The experimental Indian Nano-Satellite (INS)-2TD acquires data in a long-wave infrared (7 to 16 μm) region with a fairly good spatial resolution of 175 m. Our study focuses on the retrieval of land surface temperature (LST) using a physics-based generalized single-channel (GSC) algorithm for the INS-2TD observations. A total of 597,240 at-sensor radiance simulations were carried out using moderate resolution atmospheric transmittance 5.3 radiative transfer model for varying conditions pertaining to surface, atmosphere, and sensor geometry to develop and validate the GSC algorithm for broadband INS-2TD sensor. The result from simulated test dataset shows the algorithm’s consistent performance with root-mean-square error (RMSE) of 2.87 K and 0.97 R2. Pixel-to-pixel intercomparison of retrieved LST and standard LST product of Indian National Satellite (INSAT)-3D indicates a good agreement with 0.99 R2 and range of RMSE from 1.17 to 4.78 K over the six selected datasets of South-Asia. The results reveal that the retrieved INS-2TD LST products perform very well, except having a hot bias of around 4.78 K compared to INSAT-3D LST over the Himalayan mountains due to the topographic effect. These results show the overall reasonable accuracy of the retrieved LST over heterogeneous surfaces and highly dynamic atmospheric conditions.

By leveraging the characteristics of different optical sensors, infrared and visible image fusion generates a fused image that combines prominent thermal radiation targets with clear texture details. Existing methods often focus on a single modality or treat two modalities equally, which overlook the distinctive characteristics of each modality and fail to fully utilize their complementary information. To address this problem, we propose an end-to-end infrared and visible image fusion model based on shared-individual multi-scale feature decomposition. First, to extract multi-scale features from source images, a symmetric multi-scale decomposition encoder consisting of nest connections and a multi-scale receptive field network is designed to capture small, medium, and large-scale features. Second, to sufficiently utilize complementary information, common edge feature maps are introduced to the feature decomposition loss function to decompose extracted features into shared and individual features. Third, to aggregate shared and individual features, a shared-individual self-augmented decoder is proposed to take the individual fusion feature maps as the main input and the shared fusion feature maps as the residual input to assist the decoding process and the reconstruct the fused image. Finally, through comparing subjective evaluations and objective metrics, our method demonstrates its superiority compared with the state-of-the-art approaches.

Artificial intelligence has been widely applied to water depth retrieval across various environments, deemed essential for habitat modeling, hydraulic structure design, and watershed management. However, most of these models have been developed for deep waters, with the critical impact of the gradient descent algorithm often not evaluated. To address this gap in current research, this study adopted the artificial neural network with seven gradient descent methods, including step, momentum, quick propagation, delta-bar-delta, conjugate gradient, Levenberg–Marquardt, and resilient backpropagation (RProp), for shallow water depth modeling. Shallow water depths in Taiwan’s mountainous rivers were then modeled using multispectral imagery taken by drone and vegetation indices. From our results, it was revealed that methods optimizing weight updates were outperformed by those based on gradient information, such as RProp. The selection of gradient descent algorithm was identified as pivotal; an inappropriate selection might even result in performance inferior to a traditional linear regression model. In the sensitivity analysis, near-infrared and normalized difference water index were classified as highly sensitive. By leveraging multispectral data and vegetation indices with ANN, the optimal gradient descent algorithm and the critical model input for shallow water modeling were successfully identified, offering invaluable insights for future studies.

Infrared (IR) imaging systems have sensor and optical limitations that result in degraded imagery. Apart from imperfect optics and the finite detector size being responsible for introducing blurring and aliasing, the detector fixed-pattern noise also adds a significant layer of degradation in the collected imagery. Here, we propose a single-shot super-resolution method that compensates for the nonuniformity noise of long-wave IR imaging systems. The strategy combines wavefront modulation and a reconstruction methodology based on total variation and nonlocal means regularizers to recover high-spatial frequencies while reducing noise. In simulations and experiments, we demonstrate a clear improvement of up to 16× in image resolution while significantly decreasing the fixed-pattern noise in the reconstructed images.

Deep neural network-based synthetic aperture radar (SAR) image change detection algorithms are affected by coherent speckle noise in the original image. Existing denoising methods have predominantly focused on generating binary images based on the pre-classification of original pixels, which is insufficient in removing interfering noise. Herein, to further reduce the noise points generated in the clustering algorithm, we combined the characteristics of the fuzzy clustering algorithm, demonstrating the obvious advantages of the proposed fast and flexible denoising convolutional neural network (FFDNet-F) method. An FFDNet was used to reduce noise interference in real SAR images and improve the detection accuracy and robustness of the method. Difference operators were then drawn from the weak noise images, and fuzzy local information C-means clustering was applied for analysis to generate the change detection results. The experimental results from two real datasets and the comparative analysis with other network models demonstrated the effectiveness of this method. Simultaneously, Gaofen-3 satellite images were used to verify and analyze surface flood disasters in Zhengzhou, China. The findings of this study demonstrate a significant improvement in detection accuracy using the proposed method compared with that of other algorithms.

Due to the complexity of hyperspectral data and the scarcity of labeled samples, unsupervised clustering segmentation has become a hot spot of interest in remote sensing. Sparse subspace clustering (SSC) is the most common clustering approach at the moment, although its computational cost restricts its use on big remote sensing datasets. Furthermore, SSC’s neglect of spatial information and limited recognition ability hinder the spatial homogeneity of clustering results. Hence, this work proposes a fast spectral clustering algorithm for local cosine similarity graphs. First, the fuzzy simple linear iterative clustering superpixel method is introduced into the SSC framework to treat superpixels as homogeneous entities and obtain global similarity maps using very low computational and spatial overheads. Then, a cosine similarity measure that combines spectral information and spatial information is used to obtain a local similarity graph, which enhances the accuracy of the final classification and suppresses noise. Extensive testing demonstrates the value of the proposed method. Compared to state-of-the-art SSC-based algorithms, it offers superior classification performance, noise immunity, and very little computational overhead.

Spatial and temporal land-use patterns in the Songhua River Basin (SRB) over the past 20 years were analyzed; the influence of natural geographic, socioeconomic, and anthropogenic factors was considered. Using spatial analysis and geodetector modeling, we assessed various indicators to comprehensively analyze land-use changes in the SRB in a long time series (2001 to 2021). Our goal was to determine the extent to which each factor influences land-use change and the mechanisms of interaction. We found that natural geographic factors and anthropogenic factors, particularly elevation and population density, had a greater influence on land-use changes than climatic and socio-economic factors. Despite a positive trend in land use indicated by the composite index, the SRB is experiencing a decrease in undeveloped land resources annually. We also identified that interactions between factors had varying effects, with the superposition of multiple factors potentially exacerbating conflicts between different land-use types. These findings provide valuable insights for strategic planning, policy formulation, and optimization of land resources in the Songhua River Basin.

Hyperspectral unmixing (HU) in hyperspectral image (HSI) processing is a crucial step. However, the accuracy of unmixing methods is limited by the variability in endmember and the complexity of the HSI structure found in natural scenes. Endmember variability refers to the variations or differences exhibited by endmembers in different locations or under varying conditions within a hyperspectral remote sensing scene. Therefore, to enhance the accuracy of unmixing results, it is crucial to fully leverage spectral, geometric, and spatial information within HSIs, comprehensively exploring the spectral characteristics of endmembers. We present a cascaded dual-constrained transformer autoencoder (AE) for HU with endmember variability and spectral geometry. The model utilizes a transformer AE network to extract the global spatial features in the HSI. Additionally, it incorporates the minimum distance constraint to account for the geometric information of the HSI. Given the similarity in shape exhibited by endmembers of each individual material, with the primary endmember variability being expressed through overall intensity fluctuations, an abundance-weighted constraint method for endmember spectral angle distance is proposed. During training, the architecture utilizes two cascaded networks to preserve the detailed information in the HSI. We evaluate the proposed model using three real datasets. The experimental results indicate that the proposed method achieves superior performance in abundance estimation and endmember extraction. Furthermore, the effectiveness of the two constraint methods was verified through ablation experiments.

Radar receivers are vital components in modern radar systems, and their reliable operation is crucial for accurate target detection and tracking. However, degrading receiver components can lead to reduced gain, increased noise levels, and decreased probability of detection affecting the overall radar performance. We present an efficient real-time prognostic framework for a radar receiver. The effect of the performance degradation of critical devices on the radar receiver is analyzed. A prognostic framework is developed based on the relationship between device health and receiver performance. Subsequently, an improved prognostic model based on the integration of Weibull distribution and long short-term memory network is developed and trained to accurately estimate the remaining useful life (RUL) of the receiver. Integrating survival analysis and deep learning techniques offers a robust solution for accurate RUL estimation, which can significantly enhance maintenance strategies. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar receivers.

Inaccurate solar vector orientation knowledge can considerably deteriorate calibration results for the Visible Infrared Imaging Radiometer Suite (VIIRS). We develop a methodology to use the Suomi National Polar-orbiting Partnership (SNPP) VIIRS solar diffuser stability monitor (SDSM) sun view data to assess the knowledge accuracy of the solar angles that reside in the onboard calibrator intermediate product (OBCIP) files used for on-orbit radiometric calibration. We applied an initial version of this methodology in 2013 and found that the solar declination angle had a relative error that varied between ∼0 deg to 0.17 deg. The relative error is referenced to the error at the SNPP satellite yaw maneuver time that occurred on February 15 to 16, 2012. Our mission long results from the current methodology show that the solar vector angular knowledge error occurred from the early mission until mission day 1129 (November 30, 2014). The error undulates yearly with the largest error in the solar declination angle increasing from ∼0.17 deg in the first year to 0.19 deg in the third year, agreeing with the solar vector error root cause understanding realized in early 2014. With the reprocessed OBCIP files, we find the solar vector declination and azimuth angular knowledge errors have near zero biases. The detection limit of this methodology strongly depends on how finely the solar angle is sampled by the SDSM detectors. With the SDSM sun view data collected when the SDSM operated once per day, this methodology yields detection standard deviations of 0.013 deg and 0.024 deg for the solar declination and azimuth angles. With a 3-sigma criterion, at the detection limits, the solar orientation errors result in a calibration error of 0.088%. This method can be applied to other Earth-orbiting sensors.

Over the last several decades, large wildfires have become increasingly common across the United States causing a disproportionate impact on forest health and function, human well-being, and the economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011 to 2020) using a wide array of meteorological, land cover, and topographical features in a deep neural network model. A total of 4538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43% of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the weather, land cover, and elevation of the study area as indicated from their SHapley Additive exPlanations values. Overall, different variants of data-driven models and their results could provide useful guidance in managing landscapes for large wildfires under changing climate and disturbance regimes.

The multisource satellite observation data have been widely used in carbon cycle research owing to their long-term and large-scale characteristics. However, the sparse sampling density of satellite observation data often results in incomplete spatiotemporal coverage at certain time intervals, which hinders the accurate representation of global carbon dioxide (CO2) concentration variations and is inadequate for supporting research applications with different precision requirements. To address this issue, a new multiscale fixed rank kriging is proposed to generate long-term daily scale column-averaged dry-air mole fraction of CO2 (XCO2) products from 2016 to 2019 over the globe on grids of 1°, for which the XCO2 data from Orbiting Carbon Observatory-2, Orbiting Carbon Observatory-3, and Greenhouse gases Observing SATellite are applied. Experimental results show that the dataset has a high spatiotemporal resolution and coverage validated by the Total Carbon Column Observing Network data to effectively fill gaps in satellite observation data, with cross-validation of R2=0.93 and root mean square error = 1.06 ppm. Moreover, we analyze the spatial distribution and seasonal variation characteristics of global and Chinese XCO2 from 2016 to 2019, with XCO2 presenting an obvious latitudinal gradient and seasonal periodicity in space. The proposed method establishes a foundational research dataset for the analysis of spatiotemporal variation characteristics of CO2 concentration at global and regional scales, as well as investigations on carbon sources and sink.