattern of mangroves and the rapid expansion of S. alterniflora. The thriving S. alterniflora in Zhangjiang Estuary suggests the necessity of high-frequency and large-scale monitoring of invasive species along the coastal estuaries of China.

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: type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). After inputs incubation, the soybean was sown. Then, 42 days after sowing, we collected the foliar spectra, as well as leaves, for further analysis of the contents of macro- and micronutrients in leaves and production of LDM. We managed to adjust models at the stage of prediction with R²p  >  0.50 and RPD_p  >  1.50 for the variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented R²p above 0.65. Therefore, we conclude that Vis–NIR spectroscopy has a potential for predicting LDM and the nutrients contents of soybean subjected to the application of limestone-mining coproducts, with advantages such as speed, low cost, and no use of reagents that are toxic to the environment.

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 problem. The model attempts to learn a pair of mapping matrices and transform bi-temporal image data into a common feature space in which the contrast between the changed and unchanged pixels will be further enhanced. First, a sample selection mechanism is proposed to select training samples with high accuracy. Then, these samples are used to learn a pair of mapping matrices by minimizing the sum of the distances between the unchanged samples and maximizing the sum of the distances between the changed samples according to the CDML. Finally, the original images are mapped to the same feature space respectively by the mapping matrices, and the difference is calculated by L2 norm. The final experimental results confirm the feasibility and effectiveness of the proposed model.

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 studies, but it has rarely been used to study subsidence in mining areas. A total of 62 Sentinel-1A images taken from March 2017 to March 2019 were used to analyze the surface subsidence of the Binchang mining area in Shaanxi Province, China. The accuracy was evaluated using the measured field data from six ground control points for eight different months. The root-mean-square-error and correlation coefficient (r) for the data obtained using SBAS and field measurements were 2.27 cm and 0.944, respectively. We examined 12 main subsidence areas with a mean subsidence rate of −11.88  cm  /  year, where the maximum cumulative subsidence of a single deformation point reached 83.46 cm. The subsidence areas also had differences in spatial distribution and subsidence formation processes. The geological and groundwater conditions were the same for all areas of the study, and mining in the surrounding coal mines was the main cause of surface subsidence. We demonstrate that SBAS differential interferometric synthetic aperture radar technique can be used as an effective method for the long-term monitoring of mining deformation.

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, and corn fields in Yolo County, California, during 2015. We compared DSSAT-MIMICS modeled backscatter to Sentinel-1A backscatter and conducted sensitivity analyses to examine crop features that influence backscatter. For each crop, DSSAT-MIMICS modeled VV (vertically transmitted and received) backscatter was correlated to Sentinel-1A σVV0 (mean R-value  =  0.76, p  <  0.05), root-mean-square error <2  dB, and a model bias between −0.23 and 0.99 dB. However, there were not sufficient Sentinel-1A VH (vertically transmitted and horizontally received) backscatter observations to robustly evaluate DSSAT-MIMICS modeled VH performance. The sensitivity analyses revealed modeled backscatter was most responsive to wheat and rice stems, and corn leaves. Using the analyses, we developed a crop growth index that normalizes Sentinel-1A backscatter to modeled backscatter and mapped corn, rice, and wheat variability, identifying high and low crop growth in fields. This research contributes to the potential application of Sentinel-1A for crop monitoring.

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 receiving subsystems. Apart from providing regular base products such as reflectivity (Z), radial velocity (V), and spectral width (σ), the DWR being equipped with dual-polarization capability provides differential reflectivity (Z_dr), differential phase shift (ϕ_dp), and correlation coefficient (ρ_hv). This is the first DWR with polarimetric capability developed indigenously in India. The C-DWR was operated during North-East as well as South-West monsoon period to evaluate its performance with special emphasis on the reflectivity and rainfall intensity measurements. Before the commencement of the campaign, the C-DWR was calibrated using a metallic sphere and the appropriate corrections were made to the radar constant. The preliminary results show that the radar products are consistent with present understanding of precipitating cloud systems. The simultaneous observations of precipitating clouds by C-DWR and Global Precipitation Measurement mission are used to evaluate the former. The comparison of spatial pattern of reflectivity from space-based radars and C-DWR shows very good agreement, qualitatively. The C-DWR-derived rainfall observations also show very good agreement with ground-based laser precipitating monitor measurements. The regression analysis between the C-DWR and laser precipitation monitor-derived rainfall shows a correlation coefficient of 0.89. The C-DWR observations during the passage of tropical cyclone Ockhi are also discussed. The significance of this study lies in describing the first indigenously developed C-band dual-polarization radar and its validation using the space- and ground-based observations.

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 compositional susceptibility, which may lead to a wrong fractional map of mineral endmembers. Having such physical disputes, the present paper establishes a fraction cover mapping model by incorporating the characterization of endmember variability, optimization model of endmember extraction (EE), and inverse model of abundance estimation. In this regard, a proposition of EE method was deployed, which comprises subproblems on the minimization of endmember variability by the alternating direction method. Next, the extracted endmembers were used to estimate abundances with the Hapke model by applying the fully constrained least-squares method. Experimenting on a synthetic image, both the qualitative analysis by correlation measure and quantitative analysis by statistical error measure were evaluated for the proposed fractional cover mapping model. Using airborne visible/infrared imaging spectrometer-next generation hyperspectral imagery, the fraction cover map of a validation area was justified first, then a distributed mapping of Jahazpur-mineralized belt was achieved by the MapReduce programming of the proposed model in Hadoop architecture.

Acacia longifolia, like any other invasive species, poses a threat to the natural ecosystems in South Africa and beyond. In South Africa, the species is reported to cause significant loss to vegetation biodiversity and is associated with high water use. It remains a challenge to understand the extent of their invasion in complex landscapes, where the use of ground-based surveys is difficult. Considering the heterogeneity and complexity of hydrological and ecological processes at catchment scale, the implementation of remote sensing to detect the distribution of these species becomes critical for continuous monitoring of their extent and associated impacts. We, therefore, aim at species-specific characterization of the spatial distribution of Acacia longifolia invasive wood species at catchment scale within the Cape Floristic region located in the Western Cape Province, using Sentinel 2 remotely sensed data. Overall, the distribution of A. Longifolia was found to be patchy and heterogeneous, across the catchment. However, the main hot spots were detected within the fynbos vegetation, low-lying areas, as well as along the southern coastal belt. Although the catchment is predominantly under cultivation, A. longifolia was found to occupy almost 10% of its area. Classification using combined variables, using both vegetation indices and bands produced the highest overall accuracy of 88.66%, than when bands (66.19%) and indices (76.05%) were used as independent dataset. For example, the producer’s classification accuracy of A. longifolia increased by almost 15%, whereas the user’s increased by almost 10% from spectral bands accuracies. The classification of A. longifolia was primarily attributed to near-infrared, short wave-infrared (centered at 2190 nm), red edge (705 and 740 nm), as well as, chlorophyll vegetation index, the leaf water content index, and normalized difference vegetation index, derived using the near-infrared and red-edge band (at 740 nm) variables. The findings of this study underscore the relevance of satellite-based remote sensing in monitoring invasions in natural ecosystems and can be used as baseline information for invasive-species clearing endeavors in the area.

With the rapid increase of the world’s population, urban growth management and monitoring have become an important component in environmental, social, and economic terms. In general, automatic detection of buildings in urban areas from high-resolution satellite imagery has become an important issue. In recent years, the U-Net architecture has become one of the most popular convolutional neural networks in terms of pixel-based image segmentation. A new deep learning architecture has been developed by combining inception blocks with the convolutional layers of the original U-Net architecture to achieve remarkably high performance in building detection. First, the width of the network is increased by adding parallel filters of different sizes to the convolutional layers in the original U-Net model, and Inception UNet architecture is developed. For the proposed architecture, parallel layers were used only in feature extraction stage to reduce the number of parameters and computation time due to a large network size. In this context, performance comparisons were made with two different datasets. The results show that a significant improvement in F₁ and kappa scores compared to the original U-Net was achieved using the proposed architecture, and model size is dramatically reduced according to Inception UNet-v1.

Smallholder agroecological subzones (AEsZs) produce an array of crops occupying large areas throughout Africa but remain largely unmapped. We explored multisource satellite datasets to produce a seamless land-use and land-cover (LULC) and fragmentation dataset for upper midland (UM1 to UM4) AEsZs in central Kenya. Specifically, the utility of PlanetScope, Sentinel 2, and Landsat 8 images for mapping coffee-based landscape were tested using a random forest (RF) classifier. Vegetation indices, texture variables, and wavelength bands from all satellite data were used as inputs in generating four RF models. A LULC baseline map was produced that was further analyzed using FRAGSTAT to generate landscape metrics for each AEsZs. Wavelength bands model from Sentinel 2 had the highest overall accuracy with shortwave near-infrared and green bands as the most important variables. In UM1 and UM2, coffee was the dominant cover type, whereas annual and other perennial crops dominated the landscape in UM3 and UM4. The patch density for coffee was five times higher in UM4 than in UM1. Since Sentinel 2 is freely available, the approach used in our study can be adopted to support land-use planning in smallholder agroecosystems.

We compare the precipitable water vapor (PWV) determined using a domestic ground-based microwave radiometer (MWR PWV) with PWV measurements from radiosondes (RS PWV), the Global Navigation Satellite System (GNSS PWV), and reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) (EC PWV). The MWR PWV is affected by precipitation, and thus it differs greatly from the other three observations. The correlation coefficient between the MWR PWV and RS PWV (EC PWV) is 0.934 (0.933), and the root mean square error (RMSE) is 17.19 mm (16.05 mm), whereas the correlation coefficient between the GNSS PWV and RS PWV (EC PWV) is 0.989 (0.986), and the RMSE is 17.04 mm (15.83 mm). The scatter distributions of the MWR PWV and the other observations show a systematic deviation that is negatively correlated with the surface air temperature. After polynomial fitting and corrections are applied, the correlation coefficients between the MWR PWV and the RS PWV and EC PWV increase to 0.993 and 0.99, and the RMSEs decrease to 14.13 and 15.86 mm, respectively. The temperature and water vapor density profiles are retrieved from the bright temperature and can reflect the quality of the bright temperature. Because the PWV retrieved from the ground-based MWR has a linear relationship with the brightness temperature, the accuracy of the PWV can be analyzed in terms of the quality of the brightness temperature. We found that the differences in the temperature profile below 2000 m are smaller, whereas those in the water vapor density profile below 2000 m show the largest difference. This finding reflects the differences in the brightness temperature, which may be the cause of the inaccurate PWV observations.

Cucurbits plants are very susceptible to fungal diseases as powdery mildew (PM) infection. Currently, the PM early detection in the field is the cutting-edge research. The objectives of our study were to assess visible and near-infrared spectroscopy of normal and infected leaves for early detection of PM in Cucurbita pepo L. plants. Samples for spectral analysis were taken three days a week (Monday, Wednesday, and Friday) after the first true leaves appeared. The reflectance spectra of leaves were collected using an HR4000CG-UV-NIR spectrometer (Ocean Optics) with a fiber optics probe. Vegetative indices were used for discrimination between infected and healthy plants. The calculated vegetation indices (green normalized difference vegetation index, triangular greenness index, single-band reflectance index, simple ratio indices, and anthocyanins reflectance index) showed the highest sensitivity to differentiate healthy, infected plants at a symptomless stage, first symptoms, and diseased plants. The best prediction on early detection in sampling days were structure-independent pigment index and red-edge reflection point with an R of 0.211 and 0.1893, respectively. Our study shows the efficacy of the identification based on reflectance spectra for an early distinction of PM disease and could be used in cucurbits plants with similar characteristics on leaves.

Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA) for tea plantation mapping is more worthy of implementation. This study explored the performance of Gaofen-5 (GF-5) and copolarized SAR data for tea plantation mapping using pixel- and object-based support vector machine algorithms in Wuyishan, China. Comparison of PBIA and OBIA demonstrated the significant differences in visual effect and classification accuracy. The object-based classifications especially offered a more contiguous depiction with fewer speckles of tea plantations than pixel-based classifications did. Moreover, object-based classifications improved overall accuracy (OA) between 1.7% and 7.9% in all scenarios when compared to pixel-based classifications. As for datasets, classifications using only GF-5 data obtained an OA of over 85%, while fusing images decreased classification accuracy due to the lower separability between tea and forest, showcasing that the fusion of hyperspectral and SAR data does not guarantee the improvement of classification accuracy. The integration of GF-5, horizontal transmit and horizontal receive (HH), and vertical transmit and vertical receive (VV) polarized data outperformed other data combinations in both pixel- and object-based classifications and achieved the highest OA (95.58%) in OBIA, with a 98.13% producer accuracy and 93.34% user accuracy of tea plantations. The results indicated OBIA could overcome the shortcomings of the PBIA and effectively improve the mapping accuracy of tea plantation, and OBIA integrating GF-5, HH, and VV polarized data could play a distinguished role in tea plantation mapping. This work provides a promising approach for mapping tea plantations and demonstrates that the integration of GF-5 and copolarized data can improve spectral separability of vegetation, which is also significant for general forest mappings.

The advanced baseline imager (ABI) onboard Geostationary Operational Environmental Satellites-16 (GOES-16) provides high-quality visible and near-infrared (VNIR) imagery data. Radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) is evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 visible infrared imaging radiometer suite equivalent bands M3, M5, M7, M10, and M11, respectively. In order to minimize the uncertainties due to the difference in spectral response functions of similar bands from the different sensors, spectral band adjustment factor (SBAF) derived from Hyperion data over Sonoran Desert is used. The large viewing angle at the Sonoran Desert by ABI (56.34 deg) and a lack of comprehensive BRDF model at such viewing geometry are the main challenges for the comparison. Two schemes to address such challenges were developed. A data-derived (DD) method, based on confining the matched solar-zenith angle and view-zenith angle between ABI and VIIRS, interpolation in solar-zenithal and relative-azimuthal angles is developed to address this issue. It is shown that there is some residual bias for ABI B1 and its equivalent VIIRS M3 band, which is mainly due to unaccounted angular dependence of atmospheric scattering in this DD method. To address this issue, a radiative transfer modeling (RTM)-based method to account for atmospheric effects is developed to facilitate the comparison. The time series trending and mean bias of reflectance ratio between ABI and VIIRS measurements over the Sonoran Desert after SBAF and bidirectional reflectance distribution function corrections are derived to evaluate the radiometric performance of ABI with respect to net primary productivity and NOAA-20 VIIRS. The analysis shows that the radiometric biases of the five VNIR channels of GOES-16 ABI are all within 5% in comparison to the matched channels of NPP VIIRS after applying the RTM correction. The analysis also detects ∼6  %   drop in the radiometric bias of GOES-16 ABI 0.64  μm channel after April 23, 2019, which can be traced to the implementation of a correction of the ABI B2 calibration coefficient around this date. Further, we evaluate the relative radiometric bias for the five VNIR channels between NPP and NOAA-20 VIIIRS using double difference method, and the comparison shows that NPP VIIRS has 2% to 3% higher bias than NOAA-20 VIIRS for these spectral bands.

The Fly’s Eye GLM Simulator (FEGS) is a multi-spectral array of radiometers designed to provide a validation dataset for the geostationary lightning mapper (GLM). The main component of FEGS is a 5  ×  5 grid of radiometers, each with a square 18 deg field of view, that are sensitive to a 10-nm wide spectral band centered on 780 nm to observe a neutral atomic oxygen emission triplet at 777.4 nm. FEGS was flown as a primary payload on the high-altitude NASA ER-2 aircraft as part of the GOES-R validation flight campaign in the spring of 2017. More than 14,000 lightning flashes were recorded over a variety of thunderstorm phenomenologies and geographical regions in the continental United States and Atlantic Ocean. Following the application of a quality control process, a subset of 9160 of these flashes occurring during daylight hours was suitable for analysis. This analysis has provided an independent, lower bound estimate of the average daytime optical flash detection efficiency (FDE) of GLM at 64%. The FDE varied significantly between storms and location ranging between 44% and 88%. An upper estimate of the time-integrated cloud-top radiance detection threshold of GLM during daytime observations is calculated to be 9  μJ  /  m² sr nm. Dependencies of GLM performance on the temporal width, energy, and cloud-top area of optical pulses, and the background solar radiance conditions are presented and discussed.

The imaging instruments on the polar-orbiting S-NPP and NOAA-20 satellite platforms [e.g., Visible Infrared Imaging Radiometer Suite (VIIRS)] and on geostationary GOES and Himawari platforms [e.g., Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI)] have high horizontal spatial resolution but coarse vertical information about tropospheric gases and temperatures. An approach for fusing a high spatial resolution imager (e.g., VIIRS, ABI, and AHI) and a high information content sounder or trace gas monitor [e.g., Cross-track Infrared Sounder (CrIS) and TROPOspheric Monitoring Instrument (TROPOMI)] offers the opportunity to construct retrieval products (e.g., trace gas concentrations of CO, SO₂, and NH₃) via product fusion at high spatial resolution. Furthermore, these products can also be provided at high temporal resolution when geostationary imager (ABI and AHI) data are used in the product fusion. Promising results from VIIRS/CrIS, VIIRS/TROPOMI, and AHI/TROPOMI fusion are presented.

Phase extraction in differential synthetic aperture radar (SAR) interferometry (DInSAR) is an important tool used for detecting subcentimeter-level change in ground deformation. The evaluated phase map processing is conducted via two important and successive steps: phase denoising and phase unwrapping. We attack the first step and propose the performance of discrete Riesz wavelets transform to reduce the residual speckle noise from the generated DinSAR phase map. The performance of the proposed method is appraised using three important criteria such as peak-to-signal-noise ratio (PSNR), the quality index Q, and the edge preservation index. The obtained metric values reveal that this technique can improve the PSNR and Q in comparison with other famous techniques. Finally, we apply the algorithm to denoise a real DinSAR phase map generated with sentinel application platform software using three SAR data of the Napa city in California.

Microwave staring correlated imaging (MSCI) is a staring high-resolution microwave imaging technique, employing the temporal–spatial stochastic radiation field (TSSRF). The imaging capability of MSCI depends on the spatial correlation of the TSSRF, which is equivalent to the incoherence of the sensing matrix in discrete form. A waveform design method for MSCI using multifrequency signals to reduce the spatial correlation of the TSSRF is proposed. The amplitude of each frequency component in the multifrequency signals serves as the design variable. Considering the power and the amplitude constraints in the practical radar system, we first form the waveform design as a constrained optimization problem. Then, it is reformulated as a nonsmooth unconstrained optimization problem by the penalty function method. With the smoothing approximation, the reformulated problem is solved by alternating minimization. Numerical experiments show that the proposed method can decrease the spatial correlation of the TSSRF and improve the imaging performance of MSCI as a result.

Object detection in synthetic aperture radar (SAR) images remains a challenging problem due to the particular imaging mechanism of SAR systems. The sizes of targets are relatively small and the scenes are large, indicating that the intersection over union value between the targets and anchors is probably small. In addition, SAR images are severely polluted by speckles under normal conditions. The edges of objects in SAR images are blurred. In this study, we proposed an improved object-detection framework based on a two-stage faster region-based convolutional neural network. A feature-enhancement module based on self-attention mechanism is designed to learn a target-oriented feature map, where the spatial attention and channel attention work simultaneously, such that targets can be highlighted and the speckles can be suppressed to a certain extent. Two separate feature maps serve as the input of the region proposal network to isolate the classification and regression tasks. Because only one category exists in the ship-detection task, correctly distinguishing targets from the background results in the correct detection of ships. In the final classification stage, an extended region-of-interest pooling operation is performed on the potential proposals and contexture information. The usage of extra information can improve target fine-tuning in the final network. To avoid ignoring small targets, we carefully set the anchors’ parameters based on the analysis of ground truth and select the appropriate shapes of feature maps. With the help of these modifications, the proposed method can detect small, weak, and dense targets in SAR images. Ablation experiments over networks with different configurations prove that the proposed modules are working. Experiments on real SAR Gaofen-3 and Sentinel-1 images demonstrate the efficiency of the proposed object-detection framework.

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 for spectral shapes. Ten-fold improvement is demonstrated in retrieval accuracy using the proposed method on multiple data sets. Input spectral shape variations are simulated using spectra of TOA radiance, path radiance, exoatmospheric solar irradiance, and integrating sphere. SRF changes are simulated using SRFs of Ocean Color Monitor-2 and Sea-Viewing Wide Field-of-View Sensor. System spectral shape factor, a new parameter, is found to capture the spectral changes and helps quantitative assessment of the inaccuracy. The proposed method will be highly beneficial in deriving accurate geophysical products from the existing and upcoming ocean color remote sensing instruments.

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. In order to solve this issue, we propose a homogeneous region regularized multilayer non-negative matrix factorization (HR-MLNMF) method for hyperspectral unmixing. In HR-MLNMF, the spatial information, depicted by the homogeneous region, is applied to regularize MLNMF, which could enhance the smoothness of each homogeneous spatial field to achieve better performance. Experiments on both synthetic and real datasets have validated the effectiveness of our method and shown that it has outperformed several state-of-the-art approaches of hyperspectral unmixing.

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 to the final kernel remain major challenges for MKL. We propose a scheme to generate effective base kernels and optimize their weights, which represent their contribution to the final kernel. In addition, both spatial and spectral information are utilized to improve the classification accuracy. In the proposed scheme, the spatial features of HSIs are introduced through multiscale feature representations that preserve the relationship between the classification process and the pixel context. MKL and self-organizing maps (SOMs) are integrated and used for the unsupervised classification of HSIs. The weights of both the base kernels and neural networks are simultaneously optimized in an unsupervised manner. The results indicate that the proposed MKL-SOM scheme outperforms state-of-the-art algorithms, particularly when applied to large HSIs. Moreover, its ability to fuse multiscale features, especially in large HSIs, is useful for various analysis tasks.

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 unmixing, is also analyzed quantitatively for UTSVM. Two types of unmixing uncertainty, abundance overlap (i.e., different mixed pixels have the same abundances) and model overlap (i.e., one mixed pixel may be unmixed into different abundances), are introduced. Abundance overlap angle and abundance variability scale (AVS) are defined as two uncertainty indexes to measure abundance overlap and model overlap, respectively. The relationship between within-class endmember variability and unmixing uncertainty is discussed. When the unmixing uncertainty is high, we propose to use the mean value of abundances within AVS as the estimation of abundance to obtain the best compromised results. Experimental results show the feasibility and effectiveness of our study.

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 based on short-time time-frequency analysis, the proposed method extracts the time-varying signal feature via integration decomposition. In the proposed method, the amplitude history (AH) curve, which presents the temporal distribution of signal energy, is generated based on the intermediate data of the fractional Fourier transform integration. Then a detector is provided to locate the start and end positions of the chirp signal component in the AH curve. Monte Carlo experiments under different conditions verify the noise robustness and computational efficiency of the proposed method. In general, the proposed method provides a new perspective for extracting the time-varying feature of the signal component, and it is a useful reference for future investigations concerning integration decomposition.

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, the micro-Doppler effect of rotating target is theoretically analyzed with formulas derived. Then, the instantaneous frequencies are extracted via the short-time Fourier transform and the Viterbi algorithm. The echo signals received by these three antennas are decomposed by the intrinsic chirp component decomposition method, and the interferometric phases of different scatterers are respectively obtained. Finally, the 3-D MMTs of rotating target are reconstructed by exploring the interferometric phases and the range-slow-time data. The effectiveness of the proposed method is validated by both simulations and practical experiment using a Ka-band interferometric radar.

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 imaging method based on the prior knowledge of imaging, the method proposed in this paper directly trains the ionospheric implicit imaging model of spaceborne SAR without complicated iterative processes. First, the phase errors, which are caused by the dispersion and the scintillation in the range and azimuth directions, respectively, are analyzed. Second, an improved u-net structure that embeds the residual network between the encoder and decoder is briefly proposed. Finally, the range doppler algorithm is used to preprocess radar image as the input of the convolutional neural network (CNN) and is compare with the predicted output of the CNN. Experimental results prove the effectiveness of the proposed method in radar image focusing.

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 sea conditions are mostly neglected in the current detecting techniques, which go against the reliable parameter selection in the clutter suppression process. An innovative multi-channel space-time decorrelation analysis method based on sea clutter is proposed. First, the channel decorrelation factors caused by the channel amplitude-phase errors and the noise are calculated by fusing the calibration signal with echo data. Then, the temporal decorrelation curve caused by the internal motions of sea clutter is obtained by fitting the peak points of multi-channel correlation coefficient curve based on the multi-channel phase center. Finally, the independent spatial decorrelation curve is derived through calculating the multi-channel correlation coefficients of different range bins. Due to the utilization of space-time characteristics decomposition method for sea clutter, the space-time coupling problem can be solved effectively. The experimental results based on the measured data prove the effectiveness of the proposed method.

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 are extracted by thresholding the received signal. It is acceptable if SNR is high. The threshold must be less than the sufficient probability to avoid a high rate of false detections. This assumption is unsuitable for tracking extended-stealth targets (ESTs) with unknown detection probability. To this point, we address a track-before-detect-MB-RMM approach, which is an efficient way to solve the unknown detection profile issue. There is a considerable advantage of using unthresholded data in simultaneous detection and tracking. In this scenario, although the extension ellipsoid is efficient, it may not be accurate enough because of a lack of useful information, such as size, shape, and orientation. Therefore, we introduce a filter composed of subellipses where each one is represented by an RMM. Furthermore, a sequential Monte Carlo implementation is applied to estimate nonlinear kinematic ESTs’ state. The results confirm the effectiveness and robustness of the proposed filter.

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 polarization coherence channel is developed to retrieve the forest parameters. To evaluate the effectiveness of this method, we first apply it to simulated data obtained from PolSARProSim software. Next, the experimental L-band single-baseline single-frequency polarimetric interferometric data are used to assess the applicability of the proposed method for the actual forest topography in Malaysia and Kudara, Lake Baikal, Russia. The obtained results indicate that the proposed algorithm significantly overcomes the disadvantages of the recently published methods.

A four-directional total variation technique is proposed to encapsulate the spatial contextual information for sparse hyperspectral image (HSI) unmixing. Traditional sparse total variation techniques explore gradient information along with the horizontal and vertical directions. As a result, spatial disparity due to high noise levels within the neighboring pixels are not considered while unmixing. Moreover, oversmoothing due to total variation may depreciate the spatial details in the abundance map. In this context, we propose a four-directional regularization technique (Sparse Unmixing with Splitting Augmented Lagrangian: Four-Directional Total Variation, SUnSAL-4DTV) for sparse unmixing. The four-directional total variation scheme is transformed into the fast-Fourier-transform domain to reduce the higher computational requirements. An alternating-direction-method-of-multipliers-based iterative scheme is proposed for solving the large-scale optimization problem. An adaptive scheme is introduced to update the regularization parameters to ensure faster convergence. Extensive numerical simulations were conducted on both simulated and real hyperspectral datasets to demonstrate the robustness of proposed technique. Comparative analysis on noisy (low signal-to-noise-ratio) HSIs shows the robustness of SUnSAL-4DTV over the state-of-the-art algorithms.

In this study, a general transformation-based framework for unmanned aerial vehicle image stitching is proposed that can resist global distortion while improving the local registration accuracy. In the first step, with tie points as constraints, the global transformation function of each image is obtained in an optimization manner and no reference image is needed. In the second step, to reduce data redundancy, an image selection algorithm based on information entropy is proposed. The optimal image combination covering the entire scene is selected from the original image set. In the third step, a local optimization algorithm based on mesh deformation is proposed to further optimize the registration accuracy of the optimal image combination. Finally, all of the images are combined to obtain a high-resolution panorama. On challenging datasets, the proposed algorithm can not only reduce the global distortions caused by the accumulation of errors during image stitching, but can also reduce the redundant data, which will benefit the postprocessing. The local mesh optimization can greatly improve the registration accuracy and eliminate the obvious misalignment problems. Tested on a large number of challenging datasets, the proposed method substantially outperforms several state-of-the-art image-stitching methods and commercial software.

Much interest has arisen in the problem of detecting weak optical subpixel objects in a sequence of images immersed in a heavy homogeneous Gaussian clutter background. In optical systems, the presence of the objects changes the background plus the channel noise covariance matrix. Hence, this matrix may be different under null and alternative hypotheses. Because the maximum likelihood estimate of the background covariance matrix is not available when using the primary data set, we use an approximate estimate of a certain number of eigenvalues of the background covariance matrix that are available for estimation. We derived the general likelihood ratio test for the problem at hand and obtained the modified spectrum matching detector (MSpMD). Using the simulation results, we contrast it with well-known spectrum matching detectors and the hypothesis-independent matched subspace detectors. We also present experimental results of small subpixel objects detection on an agitated sea surface, using digital optical experiments that demonstrate the validation of the simulating results. We show the advantage of the MSpMD that allows to detect small subpixel floating objects on an agitated sea surface even with the same means and variances of the reflected signals from the sea surface and the object.

The rapid development of the aerospace industry has significantly increased the demand for remote-sensing images with high resolution and quality. Generating images with expected resolution from the samples obtained by common acquisition devices is a challenging task as the trade-off between cost and efficiency must be considered. We propose a super-resolution (SR) algorithm especially for remote-sensing images that is based on generative adversarial networks optimized by a classifier, which is called classifier-based super-resolution generative adversarial network (CSRGAN). We hypothesize that the confidence scores of classification can be a critical factor for representing the features in target remote-sensing images. To sufficiently take this factor into account during training, we add the class-score as an error into the loss function in addition to mean square error and high-dimensional features extracted from deep neural networks. Then, the classifier is utilized for both better SR performance and more precise classification. The classifier-testing branch of our system can also be flexibly combined with other network architectures to optimize SR performance on remote-sensing images. We validate the model on the NWPU-RESISC45 dataset considering both SR and classification performance. The final analysis is also provided and shows that the proposed CSRGAN outperforms existing algorithms.

Most existing multispectral fusion algorithms often suffer from spectral or spatial information distortion. Driven by this motivation, we propose an edge-guided multispectral (MS) image fusion algorithm. In particular, it combines the advantages of generative adversarial networks and improved fusion frameworks, so the merged image can better preserve the spectral information of the original multispectral image while injecting spatial detail information. Specifically, first, an MS image with more image detail is generated using the generated confrontation network for preliminary reconstruction. The panchromatic image edge information and the antagonistic learning strategy are introduced for the robust multispectral image reconstruction. Then, using the reconstructed MS image and the general component substitution image fusion framework, the whole fusion system of this paper is constructed. An enhancement operator is introduced to inject spatial details. Our extensive dataset evaluations show that our approach performs better in terms of high objective quality and human visual perception than several of the most advanced fusion methods.

Due to lacking use of prior information, the anomaly detection results are not always satisfactory. However, with the establishment of the spectral library, it becomes possible to obtain one or more spectra of the background in the image to be detected. If we can make use of such background information that is always ignored or discarded, the detection result is very likely to be improved. Hence, we proposed a hyperspectral anomaly detection method using a background endmember signature. To better separate the anomaly from the background, we first perform spectral unmixing to estimate the abundance matrix for further study instead of the original spectral data. In this process, we introduce a non-negative matrix factorization-based unmixing method and a corresponding initialization method using a background endmember. Then the low-rank property contained in the abundance matrix is exploited. A low-rank decomposition method is used to separate the anomalies. The proposed algorithm is evaluated on both synthetic and real data sets. Experiment results show the effectiveness of the proposed method and the improvement brought by the usage of a known background endmember.

Medium Resolution Spectral Imager II (MERSI-2) is a payload of the China meteorological satellite FY-3D. The sensor bands 24 (10.3 to 11.3  μm) and 25 (11.5 to 12.5  μm) images, which are most suitable for land surface temperature (LST) retrieval, have higher spatial resolution than that of similar sensors. However, bands 24/25 images with spatial resolution of 250 m (hereafter bands 24/25 250-m images) have considerable horizontal stripe noise caused by the limitation of the sensor imaging system’s multi-detector parallel scanning and the multiple interferences from space. Thus, the application value of LST retrieval using bands 24/25 250-m images is severely affected. At present, existing methods used to remove stripe noise often cannot retain the spectral information of the original image. Our research considers that MERSI-2 bands 24/25 images with spatial resolution of 1000 m (hereafter bands 24/25 1000-m images) and no stripe noise can provide reasonable reference statistics for bands 24/25 250-m images. Thus, the moment matching method considering synchronous reference image is applied to remove the stripe noise of bands 24/25 250-m images in the land area. Results shows that the moment matching method considering the synchronous reference image can effectively remove the stripe noise in the land area. In addition, quantitative evaluation criteria, such as information entropy H, mean-absolute-error (MAE), and peak signal-to-noise ratio, are better than those of the traditional moment matching method. After destriping, the LST values using MERSI-2 data are retrieved with better effect and decrease by 0.339 and 0.504 K using MAE and root-mean-square error, respectively, in contrast to the Moderate Resolution Imaging Spectroradiometer LST product.

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), the SD diffusely reflects off the incident sunlight to act as a radiance source for the calibration. An onboard solar diffuser stability monitor (SDSM) yields the on-orbit change of the SD bidirectional reflectance distribution function (BRDF) by comparing the signal strength from the SD with that from the Sun attenuated by another attenuation screen (the SDSM screen). Complications arise due to the discovery that the on-orbit change of the BRDF is angle dependent. Additionally, the SDSM does not cover the wavelengths for the short-wave infrared bands in the RSBs. Furthermore, satellite yaw maneuvers were performed in the early mission to yield data for refining the prelaunch SDSM screen relative effective transmittance and the relative product of the SD screen transmittance and the BRDF at the mission start. But the yaw maneuver data are coarse in the solar azimuth angles and thus are unable to yield accurate values between the measurement angles. Over the years of performing on-orbit radiometric calibration through the SD for the VIIRS RSBs, we have developed several highly effective calibration algorithms to address the issues mentioned above. We review these algorithms.

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 distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). We review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally, we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the Suomi National Polar-orbiting Partnership (SNPP) and the NOAA-20 VIIRS and the Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that, although the SNRs trend downward, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS.

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 was designed and implemented in the MODIS level 1B (L1B) calibration. Shortly after the Terra launch, extensive efforts were undertaken to help characterize and mitigate the impact due to the OOB response and crosstalk on the SWIR on-orbit calibration and, consequently, the associated L1B data products. In addition, special night-time-day-mode operations have been regularly scheduled to derive the crosstalk correction coefficients. Since MODIS does not have a spectral band centered at 5.3  μm, its band 28 (7.325  μm) was chosen as the surrogate sending band to simulate the OOB radiances at 5.3  μm. This was largely based on the measurements from the MODIS Airborne Simulator spectrometer field campaigns in the early months after the Terra launch. In the case of Aqua MODIS, the magnitude of the SWIR crosstalk was much smaller and band 25 (4.52  μm) was found to be more effective as the sending band for the crosstalk correction. In recent years, the Terra MODIS photovoltaic (PV) long-wave infrared (LWIR) bands (27 to 30) electronic crosstalk has increased considerably, especially after the spacecraft safe-mode event occurred in February 2016. This accentuated degradation in the PV LWIR performance has also impacted the performance of the SWIR crosstalk correction and thus its calibration and data quality. We examine the use of band 25 as the sending band for the Terra MODIS SWIR crosstalk correction and compare its performance with that based on band 28 as the sending band. Results indicate an improvement of on-orbit gain stability for the SWIR calibration and reduced detector to detector and subframe to subframe striping in the calibrated L1B imagery, especially during the period when the PV LWIR electronic crosstalk has become more severe. This approach has been implemented in the forward production of Terra MODIS Collection 6 and Collection 6.1, starting from July 2019, and is planned to be used for the future reprocessing of MODIS L1B and to help improve the mission-long reflectance calibration and trending stability of the SWIR bands.

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 was found to be higher in the beginning of the mission and to be decreasing with time. The latest calibration results show that the decrease in instrument response between 440 and 900 nm in the near-infrared band was relatively small compared to the previous official calibration coefficient, with an average attenuation rate of ∼5.3  %  . It was observed that the MERSI-II shortwave channel had a large attenuation of 21%, especially in the 412 nm band. The calibration method and parameter processing of the reflectance-based method are introduced in detail. The accuracy of the calibration process was tested using Moderate Resolution Imaging Spectroradiometer, an internationally recognized high-precision satellite instrument, which determined the calibration error to be <4  %  . The experimental results contribute to the improvement in the accuracy of satellite observation and the design of subsequent equipment.

Visible-near infrared (VIS-NIR) spectral data are widely used for remotely estimating a number of crop health metrics. In general, these indices and models do not explicitly account for leaf surface characteristics, which themselves can be indicators of plant status or environmental responses. To explicitly include leaf surface characteristics, data are required linking optical properties to surface characteristics. We present the design and experimental validation of a goniospectropolarimeter (GoSPo) that combines the capabilities of a spectrometer, goniometer, and polarimeter. GoSPo was designed with the objective of studying the relationships between leaf surface characteristics and the resulting light reflectance, transmission, and polarization as functions of both direction and VIS-NIR spectra. Using six motors, a pneumatic system, two spectrometers, and a combination of lenses, polarizers, and mirrors, GoSPo can examine a leaf from a particular angle, approximate hemispherical transmittance and reflectance (with root-mean-square error values of 0.0189 and 0.0216 for reflectance and transmittance, respectively, compared to a spectrophotometer and integrating sphere), and obtain spectral polarization measurements without disrupting the sample between measurements. The data collected with GoSPo will aid in model development for remote sensing applications.

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 addition, the data often contain a lot of noise. To address these problems, we propose an autoencoder network, referred to as MDC-SAE, that introduces two different constraints to optimize the spectral unmixing problem. Specifically, we adopt the L_1/2 norm regularizer to constrict the abundance vectors, making them sparse. At the same time, we apply the minimum distance constraint on the endmember matrix to push each endmember toward its centroid. We evaluate our method on both synthetic and real data sets, and experimental results demonstrate that the proposed method can achieve the desired solutions and outperforms several state-of-the-art methods.

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 orbit, are radiometers providing high spatial and temporal resolution radiance measurements across a more limited number of bands, typically ∼10 to ∼20. Their data are used for monitoring Earth’s weather and climate. A data fusion method (developed and previously demonstrated at University of Wisconsin-Madison) is utilized to construct high vertical resolution, sounder-like temperature and humidity retrievals at imager high spatial and temporal resolution. Using the Crosstrack Infrared Sounder (CrIS) and Advanced Baseline Imager (ABI) data, comparison of concurrent and ∼50-min time-series fusion to radiosondes launched from February 2017 to March 2019 over the Department of Energy Atmospheric Radiation Measurement Southern Great Plains Site shows that the improved spatial and temporal resolution does not come at the expense of accuracy as compared with the sounder retrieval at native resolution.

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 or cadastre information can be used. To attend this, a methodology to perform supervised classification based on the combination of statistical, textural, and shape features for the LULC classification problem is proposed. For the experiments, thousands of Landsat and Sentinel images covering all of Mexico and Europe, respectively, were used. Twelve LULC classes were established for Mexico territory, and 10 were established for Europe. This methodology was applied to both datasets and benchmarking with multiple well-known classifiers (random forest, support vector machines, extra trees, and artificial neural network). As a result, the overall accuracy (OA) for Landsat and Sentinel-2 was reached 77.1% and 96.7%, respectively. The performance of the different types of features was compared using Mexico and Europe images. Interesting results were achieved and some conclusions about using traditional and non-traditional features were found in the LULC classification task.

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 of papers. We propose a siamese network composed of three-dimensional convolutional neural networks named 3DCSN. We design a structure for 3DCSN that combines contrast information with label information and get a satisfying classification result. With only a few labeled samples, it performs better than the baseline methods. Moreover, it is an end-to-end network that can use joint training. The experiments indicate the great potential of our method.

The characteristics of ecosystem functions are of great significance for biodiversity conservation and ecosystem services. Ecosystem functional types (EFTs) are land surface areas similar in carbon dynamics that are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. However, identification of EFTs based on low-resolution remote sensing data cannot satisfy the needs of fine-scale characterization of regional ecosystem functionality patterns, and a more accurate and optimized method of identifying EFTs also deserves attention. Here, we characterize EFTs at a county scale based on subtractive fuzzy cluster means (SUBFCM) and Sentinel-2 time-series data. The normalized difference vegetation index, the fraction of absorbed photosynthetically active radiation, and canopy water content and their derived variables in the growing season were selected as ecosystem functional indicators to characterize regional EFT diversity patterns. The correspondence analysis method was used to reveal relationships between the EFTs and land cover structure information, and further analysis of EFTs was performed with soil type data. Our results showed that the selected variables indicating carbon and water flux of the regional ecosystems could be adopted in ecosystem functional classification. The SUBFCM algorithm can automatically divide EFTs with faster convergence speed and reduced subjectivity. The obtained EFTs based on Sentinel-2 images reflected the internal structure of carbon balance well and the distribution pattern of ecosystem functional diversity at a fine scale. A reference for further optimization of the EFT identification algorithm and development of the understanding of spatial heterogeneity of temperate terrestrial ecosystem functions was provided.

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

This erratum explains a correction to a figure.