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To address the challenge of detecting small objects in aerial and satellite remote sensing images with low-resolution, we propose a high-precision object detection method based on PFRNet. PFRNet incorporates parallel feature extraction branches and a progressive feature refinement mechanism, significantly enhancing the model’s ability to perceive detailed features. In addition, PFRNet introduces the spatial pyramid pooling fusion with spatial attention (SPPFSPA) module, which integrates multi-scale features with an attention mechanism, enabling the model to better focus on areas of interest, thereby improving detection performance. Results demonstrate that PFRNet achieves outstanding detection accuracy, markedly outperforming other algorithms, particularly in small object detection. Visualization analysis reveals that the PFR module effectively captures richer and more comprehensive visual features in images, providing robust input for subsequent detection tasks, which is crucial for PFRNet’s superior performance. Overall, the proposed PFRNet model makes significant strides in small object detection in UAV aerial and satellite remote sensing images, offering strong support for applications such as intelligent transportation and precision agriculture.

The near-surface air temperature (${{T}_{air}}$) is a principal variable describing energy exchange and water circulation between the land surface and the atmospheric environment. The estimation of ${{T}_{air}}$ by satellite land surface temperature (LST) is challenging due to the variable magnitude of the difference between ${{T}_{air}}$ and LST in both space and time, as well as the restriction of estimated ${{T}_{air}}$ to clear-sky conditions because of the penetration of infrared wavelengths. Moreover, the estimation suffers from low temporal resolution and primarily focuses on daily minimum, maximum, and two instantaneous ${{T}_{air}}$ per day. This study proposes a method for estimating all-sky gridded diurnal ${{T}_{air}}$ at regional scale from FY-4B/AGRI measurements. The multiscale geographically weighted regression model was investigated to establish the dynamic relationships between ground station observed ${{T}_{air}}$ and satellite LST under clear-sky conditions by employing different spatial values for each explanatory variable in localized regressions. A moving window loop based multiple linear regression was employed to establish the relationship between satellite-derived clear-sky ${{T}_{air}}$ and other variables to extrapolate ${{T}_{air}}$ in cloudy-sky pixels. The results showed that the proposed method captures the trend of ${{T}_{air}}$ variations well in hourly profiles with R values greater than 0.95. RMSE was 1.75 °C, 1.38 °C, 1.95 °C, and 2.19 °C in April, July, October, and January, respectively. The demonstration of heatwave monitoring showed that satellite-estimated ${{T}_{air}}$ provide an excellent representation of the spatial and temporal evolution of the heatwave.

Bathymetry is a critical marine parameter for applications, such as coral reef monitoring, erosion forecasting, coastal defense, and other related efforts. The predominant method for obtaining water depth data has been through satellite remote sensing. However, the utilization of multispectral processing in deeper and more complex benthic substrate environments poses significant challenges due to reduced light penetration at greater depths and the influence of benthic substrate on light reflection and absorption. In response to these challenges, a novel inversion method based on machine learning called the minimum distance from the coastline and geographical position-based machine learning bathymetry inversion model (MDCGP-ML) has been introduced in this research. The MDCGP-ML model leverages the spectral data from multispectral remote sensing images, alongside MDCGP features, with 70% of bathymetric data from the South China Sea region used for model training. The machine learning algorithms employed include random forest, support vector regression, backpropagation (BP) neural network, extreme gradient boosting, categorical boosting, and light gradient boosting machine (LightGBM). The 30% of in situ was utilized for validating model accuracy. Experimental results using WorldView-2 imagery indicate that the MDCGP-ML model delivers strong performance [root mean square error: 0.849–1.26]. Furthermore, compared to traditional log-ratio inversion models and the spectral information-based machine learning bathymetry inversion model, the MDCGP-ML approach exhibits distinct advantages. It effectively reduces the overreliance on spectral data in bathymetric inversion. This advancement offers a novel perspective for augmenting the bathymetry inversion capabilities of satellite remote sensing in complex nearshore aquatic regions.

This study introduces a data-to-data (D2D) translation approach that utilizes a conditional adversarial learning framework to generate hypothetical data at the meteorological imager (MI) sensor on the communication, ocean, and meteorological satellite (COMS). The proposed D2D model produces virtual 10-min data from actual 30-min data by exploiting the 10-min temporal resolution (TR) of the advanced MI (AMI) on GEO-KOMPSAT-2A during the overlapping observation period of the two satellites. Specifically, the D2D model uses one visible (VIS) at 0.64 μm channel and four infrared channels (3.8, 6.9, 10.8, and 12.3 μm) from AMI of the actual 30-min data from April 2020 to April 2022 to train and test the model. Subsequently, the D2D model is applied to simulate hypothetical 10-min-TR COMS data using the 30-min COMS observation data from September 2019 to March 2020 during the coexistence period of the two satellites. Regression-calibrated COMS data alleviated the spectral response function differences between MI and AMI sensors. The proposed D2D method exhibits excellent statistical performance, with an average root-mean-square error of 0.056 for the VIS channel and 3.237 K, 1.005 K, 3.251 K, and 3.184 K for 3.8 μm, 6.9 μm, 10.8 μm, and 12.2 μm channels, respectively. The findings of this study are expected to facilitate various types of remote sensing research and applications using long-term data with AMI and AMI-like past MI data.

The NASA ISRO synthetic aperture radar (NISAR) mission scheduled for launch in 2024 will provide global L-band radar observations that can be applied to estimate land surface soil moisture. The mission's soil moisture product will be provided at 200-m resolution with a global revisit frequency of 12 days (or 6 days when considering both ascending and descending observations). A time-series ratio algorithm for soil moisture retrieval has been applied to NISAR simulated datasets from airborne UAVSAR measurements in the SMAPVEX12 field campaign. Soil moisture retrieval performance using the algorithm is encouraging, with a correlation coefficient between retrievals and in situ observations greater than 0.7 and an unbiased root-mean-squared Error (RMSE) of 0.05 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$. The results suggest that the time-series ratio algorithm will provide soil moisture products that meet an accuracy goal of 0.06 ${{{\bm{m}}}^3}/{{{\bm{m}}}^3}$ unbiased RMSE.

True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the geostationary Korea multipurpose satellite (GK) 2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm2/cm/sr, and −1.858 W/cm2/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

Cirrus cloud contributes significantly to earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 μm band, sensitive to monitoring cirrus clouds. This article proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 μm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 μm bands and other highly correlated bands to cirrus from July 24, 2019 to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root-mean-square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 μm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 μm band was qualitatively analogous to the observed AMI 1.37 μm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 μm band performed similarly to the observed AMI 1.37 µm band. Consequently, this article can significantly contribute to cirrus detection and its application to climatology. [ABSTRACT FROM AUTHOR]

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from in situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

Near-surface air temperature (NSAT) playsan important role in land surface and atmosphere interactions. It is widely used in many fields, such as hydrology, climatology, and environment. Although remote sensing-based approaches for NSAT estimation have been proposed by the scientific communities, many of them are limited in cloudy areas and, thus, are not able to provide all-weather NSAT (AW-NSAT) estimates. To satisfy NSAT-related applications for all-weather conditions over the Tibetan Plateau (TP), this study develops a novel model for estimating daily 1-km AW-NSAT based on machine learning techniques. The input variables for the AW-NSAT model include land surface temperature (LST) from a newly released satellite all-weather LST dataset (i.e., TRIMS LST), as well as other parameters. The model is trained with in situ NSAT, and the results show that the random forest model with all-weather LST as the main input yields the best performance. Validation with independent in situ NSAT shows that the AW-NSAT estimate has good accuracy: An overall root-mean-square error of 2.43 °C and an R2 of 0.93. Intercomparison with an existing NSAT dataset based on MODIS LST shows that AW-NSAT has similar accuracy. Nevertheless, AW-NSAT has an evident spatial seamless characteristic, indicating that the developed model has good ability to overcome cloud contamination by introducing TRIMS LST. The developed model provides the possibility for generating the AW-NSAT for the whole TP. Furthermore, the proposed model can also be extended to other areas and then support NSATs subsequent applications.

This article developed a scalable deep-learning model, the AlgaeNet model, for floating Ulva prolifera (U. prolifera) detection in moderate resolution imaging spectroradiometer (MODIS) and synthetic aperture radar (SAR) images. We labeled 1055/4071 pairs of samples, among which 70%/30% were used for training/validation. As a result, the model reached an accuracy of 97.03%/99.83% and a mean intersection over union of 48.57%/88.43% for the MODIS/SAR images. The model was designed based on the classic U-Net model with two tailored modifications. First, the physics information input was a multichannel multisource remote sensing data. Second, a new loss function was developed to resolve the class-unbalanced samples (algae and seawater) and improve model performance. In addition, this model is expandable to process images from optical sensors (e.g., MODIS/GOCI/Landsat) and SAR (e.g., Sentinel-1/GF-3/Radarsat-1 or 2), reducing the potential biases due to the selection of extraction thresholds during the traditional threshold-based segmentation. We process satellite images containing U. prolifera in the Yellow Sea and draw two conclusions. First, adding the 10-m high-resolution SAR imagery shows a 63.66% increase in algae detection based on the 250-m resolution MODIS image alone. Second, we define a floating and submerged ratio number (FS ratio) based on the floating and submerged parts of U. prolifera detected by SAR and MODIS. A research vessel measurement confirms the FS ratio to be a good indicator for representing different life phases of U. prolifera.

True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This article presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the advanced meteorological imager (AMI) sensor onboard the geostationary Korea multipurpose satellite. The AMI datasets in the form of albedo and brightness temperature (${{\boldsymbol{T}}}_{\boldsymbol{B}}$) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or ${{\boldsymbol{T}}}_{\boldsymbol{B}}$ differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were −0.006 and 0.047 in albedo, and 0.941 for the blue band; −0.007 and 0.05 in albedo and 0.939 for the green band; −0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea meteorological administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this article could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, meteosat third generation, and geostationary operational environmental satellites.

Area-volume-elevation (AVE) curves are critical for reservoir operation rules. However, such curves are not publicly available for most global reservoirs. Here, we present a framework to derive reservoir AVE curves from TanDEM-X data, using Lake Mead (~600 km2) as an example. First, the maximum water extent from 1984 to 2018-provided by the global surface water (GSW) dataset-was used as a mask to obtain the TanDEM-X data. Then, the TanDEM-X water indication mask (WAM) was applied to the extracted TanDEM-X data to obtain the visible bathymetry, which represents the topography between the maximum extent (according to GSW) and the water extent from WAM. Last, the AVE curve was generated by integrating the volume values from the top to bottom layers. TanDEM-X also captures the elevation values of the transitional waters, which are defined as the difference between the highest and lowest water levels. The transitional waters were obtained by thresholding amplitude and coherence images, and their elevations were then added to the visible bathymetry to extend the AVE curves with an elevation range extending from 344-369 m to 341-369 m. Validation results against in situ lidar survey values suggest a high-accuracy of elevation-area (E-A) relationships with R2 values of >0.99 and NRMSE values from 2.11% to 2.45%, and elevation-volume (E-V) relationships with R2 values of 1 and NRMSE values from 1.11% to 1.29%. Results also show that TanDEM-X data can capture the interannual variations due to multiple acquisitions, and that the elevation measurements for the lake shore areas are reliable.

High spatial resolution and broad spatial coverage data on fine particulate matter (PM2.5) are of great significance to estimating the exposure to PM2.5. However, this type of data is currently very limited worldwide. In addition, the COVID-19 pandemic in China, starting in January 2020, may have led to significant variations in the PM2.5 concentrations. To identify the variations and causes of PM2.5 concentrations before and after the COVID-19 pandemic from January 23 to March 24 during 2018–2020, in this article, a geographically weighted regression model with a 1 km spatial resolution covering all of mainland China was developed. The overall R and RMSE values of the model cross validation were 0.91 and 17.19 μg/m3, respectively, indicating that the model performed satisfactorily in estimating the PM2.5 values. Then, based on the satellite-based PM2.5 values, the results show that the PM2.5 values fluctuated significantly across mainland China before and after the COVID-19 outbreak. Additionally, the mean PM2.5 values decreased by 5.41 μg/m3 in 2020 compared to 2019. In Hubei Province, the mean PM2.5 values increased by 1.85 μg/m3 in 2019 compared to 2018, whereas they dramatically decreased by 23.18 μg/m3 in 2020 compared to 2019. Finally, the results show that anthropogenic factors were primarily responsible for the variations in the PM2.5 concentrations in Heilongjiang, Jilin, and Liaoning provinces; whereas, both meteorological and anthropogenic factors were responsible for the variations in Hubei, Henan, Anhui, Shandong, and Jiangsu provinces during the study period. These results provide an important reference for the future development of air pollution control policies in China.

The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction.

The COVID-19 global pandemic has posed a serious impact on human life and health. Facing this unknown, unexpected, and devastating disease, China launched a resolute battle to prevent and control its spread. This study focused on assessing the recovery of city activity of 17 administrative regions in China around the COVID-19 pandemic period in early 2020. The night-time light (NTL) satellite remote sensing data, NPP-VIIRS dataset, was applied to evaluate the NTL changes during the coronavirus epidemic to understand the impact of the epidemic on city activity and the economy of China. A series of median composites was first constructed to analyze short-term NTL changes. Based on these median composites, the ratio map, difference map, wave index, and recovery ratio (RR) were defined to indicate the NTL recovery pattern and its spatial distribution characteristics. The results are as follows:first, the adopted median composites are able to describe the NTL changes in the short term; second, the recovery of city activity could not be exactly described by RR of a single year but could be evaluated by the comparison of RR between two adjacent years; third, a partial and spatially heterogeneous recovery of city activity is observed after the 2020 holiday compared to 2019, fourth, a better degree of recovery is observed after China announced its fully work resumption.

The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve subpixel registration. The relative registration accuracy improved by 1.6%-7.5%, whereas the overall performance improved by 4.5%-14.1%.

Satellite vertical atmospheric sounding was initiated more than 50 years ago and has evolved to provide the most critical component of today's global observation system. However, the operational use of today's polar orbiting satellite hyperspectral infrared (IR) observations in numerical weather prediction (NWP) has been limited to a small fraction of the radiance information being provided. On the other hand, research systems are in operation that combines high vertical resolution polar hyperspectral radiance measurements with high spatial and time resolution geostationary multispectral radiance measurements that demonstrate the promise of future geo-hyperspectral sounding observations to significantly improve the forecast location and warning time for the development of localized tornadic storms. This article has a twofold objective: 1) to demonstrate that there is much more information available in current IR sounding data, than is being used to benefit the current NWP operation and 2) to illustrate the importance of the spectrometer technology (i.e., Fourier transform vs. dispersive grating) used for achieving the vertical profile resolution required to improve both extended range and localized severe weather forecasts. These objectives are achieved by performing both theoretical physics-based radiance information content (IC) studies and empirical analyses of current hyperspectral radiance measurements. The IC studies clearly demonstrate the unique importance of longwave IR (9−15 μm) radiance observations. The empirical studies demonstrate the importance of using Fourier transform spectrometers for providing the high spectral fidelity needed to resolve the small-scale vertical features in atmospheric temperature and moisture profiles, which impact weather forecast accuracy.

Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011–2018), this article developed a retrieval model suitable for turbidity. The determination coefficient (R2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984–2018). The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P < 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onward). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R = 0.56, P < 0.001), followed by water temperature and wind speed (0.02 < R < 0.5, P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997–2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been increasingly employed for the estimation of ground-level atmospheric PM2.5. There have been several cross-validation (CV) approaches applied for the validation of satellite-based PM2.5 estimation models. However, these validation approaches often lead to confusion, due to the unclear applicable conditions. For this, we fully analyze and assess the existing validation approaches, and provide suggestions on applicable conditions for them. Furthermore, the existing validation approaches still have limitations to disregard the uneven distribution of ground stations, and tend to overestimate the performance of the PM2.5 estimation models. To this end, a CV-based validation approach considering the uneven spatial distribution of monitoring stations (denoted as SDCV) is proposed. SDCV introduces the spatial distance between validation station and modeling station into the CV process, and evaluates the spatial performance through a strategy of excluding modeling stations within a specific distance. Meanwhile, this approach has designed reasonable evaluation indices for the model validation. Taking China as a case study, the results indicate that SDCV can yield a more complete and effective evaluation for the popular PM2.5 estimation models than the traditional validation approaches.

Green bands in satellite remote sensing play an important role in monitoring water and vegetation information. Due to the lack of observed green band, the Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imager (ABI) sensor uses a synthetic one. This study presents an ABI green band generation method using the Pix2Pix based on conditional generative adversarial networks (CGANs) and convolutional neural network techniques with data observed in the visible range of the GOES-16/ABI sensor. Our model was constructed from the radiance data sets in the red, blue, and green bands of the Advanced Himawari Imager (AHI) onboard Himawari-8/9 satellites from August 27, 2018 to May 1, 2019, and applied to generate a GOES-16 ABI green band using the ABI blue band radiance data. A comparison between the AHI and the Pix2Pix-generated AHI green bands displayed high accuracy, evaluated through bias = 0.120, root mean square error (RMSE) = 0.983 in digital number (DN) units, and correlation coefficient (CC) = 0.999. Furthermore, comparison between the Pix2Pix-generated and synthetic ABI green bands resulted in a good agreement (bias = 1.029 and RMSE = 2.892 in DN units, CC = 0.993). The statistical comparison between the green band, and red or blue band resulted in the exceptional performance of the Pix2Pix-generated ABI green band compared to the synthetic ABI green band. Consequently, our Pix2Pix-based model can be effectively used to generate nonexistent green band of ABI sensor and be applied in a variety of scientific applications requiring green band. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) are becoming increasingly more popular to interpret remote sensing observations in many contexts. Their flexibility and accuracy have been a true advantage for the retrieval of many geophysical variables in the atmosphere, land, and ocean, over the last three decades. Uncertainty of the retrieved products is important to assess the quality of the retrievals but also to combine products a posteriori, combine them in a complex way to study for instance the water cycle, or assimilate them in numerical weather prediction (NWP) centers. However, no easy solution has been proposed so far to estimate the retrieval uncertainties of NN inversion schemes. A simple, pragmatic, and easy-to-implement scheme based on the input space clustering is proposed here to perform this task. Tests are conducted using an application aiming at retrieving atmospheric profiles based on improved atmospheric sounding in the infrared (IASI) high-spectral resolution observations in the infrared. [ABSTRACT FROM AUTHOR]

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun–Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh–ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters. [ABSTRACT FROM AUTHOR]

Changing a logic switch threshold in the linear fit sulfur dioxide (LFSO2) algorithm improves the performance based on the evaluation of the NOAA operational atmospheric SO2 near-real-time (NRT) retrieval. The LFSO2 is used to create estimates from measurements made by the Suomi NPP (S-NPP) Ozone Mapping and Profiler Suite (OMPS). We evaluate the LFSO2 and compare the results to those from a principal component analysis (PCA) offline algorithm. Twenty independent volcanic scenarios and one environmental disaster scenario spread over eight years are selected for this comparison. More than three months of Kilauea volcanic activity in 2018 are monitored and are included in this evaluation and comparison. We found that the operational LFSO2 retrievals at lower troposphere (TRL), mid-troposphere (TRM), and lower stratosphere (STL) exhibited a discontinuity and have a saturation-like relationship if compared with PCA results. Using the new retrieval logic, the discontinuity in LFSO2 retrievals and the saturation appearance in comparisons vanished and a close to a linear relationship with the matchup data from the PCA retrieval products is demonstrated. The minimum detectable values for all three SO2 layer products and the planetary boundary layer (PBL) products are estimated with the updated LFSO2 algorithm. Results for a volcanic cloud over Colombia for the updated LFSO2 for OMPS and a Differential Optical Absorption Spectroscopy (DOAS) algorithm for the Tropospheric Monitoring Instrument (TROPOMI) measurements are also examined. Similar SO2 total mass estimates over the region are obtained from the two products. [ABSTRACT FROM AUTHOR]

The severe haze pollution episode from December 1 to 10, 2013 occurred in Shanghai area, and seriously threatened human health and transportation safety. As regional air pollution, it is practical significance to the rapid monitoring and comprehensive analysis for haze treatment. Based on the moderate resolution imaging spectro- radiometer (MODIS) and cloud-aerosol LiDAR with orthogonal polarization (CALIOP) data, via combing with meteorological features during the haze pollution period, the current study analyzed and explored the spatial distribution, aerosol vertical structure, source and migration path of haze pollution. The results showed that: (1) There are obvious aerosol layers over Shanghai area, aerosol optical depth (AOD) fluctuates frequently and has distinctive features such as complexity, regionality and seasonality. (2) The aerosol layer is mainly at the heights ranging from 0 to 2 km and composed mostly of spherical aerosol but there is also non-spherical aerosol. (3) The total backscattering intensity of aerosol is centralized at the heights ranging from 0 to 1 km, especially at the heights ranging from 0 to 0.5 km. (4) The haze pollution episode was affected by both specific meteorological factors and migration of the non-local particles in the surrounding areas and northern China. [ABSTRACT FROM AUTHOR]

The prediction of PM2.5 concentration is a canonical predictive challenge due to the distribution of $PM_{2.5}$ appears serious spatiotemporal variability at multiple scales. Currently, using satellite-based remote sensing data to estimate ground-level PM2.5 is a promising method for providing spatiotemporal continuous information of PM2.5. In this letter, we proposed a deep neural network (DNN)-based PM2.5 prediction model to capture the spatiotemporal variability of ground-level PM2.5 using the remote sensing aerosol optical depth (AOD) data from the Himawari-8 satellite along with the conventional meteorological observation variables (denoted as PM25-DNN). The PM25-DNN model was trained and tested using the data from Beijing–Tianjin–Hebei region of China in 2017, and we compared the prediction performance between the PM25-DNN and the current state-of-the-art methods in this field. The results show that the PM25-DNN outperforms the other models with the cross-validated coefficient of determination ($\text{R}^{2}$), root-mean-square error (RMSE), mean prediction error (MPE), and relative prediction error (RPE) were 0.84, $19.9~\mu \text{g}/\text{m}^{3}$ , $11.89~\mu \text{g}/\text{m}^{3}$ , and 41.21%, respectively. Then, the trained PM25-DNN model was applied to estimate the hourly gridded PM2.5 with 1-km spatial resolution. Our results indicate that the DNN architecture can capture the essential spatiotemporal distribution associated with PM2.5 only using AOD data and conventional meteorological observational variables without more handcrafted features. The proposed PM25-DNN model can greatly improve the accuracy of PM2.5 estimation, and it provides a new perspective for PM2.5 monitoring using end-to-end deep learning method. [ABSTRACT FROM AUTHOR]

Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002–2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type, and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%–50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings. [ABSTRACT FROM AUTHOR]

In this letter, we propose a simple yet robust procedure to combat the residual local misalignment between the image data sets that are usually processed for pansharpening: a higher resolution panchromatic (Pan) image and a series of lower resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. Unlike a conventional coregistration, which requires a preliminary orthorectification enforced by an accurate digital surface model, the proposed method automatically exploits the characteristics of the Pan image to alleviate the effects of misalignment on fusion products, whichever is the method chosen for pansharpening. More specifically, the space-varying residue of the multivariate regression between resampled MS bands and low-pass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignments, is injected into the MS bands after being weighted by the projection coefficients of each band. Tests on simulated Pléiades images demonstrate that global shifts up to five pixels along each direction are perfectly compensated. Tests on a true GeoEye-1 image, whose shifts are space-varying and of unknown extent, highlight the attained improvement in spatial alignment. [ABSTRACT FROM AUTHOR]

A-train satellite data, acquired during the Calbuco volcano (Chile) sub-Plinian eruption in April 2015, are discussed to explore the complementarity of spaceborne observations in the microwave (MW), thermal infrared (TIR), and visible wavelengths for both near-source plume and distal ash clouds. The analysis shows that TIR-based detection techniques are not suitable near the volcanic vent where rising convective columns are associated with large optical depths. Detection and parametric estimates of near-source tephra mass loading and plume height from MW radiometric data, available 69 min after the eruption onset, are proposed. Results indicate a maximum plume altitude of about 21 km above the sea level and an ash mass of $3.65 \times 10^{10}$ kg, in agreement with mass values obtained from empirical formulas, but less than proximal–distal mass deposit of $1.86 \times 10^{11}$ kg. This discrepancy may be explained by extrapolating Advanced Technology Microwave Sounder-based estimates to 6 h, thus obtaining a total mass of about $1.90 \times 10^{11}$ kg. Distal volcanic cloud retrievals are derived from TIR imagery and results show a good agreement between Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) retrievals of total mass taking into account the overpass time shift. If only the overlapping pixels between MODIS and VIIRS are considered, the respective estimates are $1.90 \times 10^{9}$ kg and $1.80 \times 10^{9}$ kg. TIR radiometric estimates of distal ash cloud height and mass loadings are also compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar retrievals. For low-to-medium optically thick ash cloud, average Cloud-Aerosol Lidar with Orthogonal Polarization-derived mass loading is about 0.8 g/m2 against 0.4 g/m2 from VIIRS and 1.4 g/m2 from MODIS. [ABSTRACT FROM AUTHOR]

The normalized differential spectral attenuation (NDSA) approach was proposed years ago as an effective way to estimate the integrated water vapor (IWV) along a tropospheric propagation path between two low Earth orbit satellites. Two applications are possible: the retrieval of vertical profiles of WV if the sense of rotation is opposite and the retrieval of 2-D fields of WV over vertical tropospheric sections if the sense is the same. The method relies on the measurement of the socalled spectral sensitivity S at given frequencies, and on IWV-S relationships that convert S into an estimate of IWV along the radio link where S is measured. In this paper, we recompute the IWV-S relationships using synthetic atmospheres generated by means of European Centre for Medium-RangeWeather Forecasts (ECMWF) analysis data instead of radiosonde profiles as done by ourselves in the past. Thanks to the uniform spatial distribution of the ECMWF data on a global Earth scale, we were able to validate the IWV-S relationships in the Ku/K band previously found through synthetic atmospheres generated by means of the aforementioned irregularly spaced radiosonde data, and to define the IWV-S relationships at 179 and 181 GHz that are exploitable in the upper troposphere. Since the ECMWF data also include information about the liquid water (LW) content, we then show that an additional S channel at 32 GHz can be exploited to detect and correct the bias induced by LW on IWV estimates made by applying the NDSA in the Ku/K band. [ABSTRACT FROM AUTHOR]

Earth-observing satellites provide global observations of many geophysical variables. As these variables are derived from measured radiances, the underlying radiance data are the most reliable sources of information for change detection. Here, we identify statistically significant trends in the color and spatial texture of the Earth as viewed from multiple directions from the Multi-angle Imaging SpectroRadiometer (MISR), which has been sampling the angular distribution of scattered sunlight since 2000. Globally, our results show that the Earth has been appearing relatively bluer (up to 1.6% per decade from both nadir and oblique views) and smoother (up to 1.5% per decade only from oblique views) over the past 15 years. The magnitude of the global blueing trends is comparable to that of uncertainties in radiometric calibration stability. Regional shifts in color and texture, which are significantly larger than global means, are observed, particularly over polar regions, along the boundaries of the subtropical highs, the tropical western Pacific, Southwestern Asia, and Australia. We demonstrate that the large regional trends cannot be explained either by uncertainties in radiometric calibration or variability in total or spectral solar irradiance; hence, they reflect changes internal to the Earth's climate system. The 15-year-mean true color composites and texture images of the Earth at both nadir and oblique views are also presented for the first time. [ABSTRACT FROM AUTHOR]

We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique. [ABSTRACT FROM PUBLISHER]

The Altai catastrophic flood began on May 28, 2014, in the headwaters of one of the world's largest rivers, the Ob River, in the south of Western Siberia. The flood resulted from a steady rain which coincided with intensive snow melting in the Altai Mountains. As a result of inclement weather, more than 60 settlements have become flooded. The remote assessment of flooded areas was performed using the images taken by MIRAS radiometer in the microwave range from SMOS satellite. To validate SMOS data, the dependence between the soil volume moisture and emissivity was used; it was determined with laboratory measurements of dielectric parameters of soil sampled in the inundation area. The analysis of satellite images allowed us to outline wetlands and waterlogged areas. [ABSTRACT FROM PUBLISHER]